Scientific bases of human-machine communication by voice.

PubMed Central

Schafer, R W

1995-01-01

The scientific bases for human-machine communication by voice are in the fields of psychology, linguistics, acoustics, signal processing, computer science, and integrated circuit technology. The purpose of this paper is to highlight the basic scientific and technological issues in human-machine communication by voice and to point out areas of future research opportunity. The discussion is organized around the following major issues in implementing human-machine voice communication systems: (i) hardware/software implementation of the system, (ii) speech synthesis for voice output, (iii) speech recognition and understanding for voice input, and (iv) usability factors related to how humans interact with machines. PMID:7479802

Machine Learning for Detecting Gene-Gene Interactions

PubMed Central

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D.; Moore, Jason H.

2011-01-01

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics. PMID:16722772

User Centered System Design. Part II: Collected Papers from the UCSD HMI Project.

ERIC Educational Resources Information Center

California Univ., San Diego, La Jolla. Inst. for Cognitive Science.

This report is a collection of 11 recent papers by the Human-Machine Interaction Group at the University of California, San Diego. The following papers are included: (1) "Stages and Levels in Human-Machine Interaction," Donald A. Norman; (2) "The Nature of Expertise in UNIX," Stephen W. Draper; (3) "Users in the Real…

Man-systems integration and the man-machine interface

NASA Technical Reports Server (NTRS)

Hale, Joseph P.

1990-01-01

Viewgraphs on man-systems integration and the man-machine interface are presented. Man-systems integration applies the systems' approach to the integration of the user and the machine to form an effective, symbiotic Man-Machine System (MMS). A MMS is a combination of one or more human beings and one or more physical components that are integrated through the common purpose of achieving some objective. The human operator interacts with the system through the Man-Machine Interface (MMI).

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

Integrated human-machine intelligence in space systems

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1992-01-01

The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

Human-machine interface for a VR-based medical imaging environment

NASA Astrophysics Data System (ADS)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

PubMed

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

Metaphors for the Nature of Human-Computer Interaction in an Empowering Environment: Interaction Style Influences the Manner of Human Accomplishment.

ERIC Educational Resources Information Center

Weller, Herman G.; Hartson, H. Rex

1992-01-01

Describes human-computer interface needs for empowering environments in computer usage in which the machine handles the routine mechanics of problem solving while the user concentrates on its higher order meanings. A closed-loop model of interaction is described, interface as illusion is discussed, and metaphors for human-computer interaction are…

On the role of exchange of power and information signals in control and stability of the human-robot interaction

NASA Technical Reports Server (NTRS)

Kazerooni, H.

1991-01-01

A human's ability to perform physical tasks is limited, not only by his intelligence, but by his physical strength. If, in an appropriate environment, a machine's mechanical power is closely integrated with a human arm's mechanical power under the control of the human intellect, the resulting system will be superior to a loosely integrated combination of a human and a fully automated robot. Therefore, we must develop a fundamental solution to the problem of 'extending' human mechanical power. The work presented here defines 'extenders' as a class of robot manipulators worn by humans to increase human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. The human, in physical contact with the extender, exchanges power and information signals with the extender. The aim is to determine the fundamental building blocks of an intelligent controller, a controller which allows interaction between humans and a broad class of computer-controlled machines via simultaneous exchange of both power and information signals. The prevalent trend in automation has been to physically separate the human from the machine so the human must always send information signals via an intermediary device (e.g., joystick, pushbutton, light switch). Extenders, however are perfect examples of self-powered machines that are built and controlled for the optimal exchange of power and information signals with humans. The human wearing the extender is in physical contact with the machine, so power transfer is unavoidable and information signals from the human help to control the machine. Commands are transferred to the extender via the contact forces and the EMG signals between the wearer and the extender. The extender augments human motor ability without accepting any explicit commands: it accepts the EMG signals and the contact force between the person's arm and the extender, and the extender 'translates' them into a desired position. In this unique configuration, mechanical power transfer between the human and the extender occurs because the human is pushing against the extender. The extender transfers to the human's hand, in feedback fashion, a scaled-down version of the actual external load which the extender is manipulating. This natural feedback force on the human's hand allows him to 'feel' a modified version of the external forces on the extender. The information signals from the human (e.g., EMG signals) to the computer reflect human cognitive ability, and the power transfer between the human and the machine (e.g., physical interaction) reflects human physical ability. Thus the information transfer to the machine augments cognitive ability, and the power transfer augments motor ability. These two actions are coupled through the human cognitive/motor dynamic behavior. The goal is to derive the control rules for a class of computer-controlled machines that augment human physical and cognitive abilities in certain manipulative tasks.

Humans and machines in space: The vision, the challenge, the payoff; Proceedings of the 29th Goddard Memorial Symposium, Washington, Mar. 14, 15, 1991

NASA Astrophysics Data System (ADS)

Johnson, Bradley; May, Gayle L.; Korn, Paula

The present conference discusses the currently envisioned goals of human-machine systems in spacecraft environments, prospects for human exploration of the solar system, and plausible methods for meeting human needs in space. Also discussed are the problems of human-machine interaction in long-duration space flights, remote medical systems for space exploration, the use of virtual reality for planetary exploration, the alliance between U.S. Antarctic and space programs, and the economic and educational impacts of the U.S. space program.

Human-Machine Teams: The Social Frontier

DTIC Science & Technology

2015-12-01

Trust & Interaction Branch December 2015 Interim Report Distribution A. Approved for public release AIR FORCE RESEARCH LABORATORY 711TH HUMAN...711th Human Performance Wing Air Force Research Laboratory This report is published in the interest of scientific and technical information exchange... Research Laboratory 711th Human Performance Wing Human Effectiveness Directorate Human Centered ISR Division Human Trust & Interaction Branch Wright

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

A study on the application of voice interaction in automotive human machine interface experience design

NASA Astrophysics Data System (ADS)

Huang, Zhaohui; Huang, Xiemin

2018-04-01

This paper, firstly, introduces the application trend of the integration of multi-channel interactions in automotive HMI ((Human Machine Interface) from complex information models faced by existing automotive HMI and describes various interaction modes. By comparing voice interaction and touch screen, gestures and other interaction modes, the potential and feasibility of voice interaction in automotive HMI experience design are concluded. Then, the related theories of voice interaction, identification technologies, human beings' cognitive models of voices and voice design methods are further explored. And the research priority of this paper is proposed, i.e. how to design voice interaction to create more humane task-oriented dialogue scenarios to enhance interactive experiences of automotive HMI. The specific scenarios in driving behaviors suitable for the use of voice interaction are studied and classified, and the usability principles and key elements for automotive HMI voice design are proposed according to the scenario features. Then, through the user participatory usability testing experiment, the dialogue processes of voice interaction in automotive HMI are defined. The logics and grammars in voice interaction are classified according to the experimental results, and the mental models in the interaction processes are analyzed. At last, the voice interaction design method to create the humane task-oriented dialogue scenarios in the driving environment is proposed.

A Concept for Optimizing Behavioural Effectiveness & Efficiency

NASA Astrophysics Data System (ADS)

Barca, Jan Carlo; Rumantir, Grace; Li, Raymond

Both humans and machines exhibit strengths and weaknesses that can be enhanced by merging the two entities. This research aims to provide a broader understanding of how closer interactions between these two entities can facilitate more optimal goal-directed performance through the use of artificial extensions of the human body. Such extensions may assist us in adapting to and manipulating our environments in a more effective way than any system known today. To demonstrate this concept, we have developed a simulation where a semi interactive virtual spider can be navigated through an environment consisting of several obstacles and a virtual predator capable of killing the spider. The virtual spider can be navigated through the use of three different control systems that can be used to assist in optimising overall goal directed performance. The first two control systems use, an onscreen button interface and a touch sensor, respectively to facilitate human navigation of the spider. The third control system is an autonomous navigation system through the use of machine intelligence embedded in the spider. This system enables the spider to navigate and react to changes in its local environment. The results of this study indicate that machines should be allowed to override human control in order to maximise the benefits of collaboration between man and machine. This research further indicates that the development of strong machine intelligence, sensor systems that engage all human senses, extra sensory input systems, physical remote manipulators, multiple intelligent extensions of the human body, as well as a tighter symbiosis between man and machine, can support an upgrade of the human form.

Operator-coached machine vision for space telerobotics

NASA Technical Reports Server (NTRS)

Bon, Bruce; Wilcox, Brian; Litwin, Todd; Gennery, Donald B.

1991-01-01

A prototype system for interactive object modeling has been developed and tested. The goal of this effort has been to create a system which would demonstrate the feasibility of high interactive operator-coached machine vision in a realistic task environment, and to provide a testbed for experimentation with various modes of operator interaction. The purpose for such a system is to use human perception where machine vision is difficult, i.e., to segment the scene into objects and to designate their features, and to use machine vision to overcome limitations of human perception, i.e., for accurate measurement of object geometry. The system captures and displays video images from a number of cameras, allows the operator to designate a polyhedral object one edge at a time by moving a 3-D cursor within these images, performs a least-squares fit of the designated edges to edge data detected with a modified Sobel operator, and combines the edges thus detected to form a wire-frame object model that matches the Sobel data.

Impact of Human like Cues on Human Trust in Machines: Brain Imaging and Modeling Studies for Human-Machine Interactions

DTIC Science & Technology

2018-01-05

research team recorded fMRI or event-related potentials while subjects were playing two cognitive games . At the first experiment, human subjects played a...theory-of-mind bilateral game with two types of computerized agents: with or without humanlike cues. At the second experiment, human subjects played...a unilateral game in which the human subjects played the role of the Coach (or supervisor) while a computer agent played as the Player

Man Machine Systems in Education.

ERIC Educational Resources Information Center

Sall, Malkit S.

This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

The Promise of Interactive Video: An Affective Search.

ERIC Educational Resources Information Center

Hon, David

1983-01-01

Argues that factors that create a feeling of interactivity in the human situation--response time, spontaneity, lack of distractors--should be included as prime elements in the design of human/machine systems, e.g., computer assisted instruction and interactive video. A computer/videodisc learning system for cardio-pulmonary resuscitation and its…

Control system software, simulation, and robotic applications

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

All essential existing capabilities needed to create a man-machine interaction dynamics and performance (MMIDAP) capability are reviewed. The multibody system dynamics software program Order N DISCOS will be used for machine and musculo-skeletal dynamics modeling. The program JACK will be used for estimating and animating whole body human response to given loading situations and motion constraints. The basic elements of performance (BEP) task decomposition methodologies associated with the Human Performance Institute database will be used for performance assessment. Techniques for resolving the statically indeterminant muscular load sharing problem will be used for a detailed understanding of potential musculotendon or ligamentous fatigue, pain, discomfort, and trauma. The envisioned capacity is to be used for mechanical system design, human performance assessment, extrapolation of man/machine interaction test data, biomedical engineering, and soft prototyping within a concurrent engineering (CE) system.

The role of voice input for human-machine communication.

PubMed Central

Cohen, P R; Oviatt, S L

1995-01-01

Optimism is growing that the near future will witness rapid growth in human-computer interaction using voice. System prototypes have recently been built that demonstrate speaker-independent real-time speech recognition, and understanding of naturally spoken utterances with vocabularies of 1000 to 2000 words, and larger. Already, computer manufacturers are building speech recognition subsystems into their new product lines. However, before this technology can be broadly useful, a substantial knowledge base is needed about human spoken language and performance during computer-based spoken interaction. This paper reviews application areas in which spoken interaction can play a significant role, assesses potential benefits of spoken interaction with machines, and compares voice with other modalities of human-computer interaction. It also discusses information that will be needed to build a firm empirical foundation for the design of future spoken and multimodal interfaces. Finally, it argues for a more systematic and scientific approach to investigating spoken input and performance with future language technology. PMID:7479803

Sensing Pressure Distribution on a Lower-Limb Exoskeleton Physical Human-Machine Interface

PubMed Central

De Rossi, Stefano Marco Maria; Vitiello, Nicola; Lenzi, Tommaso; Ronsse, Renaud; Koopman, Bram; Persichetti, Alessandro; Vecchi, Fabrizio; Ijspeert, Auke Jan; van der Kooij, Herman; Carrozza, Maria Chiara

2011-01-01

A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer’s skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented. PMID:22346574

Literate Specification: Using Design Rationale To Support Formal Methods in the Development of Human-Machine Interfaces.

ERIC Educational Resources Information Center

Johnson, Christopher W.

1996-01-01

The development of safety-critical systems (aircraft cockpits and reactor control rooms) is qualitatively different from that of other interactive systems. These differences impose burdens on design teams that must ensure the development of human-machine interfaces. Analyzes strengths and weaknesses of formal methods for the design of user…

Adaptive displays and controllers using alternative feedback.

PubMed

Repperger, D W

2004-12-01

Investigations on the design of haptic (force reflecting joystick or force display) controllers were conducted by viewing the display of force information within the context of several different paradigms. First, using analogies from electrical and mechanical systems, certain schemes of the haptic interface were hypothesized which may improve the human-machine interaction with respect to various criteria. A discussion is given on how this interaction benefits the electrical and mechanical system. To generalize this concept to the design of human-machine interfaces, three studies with haptic mechanisms were then synthesized and analyzed.

Framework for Building Collaborative Research Environment

DOE PAGES

Devarakonda, Ranjeet; Palanisamy, Giriprakash; San Gil, Inigo

2014-10-25

Wide range of expertise and technologies are the key to solving some global problems. Semantic web technology can revolutionize the nature of how scientific knowledge is produced and shared. The semantic web is all about enabling machine-machine readability instead of a routine human-human interaction. Carefully structured data, as in machine readable data is the key to enabling these interactions. Drupal is an example of one such toolset that can render all the functionalities of Semantic Web technology right out of the box. Drupal’s content management system automatically stores the data in a structured format enabling it to be machine. Withinmore » this paper, we will discuss how Drupal promotes collaboration in a research setting such as Oak Ridge National Laboratory (ORNL) and Long Term Ecological Research Center (LTER) and how it is effectively using the Semantic Web in achieving this.« less

A video, text, and speech-driven realistic 3-d virtual head for human-machine interface.

PubMed

Yu, Jun; Wang, Zeng-Fu

2015-05-01

A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human-machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human-machine interaction.

Advice Taking from Humans and Machines: An fMRI and Effective Connectivity Study.

PubMed

Goodyear, Kimberly; Parasuraman, Raja; Chernyak, Sergey; Madhavan, Poornima; Deshpande, Gopikrishna; Krueger, Frank

2016-01-01

With new technological advances, advice can come from different sources such as machines or humans, but how individuals respond to such advice and the neural correlates involved need to be better understood. We combined functional MRI and multivariate Granger causality analysis with an X-ray luggage-screening task to investigate the neural basis and corresponding effective connectivity involved with advice utilization from agents framed as experts. Participants were asked to accept or reject good or bad advice from a human or machine agent with low reliability (high false alarm rate). We showed that unreliable advice decreased performance overall and participants interacting with the human agent had a greater depreciation of advice utilization during bad advice compared to the machine agent. These differences in advice utilization can be perceivably due to reevaluation of expectations arising from association of dispositional credibility for each agent. We demonstrated that differences in advice utilization engaged brain regions that may be associated with evaluation of personal characteristics and traits (precuneus, posterior cingulate cortex, temporoparietal junction) and interoception (posterior insula). We found that the right posterior insula and left precuneus were the drivers of the advice utilization network that were reciprocally connected to each other and also projected to all other regions. Our behavioral and neuroimaging results have significant implications for society because of progressions in technology and increased interactions with machines.

Advice Taking from Humans and Machines: An fMRI and Effective Connectivity Study

PubMed Central

Goodyear, Kimberly; Parasuraman, Raja; Chernyak, Sergey; Madhavan, Poornima; Deshpande, Gopikrishna; Krueger, Frank

2016-01-01

With new technological advances, advice can come from different sources such as machines or humans, but how individuals respond to such advice and the neural correlates involved need to be better understood. We combined functional MRI and multivariate Granger causality analysis with an X-ray luggage-screening task to investigate the neural basis and corresponding effective connectivity involved with advice utilization from agents framed as experts. Participants were asked to accept or reject good or bad advice from a human or machine agent with low reliability (high false alarm rate). We showed that unreliable advice decreased performance overall and participants interacting with the human agent had a greater depreciation of advice utilization during bad advice compared to the machine agent. These differences in advice utilization can be perceivably due to reevaluation of expectations arising from association of dispositional credibility for each agent. We demonstrated that differences in advice utilization engaged brain regions that may be associated with evaluation of personal characteristics and traits (precuneus, posterior cingulate cortex, temporoparietal junction) and interoception (posterior insula). We found that the right posterior insula and left precuneus were the drivers of the advice utilization network that were reciprocally connected to each other and also projected to all other regions. Our behavioral and neuroimaging results have significant implications for society because of progressions in technology and increased interactions with machines. PMID:27867351

A Taxonomy of Interaction for Instructional Multimedia.

ERIC Educational Resources Information Center

Schwier, Richard A.

This paper rejects the hardware-based "levels of interaction" made popular in interactive video literature to describe human-machine interaction in favor of a new taxonomy of learner-media interaction based on the type of cognitive engagement experienced by learners. Interaction can be described on three levels, based on the quality of…

Aircraft-vehicle system interaction. An evaluation of NASA's program in human factors research

NASA Technical Reports Server (NTRS)

1982-01-01

Research in the areas of man machine interaction and human factors engineering are assessed in relation to improved effeciency and aviation safety. The appropriateness, relevance, adequacy, and timeliness of the research is evaluated, and recommendations are provided regarding the objectives, approach and content.

MARTI: man-machine animation real-time interface

NASA Astrophysics Data System (ADS)

Jones, Christian M.; Dlay, Satnam S.

1997-05-01

The research introduces MARTI (man-machine animation real-time interface) for the realization of natural human-machine interfacing. The system uses simple vocal sound-tracks of human speakers to provide lip synchronization of computer graphical facial models. We present novel research in a number of engineering disciplines, which include speech recognition, facial modeling, and computer animation. This interdisciplinary research utilizes the latest, hybrid connectionist/hidden Markov model, speech recognition system to provide very accurate phone recognition and timing for speaker independent continuous speech, and expands on knowledge from the animation industry in the development of accurate facial models and automated animation. The research has many real-world applications which include the provision of a highly accurate and 'natural' man-machine interface to assist user interactions with computer systems and communication with one other using human idiosyncrasies; a complete special effects and animation toolbox providing automatic lip synchronization without the normal constraints of head-sets, joysticks, and skilled animators; compression of video data to well below standard telecommunication channel bandwidth for video communications and multi-media systems; assisting speech training and aids for the handicapped; and facilitating player interaction for 'video gaming' and 'virtual worlds.' MARTI has introduced a new level of realism to man-machine interfacing and special effect animation which has been previously unseen.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

A Qualitative Model of Human Interaction with Complex Dynamic Systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

A qualitative model of human interaction with complex dynamic systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffith, Douglas; Greitzer, Frank L.

In his 1960 paper Man-Machine Symbiosis, Licklider predicted that human brains and computing machines will be coupled in a tight partnership that will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today. Today we are on the threshold of resurrecting the vision of symbiosis. While Licklider’s original vision suggested a co-equal relationship, here we discuss an updated vision, neo-symbiosis, in which the human holds a superordinate position in an intelligent human-computer collaborative environment. This paper was originally published as a journal article and is being publishedmore » as a chapter in an upcoming book series, Advances in Novel Approaches in Cognitive Informatics and Natural Intelligence.« less

A study of speech interfaces for the vehicle environment.

DOT National Transportation Integrated Search

2013-05-01

Over the past few years, there has been a shift in automotive human machine interfaces from : visual-manual interactions (pushing buttons and rotating knobs) to speech interaction. In terms of : distraction, the industry views speech interaction as a...

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Defense Logistics Standard Systems Functional Requirements.

DTIC Science & Technology

1987-03-01

Artificial Intelligence - the development of a machine capability to perform functions normally concerned with human intelligence, such as learning , adapting...Basic Data Base Machine Configurations .... ......... D- 18 xx ~ ?f~~~vX PART I: MODELS - DEFENSE LOGISTICS STANDARD SYSTEMS FUNCTIONAL REQUIREMENTS...On-line, Interactive Access. Integrating user input and machine output in a dynamic, real-time, give-and- take process is considered the optimum mode

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human-Machine Interaction.

PubMed

Cao, Ran; Pu, Xianjie; Du, Xinyu; Yang, Wei; Wang, Jiaona; Guo, Hengyu; Zhao, Shuyu; Yuan, Zuqing; Zhang, Chi; Li, Congju; Wang, Zhong Lin

2018-05-22

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human-machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human-machine interfacing.

Tactual interfaces: The human perceiver

NASA Technical Reports Server (NTRS)

Srinivasan, M. A.

1991-01-01

Increasingly complex human-machine interactions, such as in teleoperation or in virtual environments, have necessitated the optimal use of the human tactual channel for information transfer. This need leads to a demand for a basic understanding of how the human tactual system works, so that the tactual interface between the human and the machine can receive the command signals from the human, as well as display the information to the human, in a manner that appears natural to the human. The tactual information consists of two components: (1) contact information which specifies the nature of direct contact with the object; and (2) kinesthetic information which refers to the position and motion of the limbs. This paper is mostly concerned with contact information.

The TREC Interactive Track: An Annotated Bibliography.

ERIC Educational Resources Information Center

Over, Paul

2001-01-01

Discussion of the study of interactive information retrieval (IR) at the Text Retrieval Conferences (TREC) focuses on summaries of the Interactive Track at each conference. Describes evolution of the track, which has changed from comparing human-machine systems with fully automatic systems to comparing interactive systems that focus on the search…

Hands-free human-machine interaction with voice

NASA Astrophysics Data System (ADS)

Juang, B. H.

2004-05-01

Voice is natural communication interface between a human and a machine. The machine, when placed in today's communication networks, may be configured to provide automation to save substantial operating cost, as demonstrated in AT&T's VRCP (Voice Recognition Call Processing), or to facilitate intelligent services, such as virtual personal assistants, to enhance individual productivity. These intelligent services often need to be accessible anytime, anywhere (e.g., in cars when the user is in a hands-busy-eyes-busy situation or during meetings where constantly talking to a microphone is either undersirable or impossible), and thus call for advanced signal processing and automatic speech recognition techniques which support what we call ``hands-free'' human-machine communication. These techniques entail a broad spectrum of technical ideas, ranging from use of directional microphones and acoustic echo cancellatiion to robust speech recognition. In this talk, we highlight a number of key techniques that were developed for hands-free human-machine communication in the mid-1990s after Bell Labs became a unit of Lucent Technologies. A video clip will be played to demonstrate the accomplishement.

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Study on intelligent processing system of man-machine interactive garment frame model

NASA Astrophysics Data System (ADS)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.

Human Factors Consideration for the Design of Collaborative Machine Assistants

NASA Astrophysics Data System (ADS)

Park, Sung; Fisk, Arthur D.; Rogers, Wendy A.

Recent improvements in technology have facilitated the use of robots and virtual humans not only in entertainment and engineering but also in the military (Hill et al., 2003), healthcare (Pollack et al., 2002), and education domains (Johnson, Rickel, & Lester, 2000). As active partners of humans, such machine assistants can take the form of a robot or a graphical representation and serve the role of a financial assistant, a health manager, or even a social partner. As a result, interactive technologies are becoming an integral component of people's everyday lives.

Not all trust is created equal: dispositional and history-based trust in human-automation interactions.

PubMed

Merritt, Stephanie M; Ilgen, Daniel R

2008-04-01

We provide an empirical demonstration of the importance of attending to human user individual differences in examinations of trust and automation use. Past research has generally supported the notions that machine reliability predicts trust in automation, and trust in turn predicts automation use. However, links between user personality and perceptions of the machine with trust in automation have not been empirically established. On our X-ray screening task, 255 students rated trust and made automation use decisions while visually searching for weapons in X-ray images of luggage. We demonstrate that individual differences affect perceptions of machine characteristics when actual machine characteristics are constant, that perceptions account for 52% of trust variance above the effects of actual characteristics, and that perceptions mediate the effects of actual characteristics on trust. Importantly, we also demonstrate that when administered at different times, the same six trust items reflect two types of trust (dispositional trust and history-based trust) and that these two trust constructs are differentially related to other variables. Interactions were found among user characteristics, machine characteristics, and automation use. Our results suggest that increased specificity in the conceptualization and measurement of trust is required, future researchers should assess user perceptions of machine characteristics in addition to actual machine characteristics, and incorporation of user extraversion and propensity to trust machines can increase prediction of automation use decisions. Potential applications include the design of flexible automation training programs tailored to individuals who differ in systematic ways.

Supporting the human life-raft in confronting the juggernaut of technology: Jens Rasmussen, 1961-1986.

PubMed

Kant, Vivek

2017-03-01

Jens Rasmussen's contribution to the field of human factors and ergonomics has had a lasting impact. Six prominent interrelated themes can be extracted from his research between 1961 and 1986. These themes form the basis of an engineering epistemology which is best manifested by his abstraction hierarchy. Further, Rasmussen reformulated technical reliability using systems language to enable a proper human-machine fit. To understand the concept of human-machine fit, he included the operator as a central component in the system to enhance system safety. This change resulted in the application of a qualitative and categorical approach for human-machine interaction design. Finally, Rasmussen's insistence on a working philosophy of systems design as being a joint responsibility of operators and designers provided the basis for averting errors and ensuring safe and correct system functioning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integrating Human Factors into Space Vehicle Processing for Risk Management

NASA Technical Reports Server (NTRS)

Woodbury, Sarah; Richards, Kimberly J.

2008-01-01

This presentation will discuss the multiple projects performed in United Space Alliance's Human Engineering Modeling and Performance (HEMAP) Lab, improvements that resulted from analysis, and the future applications of the HEMAP Lab for risk assessment by evaluating human/machine interaction and ergonomic designs.

Similarities and differences of emotions in human-machine and human-human interactions: what kind of emotions are relevant for future companion systems?

PubMed

Walter, Steffen; Wendt, Cornelia; Böhnke, Jan; Crawcour, Stephen; Tan, Jun-Wen; Chan, Andre; Limbrecht, Kerstin; Gruss, Sascha; Traue, Harald C

2014-01-01

Cognitive-technical intelligence is envisioned to be constantly available and capable of adapting to the user's emotions. However, the question is: what specific emotions should be reliably recognised by intelligent systems? Hence, in this study, we have attempted to identify similarities and differences of emotions between human-human (HHI) and human-machine interactions (HMI). We focused on what emotions in the experienced scenarios of HMI are retroactively reflected as compared with HHI. The sample consisted of N = 145 participants, who were divided into two groups. Positive and negative scenario descriptions of HMI and HHI were given by the first and second groups, respectively. Subsequently, the participants evaluated their respective scenarios with the help of 94 adjectives relating to emotions. The correlations between the occurrences of emotions in the HMI versus HHI were very high. The results do not support the statement that only a few emotions in HMI are relevant.

Cybernetic anthropomorphic machine systems

NASA Technical Reports Server (NTRS)

Gray, W. E.

1974-01-01

Functional descriptions are provided for a number of cybernetic man machine systems that augment the capacity of normal human beings in the areas of strength, reach or physical size, and environmental interaction, and that are also applicable to aiding the neurologically handicapped. Teleoperators, computer control, exoskeletal devices, quadruped vehicles, space maintenance systems, and communications equipment are considered.

Linear-hall sensor based force detecting unit for lower limb exoskeleton

NASA Astrophysics Data System (ADS)

Li, Hongwu; Zhu, Yanhe; Zhao, Jie; Wang, Tianshuo; Zhang, Zongwei

2018-04-01

This paper describes a knee-joint human-machine interaction force sensor for lower-limb force-assistance exoskeleton. The structure is designed based on hall sensor and series elastic actuator (SEA) structure. The work we have done includes the structure design, the parameter determination and dynamic simulation. By converting the force signal into macro displacement and output voltage, we completed the measurement of man-machine interaction force. And it is proved by experiments that the design is simple, stable and low-cost.

EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.

PubMed

Yin, Yue H; Fan, Yuan J; Xu, Li D

2012-07-01

Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.

Integration Telegram Bot on E-Complaint Applications in College

NASA Astrophysics Data System (ADS)

Rosid, M. A.; Rachmadany, A.; Multazam, M. T.; Nandiyanto, A. B. D.; Abdullah, A. G.; Widiaty, I.

2018-01-01

Internet of Things (IoT) has influenced human life where IoT internet connectivity extending from human-to-humans to human-to-machine or machine-to-machine. With this research field, it will be created a technology and concepts that allow humans to communicate with machines for a specific purpose. This research aimed to integrate between application service of the telegram sender with application of e-complaint at a college. With this application, users do not need to visit the Url of the E-compliant application; but, they can be accessed simply by submitting a complaint via Telegram, and then the complaint will be forwarded to the E-complaint Application. From the test results, e-complaint integration with Telegram Bot has been run in accordance with the design. Telegram Bot is made able to provide convenience to the user in this academician to submit a complaint, besides the telegram bot provides the user interaction with the usual interface used by people everyday on their smartphones. Thus, with this system, the complained work unit can immediately make improvements since all the complaints process can be delivered rapidly.

Kinematic design to improve ergonomics in human machine interaction.

PubMed

Schiele, André; van der Helm, Frans C T

2006-12-01

This paper introduces a novel kinematic design paradigm for ergonomic human machine interaction. Goals for optimal design are formulated generically and applied to the mechanical design of an upper-arm exoskeleton. A nine degree-of-freedom (DOF) model of the human arm kinematics is presented and used to develop, test, and optimize the kinematic structure of an human arm interfacing exoskeleton. The resulting device can interact with an unprecedented portion of the natural limb workspace, including motions in the shoulder-girdle, shoulder, elbow, and the wrist. The exoskeleton does not require alignment to the human joint axes, yet is able to actuate each DOF of our redundant limb unambiguously and without reaching into singularities. The device is comfortable to wear and does not create residual forces if misalignments exist. Implemented in a rehabilitation robot, the design features of the exoskeleton could enable longer lasting training sessions, training of fully natural tasks such as activities of daily living and shorter dress-on and dress-off times. Results from inter-subject experiments with a prototype are presented, that verify usability over the entire workspace of the human arm, including shoulder and shoulder girdle.

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles.

PubMed

Eom, Hwisoo; Lee, Sang Hun

2015-06-12

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model.

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles

PubMed Central

Eom, Hwisoo; Lee, Sang Hun

2015-01-01

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model. PMID:26076406

Watson and Siri: The Rise of the BI Smart Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Troy Hiltbrand

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

Agent Interaction with Human Systems in Complex Environments: Requirements for Automating the Function of CapCom in Apollo 17

NASA Technical Reports Server (NTRS)

Clancey, William J.

2003-01-01

A human-centered approach to computer systems design involves reframing analysis in terms of people interacting with each other, not only human-machine interaction. The primary concern is not how people can interact with computers, but how shall we design computers to help people work together? An analysis of astronaut interactions with CapCom on Earth during one traverse of Apollo 17 shows what kind of information was conveyed and what might be automated today. A variety of agent and robotic technologies are proposed that deal with recurrent problems in communication and coordination during the analyzed traverse.

Design Guidelines and Criteria for User/Operator Transactions with Battlefield Automated Systems. Volume 5. Background Literature

DTIC Science & Technology

1981-02-01

the machine . ARI’s efforts in this area focus on human perfor- mance problems related to interactions with command and control centers, and on issues...improvement of the user- machine interface. Lacking consistent design principles, current practice results in a fragmented and unsystematic approach to system...complexity in the user- machine interface of BAS, ARI supported this effort for develop- me:nt of an online language for Army tactical intelligence

Overview Electrotactile Feedback for Enhancing Human Computer Interface

NASA Astrophysics Data System (ADS)

Pamungkas, Daniel S.; Caesarendra, Wahyu

2018-04-01

To achieve effective interaction between a human and a computing device or machine, adequate feedback from the computing device or machine is required. Recently, haptic feedback is increasingly being utilised to improve the interactivity of the Human Computer Interface (HCI). Most existing haptic feedback enhancements aim at producing forces or vibrations to enrich the user’s interactive experience. However, these force and/or vibration actuated haptic feedback systems can be bulky and uncomfortable to wear and only capable of delivering a limited amount of information to the user which can limit both their effectiveness and the applications they can be applied to. To address this deficiency, electrotactile feedback is used. This involves delivering haptic sensations to the user by electrically stimulating nerves in the skin via electrodes placed on the surface of the skin. This paper presents a review and explores the capability of electrotactile feedback for HCI applications. In addition, a description of the sensory receptors within the skin for sensing tactile stimulus and electric currents alsoseveral factors which influenced electric signal to transmit to the brain via human skinare explained.

Visualization tool for human-machine interface designers

NASA Astrophysics Data System (ADS)

Prevost, Michael P.; Banda, Carolyn P.

1991-06-01

As modern human-machine systems continue to grow in capabilities and complexity, system operators are faced with integrating and managing increased quantities of information. Since many information components are highly related to each other, optimizing the spatial and temporal aspects of presenting information to the operator has become a formidable task for the human-machine interface (HMI) designer. The authors describe a tool in an early stage of development, the Information Source Layout Editor (ISLE). This tool is to be used for information presentation design and analysis; it uses human factors guidelines to assist the HMI designer in the spatial layout of the information required by machine operators to perform their tasks effectively. These human factors guidelines address such areas as the functional and physical relatedness of information sources. By representing these relationships with metaphors such as spring tension, attractors, and repellers, the tool can help designers visualize the complex constraint space and interacting effects of moving displays to various alternate locations. The tool contains techniques for visualizing the relative 'goodness' of a configuration, as well as mechanisms such as optimization vectors to provide guidance toward a more optimal design. Also available is a rule-based design checker to determine compliance with selected human factors guidelines.

Metabolome of human gut microbiome is predictive of host dysbiosis.

PubMed

Larsen, Peter E; Dai, Yang

2015-01-01

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependentmore » on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE PAGES

Larsen, Peter E.; Dai, Yang

2015-09-14

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

PubMed

Yugandhar, K; Gromiha, M Michael

2014-09-01

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions. © 2014 Wiley Periodicals, Inc.

Man-Machine Interaction Design and Analysis System (MIDAS): Memory Representation and Procedural Implications for Airborne Communication Modalities

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Pisanich, Gregory M.; Lebacqz, Victor (Technical Monitor)

1996-01-01

The Man-Machine Interaction Design and Analysis System (MIDAS) has been under development for the past ten years through a joint US Army and NASA cooperative agreement. MIDAS represents multiple human operators and selected perceptual, cognitive, and physical functions of those operators as they interact with simulated systems. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. Specific examples include: nuclear power plant crew simulation, military helicopter flight crew response, and police force emergency dispatch. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communications issues connected with aircraft-based separation assurance.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

Using Adaptive Automation to Increase Operator Performance and Decrease Stress in a Satellite Operations Environment

ERIC Educational Resources Information Center

Klein, David C.

2014-01-01

As advancements in automation continue to alter the systemic behavior of computer systems in a wide variety of industrial applications, human-machine interactions are increasingly becoming supervisory in nature, with less hands-on human involvement. This maturing of the human role within the human-computer relationship is relegating operations…

Ghost-in-the-Machine reveals human social signals for human-robot interaction.

PubMed

Loth, Sebastian; Jettka, Katharina; Giuliani, Manuel; de Ruiter, Jan P

2015-01-01

We used a new method called "Ghost-in-the-Machine" (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer's requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human-robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience.

Investigating the Human Computer Interaction Problems with Automated Teller Machine Navigation Menus

ERIC Educational Resources Information Center

Curran, Kevin; King, David

2008-01-01

Purpose: The automated teller machine (ATM) has become an integral part of our society. However, using the ATM can often be a frustrating experience as people frequently reinsert cards to conduct multiple transactions. This has led to the research question of whether ATM menus are designed in an optimal manner. This paper aims to address the…

Vision Systems with the Human in the Loop

NASA Astrophysics Data System (ADS)

Bauckhage, Christian; Hanheide, Marc; Wrede, Sebastian; Käster, Thomas; Pfeiffer, Michael; Sagerer, Gerhard

2005-12-01

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed.

A Cognitive Systems Engineering Approach to Developing Human Machine Interface Requirements for New Technologies

NASA Astrophysics Data System (ADS)

Fern, Lisa Carolynn

This dissertation examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will be deployed into complex systems. A key question for new technologies with increasingly capable automation, is how work will be accomplished by human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by designers. The human machine interface (HMI), which is intended to facilitate human-machine interaction and cooperation, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the predicted performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements for a detect and avoid (DAA) system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned in order to improve the methodology for defining technology requirements for regulators in the future. The biggest shortcoming of the presented research program was the absence of the explicit definition, generation and analysis of potential human-automation architectures. Failure to execute this step in the research process resulted in less efficient evaluation of the candidate prototypes technologies in addition to a lack of exploration of different approaches to human-automation cooperation. Defining potential human-automation architectures a priori also allows regulators to develop scenarios that will stress the performance boundaries of the technology during the evaluation phase. The importance of adding this step of generating and evaluating candidate human-automation architectures prior to formal empirical evaluation is discussed. This document concludes with a look at both the importance of, and the challenges facing, the inclusion of examining human-automation coordination issues as part of the safety assurance activities of new technologies.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction.

PubMed

Chen, Siyuan; Epps, Julien

2014-12-01

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Cognitive engineering models: A prerequisite to the design of human-computer interaction in complex dynamic systems

NASA Technical Reports Server (NTRS)

Mitchell, Christine M.

1993-01-01

This chapter examines a class of human-computer interaction applications, specifically the design of human-computer interaction for the operators of complex systems. Such systems include space systems (e.g., manned systems such as the Shuttle or space station, and unmanned systems such as NASA scientific satellites), aviation systems (e.g., the flight deck of 'glass cockpit' airplanes or air traffic control) and industrial systems (e.g., power plants, telephone networks, and sophisticated, e.g., 'lights out,' manufacturing facilities). The main body of human-computer interaction (HCI) research complements but does not directly address the primary issues involved in human-computer interaction design for operators of complex systems. Interfaces to complex systems are somewhat special. The 'user' in such systems - i.e., the human operator responsible for safe and effective system operation - is highly skilled, someone who in human-machine systems engineering is sometimes characterized as 'well trained, well motivated'. The 'job' or task context is paramount and, thus, human-computer interaction is subordinate to human job interaction. The design of human interaction with complex systems, i.e., the design of human job interaction, is sometimes called cognitive engineering.

Intent Specifications: An Approach to Building Human-Centered Specifications

NASA Technical Reports Server (NTRS)

Leveson, Nancy G.

1999-01-01

This paper examines and proposes an approach to writing software specifications, based on research in systems theory, cognitive psychology, and human-machine interaction. The goal is to provide specifications that support human problem solving and the tasks that humans must perform in software development and evolution. A type of specification, called intent specifications, is constructed upon this underlying foundation.

A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine.

PubMed

Lee, Haerin; Jung, Moonki; Lee, Ki-Kwang; Lee, Sang Hun

2017-02-06

In this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human-machine-environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines.

Computer-assisted visual interactive recognition and its prospects of implementation over the Internet

NASA Astrophysics Data System (ADS)

Zou, Jie; Gattani, Abhishek

2005-01-01

When completely automated systems don't yield acceptable accuracy, many practical pattern recognition systems involve the human either at the beginning (pre-processing) or towards the end (handling rejects). We believe that it may be more useful to involve the human throughout the recognition process rather than just at the beginning or end. We describe a methodology of interactive visual recognition for human-centered low-throughput applications, Computer Assisted Visual InterActive Recognition (CAVIAR), and discuss the prospects of implementing CAVIAR over the Internet. The novelty of CAVIAR is image-based interaction through a domain-specific parameterized geometrical model, which reduces the semantic gap between humans and computers. The user may interact with the computer anytime that she considers its response unsatisfactory. The interaction improves the accuracy of the classification features by improving the fit of the computer-proposed model. The computer makes subsequent use of the parameters of the improved model to refine not only its own statistical model-fitting process, but also its internal classifier. The CAVIAR methodology was applied to implement a flower recognition system. The principal conclusions from the evaluation of the system include: 1) the average recognition time of the CAVIAR system is significantly shorter than that of the unaided human; 2) its accuracy is significantly higher than that of the unaided machine; 3) it can be initialized with as few as one training sample per class and still achieve high accuracy; and 4) it demonstrates a self-learning ability. We have also implemented a Mobile CAVIAR system, where a pocket PC, as a client, connects to a server through wireless communication. The motivation behind a mobile platform for CAVIAR is to apply the methodology in a human-centered pervasive environment, where the user can seamlessly interact with the system for classifying field-data. Deploying CAVIAR to a networked mobile platform poses the challenge of classifying field images and programming under constraints of display size, network bandwidth, processor speed, and memory size. Editing of the computer-proposed model is performed on the handheld while statistical model fitting and classification take place on the server. The possibility that the user can easily take several photos of the object poses an interesting information fusion problem. The advantage of the Internet is that the patterns identified by different users can be pooled together to benefit all peer users. When users identify patterns with CAVIAR in a networked setting, they also collect training samples and provide opportunities for machine learning from their intervention. CAVIAR implemented over the Internet provides a perfect test bed for, and extends, the concept of Open Mind Initiative proposed by David Stork. Our experimental evaluation focuses on human time, machine and human accuracy, and machine learning. We devoted much effort to evaluating the use of our image-based user interface and on developing principles for the evaluation of interactive pattern recognition system. The Internet architecture and Mobile CAVIAR methodology have many applications. We are exploring in the directions of teledermatology, face recognition, and education.

Supporting Empathy in Online Learning with Artificial Expressions

ERIC Educational Resources Information Center

Lyons, Michael J.; Kluender, Daniel; Tetsutani, Nobuji

2005-01-01

Motivated by a consideration of the machine-mediated nature of human interaction in web-based tutoring, we propose the construction of artificial expressions, displays which reflect users' felt bodily experience, to support the development of greater empathy in remote interaction. To demonstrate the concept of artificial expressions we have…

Techniques for optimizing human-machine information transfer related to real-time interactive display systems

NASA Technical Reports Server (NTRS)

Granaas, Michael M.; Rhea, Donald C.

1989-01-01

The requirements for the development of real-time displays are reviewed. Of particular interest are the psychological aspects of design such as the layout, color selection, real-time response rate, and the interactivity of displays. Some existing Western Aeronautical Test Range displays are analyzed.

Investigation of automated task learning, decomposition and scheduling

NASA Technical Reports Server (NTRS)

Livingston, David L.; Serpen, Gursel; Masti, Chandrashekar L.

1990-01-01

The details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis.

Could robots become authentic companions in nursing care?

PubMed

Metzler, Theodore A; Lewis, Lundy M; Pope, Linda C

2016-01-01

Creating android and humanoid robots to furnish companionship in the nursing care of older people continues to attract substantial development capital and research. Some people object, though, that machines of this kind furnish human-robot interaction characterized by inauthentic relationships. In particular, robotic and artificial intelligence (AI) technologies have been charged with substituting mindless mimicry of human behaviour for the real presence of conscious caring offered by human nurses. When thus viewed as deceptive, the robots also have prompted corresponding concerns regarding their potential psychological, moral, and spiritual implications for people who will be interacting socially with these machines. The foregoing objections and concerns can be assessed quite differently, depending upon ambient religious beliefs or metaphysical presuppositions. The complaints may be set aside as unnecessary, for example, within religious traditions for which even current robots can be viewed as presenting spiritual aspects. Elsewhere, technological cultures may reject the complaints as expression of outdated superstition, holding that the machines eventually will enjoy a consciousness described entirely in materialist and behaviourist terms. While recognizing such assessments, the authors of this essay propose that the heart of the foregoing objections and concerns may be evaluated, in part, scientifically - albeit with a conclusion recommending fundamental revisions in AI modelling of human mental life. Specifically, considerations now favour introduction of AI models using interactive classical and quantum computation. Without this change, the answer to the essay's title question arguably is 'no' - with it, the answer plausibly becomes 'maybe'. Either outcome holds very interesting implications for nurses. © 2015 John Wiley & Sons Ltd.

Soft, Conformal Bioelectronics for a Wireless Human-Wheelchair Interface

PubMed Central

Mishra, Saswat; Norton, James J. S.; Lee, Yongkuk; Lee, Dong Sup; Agee, Nicolas; Chen, Yanfei; Chun, Youngjae; Yeo, Woon-Hong

2017-01-01

There are more than 3 million people in the world whose mobility relies on wheelchairs. Recent advancement on engineering technology enables more intuitive, easy-to-use rehabilitation systems. A human-machine interface that uses non-invasive, electrophysiological signals can allow a systematic interaction between human and devices; for example, eye movement-based wheelchair control. However, the existing machine-interface platforms are obtrusive, uncomfortable, and often cause skin irritations as they require a metal electrode affixed to the skin with a gel and acrylic pad. Here, we introduce a bioelectronic system that makes dry, conformal contact to the skin. The mechanically comfortable sensor records high-fidelity electrooculograms, comparable to the conventional gel electrode. Quantitative signal analysis and infrared thermographs show the advantages of the soft biosensor for an ergonomic human-machine interface. A classification algorithm with an optimized set of features shows the accuracy of 94% with five eye movements. A Bluetooth-enabled system incorporating the soft bioelectronics demonstrates a precise, hands-free control of a robotic wheelchair via electrooculograms. PMID:28152485

Rethinking Visual Analytics for Streaming Data Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crouser, R. Jordan; Franklin, Lyndsey; Cook, Kris

In the age of data science, the use of interactive information visualization techniques has become increasingly ubiquitous. From online scientific journals to the New York Times graphics desk, the utility of interactive visualization for both storytelling and analysis has become ever more apparent. As these techniques have become more readily accessible, the appeal of combining interactive visualization with computational analysis continues to grow. Arising out of a need for scalable, human-driven analysis, primary objective of visual analytics systems is to capitalize on the complementary strengths of human and machine analysis, using interactive visualization as a medium for communication between themore » two. These systems leverage developments from the fields of information visualization, computer graphics, machine learning, and human-computer interaction to support insight generation in areas where purely computational analyses fall short. Over the past decade, visual analytics systems have generated remarkable advances in many historically challenging analytical contexts. These include areas such as modeling political systems [Crouser et al. 2012], detecting financial fraud [Chang et al. 2008], and cybersecurity [Harrison et al. 2012]. In each of these contexts, domain expertise and human intuition is a necessary component of the analysis. This intuition is essential to building trust in the analytical products, as well as supporting the translation of evidence into actionable insight. In addition, each of these examples also highlights the need for scalable analysis. In each case, it is infeasible for a human analyst to manually assess the raw information unaided, and the communication overhead to divide the task between a large number of analysts makes simple parallelism intractable. Regardless of the domain, visual analytics tools strive to optimize the allocation of human analytical resources, and to streamline the sensemaking process on data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment.« less

Fusing human and machine skills for remote robotic operations

NASA Technical Reports Server (NTRS)

Schenker, Paul S.; Kim, Won S.; Venema, Steven C.; Bejczy, Antal K.

1991-01-01

The question of how computer assists can improve teleoperator trajectory tracking during both free and force-constrained motions is addressed. Computer graphics techniques which enable the human operator to both visualize and predict detailed 3D trajectories in real-time are reported. Man-machine interactive control procedures for better management of manipulator contact forces and positioning are also described. It is found that collectively, these novel advanced teleoperations techniques both enhance system performance and significantly reduce control problems long associated with teleoperations under time delay. Ongoing robotic simulations of the 1984 space shuttle Solar Maximum EVA Repair Mission are briefly described.

Man-machine interface requirements - advanced technology

NASA Technical Reports Server (NTRS)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

Machine intelligence-based decision-making (MIND) for automatic anomaly detection

NASA Astrophysics Data System (ADS)

Prasad, Nadipuram R.; King, Jason C.; Lu, Thomas

2007-04-01

Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction.

PubMed

Schermer, Maartje

2009-12-01

Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our 'symbolic order'-on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility.

Human-Vehicle Interface for Semi-Autonomous Operation of Uninhabited Aero Vehicles

NASA Technical Reports Server (NTRS)

Jones, Henry L.; Frew, Eric W.; Woodley, Bruce R.; Rock, Stephen M.

2001-01-01

The robustness of autonomous robotic systems to unanticipated circumstances is typically insufficient for use in the field. The many skills of human user often fill this gap in robotic capability. To incorporate the human into the system, a useful interaction between man and machine must exist. This interaction should enable useful communication to be exchanged in a natural way between human and robot on a variety of levels. This report describes the current human-robot interaction for the Stanford HUMMINGBIRD autonomous helicopter. In particular, the report discusses the elements of the system that enable multiple levels of communication. An intelligent system agent manages the different inputs given to the helicopter. An advanced user interface gives the user and helicopter a method for exchanging useful information. Using this human-robot interaction, the HUMMINGBIRD has carried out various autonomous search, tracking, and retrieval missions.

Holistic Modeling for Human-Autonomous System Interaction

DTIC Science & Technology

2015-01-01

piloting ...2012).  18X   Pilots  Learn  RPAs  First.      Retrieved  April  7,  2013,  from   http://www.holloman.af.mil/news/story.asp...human  processor  (QN-­‐ MHP):  a  computational  architecture  for   multitask  performance  in  human-­‐machine  

Roles of Human Factors and Ergonomics in Meeting the Challenge of Terrorism

ERIC Educational Resources Information Center

Nickerson, Raymond S.

2011-01-01

Human factors and ergonomics research focuses on questions pertaining to the design of devices, systems, and procedures with the goal of making sure that they are well suited to human use and focuses on studies of the interaction of people with simple and complex systems and machines. Problem areas studied include the allocation of function to…

Designing for flexible interaction between humans and automation: delegation interfaces for supervisory control.

PubMed

Miller, Christopher A; Parasuraman, Raja

2007-02-01

To develop a method enabling human-like, flexible supervisory control via delegation to automation. Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance. We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described. On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles. Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level. Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning.

The eXperience Induction Machine: A New Paradigm for Mixed-Reality Interaction Design and Psychological Experimentation

NASA Astrophysics Data System (ADS)

Bernardet, Ulysses; Bermúdez I Badia, Sergi; Duff, Armin; Inderbitzin, Martin; Le Groux, Sylvain; Manzolli, Jônatas; Mathews, Zenon; Mura, Anna; Väljamäe, Aleksander; Verschure, Paul F. M. J.

The eXperience Induction Machine (XIM) is one of the most advanced mixed-reality spaces available today. XIM is an immersive space that consists of physical sensors and effectors and which is conceptualized as a general-purpose infrastructure for research in the field of psychology and human-artifact interaction. In this chapter, we set out the epistemological rational behind XIM by putting the installation in the context of psychological research. The design and implementation of XIM are based on principles and technologies of neuromorphic control. We give a detailed description of the hardware infrastructure and software architecture, including the logic of the overall behavioral control. To illustrate the approach toward psychological experimentation, we discuss a number of practical applications of XIM. These include the so-called, persistent virtual community, the application in the research of the relationship between human experience and multi-modal stimulation, and an investigation of a mixed-reality social interaction paradigm.

Human-human reliance in the context of automation.

PubMed

Lyons, Joseph B; Stokes, Charlene K

2012-02-01

The current study examined human-human reliance during a computer-based scenario where participants interacted with a human aid and an automated tool simultaneously. Reliance on others is complex, and few studies have examined human-human reliance in the context of automation. Past research found that humans are biased in their perceived utility of automated tools such that they view them as more accurate than humans. Prior reviews have postulated differences in human-human versus human-machine reliance, yet few studies have examined such reliance when individuals are presented with divergent information from different sources. Participants (N = 40) engaged in the Convoy Leader experiment.They selected a convoy route based on explicit guidance from a human aid and information from an automated map. Subjective and behavioral human-human reliance indices were assessed. Perceptions of risk were manipulated by creating three scenarios (low, moderate, and high) that varied in the amount of vulnerability (i.e., potential for attack) associated with the convoy routes. Results indicated that participants reduced their behavioral reliance on the human aid when faced with higher risk decisions (suggesting increased reliance on the automation); however, there were no reported differences in intentions to rely on the human aid relative to the automation. The current study demonstrated that when individuals are provided information from both a human aid and automation,their reliance on the human aid decreased during high-risk decisions. This study adds to a growing understanding of the biases and preferences that exist during complex human-human and human-machine interactions.

Collaborative human-machine analysis using a controlled natural language

NASA Astrophysics Data System (ADS)

Mott, David H.; Shemanski, Donald R.; Giammanco, Cheryl; Braines, Dave

2015-05-01

A key aspect of an analyst's task in providing relevant information from data is the reasoning about the implications of that data, in order to build a picture of the real world situation. This requires human cognition, based upon domain knowledge about individuals, events and environmental conditions. For a computer system to collaborate with an analyst, it must be capable of following a similar reasoning process to that of the analyst. We describe ITA Controlled English (CE), a subset of English to represent analyst's domain knowledge and reasoning, in a form that it is understandable by both analyst and machine. CE can be used to express domain rules, background data, assumptions and inferred conclusions, thus supporting human-machine interaction. A CE reasoning and modeling system can perform inferences from the data and provide the user with conclusions together with their rationale. We present a logical problem called the "Analysis Game", used for training analysts, which presents "analytic pitfalls" inherent in many problems. We explore an iterative approach to its representation in CE, where a person can develop an understanding of the problem solution by incremental construction of relevant concepts and rules. We discuss how such interactions might occur, and propose that such techniques could lead to better collaborative tools to assist the analyst and avoid the "pitfalls".

Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.

PubMed

Karamzadeh, Razieh; Karimi-Jafari, Mohammad Hossein; Sharifi-Zarchi, Ali; Chitsaz, Hamidreza; Salekdeh, Ghasem Hosseini; Moosavi-Movahedi, Ali Akbar

2017-06-16

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Social Intelligence in a Human-Machine Collaboration System

NASA Astrophysics Data System (ADS)

Nakajima, Hiroshi; Morishima, Yasunori; Yamada, Ryota; Brave, Scott; Maldonado, Heidy; Nass, Clifford; Kawaji, Shigeyasu

In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

PubMed

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

A State Cyber Hub Operations Framework

DTIC Science & Technology

2016-06-01

to communicate and sense or interact with their internal states or the external environment. Machine Learning: A type of artificial intelligence that... artificial intelligence , and computational linguistics concerned with the interactions between computers and human (natural) languages. Patching: A piece...formalizing a proof of concept for cyber initiatives and developed frameworks for operationalizing the data and intelligence produced across state

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration.

PubMed

Crouser, R J; Chang, R

2012-12-01

Visual Analytics is "the science of analytical reasoning facilitated by visual interactive interfaces". The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on human and machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

PubMed Central

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon

2018-01-01

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas. PMID:29364861

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces.

PubMed

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon; Yeo, Woon-Hong

2018-01-24

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

Agile development of ontologies through conversation

NASA Astrophysics Data System (ADS)

Braines, Dave; Bhattal, Amardeep; Preece, Alun D.; de Mel, Geeth

2016-05-01

Ontologies and semantic systems are necessarily complex but offer great potential in terms of their ability to fuse information from multiple sources in support of situation awareness. Current approaches do not place the ontologies directly into the hands of the end user in the field but instead hide them away behind traditional applications. We have been experimenting with human-friendly ontologies and conversational interactions to enable non-technical business users to interact with and extend these dynamically. In this paper we outline our approach via a worked example, covering: OWL ontologies, ITA Controlled English, Sensor/mission matching and conversational interactions between human and machine agents.

Space human factors publications: 1980-1990

NASA Technical Reports Server (NTRS)

Dickson, Katherine J.

1991-01-01

A 10 year cummulative bibliography of publications resulting from research supported by the NASA Space Human Factors Program of the Life Science Division is provided. The goal of this program is to understand the basic mechanisms underlying behavioral adaptation to space and to develop and validate system design requirements, protocols, and countermeasures to ensure the psychological well-being, safety, and productivity of crewmembers. Subjects encompassed by this bibliography include selection and training, group dynamics, psychophysiological interactions, habitability issues, human-machine interactions, psychological support measures, and anthropometric data. Principal Investigators whose research tasks resulted in publication are identified by asterisk.

An evaluation of NASA's program in human factors research: Aircrew-vehicle system interaction

NASA Technical Reports Server (NTRS)

1982-01-01

Research in human factors in the aircraft cockpit and a proposed program augmentation were reviewed. The dramatic growth of microprocessor technology makes it entirely feasible to automate increasingly more functions in the aircraft cockpit; the promise of improved vehicle performance, efficiency, and safety through automation makes highly automated flight inevitable. An organized data base and validated methodology for predicting the effects of automation on human performance and thus on safety are lacking and without such a data base and validated methodology for analyzing human performance, increased automation may introduce new risks. Efforts should be concentrated on developing methods and techniques for analyzing man machine interactions, including human workload and prediction of performance.

When a robot is social: spatial arrangements and multimodal semiotic engagement in the practice of social robotics.

PubMed

Alac, Morana; Movellan, Javier; Tanaka, Fumihide

2011-12-01

Social roboticists design their robots to function as social agents in interaction with humans and other robots. Although we do not deny that the robot's design features are crucial for attaining this aim, we point to the relevance of spatial organization and coordination between the robot and the humans who interact with it. We recover these interactions through an observational study of a social robotics laboratory and examine them by applying a multimodal interactional analysis to two moments of robotics practice. We describe the vital role of roboticists and of the group of preverbal infants, who are involved in a robot's design activity, and we argue that the robot's social character is intrinsically related to the subtleties of human interactional moves in laboratories of social robotics. This human involvement in the robot's social agency is not simply controlled by individual will. Instead, the human-machine couplings are demanded by the situational dynamics in which the robot is lodged.

SAINT: A combined simulation language for modeling man-machine systems

NASA Technical Reports Server (NTRS)

Seifert, D. J.

1979-01-01

SAINT (Systems Analysis of Integrated Networks of Tasks) is a network modeling and simulation technique for design and analysis of complex man machine systems. SAINT provides the conceptual framework for representing systems that consist of discrete task elements, continuous state variables, and interactions between them. It also provides a mechanism for combining human performance models and dynamic system behaviors in a single modeling structure. The SAINT technique is described and applications of the SAINT are discussed.

Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

NASA Astrophysics Data System (ADS)

Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

2015-08-01

For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

Human-machine interface hardware: The next decade

NASA Technical Reports Server (NTRS)

Marcus, Elizabeth A.

1991-01-01

In order to understand where human-machine interface hardware is headed, it is important to understand where we are today, how we got there, and what our goals for the future are. As computers become more capable, faster, and programs become more sophisticated, it becomes apparent that the interface hardware is the key to an exciting future in computing. How can a user interact and control a seemingly limitless array of parameters effectively? Today, the answer is most often a limitless array of controls. The link between these controls and human sensory motor capabilities does not utilize existing human capabilities to their full extent. Interface hardware for teleoperation and virtual environments is now facing a crossroad in design. Therefore, we as developers need to explore how the combination of interface hardware, human capabilities, and user experience can be blended to get the best performance today and in the future.

The Naturalistic Flight Deck System: An Integrated System Concept for Improved Single-Pilot Operations

NASA Technical Reports Server (NTRS)

Schutte, Paul C.; Goodrich, Kenneth H.; Cox, David E.; Jackson, Bruce; Palmer, Michael T.; Pope, Alan T.; Schlecht, Robin W.; Tedjojuwono, Ken K.; Trujillo, Anna C.; Williams, Ralph A.;

Simulation of the «COSMONAUT-ROBOT» System Interaction on the Lunar Surface Based on Methods of Machine Vision and Computer Graphics

NASA Astrophysics Data System (ADS)

Kryuchkov, B. I.; Usov, V. M.; Chertopolokhov, V. A.; Ronzhin, A. L.; Karpov, A. A.

2017-05-01

Extravehicular activity (EVA) on the lunar surface, necessary for the future exploration of the Moon, involves extensive use of robots. One of the factors of safe EVA is a proper interaction between cosmonauts and robots in extreme environments. This requires a simple and natural man-machine interface, e.g. multimodal contactless interface based on recognition of gestures and cosmonaut's poses. When travelling in the "Follow Me" mode (master/slave), a robot uses onboard tools for tracking cosmonaut's position and movements, and on the basis of these data builds its itinerary. The interaction in the system "cosmonaut-robot" on the lunar surface is significantly different from that on the Earth surface. For example, a man, dressed in a space suit, has limited fine motor skills. In addition, EVA is quite tiring for the cosmonauts, and a tired human being less accurately performs movements and often makes mistakes. All this leads to new requirements for the convenient use of the man-machine interface designed for EVA. To improve the reliability and stability of human-robot communication it is necessary to provide options for duplicating commands at the task stages and gesture recognition. New tools and techniques for space missions must be examined at the first stage of works in laboratory conditions, and then in field tests (proof tests at the site of application). The article analyzes the methods of detection and tracking of movements and gesture recognition of the cosmonaut during EVA, which can be used for the design of human-machine interface. A scenario for testing these methods by constructing a virtual environment simulating EVA on the lunar surface is proposed. Simulation involves environment visualization and modeling of the use of the "vision" of the robot to track a moving cosmonaut dressed in a spacesuit.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

PubMed Central

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance. PMID:26422143

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

PubMed

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

What kind of time for a Time Machine?

NASA Astrophysics Data System (ADS)

Alfano, Marina; Buccheri, Rosolino

2013-09-01

The linear, unstructured, parameter t used in the equations of mechanics, in spite of its great aptness in describing the nature's laws, does not fit with the unidirectional flow of time tssubjectively experienced by humans, just the investigators of nature. Being ts the main foundation upon which we build our knowledge of nature through our continuous and inescapable reciprocal interaction - the possible key factor of our cerebral modulation, mediator between us and the world -, its objective essence appears to be inevitably destined to remain unveiled. We derive that any imagined and theoretically possible Time Machine, aimed to get us in our past or in our future allowing us to act there, does not have any practical grounds if it is built by using the illusory, impersonal, time, modeled by the parameter t, at the place of our interpersonal lived time ts. A real, humanly-tuned, Time Machine could perhaps arise by integrating tsin the body of a new kind of rationality - a `complex thought' - where empiricism and logic-mathematic are harmonized with participation and interaction. Ongoing joint research in neurophysiology and physics (without neglecting any important contribution coming from anthropology) will surely help achieving such a goal.

Live interactive computer music performance practice

NASA Astrophysics Data System (ADS)

Wessel, David

2002-05-01

A live-performance musical instrument can be assembled around current lap-top computer technology. One adds a controller such as a keyboard or other gestural input device, a sound diffusion system, some form of connectivity processor(s) providing for audio I/O and gestural controller input, and reactive real-time native signal processing software. A system consisting of a hand gesture controller; software for gesture analysis and mapping, machine listening, composition, and sound synthesis; and a controllable radiation pattern loudspeaker are described. Interactivity begins in the set up wherein the speaker-room combination is tuned with an LMS procedure. This system was designed for improvisation. It is argued that software suitable for carrying out an improvised musical dialog with another performer poses special challenges. The processes underlying the generation of musical material must be very adaptable, capable of rapid changes in musical direction. Machine listening techniques are used to help the performer adapt to new contexts. Machine learning can play an important role in the development of such systems. In the end, as with any musical instrument, human skill is essential. Practice is required not only for the development of musically appropriate human motor programs but for the adaptation of the computer-based instrument as well.

A Cognitive Systems Engineering Approach to Developing HMI Requirements for New Technologies

NASA Technical Reports Server (NTRS)

Fern, Lisa Carolynn

2016-01-01

This document examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will deployed into complex systems. A key question for new technologies, is how work will be accomplished by the human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by the designers. The human machine interface (HMI) which is intended to facilitate human-machine interaction and cooperation, however, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture, can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the expected performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements a detect and avoid system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned from a recent research effort in order to improve the methodology for defining technology requirements for regulators in the future. The biggest shortcoming of the presented research program was the absence of the explicit definition, generation and analysis of potential human-automation architectures. Failure to execute this step in the research process resulted in less efficient evaluation of the candidate prototypes technologies in addition to the complete absence of different approaches to human-automation cooperation. For example, all of the prototype technologies that were evaluated in the research program assumed a human-automation architecture that relied on serial processing from the automation to the human. While this type of human-automation architecture is typical across many different technologies and in many different domains, it ignores different architectures where humans and automation work in parallel. Defining potential human-automation architectures a priori also allows regulators to develop scenarios that will stress the performance boundaries of the technology during the evaluation phase. The importance of adding this step of generating and evaluating candidate human-automation architectures prior to formal empirical evaluation is discussed.

Intuitive Cognition and Models of Human-Automation Interaction.

PubMed

Patterson, Robert Earl

2017-02-01

The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy. Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems. One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature. Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested. Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.

A machine learning approach to improve contactless heart rate monitoring using a webcam.

PubMed

Monkaresi, Hamed; Calvo, Rafael A; Yan, Hong

2014-07-01

Unobtrusive, contactless recordings of physiological signals are very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only. The effectiveness of this technology, its limitations, and ways of overcoming them deserves particular attention. In this paper, we evaluate this technique for measuring HR in a controlled situation, in a naturalistic computer interaction session, and in an exercise situation. For comparison, HR was measured simultaneously using an electrocardiography device during all sessions. The results replicated the published results in controlled situations, but show that they cannot yet be considered as a valid measure of HR in naturalistic human-computer interaction. We propose a machine learning approach to improve the accuracy of HR detection in naturalistic measurements. The results demonstrate that the root mean squared error is reduced from 43.76 to 3.64 beats/min using the proposed method.

Human capabilities in space. [man machine interaction

NASA Technical Reports Server (NTRS)

Nicogossian, A. E.

1984-01-01

Man's ability to live and perform useful work in space was demonstrated throughout the history of manned space flight. Current planning envisions a multi-functional space station. Man's unique abilities to respond to the unforeseen and to operate at a level of complexity exceeding any reasonable amount of previous planning distinguish him from present day machines. His limitations, however, include his inherent inability to survive without protection, his limited strength, and his propensity to make mistakes when performing repetitive and monotonous tasks. By contrast, an automated system does routine and delicate tasks, exerts force smoothly and precisely, stores, and recalls large amounts of data, and performs deductive reasoning while maintaining a relative insensitivity to the environment. The establishment of a permanent presence of man in space demands that man and machines be appropriately combined in spaceborne systems. To achieve this optimal combination, research is needed in such diverse fields as artificial intelligence, robotics, behavioral psychology, economics, and human factors engineering.

Modeling Leadership Styles in Human-Robot Team Dynamics

NASA Technical Reports Server (NTRS)

Cruz, Gerardo E.

2005-01-01

The recent proliferation of robotic systems in our society has placed questions regarding interaction between humans and intelligent machines at the forefront of robotics research. In response, our research attempts to understand the context in which particular types of interaction optimize efficiency in tasks undertaken by human-robot teams. It is our conjecture that applying previous research results regarding leadership paradigms in human organizations will lead us to a greater understanding of the human-robot interaction space. In doing so, we adapt four leadership styles prevalent in human organizations to human-robot teams. By noting which leadership style is more appropriately suited to what situation, as given by previous research, a mapping is created between the adapted leadership styles and human-robot interaction scenarios-a mapping which will presumably maximize efficiency in task completion for a human-robot team. In this research we test this mapping with two adapted leadership styles: directive and transactional. For testing, we have taken a virtual 3D interface and integrated it with a genetic algorithm for use in &le-operation of a physical robot. By developing team efficiency metrics, we can determine whether this mapping indeed prescribes interaction styles that will maximize efficiency in the teleoperation of a robot.

HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy.

PubMed

Hu, Huan; Zhang, Li; Ai, Haixin; Zhang, Hui; Fan, Yetian; Zhao, Qi; Liu, Hongsheng

2018-03-27

LncRNA plays an important role in many biological and disease progression by binding to related proteins. However, the experimental methods for studying lncRNA-protein interactions are time-consuming and expensive. Although there are a few models designed to predict the interactions of ncRNA-protein, they all have some common drawbacks that limit their predictive performance. In this study, we present a model called HLPI-Ensemble designed specifically for human lncRNA-protein interactions. HLPI-Ensemble adopts the ensemble strategy based on three mainstream machine learning algorithms of Support Vector Machines (SVM), Random Forests (RF) and Extreme Gradient Boosting (XGB) to generate HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble, respectively. The results of 10-fold cross-validation show that HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble achieved AUCs of 0.95, 0.96 and 0.96, respectively, in the test dataset. Furthermore, we compared the performance of the HLPI-Ensemble models with the previous models through external validation dataset. The results show that the false positives (FPs) of HLPI-Ensemble models are much lower than that of the previous models, and other evaluation indicators of HLPI-Ensemble models are also higher than those of the previous models. It is further showed that HLPI-Ensemble models are superior in predicting human lncRNA-protein interaction compared with previous models. The HLPI-Ensemble is publicly available at: http://ccsipb.lnu.edu.cn/hlpiensemble/ .

Applications of airborne ultrasound in human-computer interaction.

PubMed

Dahl, Tobias; Ealo, Joao L; Bang, Hans J; Holm, Sverre; Khuri-Yakub, Pierre

2014-09-01

Airborne ultrasound is a rapidly developing subfield within human-computer interaction (HCI). Touchless ultrasonic interfaces and pen tracking systems are part of recent trends in HCI and are gaining industry momentum. This paper aims to provide the background and overview necessary to understand the capabilities of ultrasound and its potential future in human-computer interaction. The latest developments on the ultrasound transducer side are presented, focusing on capacitive micro-machined ultrasonic transducers, or CMUTs. Their introduction is an important step toward providing real, low-cost multi-sensor array and beam-forming options. We also provide a unified mathematical framework for understanding and analyzing algorithms used for ultrasound detection and tracking for some of the most relevant applications. Copyright © 2014. Published by Elsevier B.V.

Don't Fear the Cyborg: Toward Embracing Posthuman and Feminist Cyborg Discourses in Teacher Education and Educational Technology Research

ERIC Educational Resources Information Center

Gleason, Shannon C.

2014-01-01

I argue that larger cultural concerns about human-technology interactions are seldom addressed in teacher education. This article seeks to trace cultural anxieties about technology by addressing the long-standing trope of human versus machine; examine how these concerns are manifested and addressed (or not) in popular culture, educational…

Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

NASA Astrophysics Data System (ADS)

Wilson, Alex; Blunsom, Phil; Ker, Andrew D.

2014-02-01

This work proposes a natural language stegosystem for Twitter, modifying tweets as they are written to hide 4 bits of payload per tweet, which is a greater payload than previous systems have achieved. The system, CoverTweet, includes novel components, as well as some already developed in the literature. We believe that the task of transforming covers during embedding is equivalent to unilingual machine translation (paraphrasing), and we use this equivalence to de ne a distortion measure based on statistical machine translation methods. The system incorporates this measure of distortion to rank possible tweet paraphrases, using a hierarchical language model; we use human interaction as a second distortion measure to pick the best. The hierarchical language model is designed to model the speci c language of the covers, which in this setting is the language of the Twitter user who is embedding. This is a change from previous work, where general-purpose language models have been used. We evaluate our system by testing the output against human judges, and show that humans are unable to distinguish stego tweets from cover tweets any better than random guessing.

You Look Human, But Act Like a Machine: Agent Appearance and Behavior Modulate Different Aspects of Human-Robot Interaction.

PubMed

Abubshait, Abdulaziz; Wiese, Eva

2017-01-01

Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have toward others, and determines the degree of empathy, prosociality, and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to non-human agents like robots, as long as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human-robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human-robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze following) and attitudes (i.e., agent ratings) in human-robot interaction. The results show that both appearance and behavior affect human-robot interaction but that the two triggers seem to operate in isolation, with appearance more strongly impacting attitudes, and behavior more strongly affecting performance. The implications of these findings for human-robot interaction are discussed.

Social Engagement in Public Places: A Tale of One Robot

DTIC Science & Technology

2014-03-01

study we examined a prediction of Computers Are Social Actors (CASA) framework: the more machines present human -like characteristics in a consistent...social cues to increasing levels of social cues during story-telling to human -like game-playing interaction. We found several strong aspects of...support for CASA: the robot that provides even minimal social cues (speech) is more engaging than a robot that does nothing, and the more human -like the

Being human in a global age of technology.

PubMed

Whelton, Beverly J B

2016-01-01

This philosophical enquiry considers the impact of a global world view and technology on the meaning of being human. The global vision increases our awareness of the common bond between all humans, while technology tends to separate us from an understanding of ourselves as human persons. We review some advances in connecting as community within our world, and many examples of technological changes. This review is not exhaustive. The focus is to understand enough changes to think through the possibility of healthcare professionals becoming cyborgs, human-machine units that are subsequently neither human and nor machine. It is seen that human technology interfaces are a different way of interacting but do not change what it is to be human in our rational capacities of providing meaningful speech and freely chosen actions. In the highly technical environment of the ICU, expert nurses work in harmony with both the technical equipment and the patient. We used Heidegger to consider the nature of equipment, and Descartes to explore unique human capacities. Aristotle, Wallace, Sokolowski, and Clarke provide a summary of humanity as substantial and relational. © 2015 John Wiley & Sons Ltd.

Knowledge elicitation for an operator assistant system in process control tasks

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1988-01-01

A knowledge based system (KBS) methodology designed to study human machine interactions and levels of autonomy in allocation of process control tasks is presented. Users are provided with operation manuals to assist them in normal and abnormal situations. Unfortunately, operation manuals usually represent only the functioning logic of the system to be controlled. The user logic is often totally different. A method is focused on which illicits user logic to refine a KBS shell called an Operator Assistant (OA). If the OA is to help the user, it is necessary to know what level of autonomy gives the optimal performance of the overall man-machine system. For example, for diagnoses that must be carried out carefully by both the user and the OA, interactions are frequent, and processing is mostly sequential. Other diagnoses can be automated, in which the case the OA must be able to explain its reasoning in an appropriate level of detail. OA structure was used to design a working KBS called HORSES (Human Orbital Refueling System Expert System). Protocol analysis of pilots interacting with this system reveals that the a-priori analytical knowledge becomes more structured with training and the situation patterns more complex and dynamic. This approach can improve the a-priori understanding of human and automatic reasoning.

Improving the efficiency of a user-driven learning system with reconfigurable hardware. Application to DNA splicing.

PubMed

Lemoine, E; Merceron, D; Sallantin, J; Nguifo, E M

1999-01-01

This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The first one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on reconfigurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show how efficient the interaction between the machine and the expert is when the concept modelling is based on reconfigurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.

Communication, concepts and grounding.

PubMed

van der Velde, Frank

2015-02-01

This article discusses the relation between communication and conceptual grounding. In the brain, neurons, circuits and brain areas are involved in the representation of a concept, grounding it in perception and action. In terms of grounding we can distinguish between communication within the brain and communication between humans or between humans and machines. In the first form of communication, a concept is activated by sensory input. Due to grounding, the information provided by this communication is not just determined by the sensory input but also by the outgoing connection structure of the conceptual representation, which is based on previous experiences and actions. The second form of communication, that between humans or between humans and machines, is influenced by the first form. In particular, a more successful interpersonal communication might require forms of situated cognition and interaction in which the entire representations of grounded concepts are involved. Copyright © 2014 Elsevier Ltd. All rights reserved.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

Definition Of Touch-Sensitive Zones For Graphical Displays

NASA Technical Reports Server (NTRS)

Monroe, Burt L., III; Jones, Denise R.

1988-01-01

Touch zones defined simply by touching, while editing done automatically. Development of touch-screen interactive computing system, tedious task. Interactive Editor for Definition of Touch-Sensitive Zones computer program increases efficiency of human/machine communications by enabling user to define each zone interactively, minimizing redundancy in programming and eliminating need for manual computation of boundaries of touch areas. Information produced during editing process written to data file, to which access gained when needed by application program.

Finding Waldo: Learning about Users from their Interactions.

PubMed

Brown, Eli T; Ottley, Alvitta; Zhao, Helen; Quan Lin; Souvenir, Richard; Endert, Alex; Chang, Remco

2014-12-01

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems.

Embodied Interactions in Human-Machine Decision Making for Situation Awareness Enhancement Systems

DTIC Science & Technology

2016-06-09

characterize differences in spatial navigation strategies in a complex task, the Traveling Salesman Problem (TSP). For the second year, we developed...visual processing, leading to better solutions for spatial optimization problems . I will develop a framework to determine which body expressions best...methods include systematic characterization of gestures during complex problem solving. 15. SUBJECT TERMS Embodied interaction, gestures, one-shot

New generation of human machine interfaces for controlling UAV through depth-based gesture recognition

NASA Astrophysics Data System (ADS)

Mantecón, Tomás.; del Blanco, Carlos Roberto; Jaureguizar, Fernando; García, Narciso

2014-06-01

New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

Affective processes in human-automation interactions.

PubMed

Merritt, Stephanie M

2011-08-01

This study contributes to the literature on automation reliance by illuminating the influences of user moods and emotions on reliance on automated systems. Past work has focused predominantly on cognitive and attitudinal variables, such as perceived machine reliability and trust. However, recent work on human decision making suggests that affective variables (i.e., moods and emotions) are also important. Drawing from the affect infusion model, significant effects of affect are hypothesized. Furthermore, a new affectively laden attitude termed liking is introduced. Participants watched video clips selected to induce positive or negative moods, then interacted with a fictitious automated system on an X-ray screening task At five time points, important variables were assessed including trust, liking, perceived machine accuracy, user self-perceived accuracy, and reliance.These variables, along with propensity to trust machines and state affect, were integrated in a structural equation model. Happiness significantly increased trust and liking for the system throughout the task. Liking was the only variable that significantly predicted reliance early in the task. Trust predicted reliance later in the task, whereas perceived machine accuracy and user self-perceived accuracy had no significant direct effects on reliance at any time. Affective influences on automation reliance are demonstrated, suggesting that this decision-making process may be less rational and more emotional than previously acknowledged. Liking for a new system may be key to appropriate reliance, particularly early in the task. Positive affect can be easily induced and may be a lever for increasing liking.

An innovative multimodal virtual platform for communication with devices in a natural way

NASA Astrophysics Data System (ADS)

Kinkar, Chhayarani R.; Golash, Richa; Upadhyay, Akhilesh R.

2012-03-01

As technology grows people are diverted and are more interested in communicating with machine or computer naturally. This will make machine more compact and portable by avoiding remote, keyboard etc. also it will help them to live in an environment free from electromagnetic waves. This thought has made 'recognition of natural modality in human computer interaction' a most appealing and promising research field. Simultaneously it has been observed that using single mode of interaction limit the complete utilization of commands as well as data flow. In this paper a multimodal platform, where out of many natural modalities like eye gaze, speech, voice, face etc. human gestures are combined with human voice is proposed which will minimize the mean square error. This will loosen the strict environment needed for accurate and robust interaction while using single mode. Gesture complement Speech, gestures are ideal for direct object manipulation and natural language is used for descriptive tasks. Human computer interaction basically requires two broad sections recognition and interpretation. Recognition and interpretation of natural modality in complex binary instruction is a tough task as it integrate real world to virtual environment. The main idea of the paper is to develop a efficient model for data fusion coming from heterogeneous sensors, camera and microphone. Through this paper we have analyzed that the efficiency is increased if heterogeneous data (image & voice) is combined at feature level using artificial intelligence. The long term goal of this paper is to design a robust system for physically not able or having less technical knowledge.

Fast 3D NIR systems for facial measurement and lip-reading

NASA Astrophysics Data System (ADS)

Brahm, Anika; Ramm, Roland; Heist, Stefan; Rulff, Christian; Kühmstedt, Peter; Notni, Gunther

2017-05-01

Structured-light projection is a well-established optical method for the non-destructive contactless three-dimensional (3D) measurement of object surfaces. In particular, there is a great demand for accurate and fast 3D scans of human faces or facial regions of interest in medicine, safety, face modeling, games, virtual life, or entertainment. New developments of facial expression detection and machine lip-reading can be used for communication tasks, future machine control, or human-machine interactions. In such cases, 3D information may offer more detailed information than 2D images which can help to increase the power of current facial analysis algorithms. In this contribution, we present new 3D sensor technologies based on three different methods of near-infrared projection technologies in combination with a stereo vision setup of two cameras. We explain the optical principles of an NIR GOBO projector, an array projector and a modified multi-aperture projection method and compare their performance parameters to each other. Further, we show some experimental measurement results of applications where we realized fast, accurate, and irritation-free measurements of human faces.

Behavioral Dynamics in the Cooperative Control of Mixed Human/Robotic Teams

DTIC Science & Technology

2015-01-05

models of cognitive and social psychology play a major role in the work. A particular objective is to develop a fundamental understanding of how...dynamics. In addition to exploring cognitive and social psychological aspects of decision making, research is focused on formal approaches to...SUBJECT TERMS human-machine interactions, two-alternative-forced-choice (TAFC), cognitive and social psychological aspects of decision making, action

Open-Box Muscle-Computer Interface: Introduction to Human-Computer Interactions in Bioengineering, Physiology, and Neuroscience Courses

ERIC Educational Resources Information Center

Landa-Jiménez, M. A.; González-Gaspar, P.; Pérez-Estudillo, C.; López-Meraz, M. L.; Morgado-Valle, C.; Beltran-Parrazal, L.

2016-01-01

A Muscle-Computer Interface (muCI) is a human-machine system that uses electromyographic (EMG) signals to communicate with a computer. Surface EMG (sEMG) signals are currently used to command robotic devices, such as robotic arms and hands, and mobile robots, such as wheelchairs. These signals reflect the motor intention of a user before the…

Non-Destructive Analysis of Basic Surface Characteristics of Titanium Dental Implants Made by Miniature Machining

NASA Astrophysics Data System (ADS)

Babík, Ondrej; Czán, Andrej; Holubják, Jozef; Kameník, Roman; Pilc, Jozef

2016-12-01

One of the most best-known characteristic and important requirement of dental implant is made of biomaterials ability to create correct interaction between implant and human body. The most implemented material in manufacturing of dental implants is titanium of different grades of pureness. Since most of the implant surface is in direct contact with bone tissue, shape and integrity of said surface has great influence on the successful osseointegration. Among other characteristics of titanium that predetermine ideal biomaterial, it shows a high mechanical strength making precise machining miniature Increasingly difficult. The article is focused on evaluation of the resulting quality, integrity and characteristics of dental implants surface after machining.

Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception

PubMed Central

Złotowski, Jakub A.; Sumioka, Hidenobu; Nishio, Shuichi; Glas, Dylan F.; Bartneck, Christoph; Ishiguro, Hiroshi

2015-01-01

The uncanny valley theory proposed by Mori has been heavily investigated in the recent years by researchers from various fields. However, the videos and images used in these studies did not permit any human interaction with the uncanny objects. Therefore, in the field of human-robot interaction it is still unclear what, if any, impact an uncanny-looking robot will have in the context of an interaction. In this paper we describe an exploratory empirical study using a live interaction paradigm that involved repeated interactions with robots that differed in embodiment and their attitude toward a human. We found that both investigated components of the uncanniness (likeability and eeriness) can be affected by an interaction with a robot. Likeability of a robot was mainly affected by its attitude and this effect was especially prominent for a machine-like robot. On the other hand, merely repeating interactions was sufficient to reduce eeriness irrespective of a robot's embodiment. As a result we urge other researchers to investigate Mori's theory in studies that involve actual human-robot interaction in order to fully understand the changing nature of this phenomenon. PMID:26175702

Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception.

PubMed

Złotowski, Jakub A; Sumioka, Hidenobu; Nishio, Shuichi; Glas, Dylan F; Bartneck, Christoph; Ishiguro, Hiroshi

2015-01-01

The uncanny valley theory proposed by Mori has been heavily investigated in the recent years by researchers from various fields. However, the videos and images used in these studies did not permit any human interaction with the uncanny objects. Therefore, in the field of human-robot interaction it is still unclear what, if any, impact an uncanny-looking robot will have in the context of an interaction. In this paper we describe an exploratory empirical study using a live interaction paradigm that involved repeated interactions with robots that differed in embodiment and their attitude toward a human. We found that both investigated components of the uncanniness (likeability and eeriness) can be affected by an interaction with a robot. Likeability of a robot was mainly affected by its attitude and this effect was especially prominent for a machine-like robot. On the other hand, merely repeating interactions was sufficient to reduce eeriness irrespective of a robot's embodiment. As a result we urge other researchers to investigate Mori's theory in studies that involve actual human-robot interaction in order to fully understand the changing nature of this phenomenon.

Human operator performance of remotely controlled tasks: Teleoperator research conducted at NASA's George C. Marshall Space Flight Center. Executive summary

NASA Technical Reports Server (NTRS)

Shields, N., Jr.; Piccione, F.; Kirkpatrick, M., III; Malone, T. B.

1982-01-01

The combination of human and machine capabilities into an integrated engineering system which is complex and interactive interdisciplinary undertaking is discussed. Human controlled remote systems referred to as teleoperators, are reviewed. The human factors requirements for remotely manned systems are identified. The data were developed in three principal teleoperator laboratories and the visual, manipulator and mobility laboratories are described. Three major sections are identified: (1) remote system components, (2) human operator considerations; and (3) teleoperator system simulation and concept verification.

An argument for human exploration of the moon and Mars.

PubMed

Spudis, P D

1992-01-01

A debate of the merits of human space travel as opposed to robots is presented. While robotic space travel would be considerably less expensive, the author takes the position that there are certain skills and research abilities that only humans possess. Human contributions to past lunar exploration are considered, along with a discussion of the interaction of humans with robotics or other artificial intelligence or computer driven technologies. The author concludes that while robots and machines are tools which should be incorporated into space travel, they are not adequate substitutes for people.

Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

PubMed

Sugaya, Nobuyoshi; Ikeda, Kazuyoshi

2009-08-25

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM). To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways. Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.

Interactive Relationships with Computers in Teaching Reading.

ERIC Educational Resources Information Center

Doublier, Rene M.

This study summarizes recent achievements in the expanding development of man/machine communications and reviews current technological hurdles associated with the development of artificial intelligence systems which can generate and recognize human speech patterns. With the development of such systems, one potential application would be the…

Intelligent OCR Processing.

ERIC Educational Resources Information Center

Sun, Wei; And Others

1992-01-01

Identifies types and distributions of errors in text produced by optical character recognition (OCR) and proposes a process using machine learning techniques to recognize and correct errors in OCR texts. Results of experiments indicating that this strategy can reduce human interaction required for error correction are reported. (25 references)…

Toward interactive search in remote sensing imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Porter, Reid B; Hush, Do; Harvey, Neal

2010-01-01

To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new designmore » criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remote sensing imagery.« less

Identifying well-formed biomedical phrases in MEDLINE® text.

PubMed

Kim, Won; Yeganova, Lana; Comeau, Donald C; Wilbur, W John

2012-12-01

In the modern world people frequently interact with retrieval systems to satisfy their information needs. Humanly understandable well-formed phrases represent a crucial interface between humans and the web, and the ability to index and search with such phrases is beneficial for human-web interactions. In this paper we consider the problem of identifying humanly understandable, well formed, and high quality biomedical phrases in MEDLINE documents. The main approaches used previously for detecting such phrases are syntactic, statistical, and a hybrid approach combining these two. In this paper we propose a supervised learning approach for identifying high quality phrases. First we obtain a set of known well-formed useful phrases from an existing source and label these phrases as positive. We then extract from MEDLINE a large set of multiword strings that do not contain stop words or punctuation. We believe this unlabeled set contains many well-formed phrases. Our goal is to identify these additional high quality phrases. We examine various feature combinations and several machine learning strategies designed to solve this problem. A proper choice of machine learning methods and features identifies in the large collection strings that are likely to be high quality phrases. We evaluate our approach by making human judgments on multiword strings extracted from MEDLINE using our methods. We find that over 85% of such extracted phrase candidates are humanly judged to be of high quality. Published by Elsevier Inc.

Conversational sensing

NASA Astrophysics Data System (ADS)

Preece, Alun; Gwilliams, Chris; Parizas, Christos; Pizzocaro, Diego; Bakdash, Jonathan Z.; Braines, Dave

2014-05-01

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it pos- sible to represent information fusion and situational awareness for Intelligence, Surveillance and Reconnaissance (ISR) activities as a conversational process among actors at or near the tactical edges of a network. Motivated by use cases in the domain of Company Intelligence Support Team (CoIST) tasks, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled nat- ural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a ow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both soldier and civilian sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by man- agement and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects.

Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffith, Douglas; Greitzer, Frank L.

We re-address the vision of human-computer symbiosis expressed by J. C. R. Licklider nearly a half-century ago, when he wrote: “The hope is that in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” (Licklider, 1960). Unfortunately, little progress was made toward this vision over four decades following Licklider’s challenge, despite significant advancements in the fields of human factors and computer science. Licklider’s vision wasmore » largely forgotten. However, recent advances in information science and technology, psychology, and neuroscience have rekindled the potential of making the Licklider’s vision a reality. This paper provides a historical context for and updates the vision, and it argues that such a vision is needed as a unifying framework for advancing IS&T.« less

RAPID and HTML5's potential

NASA Technical Reports Server (NTRS)

Torosyan, David

2012-01-01

Just as important as the engineering that goes into building a robot is the method of interaction, or how human users will use the machine. As part of the Human-System Interactions group (Conductor) at JPL, I explored using a web interface to interact with ATHLETE, a prototype lunar rover. I investigated the usefulness of HTML 5 and Javascript as a telemetry viewer as well as the feasibility of having a rover communicate with a web server. To test my ideas I built a mobile-compatible website and designed primarily for an Android tablet. The website took input from ATHLETE engineers, and upon its completion I conducted a user test to assess its effectiveness.

Man-machine interfaces in LACIE/ERIPS

NASA Technical Reports Server (NTRS)

Duprey, B. B. (Principal Investigator)

1979-01-01

One of the most important aspects of the interactive portion of the LACIE/ERIPS software system is the way in which the analysis and decision-making capabilities of a human being are integrated with the speed and accuracy of a computer to produce a powerful analysis system. The three major man-machine interfaces in the system are (1) the use of menus for communications between the software and the interactive user; (2) the checkpoint/restart facility to recreate in one job the internal environment achieved in an earlier one; and (3) the error recovery capability which would normally cause job termination. This interactive system, which executes on an IBM 360/75 mainframe, was adapted for use in noninteractive (batch) mode. A case study is presented to show how the interfaces work in practice by defining some fields based on an image screen display, noting the field definitions, and obtaining a film product of the classification map.

A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine

PubMed Central

Lee, Haerin; Jung, Moonki; Lee, Ki-Kwang; Lee, Sang Hun

2017-01-01

In this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human–machine–environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines. PMID:28178184

Privacy preserving interactive record linkage (PPIRL).

PubMed

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.

Automation effects in a stereotypical multiloop manual control system. [for aircraft

NASA Technical Reports Server (NTRS)

Hess, R. A.; Mcnally, B. D.

1984-01-01

The increasing reliance of state-of-the art, high performance aircraft on high authority stability and command augmentation systems, in order to obtain satisfactory performance and handling qualities, has made critical the achievement of a better understanding of human capabilities, limitations, and preferences during interactions with complex dynamic systems that involve task allocation between man and machine. An analytical and experimental study has been undertaken to investigate human interaction with a simple, multiloop dynamic system in which human activity was systematically varied by changing the levels of automation. Task definition has led to a control loop structure which parallels that for any multiloop manual control system, and may therefore be considered a stereotype.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Defining brain-machine interface applications by matching interface performance with device requirements.

PubMed

Tonet, Oliver; Marinelli, Martina; Citi, Luca; Rossini, Paolo Maria; Rossini, Luca; Megali, Giuseppe; Dario, Paolo

2008-01-15

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC.

PubMed

Zhai, Jing-Xuan; Cao, Tian-Jie; An, Ji-Yong; Bian, Yong-Tao

2017-11-07

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB. Copyright © 2017 Elsevier Ltd. All rights reserved.

Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

PubMed

Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

2013-04-01

Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

NASA Technical Reports Server (NTRS)

Yliniemi, Logan; Agogino, Adrian K.; Tumer, Kagan

2014-01-01

Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.

Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff

2017-05-01

The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.

Re-Design and Beat Testing of the Man-Machine Integration Design and Analysis System: MIDAS

NASA Technical Reports Server (NTRS)

Shively, R. Jay; Rutkowski, Michael (Technical Monitor)

1999-01-01

The Man-machine Design and Analysis System (MIDAS) is a human factors design and analysis system that combines human cognitive models with 3D CAD models and rapid prototyping and simulation techniques. MIDAS allows designers to ask 'what if' types of questions early in concept exploration and development prior to actual hardware development. The system outputs predictions of operator workload, situational awareness and system performance as well as graphical visualization of the cockpit designs interacting with models of the human in a mission scenario. Recently, MIDAS was re-designed to enhance functionality and usability. The goals driving the redesign include more efficient processing, GUI interface, advances in the memory structures, implementation of external vision models and audition. These changes were detailed in an earlier paper. Two Beta test sites with diverse applications have been chosen. One Beta test site is investigating the development of a new airframe and its interaction with the air traffic management system. The second Beta test effort will investigate 3D auditory cueing in conjunction with traditional visual cueing strategies including panel-mounted and heads-up displays. The progress and lessons learned on each of these projects will be discussed.

Identification of four class emotion from Indonesian spoken language using acoustic and lexical features

NASA Astrophysics Data System (ADS)

Kasyidi, Fatan; Puji Lestari, Dessi

2018-03-01

One of the important aspects in human to human communication is to understand emotion of each party. Recently, interactions between human and computer continues to develop, especially affective interaction where emotion recognition is one of its important components. This paper presents our extended works on emotion recognition of Indonesian spoken language to identify four main class of emotions: Happy, Sad, Angry, and Contentment using combination of acoustic/prosodic features and lexical features. We construct emotion speech corpus from Indonesia television talk show where the situations are as close as possible to the natural situation. After constructing the emotion speech corpus, the acoustic/prosodic and lexical features are extracted to train the emotion model. We employ some machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes, and Random Forest to get the best model. The experiment result of testing data shows that the best model has an F-measure score of 0.447 by using only the acoustic/prosodic feature and F-measure score of 0.488 by using both acoustic/prosodic and lexical features to recognize four class emotion using the SVM RBF Kernel.

Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-01-01

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

State Event Models for the Formal Analysis of Human-Machine Interactions

NASA Technical Reports Server (NTRS)

Combefis, Sebastien; Giannakopoulou, Dimitra; Pecheur, Charles

2014-01-01

The work described in this paper was motivated by our experience with applying a framework for formal analysis of human-machine interactions (HMI) to a realistic model of an autopilot. The framework is built around a formally defined conformance relation called "fullcontrol" between an actual system and the mental model according to which the system is operated. Systems are well-designed if they can be described by relatively simple, full-control, mental models for their human operators. For this reason, our framework supports automated generation of minimal full-control mental models for HMI systems, where both the system and the mental models are described as labelled transition systems (LTS). The autopilot that we analysed has been developed in the NASA Ames HMI prototyping tool ADEPT. In this paper, we describe how we extended the models that our HMI analysis framework handles to allow adequate representation of ADEPT models. We then provide a property-preserving reduction from these extended models to LTSs, to enable application of our LTS-based formal analysis algorithms. Finally, we briefly discuss the analyses we were able to perform on the autopilot model with our extended framework.

Finding Waldo: Learning about Users from their Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Eli T.; Ottley, Alvitta; Zhao, Helen

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user’s interactions with a system reflect a large amount of the user’s reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user’s task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, wemore » conduct an experiment in which participants perform a visual search task and we apply well-known machine learning algorithms to three encodings of the users interaction data. We achieve, depending on algorithm and encoding, between 62% and 96% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user’s personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time, in some cases, 82% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed- initiative visual analytics systems.« less

An Overview of Computer-Based Natural Language Processing.

ERIC Educational Resources Information Center

Gevarter, William B.

Computer-based Natural Language Processing (NLP) is the key to enabling humans and their computer-based creations to interact with machines using natural languages (English, Japanese, German, etc.) rather than formal computer languages. NLP is a major research area in the fields of artificial intelligence and computational linguistics. Commercial…

Intelligent Adaptive Interface: A Design Tool for Enhancing Human-Machine System Performances

DTIC Science & Technology

2009-10-01

and customizable. Thus, an intelligent interface should tailor its parameters to certain prescribed specifications or convert itself and adjust to...Computer Interaction 3(2): 87-122. [51] Schereiber, G., Akkermans, H., Anjewierden, A., de Hoog , R., Shadbolt, N., Van de Velde, W., & Wielinga, W

Software Should be Written by Writers.

ERIC Educational Resources Information Center

Sheridan, James

1983-01-01

Considering the computer as a collaborator rather than a machine, it is encouraged that those in the humanities and the arts fields take advantage of the great potential that artificial intelligence can offer. Stresses that unless deliberately restricted, the computer is an inherently interdisciplinary medium, and capable of interacting with any…

Gesture Recognition Based on the Probability Distribution of Arm Trajectories

NASA Astrophysics Data System (ADS)

Wan, Khairunizam; Sawada, Hideyuki

The use of human motions for the interaction between humans and computers is becoming an attractive alternative to verbal media, especially through the visual interpretation of the human body motion. In particular, hand gestures are used as non-verbal media for the humans to communicate with machines that pertain to the use of the human gestures to interact with them. This paper introduces a 3D motion measurement of the human upper body for the purpose of the gesture recognition, which is based on the probability distribution of arm trajectories. In this study, by examining the characteristics of the arm trajectories given by a signer, motion features are selected and classified by using a fuzzy technique. Experimental results show that the use of the features extracted from arm trajectories effectively works on the recognition of dynamic gestures of a human, and gives a good performance to classify various gesture patterns.

Analysis in Motion Initiative – Human Machine Intelligence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blaha, Leslie

As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.

Modeling and prediction of human word search behavior in interactive machine translation

NASA Astrophysics Data System (ADS)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Human Machine Interfaces for Teleoperators and Virtual Environments Conference

NASA Technical Reports Server (NTRS)

1990-01-01

In a teleoperator system the human operator senses, moves within, and operates upon a remote or hazardous environment by means of a slave mechanism (a mechanism often referred to as a teleoperator). In a virtual environment system the interactive human machine interface is retained but the slave mechanism and its environment are replaced by a computer simulation. Video is replaced by computer graphics. The auditory and force sensations imparted to the human operator are similarly computer generated. In contrast to a teleoperator system, where the purpose is to extend the operator's sensorimotor system in a manner that facilitates exploration and manipulation of the physical environment, in a virtual environment system, the purpose is to train, inform, alter, or study the human operator to modify the state of the computer and the information environment. A major application in which the human operator is the target is that of flight simulation. Although flight simulators have been around for more than a decade, they had little impact outside aviation presumably because the application was so specialized and so expensive.

A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics.

PubMed

Beckerle, Philipp; Salvietti, Gionata; Unal, Ramazan; Prattichizzo, Domenico; Rossi, Simone; Castellini, Claudio; Hirche, Sandra; Endo, Satoshi; Amor, Heni Ben; Ciocarlie, Matei; Mastrogiovanni, Fulvio; Argall, Brenna D; Bianchi, Matteo

2017-01-01

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.

Comparison of Human and Machine Scoring of Essays: Differences by Gender, Ethnicity, and Country

ERIC Educational Resources Information Center

Bridgeman, Brent; Trapani, Catherine; Attali, Yigal

2012-01-01

Essay scores generated by machine and by human raters are generally comparable; that is, they can produce scores with similar means and standard deviations, and machine scores generally correlate as highly with human scores as scores from one human correlate with scores from another human. Although human and machine essay scores are highly related…

Research in interactive scene analysis

NASA Technical Reports Server (NTRS)

Tenenbaum, J. M.; Barrow, H. G.; Weyl, S. A.

1976-01-01

Cooperative (man-machine) scene analysis techniques were developed whereby humans can provide a computer with guidance when completely automated processing is infeasible. An interactive approach promises significant near-term payoffs in analyzing various types of high volume satellite imagery, as well as vehicle-based imagery used in robot planetary exploration. This report summarizes the work accomplished over the duration of the project and describes in detail three major accomplishments: (1) the interactive design of texture classifiers; (2) a new approach for integrating the segmentation and interpretation phases of scene analysis; and (3) the application of interactive scene analysis techniques to cartography.

Integrating artificial and human intelligence into tablet production process.

PubMed

Gams, Matjaž; Horvat, Matej; Ožek, Matej; Luštrek, Mitja; Gradišek, Anton

2014-12-01

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

Automatic decoding of facial movements reveals deceptive pain expressions

PubMed Central

Bartlett, Marian Stewart; Littlewort, Gwen C.; Frank, Mark G.; Lee, Kang

2014-01-01

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]. A subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions. A cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. Machine vision may, however, be able to distinguish deceptive from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here we show that human observers could not discriminate real from faked expressions of pain better than chance, and after training, improved accuracy to a modest 55%. However a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system’s superiority is attributable to its ability to differentiate the dynamics of genuine from faked expressions. Thus by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling. PMID:24656830

A Framework to Guide the Assessment of Human-Machine Systems.

PubMed

Stowers, Kimberly; Oglesby, James; Sonesh, Shirley; Leyva, Kevin; Iwig, Chelsea; Salas, Eduardo

2017-03-01

We have developed a framework for guiding measurement in human-machine systems. The assessment of safety and performance in human-machine systems often relies on direct measurement, such as tracking reaction time and accidents. However, safety and performance emerge from the combination of several variables. The assessment of precursors to safety and performance are thus an important part of predicting and improving outcomes in human-machine systems. As part of an in-depth literature analysis involving peer-reviewed, empirical articles, we located and classified variables important to human-machine systems, giving a snapshot of the state of science on human-machine system safety and performance. Using this information, we created a framework of safety and performance in human-machine systems. This framework details several inputs and processes that collectively influence safety and performance. Inputs are divided according to human, machine, and environmental inputs. Processes are divided into attitudes, behaviors, and cognitive variables. Each class of inputs influences the processes and, subsequently, outcomes that emerge in human-machine systems. This framework offers a useful starting point for understanding the current state of the science and measuring many of the complex variables relating to safety and performance in human-machine systems. This framework can be applied to the design, development, and implementation of automated machines in spaceflight, military, and health care settings. We present a hypothetical example in our write-up of how it can be used to aid in project success.

Man-machine interactive imaging and data processing using high-speed digital mass storage

NASA Technical Reports Server (NTRS)

Alsberg, H.; Nathan, R.

1975-01-01

The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations.

Assessing Multi-Person and Person-Machine Distributed Decision Making Using an Extended Psychological Distancing Model

DTIC Science & Technology

1990-02-01

human-to- human communication patterns during situation assessment and cooperative problem solving tasks. The research proposed for the second URRP year...Hardware development. In order to create an environment within which to study multi-channeled human-to- human communication , a multi-media observation...that machine-to- human communication can be used to increase cohesion between humans and intelligent machines and to promote human-machine team

The role of soft computing in intelligent machines.

PubMed

de Silva, Clarence W

2003-08-15

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Learning and Optimization of Cognitive Capabilities. Final Project Report.

ERIC Educational Resources Information Center

Lumsdaine, A.A.; And Others

The work of a three-year series of experimental studies of human cognition is summarized in this report. Proglem solving and learning in man-machine interaction was investigated, as well as relevant variables and processes. The work included four separate projects: (1) computer-aided problem solving, (2) computer-aided instruction techniques, (3)…

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

NASA Astrophysics Data System (ADS)

Quitadamo, L. R.; Cavrini, F.; Sbernini, L.; Riillo, F.; Bianchi, L.; Seri, S.; Saggio, G.

2017-02-01

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Modeling human-machine interactions for operations room layouts

NASA Astrophysics Data System (ADS)

Hendy, Keith C.; Edwards, Jack L.; Beevis, David

2000-11-01

The LOCATE layout analysis tool was used to analyze three preliminary configurations for the Integrated Command Environment (ICE) of a future USN platform. LOCATE develops a cost function reflecting the quality of all human-human and human-machine communications within a workspace. This proof- of-concept study showed little difference between the efficacy of the preliminary designs selected for comparison. This was thought to be due to the limitations of the study, which included the assumption of similar size for each layout and a lack of accurate measurement data for various objects in the designs, due largely to their notional nature. Based on these results, the USN offered an opportunity to conduct a LOCATE analysis using more appropriate assumptions. A standard crew was assumed, and subject matter experts agreed on the communications patterns for the analysis. Eight layouts were evaluated with the concepts of coordination and command factored into the analysis. Clear differences between the layouts emerged. The most promising design was refined further by the USN, and a working mock-up built for human-in-the-loop evaluation. LOCATE was applied to this configuration for comparison with the earlier analyses.

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2014 CFR

2014-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2012 CFR

2012-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2014 CFR

2014-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2012 CFR

2012-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2013 CFR

2013-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2013 CFR

2013-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

ODISEES: A New Paradigm in Data Access

NASA Astrophysics Data System (ADS)

Huffer, E.; Little, M. M.; Kusterer, J.

2013-12-01

As part of its ongoing efforts to improve access to data, the Atmospheric Science Data Center has developed a high-precision Earth Science domain ontology (the 'ES Ontology') implemented in a graph database ('the Semantic Metadata Repository') that is used to store detailed, semantically-enhanced, parameter-level metadata for ASDC data products. The ES Ontology provides the semantic infrastructure needed to drive the ASDC's Ontology-Driven Interactive Search Environment for Earth Science ('ODISEES'), a data discovery and access tool, and will support additional data services such as analytics and visualization. The ES ontology is designed on the premise that naming conventions alone are not adequate to provide the information needed by prospective data consumers to assess the suitability of a given dataset for their research requirements; nor are current metadata conventions adequate to support seamless machine-to-machine interactions between file servers and end-user applications. Data consumers need information not only about what two data elements have in common, but also about how they are different. End-user applications need consistent, detailed metadata to support real-time data interoperability. The ES ontology is a highly precise, bottom-up, queriable model of the Earth Science domain that focuses on critical details about the measurable phenomena, instrument techniques, data processing methods, and data file structures. Earth Science parameters are described in detail in the ES Ontology and mapped to the corresponding variables that occur in ASDC datasets. Variables are in turn mapped to well-annotated representations of the datasets that they occur in, the instrument(s) used to create them, the instrument platforms, the processing methods, etc., creating a linked-data structure that allows both human and machine users to access a wealth of information critical to understanding and manipulating the data. The mappings are recorded in the Semantic Metadata Repository as RDF-triples. An off-the-shelf Ontology Development Environment and a custom Metadata Conversion Tool comprise a human-machine/machine-machine hybrid tool that partially automates the creation of metadata as RDF-triples by interfacing with existing metadata repositories and providing a user interface that solicits input from a human user, when needed. RDF-triples are pushed to the Ontology Development Environment, where a reasoning engine executes a series of inference rules whose antecedent conditions can be satisfied by the initial set of RDF-triples, thereby generating the additional detailed metadata that is missing in existing repositories. A SPARQL Endpoint, a web-based query service and a Graphical User Interface allow prospective data consumers - even those with no familiarity with NASA data products - to search the metadata repository to find and order data products that meet their exact specifications. A web-based API will provide an interface for machine-to-machine transactions.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Program Predicts Time Courses of Human/Computer Interactions

NASA Technical Reports Server (NTRS)

Vera, Alonso; Howes, Andrew

2005-01-01

CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.

The experience of agency in human-computer interactions: a review

PubMed Central

Limerick, Hannah; Coyle, David; Moore, James W.

2014-01-01

The sense of agency is the experience of controlling both one’s body and the external environment. Although the sense of agency has been studied extensively, there is a paucity of studies in applied “real-life” situations. One applied domain that seems highly relevant is human-computer-interaction (HCI), as an increasing number of our everyday agentive interactions involve technology. Indeed, HCI has long recognized the feeling of control as a key factor in how people experience interactions with technology. The aim of this review is to summarize and examine the possible links between sense of agency and understanding control in HCI. We explore the overlap between HCI and sense of agency for computer input modalities and system feedback, computer assistance, and joint actions between humans and computers. An overarching consideration is how agency research can inform HCI and vice versa. Finally, we discuss the potential ethical implications of personal responsibility in an ever-increasing society of technology users and intelligent machine interfaces. PMID:25191256

Human Engineering Operations and Habitability Assessment: A Process for Advanced Life Support Ground Facility Testbeds

NASA Technical Reports Server (NTRS)

Connolly, Janis H.; Arch, M.; Elfezouaty, Eileen Schultz; Novak, Jennifer Blume; Bond, Robert L. (Technical Monitor)

1999-01-01

Design and Human Engineering (HE) processes strive to ensure that the human-machine interface is designed for optimal performance throughout the system life cycle. Each component can be tested and assessed independently to assure optimal performance, but it is not until full integration that the system and the inherent interactions between the system components can be assessed as a whole. HE processes (which are defining/app lying requirements for human interaction with missions/systems) are included in space flight activities, but also need to be included in ground activities and specifically, ground facility testbeds such as Bio-Plex. A unique aspect of the Bio-Plex Facility is the integral issue of Habitability which includes qualities of the environment that allow humans to work and live. HE is a process by which Habitability and system performance can be assessed.

Singularity now: using the ventricular assist device as a model for future human-robotic physiology.

PubMed

Martin, Archer K

2016-04-01

In our 21 st century world, human-robotic interactions are far more complicated than Asimov predicted in 1942. The future of human-robotic interactions includes human-robotic machine hybrids with an integrated physiology, working together to achieve an enhanced level of baseline human physiological performance. This achievement can be described as a biological Singularity. I argue that this time of Singularity cannot be met by current biological technologies, and that human-robotic physiology must be integrated for the Singularity to occur. In order to conquer the challenges we face regarding human-robotic physiology, we first need to identify a working model in today's world. Once identified, this model can form the basis for the study, creation, expansion, and optimization of human-robotic hybrid physiology. In this paper, I present and defend the line of argument that currently this kind of model (proposed to be named "IshBot") can best be studied in ventricular assist devices - VAD.

Singularity now: using the ventricular assist device as a model for future human-robotic physiology

PubMed Central

Martin, Archer K.

2016-01-01

In our 21st century world, human-robotic interactions are far more complicated than Asimov predicted in 1942. The future of human-robotic interactions includes human-robotic machine hybrids with an integrated physiology, working together to achieve an enhanced level of baseline human physiological performance. This achievement can be described as a biological Singularity. I argue that this time of Singularity cannot be met by current biological technologies, and that human-robotic physiology must be integrated for the Singularity to occur. In order to conquer the challenges we face regarding human-robotic physiology, we first need to identify a working model in today’s world. Once identified, this model can form the basis for the study, creation, expansion, and optimization of human-robotic hybrid physiology. In this paper, I present and defend the line of argument that currently this kind of model (proposed to be named “IshBot”) can best be studied in ventricular assist devices – VAD. PMID:28913480

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

PubMed Central

Zheng, Shuai; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A

2017-01-01

Background Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Objective Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. Methods A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Results Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. Conclusions IDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. PMID:28487265

Thermal expression of intersubjectivity offers new possibilities to human–machine and technologically mediated interactions

PubMed Central

Merla, Arcangelo

2014-01-01

The evaluation of the psychophysiological state of the interlocutor is an important element of interpersonal relationships and communication. Thermal infrared (IR) imaging has proved to be a reliable tool for non-invasive and contact-less evaluation of vital signs, psychophysiological responses, and emotional states. This technique is quickly spreading in many fields, from psychometrics to social and developmental psychology; and from the touch-less monitoring of vital signs and stress, up to the human–machine interaction. In particular, thermal IR imaging promises to be of use for gathering information about affective states in social situations. This paper presents the state of the art of thermal IR imaging in psychophysiology and in the assessment of affective states. The goal is to provide insights about its potentialities and limits for its use in human–artificial agent interaction in order to contribute to a major issue in the field: the perception by an artificial agent of human psychophysiological and affective states. PMID:25101046

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.

PubMed

Zander, Thorsten O; Krol, Laurens R; Birbaumer, Niels P; Gramann, Klaus

2016-12-27

The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.

Prediction of bacterial associations with plants using a supervised machine-learning approach.

PubMed

Martínez-García, Pedro Manuel; López-Solanilla, Emilia; Ramos, Cayo; Rodríguez-Palenzuela, Pablo

2016-12-01

Recent scenarios of fresh produce contamination by human enteric pathogens have resulted in severe food-borne outbreaks, and a new paradigm has emerged stating that some human-associated bacteria can use plants as secondary hosts. As a consequence, there has been growing concern in the scientific community about these interactions that have not yet been elucidated. Since this is a relatively new area, there is a lack of strategies to address the problem of food-borne illnesses due to the ingestion of fruits and vegetables. In the present study, we performed specific genome annotations to train a supervised machine-learning model that allows for the identification of plant-associated bacteria with a precision of ∼93%. The application of our method to approximately 9500 genomes predicted several unknown interactions between well-known human pathogens and plants, and it also confirmed several cases for which evidence has been reported. We observed that factors involved in adhesion, the deconstruction of the plant cell wall and detoxifying activities were highlighted as the most predictive features. The application of our strategy to sequenced strains that are involved in food poisoning can be used as a primary screening tool to determine the possible causes of contaminations. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Human-like machines: Transparency and comprehensibility.

PubMed

Patrzyk, Piotr M; Link, Daniela; Marewski, Julian N

2017-01-01

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

Experiencing the Sights, Smells, Sounds, and Climate of Southern Italy in VR.

PubMed

Manghisi, Vito M; Fiorentino, Michele; Gattullo, Michele; Boccaccio, Antonio; Bevilacqua, Vitoantonio; Cascella, Giuseppe L; Dassisti, Michele; Uva, Antonio E

2017-01-01

This article explores what it takes to make interactive computer graphics and VR attractive as a promotional vehicle, from the points of view of tourism agencies and the tourists themselves. The authors exploited current VR and human-machine interface (HMI) technologies to develop an interactive, innovative, and attractive user experience called the Multisensory Apulia Touristic Experience (MATE). The MATE system implements a natural gesture-based interface and multisensory stimuli, including visuals, audio, smells, and climate effects.

Digital Systems Validation Handbook. Volume 2. Chapter 19. Pilot - Vehicle Interface

DTIC Science & Technology

1993-11-01

checklists, and other status messages. Voice interactive systems are defi-ed as "the interface between a cooperative human and a machine, which involv -he...Pilot-Vehicle Interface 19-85 5.6.1 Crew Interaction and the Cockpit 19-85 5.6.2 Crew Resource Management and Safety 19-87 5.6.3 Pilot and Crew Training...systems was a "stand-alone" component performing its intended function. Systems and their cockpit interfaces were added as technological advances were

Children's Beliefs about the Fantasy/Reality Status of Hypothesized Machines

ERIC Educational Resources Information Center

Cook, Claire; Sobel, David M.

2011-01-01

Four-year-olds, 6-year-olds, and adults were asked to make judgments about the reality status of four different types of machines: real machines that children and adults interact with on a daily basis, real machines that children and adults interact with rarely (if at all), and impossible machines that violated a real-world physical or biological…

Can Machine Scoring Deal with Broad and Open Writing Tests as Well as Human Readers?

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

This article considers the claim that machine scoring of writing test responses agrees with human readers as much as humans agree with other humans. These claims about the reliability of machine scoring of writing are usually based on specific and constrained writing tasks, and there is reason for asking whether machine scoring of writing requires…

Study About Ceiling Design for Main Control Room of NPP with HFE

NASA Astrophysics Data System (ADS)

Gu, Pengfei; Ni, Ying; Chen, Weihua; Chen, Bo; Zhang, Jianbo; Liang, Huihui

Recently since human factor engineering (HFE) has been used in control room design of nuclear power plant (NPP), the human-machine interface (HMI) has been gradual to develop harmoniously, especially the use of the digital technology. Comparing with the analog technology which was used to human-machine interface in the past, human-machine interaction has been more enhanced. HFE and the main control room (MCR) design engineering of NPP is a combination of multidisciplinary cross, mainly related to electrical and instrument control, reactor, machinery, systems engineering and management disciplines. However, MCR is not only equipped with HMI provided by the equipments, but also more important for the operator to provide a work environment, such as the main control room ceiling. The ceiling design of main control room related to HFE which influences the performance of staff should also be considered in the design of the environment and aesthetic factors, especially the introduction of professional design experience and evaluation method. Based on Ling Ao phase II and Hong Yanhe project implementation experience, the study analyzes lighting effect, space partition, vision load about the ceiling of main control room of NPP. Combining with the requirements of standards, the advantages and disadvantages of the main control room ceiling design has been discussed, and considering the requirements of lightweight, noise reduction, fire prevention, moisture protection, the ceiling design solution of the main control room also has been discussed.

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2007-01-01

machine learning components natural language processing, and optimization...was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting...study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system

DARPA Robotics Challenge (DRC) Using Human-Machine Teamwork to Perform Disaster Response with a Humanoid Robot

DTIC Science & Technology

2017-02-01

DARPA ROBOTICS CHALLENGE (DRC) USING HUMAN-MACHINE TEAMWORK TO PERFORM DISASTER RESPONSE WITH A HUMANOID ROBOT FLORIDA INSTITUTE FOR HUMAN AND...AND SUBTITLE DARPA ROBOTICS CHALLENGE (DRC) USING HUMAN-MACHINE TEAMWORK TO PERFORM DISASTER RESPONSE WITH A HUMANOID ROBOT 5a. CONTRACT NUMBER...Human and Machine Cognition (IHMC) from 2012-2016 through three phases of the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge

Dialogue-Based Call: A Case Study on Teaching Pronouns

ERIC Educational Resources Information Center

Vlugter, P.; Knott, A.; McDonald, J.; Hall, C.

2009-01-01

We describe a computer assisted language learning (CALL) system that uses human-machine dialogue as its medium of interaction. The system was developed to help students learn the basics of the Maori language and was designed to accompany the introductory course in Maori running at the University of Otago. The student engages in a task-based…

Cognitive Support Embedded in Self-Regulated E-Learning Systems for Students with Special Learning Needs

ERIC Educational Resources Information Center

Chatzara, K.; Karagiannidis, C.; Stamatis, D.

2016-01-01

This paper presents an anthropocentric approach in human-machine interaction in the area of self-regulated e-learning. In an attempt to enhance communication mediated through computers for pedagogical use we propose the incorporation of an intelligent emotional agent that is represented by a synthetic character with multimedia capabilities,…

User-Based Information Retrieval System Interface Evaluation: An Examination of an On-Line Public Access Catalog.

ERIC Educational Resources Information Center

Hert, Carol A.; Nilan, Michael S.

1991-01-01

Presents preliminary data that characterizes the relationship between what users say they are trying to accomplish when using an online public access catalog (OPAC) and their perceptions of what input to give the system. Human-machine interaction is discussed, and appropriate methods for evaluating information retrieval systems are considered. (18…

Clustering social cues to determine social signals: developing learning algorithms using the "n-most likely states" approach

NASA Astrophysics Data System (ADS)

Best, Andrew; Kapalo, Katelynn A.; Warta, Samantha F.; Fiore, Stephen M.

2016-05-01

Human-robot teaming largely relies on the ability of machines to respond and relate to human social signals. Prior work in Social Signal Processing has drawn a distinction between social cues (discrete, observable features) and social signals (underlying meaning). For machines to attribute meaning to behavior, they must first understand some probabilistic relationship between the cues presented and the signal conveyed. Using data derived from a study in which participants identified a set of salient social signals in a simulated scenario and indicated the cues related to the perceived signals, we detail a learning algorithm, which clusters social cue observations and defines an "N-Most Likely States" set for each cluster. Since multiple signals may be co-present in a given simulation and a set of social cues often maps to multiple social signals, the "N-Most Likely States" approach provides a dramatic improvement over typical linear classifiers. We find that the target social signal appears in a "3 most-likely signals" set with up to 85% probability. This results in increased speed and accuracy on large amounts of data, which is critical for modeling social cognition mechanisms in robots to facilitate more natural human-robot interaction. These results also demonstrate the utility of such an approach in deployed scenarios where robots need to communicate with human teammates quickly and efficiently. In this paper, we detail our algorithm, comparative results, and offer potential applications for robot social signal detection and machine-aided human social signal detection.

Self-propulsion and interactions of catalytic particles in a chemically active medium.

PubMed

Banigan, Edward J; Marko, John F

2016-01-01

Enzymatic "machines," such as catalytic rods or colloids, can self-propel and interact by generating gradients of their substrates. We theoretically investigate the behaviors of such machines in a chemically active environment where their catalytic substrates are continuously synthesized and destroyed, as occurs in living cells. We show how the kinetic properties of the medium modulate self-propulsion and pairwise interactions between machines, with the latter controlled by a tunable characteristic interaction range analogous to the Debye screening length in an electrolytic solution. Finally, we discuss the effective force arising between interacting machines and possible biological applications, such as partitioning of bacterial plasmids.

What Machines Need to Learn to Support Human Problem-Solving

NASA Technical Reports Server (NTRS)

Vera, Alonso

2017-01-01

In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.

From 'automation' to 'autonomy': the importance of trust repair in human-machine interaction.

PubMed

de Visser, Ewart J; Pak, Richard; Shaw, Tyler H

2018-04-09

Modern interactions with technology are increasingly moving away from simple human use of computers as tools to the establishment of human relationships with autonomous entities that carry out actions on our behalf. In a recent commentary, Peter Hancock issued a stark warning to the field of human factors that attention must be focused on the appropriate design of a new class of technology: highly autonomous systems. In this article, we heed the warning and propose a human-centred approach directly aimed at ensuring that future human-autonomy interactions remain focused on the user's needs and preferences. By adapting literature from industrial psychology, we propose a framework to infuse a unique human-like ability, building and actively repairing trust, into autonomous systems. We conclude by proposing a model to guide the design of future autonomy and a research agenda to explore current challenges in repairing trust between humans and autonomous systems. Practitioner Summary: This paper is a call to practitioners to re-cast our connection to technology as akin to a relationship between two humans rather than between a human and their tools. To that end, designing autonomy with trust repair abilities will ensure future technology maintains and repairs relationships with their human partners.

Distributed intelligence for supervisory control

NASA Technical Reports Server (NTRS)

Wolfe, W. J.; Raney, S. D.

1987-01-01

Supervisory control systems must deal with various types of intelligence distributed throughout the layers of control. Typical layers are real-time servo control, off-line planning and reasoning subsystems and finally, the human operator. Design methodologies must account for the fact that the majority of the intelligence will reside with the human operator. Hierarchical decompositions and feedback loops as conceptual building blocks that provide a common ground for man-machine interaction are discussed. Examples of types of parallelism and parallel implementation on several classes of computer architecture are also discussed.

Human and Computer Control of Undersea Teleoperators

DTIC Science & Technology

1978-07-15

j ’Al.• /i•.f IAII•lU•I .p.ra i- . AWL• u~/K ftI&i. . .................... L HUMAN AND COMPUTER CONTROL / OF UNDERSEA TELEOPERATORS, .9 . - - I... UNDERSEA TELEOPERATORS 15 Mar 1977-14 June 1978 6. PERFORMING ORO. REPORT NUMBER,- J ". AUTHOR(e) S. CONTRACT OR GRANT NUMBER(*) Thomas B. Sheridan and...y ad Identify by block >w his is a review of factors p ertaining to man-machine interaction in remote control of undersea vehicles, especially their

Operations Research Techniques for Human Factors Engineers

DTIC Science & Technology

1985-06-01

mathematical than Raiffa’s explanations. b) Raiffa, Howard. Decision Analisis : Introductory Lectutres on Choiceg~~a hdY9ETny a--s- s- se ia fly, 1968. A...response criterion (8)- d) Pastore , R.E., and Scheirer, C.J. "Signal Detection Theory: Considerations for General Application " itPs Nholo_ cal Bulletin...Relatioship: . N9--1: Joi Vonn rey and Sons, Tr-rppnT35 p -TT. 26. Rouse, William B. System Enin eerin? Models of Human-Machine Interaction. New Y E

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

PubMed

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Almost human: Anthropomorphism increases trust resilience in cognitive agents.

PubMed

de Visser, Ewart J; Monfort, Samuel S; McKendrick, Ryan; Smith, Melissa A B; McKnight, Patrick E; Krueger, Frank; Parasuraman, Raja

2016-09-01

We interact daily with computers that appear and behave like humans. Some researchers propose that people apply the same social norms to computers as they do to humans, suggesting that social psychological knowledge can be applied to our interactions with computers. In contrast, theories of human–automation interaction postulate that humans respond to machines in unique and specific ways. We believe that anthropomorphism—the degree to which an agent exhibits human characteristics—is the critical variable that may resolve this apparent contradiction across the formation, violation, and repair stages of trust. Three experiments were designed to examine these opposing viewpoints by varying the appearance and behavior of automated agents. Participants received advice that deteriorated gradually in reliability from a computer, avatar, or human agent. Our results showed (a) that anthropomorphic agents were associated with greater trust resilience , a higher resistance to breakdowns in trust; (b) that these effects were magnified by greater uncertainty; and c) that incorporating human-like trust repair behavior largely erased differences between the agents. Automation anthropomorphism is therefore a critical variable that should be carefully incorporated into any general theory of human–agent trust as well as novel automation design. PsycINFO Database Record (c) 2016 APA, all rights reserved

Cutting the Cord: Discrimination and Command Responsibility in Autonomous Lethal Weapons

DTIC Science & Technology

2014-02-13

machine responses to identical stimuli, and it was the job of a third party human “witness” to determine which participant was man and which was...machines may be error free, but there are potential benefits to be gained through autonomy if machines can meet or exceed human performance in...lieu of human operators and reap the benefits that autonomy provides. Human and Machine Error It would be foolish to assert that either humans

KARL: A Knowledge-Assisted Retrieval Language. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Triantafyllopoulos, Spiros

1985-01-01

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Quadcopter control using a BCI

NASA Astrophysics Data System (ADS)

Rosca, S.; Leba, M.; Ionica, A.; Gamulescu, O.

2018-01-01

The paper presents how there can be interconnected two ubiquitous elements nowadays. On one hand, the drones, which are increasingly present and integrated into more and more fields of activity, beyond the military applications they come from, moving towards entertainment, real-estate, delivery and so on. On the other hand, unconventional man-machine interfaces, which are generous topics to explore now and in the future. Of these, we chose brain computer interface (BCI), which allows human-machine interaction without requiring any moving elements. The research consists of mathematical modeling and numerical simulation of a drone and a BCI. Then there is presented an application using a Parrot mini-drone and an Emotiv Insight BCI.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamagnini, Paolo; Krause, Josua W.; Dasgupta, Aritra

2017-05-14

To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treatingmore » a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.« less

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

[Human machines--mechanical humans? The industrial arrangement of the relation between human being and machine on the basis of psychotechnik and Georg Schlesingers work with disabled soldiers].

PubMed

Patzel-Mattern, Katja

2005-01-01

The 20th Century is the century of of technical artefacts. With their existance and use they create an artificial reality, within which humans have to position themselves. Psychotechnik is an attempt to enable humans for this positioning. It gained importance in Germany after World War I and had its heyday between 1919 and 1926. On the basis of the activity of the engineer and supporter of Psychotechnik Georg Schlesinger, whose particular interest were disabled soldiers, the essay on hand will investigate the understanding of the body and the human being of Psychotechnik as an applied science. It turned out, that the biggest achievement of Psychotechnik was to establish a new view of the relation between human being and machine. Thus it helped to show that the human-machine-interface is a shapable unit. Psychotechnik sees the human body and its physique as the last instance for the design of machines. Its main concern is to optimize the relation between human being and machine rather than to standardize human beings according to the construction of machines. After her splendid rise during the Weimar Republic and her rapid decline since the late 1920s Psychotechnik nowadays gains scientifical attention as a historical phenomenon. The main attention in the current discourse lies on the aspects conserning philosophy of science: the unity of body and soul, the understanding of the human-machine-interface as a shapable unit and the human being as a last instance of this unit.

Brain-Computer Interfaces: A Neuroscience Paradigm of Social Interaction? A Matter of Perspective

PubMed Central

Mattout, Jérémie

2012-01-01

A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment. PMID:22675291

The Importance of HRA in Human Space Flight: Understanding the Risks

NASA Technical Reports Server (NTRS)

Hamlin, Teri

2010-01-01

Human performance is critical to crew safety during space missions. Humans interact with hardware and software during ground processing, normal flight, and in response to events. Human interactions with hardware and software can cause Loss of Crew and/or Vehicle (LOCV) through improper actions, or may prevent LOCV through recovery and control actions. Humans have the ability to deal with complex situations and system interactions beyond the capability of machines. Human Reliability Analysis (HRA) is a method used to qualitatively and quantitatively assess the occurrence of human failures that affect availability and reliability of complex systems. Modeling human actions with their corresponding failure probabilities in a Probabilistic Risk Assessment (PRA) provides a more complete picture of system risks and risk contributions. A high-quality HRA can provide valuable information on potential areas for improvement, including training, procedures, human interfaces design, and the need for automation. Modeling human error has always been a challenge in part because performance data is not always readily available. For spaceflight, the challenge is amplified not only because of the small number of participants and limited amount of performance data available, but also due to the lack of definition of the unique factors influencing human performance in space. These factors, called performance shaping factors in HRA terminology, are used in HRA techniques to modify basic human error probabilities in order to capture the context of an analyzed task. Many of the human error modeling techniques were developed within the context of nuclear power plants and therefore the methodologies do not address spaceflight factors such as the effects of microgravity and longer duration missions. This presentation will describe the types of human error risks which have shown up as risk drivers in the Shuttle PRA which may be applicable to commercial space flight. As with other large PRAs of complex machines, human error in the Shuttle PRA proved to be an important contributor (12 percent) to LOCV. An existing HRA technique was adapted for use in the Shuttle PRA, but additional guidance and improvements are needed to make the HRA task in space-related PRAs easier and more accurate. Therefore, this presentation will also outline plans for expanding current HRA methodology to more explicitly cover spaceflight performance shaping factors.

Vocal emotion of humanoid robots: a study from brain mechanism.

PubMed

Wang, Youhui; Hu, Xiaohua; Dai, Weihui; Zhou, Jie; Kuo, Taitzong

2014-01-01

Driven by rapid ongoing advances in humanoid robot, increasing attention has been shifted into the issue of emotion intelligence of AI robots to facilitate the communication between man-machines and human beings, especially for the vocal emotion in interactive system of future humanoid robots. This paper explored the brain mechanism of vocal emotion by studying previous researches and developed an experiment to observe the brain response by fMRI, to analyze vocal emotion of human beings. Findings in this paper provided a new approach to design and evaluate the vocal emotion of humanoid robots based on brain mechanism of human beings.

An operator interface design for a telerobotic inspection system

NASA Technical Reports Server (NTRS)

Kim, Won S.; Tso, Kam S.; Hayati, Samad

1993-01-01

The operator interface has recently emerged as an important element for efficient and safe interactions between human operators and telerobotics. Advances in graphical user interface and graphics technologies enable us to produce very efficient operator interface designs. This paper describes an efficient graphical operator interface design newly developed for remote surface inspection at NASA-JPL. The interface, designed so that remote surface inspection can be performed by a single operator with an integrated robot control and image inspection capability, supports three inspection strategies of teleoperated human visual inspection, human visual inspection with automated scanning, and machine-vision-based automated inspection.

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.

PubMed

Zheng, Shuai; Lu, James J; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A; Wang, Fusheng

2017-05-09

Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports-each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. IDEAL-X adopts a unique online machine learning-based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. ©Shuai Zheng, James J Lu, Nima Ghasemzadeh, Salim S Hayek, Arshed A Quyyumi, Fusheng Wang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.05.2017.

Gloved Human-Machine Interface

NASA Technical Reports Server (NTRS)

Adams, Richard (Inventor); Hannaford, Blake (Inventor); Olowin, Aaron (Inventor)

2015-01-01

Certain exemplary embodiments can provide a system, machine, device, manufacture, circuit, composition of matter, and/or user interface adapted for and/or resulting from, and/or a method and/or machine-readable medium comprising machine-implementable instructions for, activities that can comprise and/or relate to: tracking movement of a gloved hand of a human; interpreting a gloved finger movement of the human; and/or in response to interpreting the gloved finger movement, providing feedback to the human.

Knowledge-based load leveling and task allocation in human-machine systems

NASA Technical Reports Server (NTRS)

Chignell, M. H.; Hancock, P. A.

1986-01-01

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

An Experience of Teaching for Learning by Observation: Remote-Controlled Experiments on Electrical Circuits

ERIC Educational Resources Information Center

Kong, Siu Cheung; Yeung, Yau Yuen; Wu, Xian Qiu

2009-01-01

In order to facilitate senior primary school students in Hong Kong to engage in learning by observation of the phenomena related to electrical circuits, a design of a specific courseware system, of which the interactive human-machine interface was created with the use of an open-source software called the LabVNC, for conducting online…

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

PubMed Central

Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

PubMed

Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

Future of Mechatronics and Human

NASA Astrophysics Data System (ADS)

Harashima, Fumio; Suzuki, Satoshi

This paper mentions circumstance of mechatronics that sustain our human society, and introduces HAM(Human Adaptive Mechatronics)-project as one of research projects to create new human-machine system. The key point of HAM is skill, and analysis of skill and establishment of assist method to enhance total performance of human-machine system are main research concerns. As study of skill is an elucidation of human itself, analyses of human higher function are significant. In this paper, after surveying researches of human brain functions, an experimental analysis of human characteristic in machine operation is shown as one example of our research activities. We used hovercraft simulator as verification system including observation, voluntary motion control and machine operation that are needed to general machine operation. Process and factors to become skilled were investigated by identification of human control characteristics with measurement of the operator's line-of sight. It was confirmed that early switching of sub-controllers / reference signals in human and enhancement of space perception are significant.

A design for living technology: experiments with the mind time machine.

PubMed

Ikegami, Takashi

2013-01-01

Living technology aims to help people expand their experiences in everyday life. The environment offers people ways to interact with it, which we call affordances. Living technology is a design for new affordances. When we experience something new, we remember it by the way we perceive and interact with it. Recent studies in neuroscience have led to the idea of a default mode network, which is a baseline activity of a brain system. The autonomy of artificial life must be understood as a sort of default mode that self-organizes its baseline activity, preparing for its external inputs and its interaction with humans. I thus propose a method for creating a suitable default mode as a design principle for living technology. I built a machine called the mind time machine (MTM), which runs continuously for 10 h per day and receives visual data from its environment using 15 video cameras. The MTM receives and edits the video inputs while it self-organizes the momentary now. Its base program is a neural network that includes chaotic dynamics inside the system and a meta-network that consists of video feedback systems. Using this system as the hardware and a default mode network as a conceptual framework, I describe the system's autonomous behavior. Using the MTM as a testing ground, I propose a design principle for living technology.

The remapping of space in motor learning and human-machine interfaces

PubMed Central

Mussa-Ivaldi, F.A.; Danziger, Z.

2009-01-01

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human-machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human machine interfaces and optimal control. PMID:19665553

A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

PubMed Central

Beckerle, Philipp; Salvietti, Gionata; Unal, Ramazan; Prattichizzo, Domenico; Rossi, Simone; Castellini, Claudio; Hirche, Sandra; Endo, Satoshi; Amor, Heni Ben; Ciocarlie, Matei; Mastrogiovanni, Fulvio; Argall, Brenna D.; Bianchi, Matteo

2017-01-01

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions. PMID:28588473

Human Machine Learning Symbiosis

ERIC Educational Resources Information Center

Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Ultrahigh Sensitive and Flexible Magnetoelectronics with Magnetic Nanocomposites: Toward an Additional Perception of Artificial Intelligence.

PubMed

Cai, Shu-Yi; Chang, Cheng-Han; Lin, Hung-I; Huang, Yuan-Fu; Lin, Wei-Ju; Lin, Shih-Yao; Liou, Yi-Rou; Shen, Tien-Lin; Huang, Yen-Hsiang; Tsao, Po-Wei; Tzou, Chen-Yang; Liao, Yu-Ming; Chen, Yang-Fang

2018-05-23

In recent years, flexible magnetoelectronics has attracted a great attention for its intriguing functionalities and potential applications, such as healthcare, memory, soft robots, navigation, and touchless human-machine interaction systems. Here, we provide the first attempt to demonstrate a new type of magneto-piezoresistance device, which possesses an ultrahigh sensitivity with several orders of resistance change under an external magnetic field (100 mT). In our device, Fe-Ni alloy powders are embedded in the silver nanowire-coated micropyramid polydimethylsiloxane films. Our devices can not only serve as an on/off switch but also act as a sensor that can detect different magnetic fields because of its ultrahigh sensitivity, which is very useful for the application in analog signal communication. Moreover, our devices contain several key features, including large-area and easy fabrication processes, fast response time, low working voltage, low power consumption, excellent flexibility, and admirable compatibility onto a freeform surface, which are the critical criteria for the future development of touchless human-machine interaction systems. On the basis of all of these unique characteristics, we have demonstrated a nontouch piano keyboard, instantaneous magnetic field visualization, and autonomous power system, making our new devices be integrable with magnetic field and enable to be implemented into our daily life applications with unfamiliar human senses. Our approach therefore paves a useful route for the development of wearable electronics and intelligent systems.

Wargaming and interactive color graphics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bly, S.; Buzzell, C.; Smith, G.

1980-08-04

JANUS is a two-sided interactive color graphic simulation in which human commanders can direct their forces, each trying to accomplish their mission. This competitive synthetic battlefield is used to explore the range of human ingenuity under conditions of incomplete information about enemy strength and deployment. Each player can react to new situations by planning new unit movements, using conventional and nuclear weapons, or modifying unit objectives. Conventional direct fire among tanks, infantry fighting vehicles, helicopters, and other units is automated subject to constraints of target acquisition, reload rate, range, suppression, etc. Artillery and missile indirect fire systems deliver conventional munitions,more » smoke, and nuclear weapons. Players use reconnaissance units, helicopters, or fixed wing aircraft to search for enemy unit locations. Counter-battery radars acquire enemy artillery. The JANUS simulation at LLL has demonstrated the value of the computer as a sophisticated blackboard. A small dedicated minicomputer is adequate for detailed calculations, and may be preferable to sharing a more powerful machine. Real-time color interactive graphics are essential to allow realistic command decision inputs. Competitive human-versus-human synthetic experiences are intense and well-remembered. 2 figures.« less

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

Media-Augmented Exercise Machines

NASA Astrophysics Data System (ADS)

Krueger, T.

2002-01-01

Cardio-vascular exercise has been used to mitigate the muscle and cardiac atrophy associated with adaptation to micro-gravity environments. Several hours per day may be required. In confined spaces and long duration missions this kind of exercise is inevitably repetitive and rapidly becomes uninteresting. At the same time, there are pressures to accomplish as much as possible given the cost- per-hour for humans occupying orbiting or interplanetary. Media augmentation provides a the means to overlap activities in time by supplementing the exercise with social, recreational, training or collaborative activities and thereby reducing time pressures. In addition, the machine functions as an interface to a wide range of digital environments allowing for spatial variety in an otherwise confined environment. We hypothesize that the adoption of media augmented exercise machines will have a positive effect on psycho-social well-being on long duration missions. By organizing and supplementing exercise machines, data acquisition hardware, computers and displays into an interacting system this proposal increases functionality with limited additional mass. This paper reviews preliminary work on a project to augment exercise equipment in a manner that addresses these issues and at the same time opens possibilities for additional benefits. A testbed augmented exercise machine uses a specialty built cycle trainer as both input to a virtual environment and as an output device from it using spatialized sound, and visual displays, vibration transducers and variable resistance. The resulting interactivity increases a sense of engagement in the exercise, provides a rich experience of the digital environments. Activities in the virtual environment and accompanying physiological and psychological indicators may be correlated to track and evaluate the health of the crew.

Unmanned tactical autonomous control and collaboration (utacc) human machine integration measures of performance and measures of effectiveness

DTIC Science & Technology

2017-06-01

AUTONOMOUS CONTROL AND COLLABORATION (UTACC) HUMAN-MACHINE INTEGRATION MEASURES OF PERFORMANCE AND MEASURES OF EFFECTIVENESS by Thomas A...TACTICAL AUTONOMOUS CONTROL AND COLLABORATION (UTACC) HUMAN-MACHINE INTEGRATION MEASURES OF PERFORMANCE AND MEASURES OF EFFECTIVENESS 5. FUNDING...Tactical Autonomous Control and Collaboration (UTACC) program seeks to integrate Marines and autonomous machines to address the challenges encountered in

The use of affective interaction design in car user interfaces.

PubMed

Gkouskos, Dimitrios; Chen, Fang

2012-01-01

Recent developments in the car industry have put Human Machine Interfaces under the spotlight. Developing gratifying human-car interactions has become one of the more prominent areas that car manufacturers want to invest in. However, concepts like emotional design remain foreign to the industry. In this study 12 experts on the field of automobile HMI design were interviewed in order to investigate their needs and opinions of emotional design. Results show that emotional design has yet to be introduced for this context of use. Designers need a tool customized for the intricacies of the car HMI field that can provide them with support and guidance so that they can create emotionally attractive experiences for drivers and passengers alike.

Learning to represent spatial transformations with factored higher-order Boltzmann machines.

PubMed

Memisevic, Roland; Hinton, Geoffrey E

2010-06-01

To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.

Telkom UData sentiment analysis using crowdsourcing and trust

NASA Astrophysics Data System (ADS)

Noer, Edvya; Sulistyo Kusumo, Dana; Rusmawati, Yanti

2018-03-01

Microblogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. Sentiments analysis are often being used to analyse the user customer opinions regarding brand images or products. For some reasons, not all sentiment generated using this existing machine-based algorithms yields satisfying results. This is mostly due to the uniformity of the informal language used in the social media sentences. This condition also occurs in Telkom UData on our preliminary study, where the machine-based provided less then optimal results in analysing the sentiment. This research offers concepts with human interaction using crowdsourcing where people are involved to analyse sentiments, while forming the new training dataset at the same time. From the research results found that sarcastic and contradictory sentences can be recognized by humans, to be utilized as new training datasets for further machine learning. From this experiments, that approach are likely increase the accuracy of the sentiments in UData from neutral to become positive or negative polarized up to 39%. We do as well simulated trust concept through sociometric to ensure the crowdsource workers are trusted and capable enough in analysing the sentiments on social media.

Human-directed local autonomy for motion guidance and coordination in an intelligent manufacturing system

NASA Astrophysics Data System (ADS)

Alford, W. A.; Kawamura, Kazuhiko; Wilkes, Don M.

1997-12-01

This paper discusses the problem of integrating human intelligence and skills into an intelligent manufacturing system. Our center has jointed the Holonic Manufacturing Systems (HMS) Project, an international consortium dedicated to developing holonic systems technologies. One of our contributions to this effort is in Work Package 6: flexible human integration. This paper focuses on one activity, namely, human integration into motion guidance and coordination. Much research on intelligent systems focuses on creating totally autonomous agents. At the Center for Intelligent Systems (CIS), we design robots that interact directly with a human user. We focus on using the natural intelligence of the user to simplify the design of a robotic system. The problem is finding ways for the user to interact with the robot that are efficient and comfortable for the user. Manufacturing applications impose the additional constraint that the manufacturing process should not be disturbed; that is, frequent interacting with the user could degrade real-time performance. Our research in human-robot interaction is based on a concept called human directed local autonomy (HuDL). Under this paradigm, the intelligent agent selects and executes a behavior or skill, based upon directions from a human user. The user interacts with the robot via speech, gestures, or other media. Our control software is based on the intelligent machine architecture (IMA), an object-oriented architecture which facilitates cooperation and communication among intelligent agents. In this paper we describe our research testbed, a dual-arm humanoid robot and human user, and the use of this testbed for a human directed sorting task. We also discuss some proposed experiments for evaluating the integration of the human into the robot system. At the time of this writing, the experiments have not been completed.

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

PubMed

You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

2014-01-01

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

Machines and Human Beings in the Movies

ERIC Educational Resources Information Center

van der Laan, J. M.

2006-01-01

Over the years, many movies have presented on-screen a struggle between machines and human beings. Typically, the machines have come to rule and threaten the existence of humanity. They must be conquered to ensure the survival of and to secure the freedom of the human race. Although these movies appear to expose the dangers of an autonomous and…

A new machine classification method applied to human peripheral blood leukocytes

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.; Fitzpatrick, Steven J.; Vitthal, Sanjay; Ladoulis, Charles T.

1994-01-01

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Observing Consistency in Online Communication Patterns for User Re-Identification.

PubMed

Adeyemi, Ikuesan Richard; Razak, Shukor Abd; Salleh, Mazleena; Venter, Hein S

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas.

Analysis of Feedback in after Action Reviews

DTIC Science & Technology

1987-06-01

CONNTSM Page INTRODUCTIUN . . . . . . . . . . . . . . . . . . . A Perspective on Feedback. . ....... • • ..... • 1 Overviev of %,•urrent Research...part of their training program . The AAR is in marked contrast to the critique method of feedback which is often used in military training. The AAR...feedback is task-inherent feedback. Task-inherent feedback refers to human-machine interacting systems, e.g., computers , where in a visual tracking task

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Labacqz, J. Victor (Technical Monitor)

1997-01-01

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Generating a Reduced Gravity Environment on Earth

NASA Technical Reports Server (NTRS)

Dungan, Larry K.; Cunningham, Tom; Poncia, Dina

2010-01-01

Since the 1950s several reduced gravity simulators have been designed and utilized in preparing humans for spaceflight and in reduced gravity system development. The Active Response Gravity Offload System (ARGOS) is the newest and most realistic gravity offload simulator. ARGOS provides three degrees of motion within the test area and is scalable for full building deployment. The inertia of the overhead system is eliminated by an active motor and control system. This presentation will discuss what ARGOS is, how it functions, and the unique challenges of interfacing to the human. Test data and video for human and robotic systems will be presented. A major variable in the human machine interaction is the interface of ARGOS to the human. These challenges along with design solutions will be discussed.

Advanced human machine interaction for an image interpretation workstation

NASA Astrophysics Data System (ADS)

Maier, S.; Martin, M.; van de Camp, F.; Peinsipp-Byma, E.; Beyerer, J.

2016-05-01

In recent years, many new interaction technologies have been developed that enhance the usability of computer systems and allow for novel types of interaction. The areas of application for these technologies have mostly been in gaming and entertainment. However, in professional environments, there are especially demanding tasks that would greatly benefit from improved human machine interfaces as well as an overall improved user experience. We, therefore, envisioned and built an image-interpretation-workstation of the future, a multi-monitor workplace comprised of four screens. Each screen is dedicated to a complex software product such as a geo-information system to provide geographic context, an image annotation tool, software to generate standardized reports and a tool to aid in the identification of objects. Using self-developed systems for hand tracking, pointing gestures and head pose estimation in addition to touchscreens, face identification, and speech recognition systems we created a novel approach to this complex task. For example, head pose information is used to save the position of the mouse cursor on the currently focused screen and to restore it as soon as the same screen is focused again while hand gestures allow for intuitive manipulation of 3d objects in mid-air. While the primary focus is on the task of image interpretation, all of the technologies involved provide generic ways of efficiently interacting with a multi-screen setup and could be utilized in other fields as well. In preliminary experiments, we received promising feedback from users in the military and started to tailor the functionality to their needs

Vocal Emotion of Humanoid Robots: A Study from Brain Mechanism

PubMed Central

Wang, Youhui; Hu, Xiaohua; Zhou, Jie; Kuo, Taitzong

2014-01-01

Driven by rapid ongoing advances in humanoid robot, increasing attention has been shifted into the issue of emotion intelligence of AI robots to facilitate the communication between man-machines and human beings, especially for the vocal emotion in interactive system of future humanoid robots. This paper explored the brain mechanism of vocal emotion by studying previous researches and developed an experiment to observe the brain response by fMRI, to analyze vocal emotion of human beings. Findings in this paper provided a new approach to design and evaluate the vocal emotion of humanoid robots based on brain mechanism of human beings. PMID:24587712

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

2016-01-01

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Robots with a gentle touch: advances in assistive robotics and prosthetics.

PubMed

Harwin, W S

1999-01-01

As healthcare costs rise and an aging population makes an increased demand on services, so new techniques must be introduced to promote an individuals independence and provide these services. Robots can now be designed so they can alter their dynamic properties changing from stiff to flaccid, or from giving no resistance to movement, to damping any large and sudden movements. This has some strong implications in health care in particular for rehabilitation where a robot must work in conjunction with an individual, and might guiding or assist a persons arm movements, or might be commanded to perform some set of autonomous actions. This paper presents the state-of-the-art of rehabilitation robots with examples from prosthetics, aids for daily living and physiotherapy. In all these situations there is the potential for the interaction to be non-passive with a resulting potential for the human/machine/environment combination to become unstable. To understand this instability we must develop better models of the human motor system and fit these models with realistic parameters. This paper concludes with a discussion of this problem and overviews some human models that can be used to facilitate the design of the human/machine interfaces.

A concept for ubiquitous robotics in industrial environment

NASA Astrophysics Data System (ADS)

Sallinen, Mikko; Heilala, Juhani; Kivikunnas, Sauli

2007-09-01

In this paper a concept for industrial ubiquitous robotics is presented. The concept combines two different approaches to manage agile, adaptable production: firstly the human operator is strongly in the production loop and secondly, the robot workcell will be more autonomous and smarter to manage production. This kind of autonomous robot cell can be called production island. Communication to the human operator working in this kind of smart industrial environment can be divided into two levels: body area communication and operator-infrastructure communication including devices, machines and infra. Body area communication can be supportive in two directions: data is recorded by means of measuring physical actions, such as hand movements, body gestures or supportive when it will provide information to user such as guides or manuals for operation. Body area communication can be carried out using short range communication technologies such as NFC (Near Field communication) which is RFID type of communication. In the operator-infrastructure communication, WLAN or Bluetooth -communication can be used. Beyond the current Human Machine interaction HMI systems, the presented system concept is designed to fulfill the requirements for hybrid, knowledge intensive manufacturing in the future, where humans and robots operate in close co-operation.

Interactome INSIDER: a structural interactome browser for genomic studies.

PubMed

Meyer, Michael J; Beltrán, Juan Felipe; Liang, Siqi; Fragoza, Robert; Rumack, Aaron; Liang, Jin; Wei, Xiaomu; Yu, Haiyuan

2018-01-01

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Adding Pluggable and Personalized Natural Control Capabilities to Existing Applications

PubMed Central

Lamberti, Fabrizio; Sanna, Andrea; Carlevaris, Gilles; Demartini, Claudio

2015-01-01

Advancements in input device and sensor technologies led to the evolution of the traditional human-machine interaction paradigm based on the mouse and keyboard. Touch-, gesture- and voice-based interfaces are integrated today in a variety of applications running on consumer devices (e.g., gaming consoles and smartphones). However, to allow existing applications running on desktop computers to utilize natural interaction, significant re-design and re-coding efforts may be required. In this paper, a framework designed to transparently add multi-modal interaction capabilities to applications to which users are accustomed is presented. Experimental observations confirmed the effectiveness of the proposed framework and led to a classification of those applications that could benefit more from the availability of natural interaction modalities. PMID:25635410

Adding pluggable and personalized natural control capabilities to existing applications.

PubMed

Lamberti, Fabrizio; Sanna, Andrea; Carlevaris, Gilles; Demartini, Claudio

2015-01-28

Advancements in input device and sensor technologies led to the evolution of the traditional human-machine interaction paradigm based on the mouse and keyboard. Touch-, gesture- and voice-based interfaces are integrated today in a variety of applications running on consumer devices (e.g., gaming consoles and smartphones). However, to allow existing applications running on desktop computers to utilize natural interaction, significant re-design and re-coding efforts may be required. In this paper, a framework designed to transparently add multi-modal interaction capabilities to applications to which users are accustomed is presented. Experimental observations confirmed the effectiveness of the proposed framework and led to a classification of those applications that could benefit more from the availability of natural interaction modalities.

Method and system for rendering and interacting with an adaptable computing environment

DOEpatents

Osbourn, Gordon Cecil [Albuquerque, NM; Bouchard, Ann Marie [Albuquerque, NM

2012-06-12

An adaptable computing environment is implemented with software entities termed "s-machines", which self-assemble into hierarchical data structures capable of rendering and interacting with the computing environment. A hierarchical data structure includes a first hierarchical s-machine bound to a second hierarchical s-machine. The first hierarchical s-machine is associated with a first layer of a rendering region on a display screen and the second hierarchical s-machine is associated with a second layer of the rendering region overlaying at least a portion of the first layer. A screen element s-machine is linked to the first hierarchical s-machine. The screen element s-machine manages data associated with a screen element rendered to the display screen within the rendering region at the first layer.

Real-time face and gesture analysis for human-robot interaction

NASA Astrophysics Data System (ADS)

Wallhoff, Frank; Rehrl, Tobias; Mayer, Christoph; Radig, Bernd

2010-05-01

Human communication relies on a large number of different communication mechanisms like spoken language, facial expressions, or gestures. Facial expressions and gestures are one of the main nonverbal communication mechanisms and pass large amounts of information between human dialog partners. Therefore, to allow for intuitive human-machine interaction, a real-time capable processing and recognition of facial expressions, hand and head gestures are of great importance. We present a system that is tackling these challenges. The input features for the dynamic head gestures and facial expressions are obtained from a sophisticated three-dimensional model, which is fitted to the user in a real-time capable manner. Applying this model different kinds of information are extracted from the image data and afterwards handed over to a real-time capable data-transferring framework, the so-called Real-Time DataBase (RTDB). In addition to the head and facial-related features, also low-level image features regarding the human hand - optical flow, Hu-moments are stored into the RTDB for the evaluation process of hand gestures. In general, the input of a single camera is sufficient for the parallel evaluation of the different gestures and facial expressions. The real-time capable recognition of the dynamic hand and head gestures are performed via different Hidden Markov Models, which have proven to be a quick and real-time capable classification method. On the other hand, for the facial expressions classical decision trees or more sophisticated support vector machines are used for the classification process. These obtained results of the classification processes are again handed over to the RTDB, where other processes (like a Dialog Management Unit) can easily access them without any blocking effects. In addition, an adjustable amount of history can be stored by the RTDB buffer unit.

Complementary Machine Intelligence and Human Intelligence in Virtual Teaching Assistant for Tutoring Program Tracing

ERIC Educational Resources Information Center

Chou, Chih-Yueh; Huang, Bau-Hung; Lin, Chi-Jen

2011-01-01

This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate…

The Trust Project - Symbiotic Human Machine Teams: Social Cueing for Trust and Reliance

DTIC Science & Technology

2016-06-30

AFRL-RH-WP-TR-2016-0096 THE TRUST PROJECT - SYMBIOTIC HUMAN-MACHINE TEAMS: SOCIAL CUEING FOR TRUST & RELIANCE Susan Rivers, Monika Lohani, Marissa...30 JUN 2012 – 30 JUN 2016 4. TITLE AND SUBTITLE THE TRUST PROJECT - SYMBIOTIC HUMAN-MACHINE TEAMS: SOCIAL CUEING FOR TRUST & RELIANCE 5a. CONTRACT

A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Understanding Autonomy in Future Systems.

PubMed

Schaefer, Kristin E; Chen, Jessie Y C; Szalma, James L; Hancock, P A

2016-05-01

We used meta-analysis to assess research concerning human trust in automation to understand the foundation upon which future autonomous systems can be built. Trust is increasingly important in the growing need for synergistic human-machine teaming. Thus, we expand on our previous meta-analytic foundation in the field of human-robot interaction to include all of automation interaction. We used meta-analysis to assess trust in automation. Thirty studies provided 164 pairwise effect sizes, and 16 studies provided 63 correlational effect sizes. The overall effect size of all factors on trust development was ḡ = +0.48, and the correlational effect was [Formula: see text]  = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (ḡ  = +0.49; [Formula: see text] = +0.16) and automation-related (ḡ = +0.53; [Formula: see text] = +0.41) factors. Moderator effects specific to environmental factors proved insufficient in number to calculate at this time. Findings provide a quantitative representation of factors influencing the development of trust in automation as well as identify additional areas of needed empirical research. This work has important implications to the enhancement of current and future human-automation interaction, especially in high-risk or extreme performance environments. © 2016, Human Factors and Ergonomics Society.

Activity Catalog Tool (ACT) user manual, version 2.0

NASA Technical Reports Server (NTRS)

Segal, Leon D.; Andre, Anthony D.

1994-01-01

This report comprises the user manual for version 2.0 of the Activity Catalog Tool (ACT) software program, developed by Leon D. Segal and Anthony D. Andre in cooperation with NASA Ames Aerospace Human Factors Research Division, FLR branch. ACT is a software tool for recording and analyzing sequences of activity over time that runs on the Macintosh platform. It was designed as an aid for professionals who are interested in observing and understanding human behavior in field settings, or from video or audio recordings of the same. Specifically, the program is aimed at two primary areas of interest: human-machine interactions and interactions between humans. The program provides a means by which an observer can record an observed sequence of events, logging such parameters as frequency and duration of particular events. The program goes further by providing the user with a quantified description of the observed sequence, through application of a basic set of statistical routines, and enables merging and appending of several files and more extensive analysis of the resultant data.

Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human-Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers.

PubMed

Roh, Eun; Hwang, Byeong-Ung; Kim, Doil; Kim, Bo-Yeong; Lee, Nae-Eung

2015-06-23

Interactivity between humans and smart systems, including wearable, body-attachable, or implantable platforms, can be enhanced by realization of multifunctional human-machine interfaces, where a variety of sensors collect information about the surrounding environment, intentions, or physiological conditions of the human to which they are attached. Here, we describe a stretchable, transparent, ultrasensitive, and patchable strain sensor that is made of a novel sandwich-like stacked piezoresisitive nanohybrid film of single-wall carbon nanotubes (SWCNTs) and a conductive elastomeric composite of polyurethane (PU)-poly(3,4-ethylenedioxythiophene) polystyrenesulfonate ( PSS). This sensor, which can detect small strains on human skin, was created using environmentally benign water-based solution processing. We attributed the tunability of strain sensitivity (i.e., gauge factor), stability, and optical transparency to enhanced formation of percolating networks between conductive SWCNTs and PEDOT phases at interfaces in the stacked PU-PEDOT:PSS/SWCNT/PU-PEDOT:PSS structure. The mechanical stability, high stretchability of up to 100%, optical transparency of 62%, and gauge factor of 62 suggested that when attached to the skin of the face, this sensor would be able to detect small strains induced by emotional expressions such as laughing and crying, as well as eye movement, and we confirmed this experimentally.

Manipulator Performance Evaluation Using Fitts' Taping Task

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draper, J.V.; Jared, B.C.; Noakes, M.W.

1999-04-25

Metaphorically, a teleoperator with master controllers projects the user's arms and hands into a re- mote area, Therefore, human users interact with teleoperators at a more fundamental level than they do with most human-machine systems. Instead of inputting decisions about how the system should func- tion, teleoperator users input the movements they might make if they were truly in the remote area and the remote machine must recreate their trajectories and impedance. This intense human-machine inter- action requires displays and controls more carefully attuned to human motor capabilities than is neces- sary with most systems. It is important for teleoperatedmore » manipulators to be able to recreate human trajectories and impedance in real time. One method for assessing manipulator performance is to observe how well a system be- haves while a human user completes human dexterity tasks with it. Fitts' tapping task has been, used many times in the past for this purpose. This report describes such a performance assessment. The International Submarine Engineering (ISE) Autonomous/Teleoperated Operations Manipulator (ATOM) servomanipulator system was evalu- ated using a generic positioning accuracy task. The task is a simple one but has the merits of (1) pro- ducing a performance function estimate rather than a point estimate and (2) being widely used in the past for human and servomanipulator dexterity tests. Results of testing using this task may, therefore, allow comparison with other manipulators, and is generically representative of a broad class of tasks. Results of the testing indicate that the ATOM manipulator is capable of performing the task. Force reflection had a negative impact on task efficiency in these data. This was most likely caused by the high resistance to movement the master controller exhibited with the force reflection engaged. Measurements of exerted forces were not made, so it is not possible to say whether the force reflection helped partici- pants control force during testing.« less

European Scientific Notes. Volume 36, Number 7,

DTIC Science & Technology

1982-07-31

services digital net- works, lierarhicel processing, of structural information in artificial intelligence. and human-machine interaction and digital...reduction in the rate of in those days were connected by "fossae" or growth and even the initiation of erosion along artificial channels. Thus littoral...fisher- Comacchio (see map) in the eighth century. men who would like to increase it. Additional The present Po delta is artificial in that conflicts

Buckling of Elastomeric Beams Enables Actuation of Soft Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Dian; Mosadegh, Bobak; Ainla, Alar

2015-09-21

Soft, pneumatic actuators that buckle when interior pressure is less than exterior provide a new mechanism of actuation. Upon application of negative pneumatic pressure, elastic beam elements in these actuators undergo reversible, cooperative collapse, and generate a rotational motion. These actuators are inexpensive to fabricate, lightweight, easy to control, and safe to operate. They can be used in devices that manipulate objects, locomote, or interact cooperatively with humans.

Nematic order-disorder state transition in a liquid crystal analogue formed by oriented and migrating amoeboid cells

NASA Astrophysics Data System (ADS)

Kemkemer, R.; Teichgräber, V.; Schrank-Kaufmann, S.; Kaufmann, D.; Gruler, H.

2000-10-01

In cell culture, liquid crystal analogues are formed by elongated, migrating, and interacting amoeboid cells. An apolar nematic liquid crystal analogue is formed by different cell types like human melanocytes (=pigment cells of the skin), human fibroblasts (=connective tissue cells), human osteoblasts (=bone cells), human adipocytes (=fat cells), etc. The nematic analogue is quite well described by i) a stochastic machine equation responsible for cell orientation and ii) a self-organized extracellular guiding signal, E_2, which is proportional to the orientational order parameter as well as to the cell density. The investigations were mainly made with melanocytes. The transition to an isotropic state analogue can be accomplished either by changing the strength of interaction (e.g. variation of the cell density) or by influencing the cellular machinery by an externally applied signal: i) An isotropic gaseous state analogue is observed at low cell density (ρ < 110melanocytes/mm^2) and a nematic liquid crystal state analogue at higher cell density. ii) The nematic state analogue disappears if the bipolar shaped melanocytes are forced to become a star-like shape (induced by colchicine or staurosporine). The analogy between nematic liquid crystal state analogue formed by elongated, migrating and interacting cells and the nematic liquid crystal phase formed by interacting elongated molecules is discussed.

Interaction between emotional state and learning underlies mood instability.

PubMed

Eldar, Eran; Niv, Yael

2015-01-21

Intuitively, good and bad outcomes affect our emotional state, but whether the emotional state feeds back onto the perception of outcomes remains unknown. Here, we use behaviour and functional neuroimaging of human participants to investigate this bidirectional interaction, by comparing the evaluation of slot machines played before and after an emotion-impacting wheel-of-fortune draw. Results indicate that self-reported mood instability is associated with a positive-feedback effect of emotional state on the perception of outcomes. We then use theoretical simulations to demonstrate that such positive feedback would result in mood destabilization. Taken together, our results suggest that the interaction between emotional state and learning may play a significant role in the emergence of mood instability.

Using lean "automation with a human touch" to improve medication safety: a step closer to the "perfect dose".

PubMed

Ching, Joan M; Williams, Barbara L; Idemoto, Lori M; Blackmore, C Craig

2014-08-01

Virginia Mason Medical Center (Seattle) employed the Lean concept of Jidoka (automation with a human touch) to plan for and deploy bar code medication administration (BCMA) to hospitalized patients. Integrating BCMA technology into the nursing work flow with minimal disruption was accomplished using three steps ofJidoka: (1) assigning work to humans and machines on the basis of their differing abilities, (2) adapting machines to the human work flow, and (3) monitoring the human-machine interaction. Effectiveness of BCMA to both reinforce safe administration practices and reduce medication errors was measured using the Collaborative Alliance for Nursing Outcomes (CALNOC) Medication Administration Accuracy Quality Study methodology. Trained nurses observed a total of 16,149 medication doses for 3,617 patients in a three-year period. Following BCMA implementation, the number of safe practice violations decreased from 54.8 violations/100 doses (January 2010-September 2011) to 29.0 violations/100 doses (October 2011-December 2012), resulting in an absolute risk reduction of 25.8 violations/100 doses (95% confidence interval [CI]: 23.7, 27.9, p < .001). The number of medication errors decreased from 5.9 errors/100 doses at baseline to 3.0 errors/100 doses after BCMA implementation (absolute risk reduction: 2.9 errors/100 doses [95% CI: 2.2, 3.6,p < .001]). The number of unsafe administration practices (estimate, -5.481; standard error 1.133; p < .001; 95% CI: -7.702, -3.260) also decreased. As more hospitals respond to health information technology meaningful use incentives, thoughtful, methodical, and well-managed approaches to technology deployment are crucial. This work illustrates how Jidoka offers opportunities for a smooth transition to new technology.

Partonomies for interactive explorable 3D-models of anatomy.

PubMed

Schubert, R; Höhne, K H

1998-01-01

We introduce a concept to model subtle part-whole-semantics for the use with interactive 3d-models of human anatomy. Similar to experiences with modeling partonomies for physical artifacts like machines or buildings we found one unique part-whole-relation to be insufficient to represent anatomical reality. This claim will be illustrated with anatomical examples. According to the requirements these examples demand, a semantic classification of part-whole-relations is introduced. Initial results in modeling anatomical partonomies for a 3d-visualization environment proved this approach to be an promising way to represent anatomy and to enable powerful complex inferences.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

The Nature of Indexing: How Humans and Machines Analyze Messages and Texts for Retrieval. Part II: Machine Indexing, and the Allocation of Human versus Machine Effort.

ERIC Educational Resources Information Center

Anderson, James D.; Perez-Carballo, Jose

2001-01-01

Discussion of human intellectual indexing versus automatic indexing focuses on automatic indexing. Topics include keyword indexing; negative vocabulary control; counting words; comparative counting and weighting; stemming; words versus phrases; clustering; latent semantic indexing; citation indexes; bibliographic coupling; co-citation; relevance…

Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

NASA Astrophysics Data System (ADS)

Ardi, S.; Ardyansyah, D.

2018-02-01

In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

PubMed Central

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

PubMed

Sutphin, George L; Mahoney, J Matthew; Sheppard, Keith; Walton, David O; Korstanje, Ron

2016-11-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

BaffleText: a Human Interactive Proof

NASA Astrophysics Data System (ADS)

Chew, Monica; Baird, Henry S.

2003-01-01

Internet services designed for human use are being abused by programs. We present a defense against such attacks in the form of a CAPTCHA (Completely Automatic Public Turing test to tell Computers and Humans Apart) that exploits the difference in ability between humans and machines in reading images of text. CAPTCHAs are a special case of 'human interactive proofs,' a broad class of security protocols that allow people to identify themselves over networks as members of given groups. We point out vulnerabilities of reading-based CAPTCHAs to dictionary and computer-vision attacks. We also draw on the literature on the psychophysics of human reading, which suggests fresh defenses available to CAPTCHAs. Motivated by these considerations, we propose BaffleText, a CAPTCHA which uses non-English pronounceable words to defend against dictionary attacks, and Gestalt-motivated image-masking degradations to defend against image restoration attacks. Experiments on human subjects confirm the human legibility and user acceptance of BaffleText images. We have found an image-complexity measure that correlates well with user acceptance and assists in engineering the generation of challenges to fit the ability gap. Recent computer-vision attacks, run independently by Mori and Jitendra, suggest that BaffleText is stronger than two existing CAPTCHAs.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Current trends in small vocabulary speech recognition for equipment control

NASA Astrophysics Data System (ADS)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Biological ageing and clinical consequences of modern technology.

PubMed

Kyriazis, Marios

2017-08-01

The pace of technology is steadily increasing, and this has a widespread effect on all areas of health and society. When we interact with this technological environment we are exposed to a wide variety of new stimuli and challenges, which may modulate the stress response and thus change the way we respond and adapt. In this Opinion paper I will examine certain aspects of the human-computer interaction with regards to health and ageing. There are practical, everyday effects which also include social and cultural elements. I will discuss how human evolution may be affected by this new environmental change (the hormetic immersion in a virtual/technological environment). Finally, I will also explore certain biological aspects which have direct relevance to the ageing human. By embracing new technologies and engaging with a techno-social ecosystem (which is no longer formed by several interacting species, but by just two main elements: humans and machines), we may be subjected to beneficial hormetic effects, which upregulate the stress response and modulate adaptation. This is likely to improve overall health as we age and, as I speculate here, may also result in the reduction of age-related dysfunction.

Generating Phenotypical Erroneous Human Behavior to Evaluate Human-automation Interaction Using Model Checking

PubMed Central

Bolton, Matthew L.; Bass, Ellen J.; Siminiceanu, Radu I.

2012-01-01

Breakdowns in complex systems often occur as a result of system elements interacting in unanticipated ways. In systems with human operators, human-automation interaction associated with both normative and erroneous human behavior can contribute to such failures. Model-driven design and analysis techniques provide engineers with formal methods tools and techniques capable of evaluating how human behavior can contribute to system failures. This paper presents a novel method for automatically generating task analytic models encompassing both normative and erroneous human behavior from normative task models. The generated erroneous behavior is capable of replicating Hollnagel’s zero-order phenotypes of erroneous action for omissions, jumps, repetitions, and intrusions. Multiple phenotypical acts can occur in sequence, thus allowing for the generation of higher order phenotypes. The task behavior model pattern capable of generating erroneous behavior can be integrated into a formal system model so that system safety properties can be formally verified with a model checker. This allows analysts to prove that a human-automation interactive system (as represented by the model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. We present benchmarks related to the size of the statespace and verification time of models to show how the erroneous human behavior generation process scales. We demonstrate the method with a case study: the operation of a radiation therapy machine. A potential problem resulting from a generated erroneous human action is discovered. A design intervention is presented which prevents this problem from occurring. We discuss how our method could be used to evaluate larger applications and recommend future paths of development. PMID:23105914

Analytical design of intelligent machines

NASA Technical Reports Server (NTRS)

Saridis, George N.; Valavanis, Kimon P.

1987-01-01

The problem of designing 'intelligent machines' to operate in uncertain environments with minimum supervision or interaction with a human operator is examined. The structure of an 'intelligent machine' is defined to be the structure of a Hierarchically Intelligent Control System, composed of three levels hierarchically ordered according to the principle of 'increasing precision with decreasing intelligence', namely: the organizational level, performing general information processing tasks in association with a long-term memory; the coordination level, dealing with specific information processing tasks with a short-term memory; and the control level, which performs the execution of various tasks through hardware using feedback control methods. The behavior of such a machine may be managed by controls with special considerations and its 'intelligence' is directly related to the derivation of a compatible measure that associates the intelligence of the higher levels with the concept of entropy, which is a sufficient analytic measure that unifies the treatment of all the levels of an 'intelligent machine' as the mathematical problem of finding the right sequence of internal decisions and controls for a system structured in the order of intelligence and inverse order of precision such that it minimizes its total entropy. A case study on the automatic maintenance of a nuclear plant illustrates the proposed approach.

Should You Trust Your Money to a Robot?

PubMed

Dhar, Vasant

2015-06-01

Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions. The analysis reveals areas in the investment landscape where machines are already very active and those where machines are likely to make significant inroads in the next few years.

Errare machinale est: the use of error-related potentials in brain-machine interfaces

PubMed Central

Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

2014-01-01

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach.

PubMed

Feng, Chunliang; Zhu, Zhiyuan; Gu, Ruolei; Wu, Xia; Luo, Yue-Jia; Krueger, Frank

2018-06-08

A large number of studies have demonstrated costly punishment to unfair events across human societies. However, individuals exhibit a large heterogeneity in costly punishment decisions, whereas the neuropsychological substrates underlying the heterogeneity remain poorly understood. Here, we addressed this issue by applying a multivariate machine-learning approach to compare topological properties of resting-state brain networks as a potential neuromarker between individuals exhibiting different punishment propensities. A linear support vector machine classifier obtained an accuracy of 74.19% employing the features derived from resting-state brain networks to distinguish two groups of individuals with different punishment tendencies. Importantly, the most discriminative features that contributed to the classification were those regions frequently implicated in costly punishment decisions, including dorsal anterior cingulate cortex (dACC) and putamen (salience network), dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (mentalizing network), and lateral prefrontal cortex (central-executive network). These networks are previously implicated in encoding norm violation and intentions of others and integrating this information for punishment decisions. Our findings thus demonstrated that resting-state functional connectivity (RSFC) provides a promising neuromarker of social preferences, and bolster the assertion that human costly punishment behaviors emerge from interactions among multiple neural systems. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

PubMed

Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

2017-04-04

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

Electric field prediction for a human body-electric machine system.

PubMed

Ioannides, Maria G; Papadopoulos, Peter J; Dimitropoulou, Eugenia

2004-01-01

A system consisting of an electric machine and a human body is studied and the resulting electric field is predicted. A 3-phase induction machine operating at full load is modeled considering its geometry, windings, and materials. A human model is also constructed approximating its geometry and the electric properties of tissues. Using the finite element technique the electric field distribution in the human body is determined for a distance of 1 and 5 m from the machine and its effects are studied. Particularly, electric field potential variations are determined at specific points inside the human body and for these points the electric field intensity is computed and compared to the limit values for exposure according to international standards.

Observing Consistency in Online Communication Patterns for User Re-Identification

PubMed Central

Venter, Hein S.

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas. PMID:27918593

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

PubMed

de Ávila, Maurício Boff; de Azevedo, Walter Filgueira

2018-04-20

In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure. © 2018 John Wiley & Sons A/S.

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

A truly human interface: interacting face-to-face with someone whose words are determined by a computer program

PubMed Central

Corti, Kevin; Gillespie, Alex

2015-01-01

We use speech shadowing to create situations wherein people converse in person with a human whose words are determined by a conversational agent computer program. Speech shadowing involves a person (the shadower) repeating vocal stimuli originating from a separate communication source in real-time. Humans shadowing for conversational agent sources (e.g., chat bots) become hybrid agents (“echoborgs”) capable of face-to-face interlocution. We report three studies that investigated people’s experiences interacting with echoborgs and the extent to which echoborgs pass as autonomous humans. First, participants in a Turing Test spoke with a chat bot via either a text interface or an echoborg. Human shadowing did not improve the chat bot’s chance of passing but did increase interrogators’ ratings of how human-like the chat bot seemed. In our second study, participants had to decide whether their interlocutor produced words generated by a chat bot or simply pretended to be one. Compared to those who engaged a text interface, participants who engaged an echoborg were more likely to perceive their interlocutor as pretending to be a chat bot. In our third study, participants were naïve to the fact that their interlocutor produced words generated by a chat bot. Unlike those who engaged a text interface, the vast majority of participants who engaged an echoborg did not sense a robotic interaction. These findings have implications for android science, the Turing Test paradigm, and human–computer interaction. The human body, as the delivery mechanism of communication, fundamentally alters the social psychological dynamics of interactions with machine intelligence. PMID:26042066

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Future Cyborgs: Human-Machine Interface for Virtual Reality Applications

DTIC Science & Technology

2007-04-01

FUTURE CYBORGS : HUMAN-MACHINE INTERFACE FOR VIRTUAL REALITY APPLICATIONS Robert R. Powell, Major, USAF April 2007 Blue Horizons...SUBTITLE Future Cyborgs : Human-Machine Interface for Virtual Reality Applications 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...Nicholas Negroponte, Being Digital (New York: Alfred A Knopf, Inc, 1995), 123. 23 Ibid. 24 Andy Clark, Natural-Born Cyborgs (New York: Oxford

Exploring cluster Monte Carlo updates with Boltzmann machines

NASA Astrophysics Data System (ADS)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

32 CFR 286.29 - Collection of fees and fee rates.

Code of Federal Regulations, 2013 CFR

2013-07-01

... consists of two parts; individual time (hereafter referred to as human time), and machine time. (i) Human... support, operator, programmer, database administrator, or action officer). (ii) Machine time. Machine time... the time of providing the documents to the requester or recipient when the requester specifically...

32 CFR 286.29 - Collection of fees and fee rates.

Code of Federal Regulations, 2012 CFR

2012-07-01

... consists of two parts; individual time (hereafter referred to as human time), and machine time. (i) Human... support, operator, programmer, database administrator, or action officer). (ii) Machine time. Machine time... the time of providing the documents to the requester or recipient when the requester specifically...

32 CFR 286.29 - Collection of fees and fee rates.

Code of Federal Regulations, 2014 CFR

2014-07-01

... consists of two parts; individual time (hereafter referred to as human time), and machine time. (i) Human... support, operator, programmer, database administrator, or action officer). (ii) Machine time. Machine time... the time of providing the documents to the requester or recipient when the requester specifically...

Creative Technology and Rap

ERIC Educational Resources Information Center

Ch'ien, Evelyn

2011-01-01

This paper describes how a linguistic form, rap, can evolve in tandem with technological advances and manifest human-machine creativity. Rather than assuming that the interplay between machines and technology makes humans robotic or machine-like, the paper explores how the pressure of executing artistic visions using technology can drive…

Environmental metabolomics with data science for investigating ecosystem homeostasis.

PubMed

Kikuchi, Jun; Ito, Kengo; Date, Yasuhiro

2018-02-01

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches. Copyright © 2017. Published by Elsevier B.V.

Optical HMI with biomechanical energy harvesters integrated in textile supports

NASA Astrophysics Data System (ADS)

De Pasquale, G.; Kim, SG; De Pasquale, D.

2015-12-01

This paper reports the design, prototyping and experimental validation of a human-machine interface (HMI), named GoldFinger, integrated into a glove with energy harvesting from fingers motion. The device is addressed to medical applications, design tools, virtual reality field and to industrial applications where the interaction with machines is restricted by safety procedures. The HMI prototype includes four piezoelectric transducers applied to the fingers backside at PIP (proximal inter-phalangeal) joints, electric wires embedded in the fabric connecting the transducers, aluminum case for the electronics, wearable switch made with conductive fabrics to turn the communication channel on and off, and a LED. The electronic circuit used to manage the power and to control the light emitter includes a diodes bridge, leveling capacitors, storage battery and switch made by conductive fabric. The communication with the machine is managed by dedicated software, which includes the user interface, the optical tracking, and the continuous updating of the machine microcontroller. The energetic benefit of energy harvester on the battery lifetime is inversely proportional to the activation time of the optical emitter. In most applications, the optical port is active for 1 to 5% of the time, corresponding to battery lifetime increasing between about 14% and 70%.

Multiple man-machine interfaces

NASA Technical Reports Server (NTRS)

Stanton, L.; Cook, C. W.

1981-01-01

The multiple man machine interfaces inherent in military pilot training, their social implications, and the issue of possible negative feedback were explored. Modern technology has produced machines which can see, hear, and touch with greater accuracy and precision than human beings. Consequently, the military pilot is more a systems manager, often doing battle against a target he never sees. It is concluded that unquantifiable human activity requires motivation that is not intrinsic in a machine.

An Interactive Astronaut-Robot System with Gesture Control

PubMed Central

Liu, Jinguo; Luo, Yifan; Ju, Zhaojie

2016-01-01

Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system. PMID:27190503

Interaction between emotional state and learning underlies mood instability

PubMed Central

Eldar, Eran; Niv, Yael

2015-01-01

Intuitively, good and bad outcomes affect our emotional state, but whether the emotional state feeds back onto the perception of outcomes remains unknown. Here, we use behaviour and functional neuroimaging of human participants to investigate this bidirectional interaction, by comparing the evaluation of slot machines played before and after an emotion-impacting wheel-of-fortune draw. Results indicate that self-reported mood instability is associated with a positive-feedback effect of emotional state on the perception of outcomes. We then use theoretical simulations to demonstrate that such positive feedback would result in mood destabilization. Taken together, our results suggest that the interaction between emotional state and learning may play a significant role in the emergence of mood instability. PMID:25608088

Interface Metaphors for Interactive Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jasper, Robert J.; Blaha, Leslie M.

To promote more interactive and dynamic machine learn- ing, we revisit the notion of user-interface metaphors. User-interface metaphors provide intuitive constructs for supporting user needs through interface design elements. A user-interface metaphor provides a visual or action pattern that leverages a user’s knowledge of another domain. Metaphors suggest both the visual representations that should be used in a display as well as the interactions that should be afforded to the user. We argue that user-interface metaphors can also offer a method of extracting interaction-based user feedback for use in machine learning. Metaphors offer indirect, context-based information that can be usedmore » in addition to explicit user inputs, such as user-provided labels. Implicit information from user interactions with metaphors can augment explicit user input for active learning paradigms. Or it might be leveraged in systems where explicit user inputs are more challenging to obtain. Each interaction with the metaphor provides an opportunity to gather data and learn. We argue this approach is especially important in streaming applications, where we desire machine learning systems that can adapt to dynamic, changing data.« less

Proposal for study on IR light and glucose phantom interaction for human glucose quantification applications

NASA Astrophysics Data System (ADS)

Romo-Cárdenas, Gerardo S.; Sanchez-Lopez, Juan D.; Nieto-Hipolito, Juan I.; Cosio-León, María.; Luque-Morales, Priscy; Vazquez-Briseno, Mabel

2016-09-01

It has been established the importance of a constant glucose monitoring in order to keep a regular control for diabetes patients. Several medical studies accept the necessity of exploring alternatives for the traditional digital glucometer, given the pain and discomfort related to this technique, which can lead to a compromised control of the disease. Several efforts based on the application of IR spectroscopy had been done with favorable, yet not conclusive results. Therefore it's necessary to apply a comprehensive and interdisciplinary study based on the biochemical and optical properties of the glucose in the human body, in order to understand the interaction between this substance, its surroundings and IR light. These study propose a comprehensive approach of the glucose and IR light interaction, considering and combining important biochemical, physiological and optical properties, as well as some machine learning techniques for the data analysis. The results of this work would help to define the right parameters aiming to obtain an optical glucose quantification system and protocol.

In Silico Investigation of Traditional Chinese Medicine Compounds to Inhibit Human Histone Deacetylase 2 for Patients with Alzheimer's Disease

PubMed Central

Hung, Tzu-Chieh; Lee, Wen-Yuan; Chen, Kuen-Bao; Chan, Yueh-Chiu; Lee, Cheng-Chun

2014-01-01

Human histone deacetylase 2 (HDAC2) has been identified as being associated with Alzheimer's disease (AD), a neuropathic degenerative disease. In this study, we screen the world's largest Traditional Chinese Medicine (TCM) database for natural compounds that may be useful as lead compounds in the search for inhibitors of HDAC2 function. The technique of molecular docking was employed to select the ten top TCM candidates. We used three prediction models, multiple linear regression (MLR), support vector machine (SVM), and the Bayes network toolbox (BNT), to predict the bioactivity of the TCM candidates. Molecular dynamics simulation provides the protein-ligand interactions of compounds. The bioactivity predictions of pIC50 values suggest that the TCM candidatesm, (−)-Bontl ferulate, monomethylcurcumin, and ningposides C, have a greater effect on HDAC2 inhibition. The structure variation caused by the hydrogen bonds and hydrophobic interactions between protein-ligand interactions indicates that these compounds have an inhibitory effect on the protein. PMID:25045700

Knowledge Acquisition, Knowledge Programming, and Knowledge Refinement.

ERIC Educational Resources Information Center

Hayes-Roth, Frederick; And Others

This report describes the principal findings and recommendations of a 2-year Rand research project on machine-aided knowledge acquisition and discusses the transfer of expertise from humans to machines, as well as the functions of planning, debugging, knowledge refinement, and autonomous machine learning. The relative advantages of humans and…

Learning Machine, Vietnamese Based Human-Computer Interface.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach,…

Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

Inoue, Masashi; Ogihara, Mitsunori; Hanada, Ryoko; Furuyama, Nobuhiro

2011-01-01

The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting. PMID:23585724

Considerations for human-machine interfaces in tele-operations

NASA Technical Reports Server (NTRS)

Newport, Curt

1991-01-01

Numerous factors impact on the efficiency of tele-operative manipulative work. Generally, these are related to the physical environment of the tele-operator and how he interfaces with robotic control consoles. The capabilities of the operator can be influenced by considerations such as temperature, eye strain, body fatigue, and boredom created by repetitive work tasks. In addition, the successful combination of man and machine will, in part, be determined by the configuration of the visual and physical interfaces available to the teleoperator. The design and operation of system components such as full-scale and mini-master manipulator controllers, servo joysticks, and video monitors will have a direct impact on operational efficiency. As a result, the local environment and the interaction of the operator with the robotic control console have a substantial effect on mission productivity.

One Dimensional Turing-Like Handshake Test for Motor Intelligence

PubMed Central

Karniel, Amir; Avraham, Guy; Peles, Bat-Chen; Levy-Tzedek, Shelly; Nisky, Ilana

2010-01-01

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake. PMID:21206462

Controlled English to facilitate human/machine analytical processing

NASA Astrophysics Data System (ADS)

Braines, Dave; Mott, David; Laws, Simon; de Mel, Geeth; Pham, Tien

2013-06-01

Controlled English is a human-readable information representation format that is implemented using a restricted subset of the English language, but which is unambiguous and directly accessible by simple machine processes. We have been researching the capabilities of CE in a number of contexts, and exploring the degree to which a flexible and more human-friendly information representation format could aid the intelligence analyst in a multi-agent collaborative operational environment; especially in cases where the agents are a mixture of other human users and machine processes aimed at assisting the human users. CE itself is built upon a formal logic basis, but allows users to easily specify models for a domain of interest in a human-friendly language. In our research we have been developing an experimental component known as the "CE Store" in which CE information can be quickly and flexibly processed and shared between human and machine agents. The CE Store environment contains a number of specialized machine agents for common processing tasks and also supports execution of logical inference rules that can be defined in the same CE language. This paper outlines the basic architecture of this approach, discusses some of the example machine agents that have been developed, and provides some typical examples of the CE language and the way in which it has been used to support complex analytical tasks on synthetic data sources. We highlight the fusion of human and machine processing supported through the use of the CE language and CE Store environment, and show this environment with examples of highly dynamic extensions to the model(s) and integration between different user-defined models in a collaborative setting.

Wearable health monitoring using capacitive voltage-mode Human Body Communication.

PubMed

Maity, Shovan; Das, Debayan; Sen, Shreyas

2017-07-01

Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices. One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person. Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission. Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN. In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The-Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine. The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8× power savings compared to Bluetooth low-energy (BTLE).

A Non-Verbal Turing Test: Differentiating Mind from Machine in Gaze-Based Social Interaction

PubMed Central

Pfeiffer, Ulrich J.; Timmermans, Bert; Bente, Gary; Vogeley, Kai; Schilbach, Leonhard

2011-01-01

In social interaction, gaze behavior provides important signals that have a significant impact on our perception of others. Previous investigations, however, have relied on paradigms in which participants are passive observers of other persons’ gazes and do not adjust their gaze behavior as is the case in real-life social encounters. We used an interactive eye-tracking paradigm that allows participants to interact with an anthropomorphic virtual character whose gaze behavior is responsive to where the participant looks on the stimulus screen in real time. The character’s gaze reactions were systematically varied along a continuum from a maximal probability of gaze aversion to a maximal probability of gaze-following during brief interactions, thereby varying contingency and congruency of the reactions. We investigated how these variations influenced whether participants believed that the character was controlled by another person (i.e., a confederate) or a computer program. In a series of experiments, the human confederate was either introduced as naïve to the task, cooperative, or competitive. Results demonstrate that the ascription of humanness increases with higher congruency of gaze reactions when participants are interacting with a naïve partner. In contrast, humanness ascription is driven by the degree of contingency irrespective of congruency when the confederate was introduced as cooperative. Conversely, during interaction with a competitive confederate, judgments were neither based on congruency nor on contingency. These results offer important insights into what renders the experience of an interaction truly social: Humans appear to have a default expectation of reciprocation that can be influenced drastically by the presumed disposition of the interactor to either cooperate or compete. PMID:22096599

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

PubMed

Saproo, Sameer; Shih, Victor; Jangraw, David C; Sajda, Paul

2016-12-01

We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

NASA Astrophysics Data System (ADS)

Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

2016-12-01

Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Implicit prosody mining based on the human eye image capture technology

NASA Astrophysics Data System (ADS)

Gao, Pei-pei; Liu, Feng

2013-08-01

The technology of eye tracker has become the main methods of analyzing the recognition issues in human-computer interaction. Human eye image capture is the key problem of the eye tracking. Based on further research, a new human-computer interaction method introduced to enrich the form of speech synthetic. We propose a method of Implicit Prosody mining based on the human eye image capture technology to extract the parameters from the image of human eyes when reading, control and drive prosody generation in speech synthesis, and establish prosodic model with high simulation accuracy. Duration model is key issues for prosody generation. For the duration model, this paper put forward a new idea for obtaining gaze duration of eyes when reading based on the eye image capture technology, and synchronous controlling this duration and pronunciation duration in speech synthesis. The movement of human eyes during reading is a comprehensive multi-factor interactive process, such as gaze, twitching and backsight. Therefore, how to extract the appropriate information from the image of human eyes need to be considered and the gaze regularity of eyes need to be obtained as references of modeling. Based on the analysis of current three kinds of eye movement control model and the characteristics of the Implicit Prosody reading, relative independence between speech processing system of text and eye movement control system was discussed. It was proved that under the same text familiarity condition, gaze duration of eyes when reading and internal voice pronunciation duration are synchronous. The eye gaze duration model based on the Chinese language level prosodic structure was presented to change previous methods of machine learning and probability forecasting, obtain readers' real internal reading rhythm and to synthesize voice with personalized rhythm. This research will enrich human-computer interactive form, and will be practical significance and application prospect in terms of disabled assisted speech interaction. Experiments show that Implicit Prosody mining based on the human eye image capture technology makes the synthesized speech has more flexible expressions.

Best face forward.

PubMed

Rayport, Jeffrey F; Jaworski, Bernard J

2004-12-01

Most companies serve customers through a broad array of interfaces, from retail sales clerks to Web sites to voice-response telephone systems. But while the typical company has an impressive interface collection, it doesn't have an interface system. That is, the whole set does not add up to the sum of its parts in its ability to provide service and build customer relationships. Too many people and too many machines operating with insufficient coordination (and often at cross-purposes) mean rising complexity, costs, and customer dissatisfaction. In a world where companies compete not on what they sell but on how they sell it, turning that liability into an asset is what separates winners from losers. In this adaptation of their forthcoming book by the same title, Jeffrey Rayport and Bernard Jaworski explain how companies must reengineer their customer interface systems for optimal efficiency and effectiveness. Part of that transformation, they observe, will involve a steady encroachment by machine interfaces into areas that have long been the sacred province of humans. Managers now have opportunities unprecedented in the history of business to use machines, not just people, to credibly manage their interactions with customers. Because people and machines each have their strengths and weaknesses, company executives must identify what people do best, what machines do best, and how to deploy them separately and together. Front-office reengineering subjects every current and potential service interface to an analysis of opportunities for substitution (using machines instead of people), complementarity (using a mix of machines and people), and displacement (using networks to shift physical locations of people and machines), with the twin objectives of compressing costs and driving top-line growth through increased customer value.

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures.

PubMed

Goodman, Philip H; Buntha, Sermsak; Zou, Quan; Dascalu, Sergiu-Mihai

2007-01-01

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly "intelligent" systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material.

Privacy preserving interactive record linkage (PPIRL)

PubMed Central

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Objective Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human–machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. Methods In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. Results We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Conclusions Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human–machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility. PMID:24201028

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

PubMed

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Society of Brains: How Alan Turing and Marvin Minsky Were Both Right

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.

2015-04-01

In his well-known prediction, Alan Turing stated that computer intelligence would surpass human intelligence by the year 2000. Although the Turing Test, as it became known, was devised to be played by one human against one computer, this is not a fair setup. Every human is a part of a social network, and a fairer comparison would be a contest between one human at the console and a network of computers behind the console. Around the year 2000, the number of web pages on the WWW overtook the number of neurons in the human brain. But these websites would be of little use without the ability to search for knowledge. By the year 2000 Google Inc. had become the search engine of choice, and the WWW became an intelligent entity. This was not without good reason. The basis for the search engine was the analysis of the ’network of knowledge’. The PageRank algorithm, linking information on the web according to the hierarchy of ‘link popularity’, continues to provide the basis for all of Google's web search tools. While PageRank was developed by Larry Page and Sergey Brin in 1996 as part of a research project about a new kind of search engine, PageRank is in its essence the key to representing and using static knowledge in an emergent intelligent system. Here I argue that Alan Turing was right, as hybrid human-computer internet machines have already surpassed our individual intelligence - this was done around the year 2000 by the Internet - the socially-minded, human-computer hybrid Homo computabilis-socialis. Ironically, the Internet's intelligence also emerged to a large extent from ‘exploiting’ humans - the key to the emergence of machine intelligence has been discussed by Marvin Minsky in his work on the foundations of intelligence through interacting agents’ knowledge. As a consequence, a decade and a half decade into the 21st century, we appear to be much better equipped to tackle the problem of the social origins of humanity - in particular thanks to the power of the intelligent partner-in-the-quest machine, however, we should not wait too long...

Are human beings humean robots?

NASA Astrophysics Data System (ADS)

Génova, Gonzalo; Quintanilla Navarro, Ignacio

2018-01-01

David Hume, the Scottish philosopher, conceives reason as the slave of the passions, which implies that human reason has predetermined objectives it cannot question. An essential element of an algorithm running on a computational machine (or Logical Computing Machine, as Alan Turing calls it) is its having a predetermined purpose: an algorithm cannot question its purpose, because it would cease to be an algorithm. Therefore, if self-determination is essential to human intelligence, then human beings are neither Humean beings, nor computational machines. We examine also some objections to the Turing Test as a model to understand human intelligence.

Microgravity simulations with human lymphocytes in the free fall machine and in the random positioning machine

NASA Technical Reports Server (NTRS)

Schwarzenberg, M.; Pippia, P.; Meloni, M. A.; Cossu, G.; Cogoli-Greuter, M.; Cogoli, A.

1998-01-01

The purpose of this paper is to present the results obtained in our laboratory with both instruments, the FFM [free fall machine] and the RPM [random positioning machine], to compare them with the data from earlier experiments with human lymphocytes conducted in the FRC [fast rotating clinostat] and in space. Furthermore, the suitability of the FFM and RPM for research in gravitational cell biology is discussed.

Amplifying human ability through autonomics and machine learning in IMPACT

NASA Astrophysics Data System (ADS)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

An Evaluation of New After-Action Review Tools in Exercise Black Skies 10 & Exercise Black Skies 12

DTIC Science & Technology

2013-10-01

impacting on participant learning . AWAR also enabled an objective ground truth to be readily available to learners, to overcome the shortcomings of...memory of historical events in a training mission. AWAR also appeared to enhance the opportunity for less experienced participants to learn through...human- machine interaction, team performance, and team training. Dr. Best is Science Team Leader for the collective training component of DSTO task AIR

Human factors - Man-machine symbiosis in space

NASA Technical Reports Server (NTRS)

Brown, Jeri W.

1987-01-01

The relation between man and machine in space is studied. Early spaceflight and the goal of establishing a permanent space presence are described. The need to consider the physiological, psychological, and social integration of humans for each space mission is examined. Human factors must also be considered in the design of spacecraft. The effective utilization of man and machine capabilities, and research in anthropometry and biomechanics aimed at determining the limitations of spacecrews are discussed.

A learning-based markerless approach for full-body kinematics estimation in-natura from a single image.

PubMed

Drory, Ami; Li, Hongdong; Hartley, Richard

2017-04-11

We present a supervised machine learning approach for markerless estimation of human full-body kinematics for a cyclist from an unconstrained colour image. This approach is motivated by the limitations of existing marker-based approaches restricted by infrastructure, environmental conditions, and obtrusive markers. By using a discriminatively learned mixture-of-parts model, we construct a probabilistic tree representation to model the configuration and appearance of human body joints. During the learning stage, a Structured Support Vector Machine (SSVM) learns body parts appearance and spatial relations. In the testing stage, the learned models are employed to recover body pose via searching in a test image over a pyramid structure. We focus on the movement modality of cycling to demonstrate the efficacy of our approach. In natura estimation of cycling kinematics using images is challenging because of human interaction with a bicycle causing frequent occlusions. We make no assumptions in relation to the kinematic constraints of the model, nor the appearance of the scene. Our technique finds multiple quality hypotheses for the pose. We evaluate the precision of our method on two new datasets using loss functions. Our method achieves a score of 91.1 and 69.3 on mean Probability of Correct Keypoint (PCK) measure and 88.7 and 66.1 on the Average Precision of Keypoints (APK) measure for the frontal and sagittal datasets respectively. We conclude that our method opens new vistas to robust user-interaction free estimation of full body kinematics, a prerequisite to motion analysis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

PubMed Central

Roetker, Nicholas S.; Yonker, James A.; Chang, Vicky; Roan, Carol L.; Herd, Pamela; Hauser, Taissa S.; Hauser, Robert M.

2013-01-01

Objectives. We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. Methods. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors—13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors—18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. Results. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. Conclusions. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic–environmental–sociobehavioral interactions in depressive symptoms. PMID:23927508

Adaptive automation of human-machine system information-processing functions.

PubMed

Kaber, David B; Wright, Melanie C; Prinzel, Lawrence J; Clamann, Michael P

2005-01-01

The goal of this research was to describe the ability of human operators to interact with adaptive automation (AA) applied to various stages of complex systems information processing, defined in a model of human-automation interaction. Forty participants operated a simulation of an air traffic control task. Automated assistance was adaptively applied to information acquisition, information analysis, decision making, and action implementation aspects of the task based on operator workload states, which were measured using a secondary task. The differential effects of the forms of automation were determined and compared with a manual control condition. Results of two 20-min trials of AA or manual control revealed a significant effect of the type of automation on performance, particularly during manual control periods as part of the adaptive conditions. Humans appear to better adapt to AA applied to sensory and psychomotor information-processing functions (action implementation) than to AA applied to cognitive functions (information analysis and decision making), and AA is superior to completely manual control. Potential applications of this research include the design of automation to support air traffic controller information processing.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

PubMed

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

Techniques for optimizing human-machine information transfer related to real-time interactive display systems

NASA Technical Reports Server (NTRS)

Granaas, Michael M.; Rhea, Donald C.

1989-01-01

In recent years the needs of ground-based researcher-analysts to access real-time engineering data in the form of processed information has expanded rapidly. Fortunately, the capacity to deliver that information has also expanded. The development of advanced display systems is essential to the success of a research test activity. Those developed at the National Aeronautics and Space Administration (NASA), Western Aeronautical Test Range (WATR), range from simple alphanumerics to interactive mapping and graphics. These unique display systems are designed not only to meet basic information display requirements of the user, but also to take advantage of techniques for optimizing information display. Future ground-based display systems will rely heavily not only on new technologies, but also on interaction with the human user and the associated productivity with that interaction. The psychological abilities and limitations of the user will become even more important in defining the difference between a usable and a useful display system. This paper reviews the requirements for development of real-time displays; the psychological aspects of design such as the layout, color selection, real-time response rate, and interactivity of displays; and an analysis of some existing WATR displays.

Towards a framework of human factors certification of complex human-machine systems

NASA Technical Reports Server (NTRS)

Bukasa, Birgit

1994-01-01

As far as total automation is not realized, the combination of technical and social components in man-machine systems demands not only contributions from engineers but at least to an equal extent from behavioral scientists. This has been neglected far too long. The psychological, social and cultural aspects of technological innovations were almost totally overlooked. Yet, along with expected safety improvements the institutionalization of human factors is on the way. The introduction of human factors certification of complex man-machine systems will be a milestone in this process.

Human-Robot Control Strategies for the NASA/DARPA Robonaut

NASA Technical Reports Server (NTRS)

Diftler, M. A.; Culbert, Chris J.; Ambrose, Robert O.; Huber, E.; Bluethmann, W. J.

2003-01-01

The Robotic Systems Technology Branch at the NASA Johnson Space Center (JSC) is currently developing robot systems to reduce the Extra-Vehicular Activity (EVA) and planetary exploration burden on astronauts. One such system, Robonaut, is capable of interfacing with external Space Station systems that currently have only human interfaces. Robonaut is human scale, anthropomorphic, and designed to approach the dexterity of a space-suited astronaut. Robonaut can perform numerous human rated tasks, including actuating tether hooks, manipulating flexible materials, soldering wires, grasping handrails to move along space station mockups, and mating connectors. More recently, developments in autonomous control and perception for Robonaut have enabled dexterous, real-time man-machine interaction. Robonaut is now capable of acting as a practical autonomous assistant to the human, providing and accepting tools by reacting to body language. A versatile, vision-based algorithm for matching range silhouettes is used for monitoring human activity as well as estimating tool pose.

Control Automation in Undersea Search and Manipulation

NASA Technical Reports Server (NTRS)

Weltman, Gershon; Freedy, Amos

1974-01-01

Automatic decision making and control mechanisms of the type termed "adaptive" or "intelligent" offer unique advantages for exploration and manipulation of the undersea environment, particularly at great depths. Because they are able to carry out human-like functions autonomously, such mechanisms can aid and extend the capabilities of the human operator. This paper reviews past and present work in the areas of adaptive control and robotics with the purpose of establishing logical guidelines for the application of automatic techniques underwater. Experimental research data are used to illustrate the importance of information feedback, personnel training, and methods of control allocation in the interaction between operator and intelligent machine.

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors.

PubMed

Boudry, Maarten; Pigliucci, Massimo

2013-12-01

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the "blueprint" of an organism, organisms are "reverse engineered" to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on "designs" that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with "programming" the right "software" would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes-isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms-pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for "engineering" a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic "instructions" is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to "search" for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

PubMed

Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

2018-05-01

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

Foundations for a New Science of Learning

PubMed Central

Meltzoff, Andrew N.; Kuhl, Patricia K.; Movellan, Javier; Sejnowski, Terrence J.

2009-01-01

Human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved compared to other species. Humans are also the only species that has developed formal ways to enhance learning: teachers, schools, and curricula. Human infants have an intense interest in people and their behavior, and possess powerful implicit learning mechanisms that are affected by social interaction. Neuroscientists are beginning to understand the brain mechanisms underlying learning and how shared brain systems for perception and action support social learning. Machine learning algorithms are being developed that allow robots and computers to learn autonomously. New insights from many different fields are converging to create a new science of learning that may transform educational practices. PMID:19608908

Physics-based and human-derived information fusion for analysts

NASA Astrophysics Data System (ADS)

Blasch, Erik; Nagy, James; Scott, Steve; Okoth, Joshua; Hinman, Michael

2017-05-01

Recent trends in physics-based and human-derived information fusion (PHIF) have amplified the capabilities of analysts; however with the big data opportunities there is a need for open architecture designs, methods of distributed team collaboration, and visualizations. In this paper, we explore recent trends in the information fusion to support user interaction and machine analytics. Challenging scenarios requiring PHIF include combing physics-based video data with human-derived text data for enhanced simultaneous tracking and identification. A driving effort would be to provide analysts with applications, tools, and interfaces that afford effective and affordable solutions for timely decision making. Fusion at scale should be developed to allow analysts to access data, call analytics routines, enter solutions, update models, and store results for distributed decision making.

Decision making and problem solving with computer assistance

NASA Technical Reports Server (NTRS)

Kraiss, F.

1980-01-01

In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.

Cooperation in Human-Agent Systems to Support Resilience: A Microworld Experiment.

PubMed

Chiou, Erin K; Lee, John D

2016-09-01

This study uses a dyadic approach to understand human-agent cooperation and system resilience. Increasingly capable technology fundamentally changes human-machine relationships. Rather than reliance on or compliance with more or less reliable automation, we investigate interaction strategies with more or less cooperative agents. A joint-task microworld scenario was developed to explore the effects of agent cooperation on participant cooperation and system resilience. To assess the effects of agent cooperation on participant cooperation, 36 people coordinated with a more or less cooperative agent by requesting resources and responding to requests for resources in a dynamic task environment. Another 36 people were recruited to assess effects following a perturbation in their own hospital. Experiment 1 shows people reciprocated the cooperative behaviors of the agents; a low-cooperation agent led to less effective interactions and less resource sharing, whereas a high-cooperation agent led to more effective interactions and greater resource sharing. Experiment 2 shows that an initial fast-tempo perturbation undermined proactive cooperation-people tended to not request resources. However, the initial fast tempo had little effect on reactive cooperation-people tended to accept resource requests according to cooperation level. This study complements the supervisory control perspective of human-automation interaction by considering interdependence and cooperation rather than the more common focus on reliability and reliance. The cooperativeness of automated agents can influence the cooperativeness of human agents. Design and evaluation for resilience in teams involving increasingly autonomous agents should consider the cooperative behaviors of these agents. © 2016, Human Factors and Ergonomics Society.

Multimodal Neuroelectric Interface Development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Totah, Joseph (Technical Monitor)

2001-01-01

This project aims to improve performance of NASA missions by developing multimodal neuroelectric technologies for augmented human-system interaction. Neuroelectric technologies will add completely new modes of interaction that operate in parallel with keyboards, speech, or other manual controls, thereby increasing the bandwidth of human-system interaction. We recently demonstrated the feasibility of real-time electromyographic (EMG) pattern recognition for a direct neuroelectric human-computer interface. We recorded EMG signals from an elastic sleeve with dry electrodes, while a human subject performed a range of discrete gestures. A machine-teaming algorithm was trained to recognize the EMG patterns associated with the gestures and map them to control signals. Successful applications now include piloting two Class 4 aircraft simulations (F-15 and 757) and entering data with a "virtual" numeric keyboard. Current research focuses on on-line adaptation of EMG sensing and processing and recognition of continuous gestures. We are also extending this on-line pattern recognition methodology to electroencephalographic (EEG) signals. This will allow us to bypass muscle activity and draw control signals directly from the human brain. Our system can reliably detect P-rhythm (a periodic EEG signal from motor cortex in the 10 Hz range) with a lightweight headset containing saline-soaked sponge electrodes. The data show that EEG p-rhythm can be modulated by real and imaginary motions. Current research focuses on using biofeedback to train of human subjects to modulate EEG rhythms on demand, and to examine interactions of EEG-based control with EMG-based and manual control. Viewgraphs on these neuroelectric technologies are also included.

Comparative study of CW, nanosecond- and femtosecond-pulsed laser microcutting of AZ31 magnesium alloy stents.

PubMed

Gökhan Demir, Ali; Previtali, Barbara

2014-06-01

Magnesium alloys constitute an interesting solution for cardiovascular stents due to their biocompatibility and biodegradability in human body. Laser microcutting is the industrially accepted method for stent manufacturing. However, the laser-material interaction should be well investigated to control the quality characteristics of the microcutting process that concern the surface roughness, chemical composition, and microstructure of the final device. Despite the recent developments in industrial laser systems, a universal laser source that can be manipulated flexibly in terms of process parameters is far from reality. Therefore, comparative studies are required to demonstrate processing capabilities. In particular, the laser pulse duration is a key factor determining the processing regime. This work approaches the laser microcutting of AZ31 Mg alloy from the perspective of a comparative study to evaluate the machining capabilities in continuous wave (CW), ns- and fs-pulsed regimes. Three industrial grade machining systems were compared to reach a benchmark in machining quality, productivity, and ease of postprocessing. The results confirmed that moving toward the ultrashort pulse domain the machining quality increases, but the need for postprocessing remains. The real advantage of ultrashort pulsed machining was the ease in postprocessing and maintaining geometrical integrity of the stent mesh after chemical etching. Resultantly, the overall production cycle time was shortest for fs-pulsed laser system, despite the fact that CW laser system provided highest cutting speed.

AIonAI: a humanitarian law of artificial intelligence and robotics.

PubMed

Ashrafian, Hutan

2015-02-01

The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of preventing human harm, stipulating obedience to humans and incorporating robotic self-protection. However the overwhelming predominance in the study of this field has focussed on human-robot interactions without fully considering the ethical inevitability of future artificial intelligences communicating together and has not addressed the moral nature of robot-robot interactions. A new robotic law is proposed and termed AIonAI or artificial intelligence-on-artificial intelligence. This law tackles the overlooked area where future artificial intelligences will likely interact amongst themselves, potentially leading to exploitation. As such, they would benefit from adopting a universal law of rights to recognise inherent dignity and the inalienable rights of artificial intelligences. Such a consideration can help prevent exploitation and abuse of rational and sentient beings, but would also importantly reflect on our moral code of ethics and the humanity of our civilisation.

Automating annotation of information-giving for analysis of clinical conversation.

PubMed

Mayfield, Elijah; Laws, M Barton; Wilson, Ira B; Penstein Rosé, Carolyn

2014-02-01

Coding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters. The data were transcripts of 415 routine outpatient visits of HIV patients which had previously been coded for speech acts using the Generalized Medical Interaction Analysis System (GMIAS); 50 had also been coded for larger scale features using the Comprehensive Analysis of the Structure of Encounters System (CASES). We aggregated selected speech acts into information-giving and requesting, then trained the machine to automatically annotate using logistic regression classification. We evaluated reliability by per-speech act accuracy. We used multiple regression to predict patient reports of communication quality from post-visit surveys using the patient and provider information-giving to information-requesting ratio (briefly, information-giving ratio) and patient gender. Automated coding produces moderate reliability with human coding (accuracy 71.2%, κ=0.57), with high correlation between machine and human prediction of the information-giving ratio (r=0.96). The regression significantly predicted four of five patient-reported measures of communication quality (r=0.263-0.344). The information-giving ratio is a useful and intuitive measure for predicting patient perception of provider-patient communication quality. These predictions can be made with automated annotation, which is a practical option for studying large collections of clinical encounters with objectivity, consistency, and low cost, providing greater opportunity for training and reflection for care providers.

How do we think machines think? An fMRI study of alleged competition with an artificial intelligence

PubMed Central

Chaminade, Thierry; Rosset, Delphine; Da Fonseca, David; Nazarian, Bruno; Lutcher, Ewald; Cheng, Gordon; Deruelle, Christine

2012-01-01

Mentalizing is defined as the inference of mental states of fellow humans, and is a particularly important skill for social interactions. Here we assessed whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by comparing brain responses during the interaction with an intentional and an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing their opponent was a fellow human (Intentional agent, Int), a humanoid robot endowed with an artificial intelligence (Artificial agent, Art), or a computer playing randomly (Random agent, Rnd). Participants' subjective reports indicated that they adopted different stances against the three agents. The contrast of brain activity during interaction with the artificial and the random agents didn't yield any cluster at the threshold used, suggesting the absence of a reproducible stance when interacting with an artificial intelligence. We probed response to the artificial agent in regions of interest corresponding to clusters found in the contrast between the intentional and the random agents. In the precuneus involved in working memory, the posterior intraparietal suclus, in the control of attention and the dorsolateral prefrontal cortex, in executive functions, brain activity for Art was larger than for Rnd but lower than for Int, supporting the intrinsically engaging nature of social interactions. A similar pattern in the left premotor cortex and anterior intraparietal sulcus involved in motor resonance suggested that participants simulated human, and to a lesser extend humanoid robot actions, when playing the game. Finally, mentalizing regions, the medial prefrontal cortex and right temporoparietal junction, responded to the human only, supporting the specificity of mentalizing areas for interactions with intentional agents. PMID:22586381

How do we think machines think? An fMRI study of alleged competition with an artificial intelligence.

PubMed

Chaminade, Thierry; Rosset, Delphine; Da Fonseca, David; Nazarian, Bruno; Lutcher, Ewald; Cheng, Gordon; Deruelle, Christine

2012-01-01

Mentalizing is defined as the inference of mental states of fellow humans, and is a particularly important skill for social interactions. Here we assessed whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by comparing brain responses during the interaction with an intentional and an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing their opponent was a fellow human (Intentional agent, Int), a humanoid robot endowed with an artificial intelligence (Artificial agent, Art), or a computer playing randomly (Random agent, Rnd). Participants' subjective reports indicated that they adopted different stances against the three agents. The contrast of brain activity during interaction with the artificial and the random agents didn't yield any cluster at the threshold used, suggesting the absence of a reproducible stance when interacting with an artificial intelligence. We probed response to the artificial agent in regions of interest corresponding to clusters found in the contrast between the intentional and the random agents. In the precuneus involved in working memory, the posterior intraparietal suclus, in the control of attention and the dorsolateral prefrontal cortex, in executive functions, brain activity for Art was larger than for Rnd but lower than for Int, supporting the intrinsically engaging nature of social interactions. A similar pattern in the left premotor cortex and anterior intraparietal sulcus involved in motor resonance suggested that participants simulated human, and to a lesser extend humanoid robot actions, when playing the game. Finally, mentalizing regions, the medial prefrontal cortex and right temporoparietal junction, responded to the human only, supporting the specificity of mentalizing areas for interactions with intentional agents.

Mistaking minds and machines: How speech affects dehumanization and anthropomorphism.

PubMed

Schroeder, Juliana; Epley, Nicholas

2016-11-01

Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

[Re-signification of the human in the context of the "ciborgzation": a look at the human being-machine relationship in intensive care].

PubMed

Vargas, Mara Ambrosina de O; Meyer, Dagmar Estermann

2005-06-01

This study discusses the human being-machine relationship in the process called "cyborgzation" of the nurse who works in intensive care, based on post-structuralist Cultural Studies and highlighting Haraway's concept of cyborg. In it, manuals used by nurses in Intensive Care Units have been examined as cultural texts. This cultural analysis tries to decode the various senses of "human" and "machine", with the aim of recognizing processes that turn nurses into cyborgs. The argument is that intensive care nurses fall into a process of "technology embodiment" that turns the body-professional into a hybrid that makes possible to disqualify, at the same time, notions such as machine and body "proper", since it is the hybridization between one and the other that counts there. Like cyborgs, intensive care nurses learn to "be with" the machine, and this connection limits the specificity of their actions. It is suggested that processes of "cyborgzation" such as this are useful for questioning - and to deal with in different ways - the senses of "human" and "humanity" that support a major part of knowledge/action in health.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An evaluative model of system performance in manned teleoperational systems

NASA Technical Reports Server (NTRS)

Haines, Richard F.

1989-01-01

Manned teleoperational systems are used in aerospace operations in which humans must interact with machines remotely. Manual guidance of remotely piloted vehicles, controling a wind tunnel, carrying out a scientific procedure remotely are examples of teleoperations. A four input parameter throughput (Tp) model is presented which can be used to evaluate complex, manned, teleoperations-based systems and make critical comparisons among candidate control systems. The first two parameters of this model deal with nominal (A) and off-nominal (B) predicted events while the last two focus on measured events of two types, human performance (C) and system performance (D). Digital simulations showed that the expression A(1-B)/C+D) produced the greatest homogeneity of variance and distribution symmetry. Results from a recently completed manned life science telescience experiment will be used to further validate the model. Complex, interacting teleoperational systems may be systematically evaluated using this expression much like a computer benchmark is used.

Contrasting State-of-the-Art in the Machine Scoring of Short-Form Constructed Responses

ERIC Educational Resources Information Center

Shermis, Mark D.

2015-01-01

This study compared short-form constructed responses evaluated by both human raters and machine scoring algorithms. The context was a public competition on which both public competitors and commercial vendors vied to develop machine scoring algorithms that would match or exceed the performance of operational human raters in a summative high-stakes…

Cooperating with machines.

PubMed

Crandall, Jacob W; Oudah, Mayada; Tennom; Ishowo-Oloko, Fatimah; Abdallah, Sherief; Bonnefon, Jean-François; Cebrian, Manuel; Shariff, Azim; Goodrich, Michael A; Rahwan, Iyad

2018-01-16

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

Coordinating Communities and Building Governance in the Development of Schematic and Semantic Standards: the Key to Solving Global Earth and Space Science Challenges in the 21st Century.

NASA Astrophysics Data System (ADS)

Wyborn, L. A.

2007-12-01

The Information Age in Science is being driven partly by the data deluge as exponentially growing volumes of data are being generated by research. Such large volumes of data cannot be effectively processed by humans and efficient and timely processing by computers requires development of specific machine readable formats. Further, as key challenges in earth and space sciences, such as climate change, hazard prediction and sustainable development resources require a cross disciplinary approach, data from various domains will need to be integrated from globally distributed sources also via machine to machine formats. However, it is becoming increasingly apparent that the existing standards can be very domain specific and most existing data transfer formats require human intervention. Where groups from different communities do try combine data across the domain/discipline boundaries much time is spent reformatting and reorganizing the data and it is conservatively estimated that this can take 80% of a project's time and resources. Four different types of standards are required for machine to machine interaction: systems, syntactic, schematic and semantic. Standards at the systems (WMS, WFS, etc) and at the syntactic level (GML, Observation and Measurement, SensorML) are being developed through international standards bodies such as ISO, OGC, W3C, IEEE etc. In contrast standards at the schematic level (e.g., GeoSciML, LandslidesML, WaterML, QuakeML) and at the semantic level (ie ontologies and vocabularies) are currently developing rapidly, in a very uncoordinated way and with little governance. As the size of the community that can machine read each others data depends on the size of the community that has developed the schematic or semantic standards, it is essential that to achieve global integration of earth and space science data, the required standards need to be developed through international collaboration using accepted standard proceedures. Once developed the standards also require some form of governance to maintain and then extend the standard as the science evolves to meet new challenges. A standard that does have some governance is GeoSciML, a data transfer standard for geoscience map data. GeoSciML is currently being developed by a consortium of 7 countries under the auspices of the Commission for the Management of and Application of Geoscience Information (CGI), a commission of the International Union of Geological Sciences. Perhaps other `ML' or ontology and vocabulary development `teams' need to look to their international domain specific specialty societies for endorsement and governance. But the issue goes beyond Earth and Space Sciences, as increasingly cross and intra disciplinary science requires machine to machine interaction with other science disciplines such as physics, chemistry and astronomy. For example, for geochemistry do we develop GeochemistryML or do we extend the existing Chemical Markup Language? Again, the question is who will provide the coordination of the development of the required schematic and semantic standards that underpin machine to machine global integration of science data. Is this a role for ICSU or CODATA or who? In order to address this issue, Geoscience Australia and CSIRO established the Solid Earth and Environmental Grid Community website to enable communities to `advertise' standards development and to provide a community TWIKI where standards can be developed in a globally `open' environment.

Automated negotiation in environmental resource management: Review and assessment.

PubMed

Eshragh, Faezeh; Pooyandeh, Majeed; Marceau, Danielle J

2015-10-01

Negotiation is an integral part of our daily life and plays an important role in resolving conflicts and facilitating human interactions. Automated negotiation, which aims at capturing the human negotiation process using artificial intelligence and machine learning techniques, is well-established in e-commerce, but its application in environmental resource management remains limited. This is due to the inherent uncertainties and complexity of environmental issues, along with the diversity of stakeholders' perspectives when dealing with these issues. The objective of this paper is to describe the main components of automated negotiation, review and compare machine learning techniques in automated negotiation, and provide a guideline for the selection of suitable methods in the particular context of stakeholders' negotiation over environmental resource issues. We advocate that automated negotiation can facilitate the involvement of stakeholders in the exploration of a plurality of solutions in order to reach a mutually satisfying agreement and contribute to informed decisions in environmental management along with the need for further studies to consolidate the potential of this modeling approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

Dual-Schemata Model

NASA Astrophysics Data System (ADS)

Taniguchi, Tadahiro; Sawaragi, Tetsuo

In this paper, a new machine-learning method, called Dual-Schemata model, is presented. Dual-Schemata model is a kind of self-organizational machine learning methods for an autonomous robot interacting with an unknown dynamical environment. This is based on Piaget's Schema model, that is a classical psychological model to explain memory and cognitive development of human beings. Our Dual-Schemata model is developed as a computational model of Piaget's Schema model, especially focusing on sensori-motor developing period. This developmental process is characterized by a couple of two mutually-interacting dynamics; one is a dynamics formed by assimilation and accommodation, and the other dynamics is formed by equilibration and differentiation. By these dynamics schema system enables an agent to act well in a real world. This schema's differentiation process corresponds to a symbol formation process occurring within an autonomous agent when it interacts with an unknown, dynamically changing environment. Experiment results obtained from an autonomous facial robot in which our model is embedded are presented; an autonomous facial robot becomes able to chase a ball moving in various ways without any rewards nor teaching signals from outside. Moreover, emergence of concepts on the target movements within a robot is shown and discussed in terms of fuzzy logics on set-subset inclusive relationships.

Information, knowledge and the future of machines.

PubMed

MacFarlane, Alistair G J

2003-08-15

This wide-ranging survey considers the future of machines in terms of information, complexity and the growth of knowledge shared amongst agents. Mechanical and human agents are compared and contrasted, and it is argued that, for the foreseeable future, their roles will be complementary. The future development of machines is examined in terms of unions of human and machine agency evolving as part of economic activity. Limits to, and threats posed by, the continuing evolution of such a society of agency are considered.

Virtual reality and planetary exploration

NASA Technical Reports Server (NTRS)

Mcgreevy, Michael W.

1992-01-01

NASA-Ames is intensively developing virtual-reality (VR) capabilities that can extend and augment computer-generated and remote spatial environments. VR is envisioned not only as a basis for improving human/machine interactions involved in planetary exploration, but also as a medium for the more widespread sharing of the experience of exploration, thereby broadening the support-base for the lunar and planetary-exploration endeavors. Imagery representative of Mars are being gathered for VR presentation at such terrestrial sites as Antarctica and Death Valley.

Electric and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine. Held in Trondheim, Norway on 25-29 May 1987

DTIC Science & Technology

1988-02-01

research dealing with the pharmacological control of states of vigilance, in the context of maximizing the operational value of combat arms personnel...brain activity of human subjects while they process cognitive information, with the research based on care- ful stimulus control , systematic task... control in man-machine interaction. Annual Technical Report 1975-1976, Report # UCLA-ENG-7J51 for Advanced Research Projecto Agency. University of

Design of penicillin fermentation process simulation system

NASA Astrophysics Data System (ADS)

Qi, Xiaoyu; Yuan, Zhonghu; Qi, Xiaoxuan; Zhang, Wenqi

2011-10-01

Real-time monitoring for batch process attracts increasing attention. It can ensure safety and provide products with consistent quality. The design of simulation system of batch process fault diagnosis is of great significance. In this paper, penicillin fermentation, a typical non-linear, dynamic, multi-stage batch production process, is taken as the research object. A visual human-machine interactive simulation software system based on Windows operation system is developed. The simulation system can provide an effective platform for the research of batch process fault diagnosis.

A Collaborative 20 Questions Model for Target Search with Human-Machine Interaction

DTIC Science & Technology

2013-05-01

optimal policies for entropy loss,” Journal of Applied Probability, vol. 49, pp. 114–136, 2012. [2] R. Castro and R. Nowak, “ Active learning and...vol. 10, pp. 223231, 1974. [8] R. Castro, Active Learning and Adaptive Sampling for Non- parametric Inference, Ph.D. thesis, Rice University, August...2007. [9] R. Castro and R. D. Nowak, “Upper and lower bounds for active learning ,” in 44th Annual Allerton Conference on Communica- tion, Control and Computing, 2006.

To select the best tool for generating 3D maintenance data and to set the detailed process for obtaining the 3D maintenance data

NASA Astrophysics Data System (ADS)

Prashanth, B. N.; Roy, Kingshuk

2017-07-01

Three Dimensional (3D) maintenance data provides a link between design and technical documentation creating interactive 3D graphical training and maintenance material. It becomes difficult for an operator to always go through huge paper manuals or come running to the computer for doing maintenance of a machine which makes the maintenance work fatigue. Above being the case, a 3D animation makes maintenance work very simple since, there is no language barrier. The research deals with the generation of 3D maintenance data of any given machine. The best tool for obtaining the 3D maintenance is selected and the tool is analyzed. Using the same tool, a detailed process for extracting the 3D maintenance data for any machine is set. This project aims at selecting the best tool for obtaining 3D maintenance data and to select the detailed process for obtaining 3D maintenance data. 3D maintenance reduces use of big volumes of manuals which creates human errors and makes the work of an operator fatiguing. Hence 3-D maintenance would help in training and maintenance and would increase productivity. 3Dvia when compared with Cortona 3D and Deep Exploration proves to be better than them. 3Dvia is good in data translation and it has the best renderings compared to the other two 3D maintenance software. 3Dvia is very user friendly and it has various options for creating 3D animations. Its Interactive Electronic Technical Publication (IETP) integration is also better than the other two software. Hence 3Dvia proves to be the best software for obtaining 3D maintenance data of any machine.

Support patient search on pathology reports with interactive online learning based data extraction.

PubMed

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

Automation effects in a multiloop manual control system

NASA Technical Reports Server (NTRS)

Hess, R. A.; Mcnally, B. D.

1986-01-01

An experimental and analytical study was undertaken to investigate human interaction with a simple multiloop manual control system in which the human's activity was systematically varied by changing the level of automation. The system simulated was the longitudinal dynamics of a hovering helicopter. The automation-systems-stabilized vehicle responses from attitude to velocity to position and also provided for display automation in the form of a flight director. The control-loop structure resulting from the task definition can be considered a simple stereotype of a hierarchical control system. The experimental study was complemented by an analytical modeling effort which utilized simple crossover models of the human operator. It was shown that such models can be extended to the description of multiloop tasks involving preview and precognitive human operator behavior. The existence of time optimal manual control behavior was established for these tasks and the role which internal models may play in establishing human-machine performance was discussed.

The "extended mind" approach for a new paradigm of psychology.

PubMed

Kono, Tetsuya

2010-12-01

In this paper, I would like to propose the idea of "extended mind" for a new paradigm of psychology. Kohler (Integrative Psychology & Behavioral Science 44:39-57, 2010) correctly pointed out the serious problems of the machine paradigm, and proposed the "organic" view as a new paradigm. But the term "organic" signifying the processes inside the body, is inadequate to express the characteristic of human mind. The recent philosophy of mind suggests that the mind is realized neither only in the brain nor only in the body, but in the whole system of brain-body-environment, namely, in the "extended mind". The characteristic of human mind resides in the interaction with the mediating tools, artifacts, and the humanized environment. We should propose an "extended mind approach" or an "ecological approach to humanized environment" as a new paradigm for a psychology.

Simulating Human Cognition in the Domain of Air Traffic Control

NASA Technical Reports Server (NTRS)

Freed, Michael; Johnston, James C.; Null, Cynthia H. (Technical Monitor)

1995-01-01

Experiments intended to assess performance in human-machine interactions are often prohibitively expensive, unethical or otherwise impractical to run. Approximations of experimental results can be obtained, in principle, by simulating the behavior of subjects using computer models of human mental behavior. Computer simulation technology has been developed for this purpose. Our goal is to produce a cognitive model suitable to guide the simulation machinery and enable it to closely approximate a human subject's performance in experimental conditions. The described model is designed to simulate a variety of cognitive behaviors involved in routine air traffic control. As the model is elaborated, our ability to predict the effects of novel circumstances on controller error rates and other performance characteristics should increase. This will enable the system to project the impact of proposed changes to air traffic control procedures and equipment on controller performance.

Nanoscale swimmers: hydrodynamic interactions and propulsion of molecular machines

NASA Astrophysics Data System (ADS)

Sakaue, T.; Kapral, R.; Mikhailov, A. S.

2010-06-01

Molecular machines execute nearly regular cyclic conformational changes as a result of ligand binding and product release. This cyclic conformational dynamics is generally non-reciprocal so that under time reversal a different sequence of machine conformations is visited. Since such changes occur in a solvent, coupling to solvent hydrodynamic modes will generally result in self-propulsion of the molecular machine. These effects are investigated for a class of coarse grained models of protein machines consisting of a set of beads interacting through pair-wise additive potentials. Hydrodynamic effects are incorporated through a configuration-dependent mobility tensor, and expressions for the propulsion linear and angular velocities, as well as the stall force, are obtained. In the limit where conformational changes are small so that linear response theory is applicable, it is shown that propulsion is exponentially small; thus, propulsion is nonlinear phenomenon. The results are illustrated by computations on a simple model molecular machine.

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome

PubMed Central

Hannigan, Geoffrey D.; Duhaime, Melissa B.; Koutra, Danai

2018-01-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks. PMID:29668682

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome.

PubMed

Hannigan, Geoffrey D; Duhaime, Melissa B; Koutra, Danai; Schloss, Patrick D

2018-04-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.

Human factors evaluation of teletherapy: Function and task analysis. Volume 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaye, R.D.; Henriksen, K.; Jones, R.

1995-07-01

As a treatment methodology, teletherapy selectively destroys cancerous and other tissue by exposure to an external beam of ionizing radiation. Sources of radiation are either a radioactive isotope, typically Cobalt-60 (Co-60), or a linear accelerator. Records maintained by the NRC have identified instances of teletherapy misadministration where the delivered radiation dose has differed from the radiation prescription (e.g., instances where fractions were delivered to the wrong patient, to the wrong body part, or were too great or too little with respect to the defined treatment volume). Both human error and machine malfunction have led to misadministrations. Effective and safe treatmentmore » requires a concern for precision and consistency of human-human and human-machine interactions throughout the course of therapy. The present study is the first part of a series of human factors evaluations for identifying the root causes that lead to human error in the teletherapy environment. The human factors evaluations included: (1) a function and task analysis of teletherapy activities, (2) an evaluation of the human-system interfaces, (3) an evaluation of procedures used by teletherapy staff, (4) an evaluation of the training and qualifications of treatment staff (excluding the oncologists), (5) an evaluation of organizational practices and policies, and (6) an identification of problems and alternative approaches for NRC and industry attention. The present report addresses the function and task analysis of teletherapy activities and provides the foundation for the conduct of the subsequent evaluations. The report includes sections on background, methodology, a description of the function and task analysis, and use of the task analysis findings for the subsequent tasks. The function and task analysis data base also is included.« less

Noise characteristics of grass-trimming machine engines and their effect on operators.

PubMed

Mallick, Zulquernain; Badruddin, Irfan Anjum; Khaleed Hussain, M T; Salman Ahmed, N J; Kanesan, Jeevan

2009-01-01

Over the last few years, interaction of humans with noisy power-driven agricultural tools and its possible adverse after effects have been realized. Grass-trimmer engine is the primary source of noise and the use of motorized cutter, spinning at high speed, is the secondary source of noise to which operators are exposed. In the present study, investigation was carried out to determine the effect of two types of grass-trimming machine engines (SUM 328 SE and BG 328) noise on the operators in real working environment. It was found that BG-328 and SUM-328 SE produced high levels of noise, of the order of 100 and 105 dB(A), respectively, to which operators are exposed while working. It was also observed that situation aggravates when a number of operators simultaneously operate resulting in still higher levels of noise. Operators should be separated 15 meters from each other in order to avoid the combined level of noise exposure while working with these machines. It was found that SPL, of the grass-trimmer machine engines (BG-328 and SUM-328 SE), were higher than the limit of noise recommended by ISO, NIOSH, and OSHA for an 8-hour workday. Such a high level of noise exposure may cause physiological and psychological problems to the operators in long run.

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

PubMed Central

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them. PMID:24567704

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

PubMed

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

Ghost-in-the-Machine reveals human social signals for human–robot interaction

PubMed Central

Loth, Sebastian; Jettka, Katharina; Giuliani, Manuel; de Ruiter, Jan P.

2015-01-01

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience. PMID:26582998

The perception of spatial layout in real and virtual worlds.

PubMed

Arthur, E J; Hancock, P A; Chrysler, S T

1997-01-01

As human-machine interfaces grow more immersive and graphically-oriented, virtual environment systems become more prominent as the medium for human-machine communication. Often, virtual environments (VE) are built to provide exact metrical representations of existing or proposed physical spaces. However, it is not known how individuals develop representational models of these spaces in which they are immersed and how those models may be distorted with respect to both the virtual and real-world equivalents. To evaluate the process of model development, the present experiment examined participant's ability to reproduce a complex spatial layout of objects having experienced them previously under different viewing conditions. The layout consisted of nine common objects arranged on a flat plane. These objects could be viewed in a free binocular virtual condition, a free binocular real-world condition, and in a static monocular view of the real world. The first two allowed active exploration of the environment while the latter condition allowed the participant only a passive opportunity to observe from a single viewpoint. Viewing conditions were a between-subject variable with 10 participants randomly assigned to each condition. Performance was assessed using mapping accuracy and triadic comparisons of relative inter-object distances. Mapping results showed a significant effect of viewing condition where, interestingly, the static monocular condition was superior to both the active virtual and real binocular conditions. Results for the triadic comparisons showed a significant interaction for gender by viewing condition in which males were more accurate than females. These results suggest that the situation model resulting from interaction with a virtual environment was indistinguishable from interaction with real objects at least within the constraints of the present procedure.

Two Dimensional Display for a Naval Duel: Man-Machine Interactive Game.

DTIC Science & Technology

Man-machine interactive games simulating naval duels are being conducted at the University of Pennsylvania. The players act as the commanding...officers of their respective vessels. They navigate, detect, and analyze their own and their opponent’s activities in the duel . The report describes the two

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

PubMed Central

Liu, Kai-Chun; Chan, Chia-Tai

2017-01-01

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. PMID:28106853

Can machines think? A report on Turing test experiments at the Royal Society

NASA Astrophysics Data System (ADS)

Warwick, Kevin; Shah, Huma

2016-11-01

In this article we consider transcripts that originated from a practical series of Turing's Imitation Game that was held on 6 and 7 June 2014 at the Royal Society London. In all cases the tests involved a three-participant simultaneous comparison by an interrogator of two hidden entities, one being a human and the other a machine. Each of the transcripts considered here resulted in a human interrogator being fooled such that they could not make the 'right identification', that is, they could not say for certain which was the machine and which was the human. The transcripts presented all involve one machine only, namely 'Eugene Goostman', the result being that the machine became the first to pass the Turing test, as set out by Alan Turing, on unrestricted conversation. This is the first time that results from the Royal Society tests have been disclosed and discussed in a paper.

Applying Spatial Audio to Human Interfaces: 25 Years of NASA Experience

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.; Godfrey, Martine; Miller, Joel D.; Anderson, Mark R.

2010-01-01

From the perspective of human factors engineering, the inclusion of spatial audio within a human-machine interface is advantageous from several perspectives. Demonstrated benefits include the ability to monitor multiple streams of speech and non-speech warning tones using a cocktail party advantage, and for aurally-guided visual search. Other potential benefits include the spatial coordination and interaction of multimodal events, and evaluation of new communication technologies and alerting systems using virtual simulation. Many of these technologies were developed at NASA Ames Research Center, beginning in 1985. This paper reviews examples and describes the advantages of spatial sound in NASA-related technologies, including space operations, aeronautics, and search and rescue. The work has involved hardware and software development as well as basic and applied research.

Absorption of language concepts in the machine mind

NASA Astrophysics Data System (ADS)

Kollár, Ján

2016-06-01

In our approach, the machine mind is the applicative dynamic system represented by its algorithmically evolvable internal language. By other words, the mind and the language of mind are synonyms. Coming out from Shaumyan's semiotic theory of languages, we present the representation of language concepts in the machine mind as a result of our experiment, to show non-redundancy of the language of mind. To provide useful restriction for further research, we also introduce the hypothesis of semantic saturation in Computer-Computer communication, which indicates that a set of machines is not self-evolvable. The goal of our research is to increase the abstraction of Human-Computer and Computer-Computer communication. If we want humans and machines comunicate as a parent with the child, using different symbols and media, we must find the language of mind commonly usable by both machines and humans. In our opinion, there exist a kind of calm language of thinking, which we try to propose for machines in this paper. We separate the layers of a machine mind, we present the structure of the evolved mind and we discuss the selected properties. We are concentrating on the representation of symbolized concepts in the mind, that are languages, not just grammars, since they have meaning.

3-D Human body models in C.A.D. : Anthropometric Aspects

NASA Astrophysics Data System (ADS)

Renaud, C.; Steck, R.; Pineau, J. C.

1986-07-01

Modeling and simulation methods of man-machine systems are developed at the laboratory by interactive infography and C.A.D. technics. In order to better apprehend the morphological variability of populations we have enriched the 3-D model with a parametric function using classical anthropometric dimensions. We have selected reference, associate and complementary dimensions : lengths, breadths, circumferences and depths, which depend on operator's tasks and characteristics of workplaces. All anthropometric values come from the International Data Bank of Human Biometry of ERGODATA System. The utilization of the parametric function brings a quick and accurate description of morphology for theoretic subjects and can be used in C.A.D. analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A

Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When combined with automated data analytics, data visualization systems orchestrate the strengths of humans with the computational power of machines to solve problems neither approach can manage in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will describe practicalmore » design principles. The chapter is concluded with a detailed case study involving the design of a multivariate visualization tool.« less

A human-machine cooperation route planning method based on improved A* algorithm

NASA Astrophysics Data System (ADS)

Zhang, Zhengsheng; Cai, Chao

2011-12-01

To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Matching brain-machine interface performance to space applications.

PubMed

Citi, Luca; Tonet, Oliver; Marinelli, Martina

2009-01-01

A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied users, such as astronauts, a BMI would only be practical if conceived as an augmenting interface. A method is presented for pointing out effective combinations of HMIs and applications of robotics and automation to space. Latency and throughput are selected as performance measures for a hybrid bionic system (HBS), that is, the combination of a user, a device, and a HMI. We classify and briefly describe HMIs and space applications and then compare the performance of classes of interfaces with the requirements of classes of applications, both in terms of latency and throughput. Regions of overlap correspond to effective combinations. Devices requiring simpler control, such as a rover, a robotic camera, or environmental controls are suitable to be driven by means of BMI technology. Free flyers and other devices with six degrees of freedom can be controlled, but only at low-interactivity levels. More demanding applications require conventional interfaces, although they could be controlled by BMIs once the same levels of performance as currently recorded in animal experiments are attained. Robotic arms and manipulators could be the next frontier for noninvasive BMIs. Integrating smart controllers in HBSs could improve interactivity and boost the use of BMI technology in space applications.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

NASA Astrophysics Data System (ADS)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples produced by machines are typically more complete. These findings set the stage for further investigations, with the ultimate goal of efficiently and accurately labeling the wide range of data classes that will arise from the planned large astronomical surveys.

Telepresence and telerobotics

NASA Technical Reports Server (NTRS)

Garin, John; Matteo, Joseph; Jennings, Von Ayre

1988-01-01

The capability for a single operator to simultaneously control complex remote multi degree of freedom robotic arms and associated dextrous end effectors is being developed. An optimal solution within the realm of current technology, can be achieved by recognizing that: (1) machines/computer systems are more effective than humans when the task is routine and specified, and (2) humans process complex data sets and deal with the unpredictable better than machines. These observations lead naturally to a philosophy in which the human's role becomes a higher level function associated with planning, teaching, initiating, monitoring, and intervening when the machine gets into trouble, while the machine performs the codifiable tasks with deliberate efficiency. This concept forms the basis for the integration of man and telerobotics, i.e., robotics with the operator in the control loop. The concept of integration of the human in the loop and maximizing the feed-forward and feed-back data flow is referred to as telepresence.

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Army-NASA aircrew/aircraft integration program (A3I) software detailed design document, phase 3

NASA Technical Reports Server (NTRS)

Banda, Carolyn; Chiu, Alex; Helms, Gretchen; Hsieh, Tehming; Lui, Andrew; Murray, Jerry; Shankar, Renuka

1990-01-01

The capabilities and design approach of the MIDAS (Man-machine Integration Design and Analysis System) computer-aided engineering (CAE) workstation under development by the Army-NASA Aircrew/Aircraft Integration Program is detailed. This workstation uses graphic, symbolic, and numeric prototyping tools and human performance models as part of an integrated design/analysis environment for crewstation human engineering. Developed incrementally, the requirements and design for Phase 3 (Dec. 1987 to Jun. 1989) are described. Software tools/models developed or significantly modified during this phase included: an interactive 3-D graphic cockpit design editor; multiple-perspective graphic views to observe simulation scenarios; symbolic methods to model the mission decomposition, equipment functions, pilot tasking and loading, as well as control the simulation; a 3-D dynamic anthropometric model; an intermachine communications package; and a training assessment component. These components were successfully used during Phase 3 to demonstrate the complex interactions and human engineering findings involved with a proposed cockpit communications design change in a simulated AH-64A Apache helicopter/mission that maps to empirical data from a similar study and AH-1 Cobra flight test.

An Interactive Simulation System for Modeling Stands, Harvests, and Machines

Treesearch

Jingxin Wang; W. Dale Greene

1999-01-01

A interactive computer simulation program models stands, harvest, and machine factors and evaluates their interatcitons while performing felling, skidding, or fowarding activities. A stand generator allows the user to generate either natural or planted stands. Fellings with chainsaw, drive-to-tree feller-bunchers, or harvesters and extraction with grapple skidders or...

Biosleeve Human-Machine Interface

NASA Technical Reports Server (NTRS)

Assad, Christopher (Inventor)

2016-01-01

Systems and methods for sensing human muscle action and gestures in order to control machines or robotic devices are disclosed. One exemplary system employs a tight fitting sleeve worn on a user arm and including a plurality of electromyography (EMG) sensors and at least one inertial measurement unit (IMU). Power, signal processing, and communications electronics may be built into the sleeve and control data may be transmitted wirelessly to the controlled machine or robotic device.

Development of a body motion interactive system with a weight voting mechanism and computer vision technology

NASA Astrophysics Data System (ADS)

Lin, Chern-Sheng; Chen, Chia-Tse; Shei, Hung-Jung; Lay, Yun-Long; Chiu, Chuang-Chien

2012-09-01

This study develops a body motion interactive system with computer vision technology. This application combines interactive games, art performing, and exercise training system. Multiple image processing and computer vision technologies are used in this study. The system can calculate the characteristics of an object color, and then perform color segmentation. When there is a wrong action judgment, the system will avoid the error with a weight voting mechanism, which can set the condition score and weight value for the action judgment, and choose the best action judgment from the weight voting mechanism. Finally, this study estimated the reliability of the system in order to make improvements. The results showed that, this method has good effect on accuracy and stability during operations of the human-machine interface of the sports training system.

Adjustable impedance, force feedback and command language aids for telerobotics (parts 1-4 of an 8-part MIT progress report)

NASA Technical Reports Server (NTRS)

Sheridan, Thomas B.; Raju, G. Jagganath; Buzan, Forrest T.; Yared, Wael; Park, Jong

1989-01-01

Projects recently completed or in progress at MIT Man-Machine Systems Laboratory are summarized. (1) A 2-part impedance network model of a single degree of freedom remote manipulation system is presented in which a human operator at the master port interacts with a task object at the slave port in a remote location is presented. (2) The extension of the predictor concept to include force feedback and dynamic modeling of the manipulator and the environment is addressed. (3) A system was constructed to infer intent from the operator's commands and the teleoperation context, and generalize this information to interpret future commands. (4) A command language system is being designed that is robust, easy to learn, and has more natural man-machine communication. A general telerobot problem selected as an important command language context is finding a collision-free path for a robot.

LIQUID CRYSTAL POLYMERS (LCP) USED AS A MACHINING FLUID CD

EPA Science Inventory

This interactive CD was produced to present the science, research activities, and beneficial environmental and machining advantages for utilizing Liquid Crystal Polymers (LCPs) as a machine fluid in the manufacturing industry.

In 1995, the USEPA funded a project to cut flu...

Artificial intelligence and its impact on combat aircraft

NASA Technical Reports Server (NTRS)

Ott, Lawrence M.; Abbot, Kathy; Kleider, Alfred; Moon, D.; Retelle, John

1987-01-01

As the threat becomes more sophisticated and weapon systems more complex to meet the threat, the need for machines to assist the pilot in the assessment of information becomes paramount. This is particularly true in real-time, high stress situations. The advent of artificial intelligence (AI) technology offers the opportunity to make quantum advances in the application of machine technology. However, if AI systems are to find their way into combat aircraft, they must meet certain criteria. The systems must be responsive, reliable, easy to use, flexible, and understandable. These criteria are compared with the current status used in a combat airborne application. Current AI systems deal with nonreal time applications and require significant user interaction. On the other hand, aircraft applications require real time, minimum human interaction systems. In order to fill the gap between where technology is now and where it must be for aircraft applications, considerable government research is ongoing in NASA, DARPA, and three services. The ongoing research is briefly summarized. Finally, recognizing that AI technology is in its embryonic stage, and the aircraft needs are very demanding, a number of issues arise. These issues are delineated and findings are provided where appropriate.

Improvement of human operator vibroprotection system in the utility machine

NASA Astrophysics Data System (ADS)

Korchagin, P. A.; Teterina, I. A.; Rahuba, L. F.

2018-01-01

The article is devoted to an urgent problem of improving efficiency of road-building utility machines in terms of improving human operator vibroprotection system by determining acceptable values of the rigidity coefficients and resistance coefficients of operator’s cab suspension system elements and those of operator’s seat. Negative effects of vibration result in labour productivity decrease and occupational diseases. Besides, structure vibrations have a damaging impact on the machine units and mechanisms, which leads to reducing an overall service life of the machine. Results of experimental and theoretical research of operator vibroprotection system in the road-building utility machine are presented. An algorithm for the program to calculate dynamic impacts on the operator in terms of different structural and performance parameters of the machine and considering combination of external pertrubation influences was proposed.

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

A voyage to Mars: A challenge to collaboration between man and machines

NASA Technical Reports Server (NTRS)

Statler, Irving C.

1991-01-01

A speech addressing the design of man machine systems for exploration of space beyond Earth orbit from the human factors perspective is presented. Concerns relative to the design of automated and intelligent systems for the NASA Space Exploration Initiative (SEI) missions are largely based on experiences with integrating humans and comparable systems in aviation. The history, present status, and future prospect, of human factors in machine design are discussed in relation to a manned voyage to Mars. Three different cases for design philosophy are presented. The use of simulation is discussed. Recommendations for required research are given.

'Tagger' - a Mac OS X Interactive Graphical Application for Data Inference and Analysis of N-Dimensional Datasets in the Natural Physical Sciences.

NASA Astrophysics Data System (ADS)

Morse, P. E.; Reading, A. M.; Lueg, C.

2014-12-01

Pattern-recognition in scientific data is not only a computational problem but a human-observer problem as well. Human observation of - and interaction with - data visualization software can augment, select, interrupt and modify computational routines and facilitate processes of pattern and significant feature recognition for subsequent human analysis, machine learning, expert and artificial intelligence systems.'Tagger' is a Mac OS X interactive data visualisation tool that facilitates Human-Computer interaction for the recognition of patterns and significant structures. It is a graphical application developed using the Quartz Composer framework. 'Tagger' follows a Model-View-Controller (MVC) software architecture: the application problem domain (the model) is to facilitate novel ways of abstractly representing data to a human interlocutor, presenting these via different viewer modalities (e.g. chart representations, particle systems, parametric geometry) to the user (View) and enabling interaction with the data (Controller) via a variety of Human Interface Devices (HID). The software enables the user to create an arbitrary array of tags that may be appended to the visualised data, which are then saved into output files as forms of semantic metadata. Three fundamental problems that are not strongly supported by conventional scientific visualisation software are addressed:1] How to visually animate data over time, 2] How to rapidly deploy unconventional parametrically driven data visualisations, 3] How to construct and explore novel interaction models that capture the activity of the end-user as semantic metadata that can be used to computationally enhance subsequent interrogation. Saved tagged data files may be loaded into Tagger, so that tags may be tagged, if desired. Recursion opens up the possibility of refining or overlapping different types of tags, tagging a variety of different POIs or types of events, and of capturing different types of specialist observations of important or noticeable events. Other visualisations and modes of interaction will also be demonstrated, with the aim of discovering knowledge in large datasets in the natural, physical sciences. Fig.1 Wave height data from an oceanographic Wave Rider Buoy. Colors/radii are driven by wave height data.

Conformal Predictions in Multimedia Pattern Recognition

ERIC Educational Resources Information Center

Nallure Balasubramanian, Vineeth

2010-01-01

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Efficient cost-sensitive human-machine collaboration for offline signature verification

NASA Astrophysics Data System (ADS)

Coetzer, Johannes; Swanepoel, Jacques; Sabourin, Robert

2012-01-01

We propose a novel strategy for the optimal combination of human and machine decisions in a cost-sensitive environment. The proposed algorithm should be especially beneficial to financial institutions where off-line signatures, each associated with a specific transaction value, require authentication. When presented with a collection of genuine and fraudulent training signatures, produced by so-called guinea pig writers, the proficiency of a workforce of human employees and a score-generating machine can be estimated and represented in receiver operating characteristic (ROC) space. Using a set of Boolean fusion functions, the majority vote decision of the human workforce is combined with each threshold-specific machine-generated decision. The performance of the candidate ensembles is estimated and represented in ROC space, after which only the optimal ensembles and associated decision trees are retained. When presented with a questioned signature linked to an arbitrary writer, the system first uses the ROC-based cost gradient associated with the transaction value to select the ensemble that minimises the expected cost, and then uses the corresponding decision tree to authenticate the signature in question. We show that, when utilising the entire human workforce, the incorporation of a machine streamlines the authentication process and decreases the expected cost for all operating conditions.

Soft brain-machine interfaces for assistive robotics: A novel control approach.

PubMed

Schiatti, Lucia; Tessadori, Jacopo; Barresi, Giacinto; Mattos, Leonardo S; Ajoudani, Arash

2017-07-01

Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

Developing and Validating Practical Eye Metrics for the Sense-Assess-Augment Framework

DTIC Science & Technology

2015-09-29

Sense-Assess-Augment ( SAA ) Framework. To better close the loop between the human and machine teammates AFRL’s Human Performance Wing and Human...Sense-Assess-Augment ( SAA ) framework, which is designed to sense a suite of physiological signals from the operator, use these signals to assess the...to use psychophysiological measures to improve human-machine teamwork (such as Biocybernetics or Augmented Cognition) the AFRL- SAA research program

The JPL telerobot operator control station. Part 2: Software

NASA Technical Reports Server (NTRS)

Kan, Edwin P.; Landell, B. Patrick; Oxenberg, Sheldon; Morimoto, Carl

1989-01-01

The Operator Control Station of the Jet Propulsion Laboratory (JPL)/NASA Telerobot Demonstrator System provides the man-machine interface between the operator and the system. It provides all the hardware and software for accepting human input for the direct and indirect (supervised) manipulation of the robot arms and tools for task execution. Hardware and software are also provided for the display and feedback of information and control data for the operator's consumption and interaction with the task being executed. The software design of the operator control system is discussed.

Hypermedia = hypercommunication

NASA Technical Reports Server (NTRS)

Laff, Mark R.

1990-01-01

New hardware and software technology gave application designers the freedom to use new realism in human computer interaction. High-quality images, motion video, stereo sound and music, speech, touch, gesture provide richer data channels between the person and the machine. Ultimately, this will lead to richer communication between people with the computer as an intermediary. The whole point of hyper-books, hyper-newspapers, virtual worlds, is to transfer the concept and relationships, the 'data structure', from the mind of creator to that of user. Some of the characteristics of this rich information channel are discussed, and some examples are presented.

An Ab Initio Solution Of Interdependence: Social Organization With First Principles

NASA Astrophysics Data System (ADS)

Lawless, W. F.; Rifkin, Stan; Sofge, D. A.

2011-03-01

A special issue of Science, the National Academy of Sciences, the military, and economists have called for a new theory of interdependence, ι. Constructed around the notion of bistable social reality (i.e., complementarity between conjugate or Fourier pairs), we have developed a social physics of ι for organizations and systems of humans, machines and robots that has shown some validity. But because of the loss of meaning associated with understanding ι states or interactions between social Fourier pairs, we consider it high-risk research.

Proceedings of the NATO-Advanced Study Institute on Computer Aided Analysis of Rigid and Flexible Mechanical Systems Held in Troia, Portugal on June 27-July 9, 1993. Volume 1. Main Lectures

DTIC Science & Technology

1993-07-09

real-time simulation capabilities, highly non -linear control devices, work space path planing, active control of machine flexibilities and reliability...P.M., "The Information Capacity of the Human Motor System in Controlling the Amplitude of Movement," Journal of Experimental Psychology, Vol 47, No...driven many research groups in the challenging problem of flexible sy,;tems with an increasing interaction with finite element methodologies. Basic

IEEE 1982. Proceedings of the international conference on cybernetics and society

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1982-01-01

The following topics were dealt with: knowledge-based systems; risk analysis; man-machine interactions; human information processing; metaphor, analogy and problem-solving; manual control modelling; transportation systems; simulation; adaptive and learning systems; biocybernetics; cybernetics; mathematical programming; robotics; decision support systems; analysis, design and validation of models; computer vision; systems science; energy systems; environmental modelling and policy; pattern recognition; nuclear warfare; technological forecasting; artificial intelligence; the Turin shroud; optimisation; workloads. Abstracts of individual papers can be found under the relevant classification codes in this or future issues.

CMLLite: a design philosophy for CML

PubMed Central

2011-01-01

CMLLite is a collection of definitions and processes which provide strong and flexible validation for a document in Chemical Markup Language (CML). It consists of an updated CML schema (schema3), conventions specifying rules in both human and machine-understandable forms and a validator available both online and offline to check conformance. This article explores the rationale behind the changes which have been made to the schema, explains how conventions interact and how they are designed, formulated, implemented and tested, and gives an overview of the validation service. PMID:21999395

Improving air traffic control: Proving new tools or approving the joint human-machine system?

NASA Technical Reports Server (NTRS)

Gaillard, Irene; Leroux, Marcel

1994-01-01

From the description of a field problem (i.e., designing decision aids for air traffic controllers), this paper points out how a cognitive engineering approach provides the milestones for the evaluation of future joint human-machine systems.

Virtual Machine Language

NASA Technical Reports Server (NTRS)

Grasso, Christopher; Page, Dennis; O'Reilly, Taifun; Fteichert, Ralph; Lock, Patricia; Lin, Imin; Naviaux, Keith; Sisino, John

2005-01-01

Virtual Machine Language (VML) is a mission-independent, reusable software system for programming for spacecraft operations. Features of VML include a rich set of data types, named functions, parameters, IF and WHILE control structures, polymorphism, and on-the-fly creation of spacecraft commands from calculated values. Spacecraft functions can be abstracted into named blocks that reside in files aboard the spacecraft. These named blocks accept parameters and execute in a repeatable fashion. The sizes of uplink products are minimized by the ability to call blocks that implement most of the command steps. This block approach also enables some autonomous operations aboard the spacecraft, such as aerobraking, telemetry conditional monitoring, and anomaly response, without developing autonomous flight software. Operators on the ground write blocks and command sequences in a concise, high-level, human-readable programming language (also called VML ). A compiler translates the human-readable blocks and command sequences into binary files (the operations products). The flight portion of VML interprets the uplinked binary files. The ground subsystem of VML also includes an interactive sequence- execution tool hosted on workstations, which runs sequences at several thousand times real-time speed, affords debugging, and generates reports. This tool enables iterative development of blocks and sequences within times of the order of seconds.

Combined Auditory and Vibrotactile Feedback for Human-Machine-Interface Control.

PubMed

Thorp, Elias B; Larson, Eric; Stepp, Cara E

2014-01-01

The purpose of this study was to determine the effect of the addition of binary vibrotactile stimulation to continuous auditory feedback (vowel synthesis) for human-machine interface (HMI) control. Sixteen healthy participants controlled facial surface electromyography to achieve 2-D targets (vowels). Eight participants used only real-time auditory feedback to locate targets whereas the other eight participants were additionally alerted to having achieved targets with confirmatory vibrotactile stimulation at the index finger. All participants trained using their assigned feedback modality (auditory alone or combined auditory and vibrotactile) over three sessions on three days and completed a fourth session on the third day using novel targets to assess generalization. Analyses of variance performed on the 1) percentage of targets reached and 2) percentage of trial time at the target revealed a main effect for feedback modality: participants using combined auditory and vibrotactile feedback performed significantly better than those using auditory feedback alone. No effect was found for session or the interaction of feedback modality and session, indicating a successful generalization to novel targets but lack of improvement over training sessions. Future research is necessary to determine the cognitive cost associated with combined auditory and vibrotactile feedback during HMI control.

Effects of optimism on gambling in the rat slot machine task.

PubMed

Rafa, Dominik; Kregiel, Jakub; Popik, Piotr; Rygula, Rafal

2016-03-01

Although gambling disorder is a serious social problem in modern societies, information about the behavioral traits that could determine vulnerability to this psychopathology is still scarce. In this study, we used a recently developed ambiguous-cue interpretation ​(ACI)​ paradigm to investigate whether 'optimism' and 'pessimism' as behavioral traits may determine the gambling-like behavior of rodents. In a series of ACI tests (cognitive bias screening), we identified rats that displayed 'pessimistic' and 'optimistic' traits. Subsequently, using the rat slot machine task (rSMT), we investigated if the 'optimistic'/'pessimistic' traits could determine the crucial feature of gambling-like behavior that has been investigated in rats and humans: the ​interpretation of 'near-miss' outcomes as a positive (i.e., win) situation. We found that 'optimists' did not interpret 'near-miss', 'near loss', or 'clear win' as win trials more often than ​their 'pessimistic' ​conspecifics; however, the 'optimists' were statistically more likely to reach for a reward in the hopeless 'clear loss' situation. This agrees with human studies and provides a platform for modeling interactions between behavioral traits and gambling in animals. Copyright © 2015 Elsevier B.V. All rights reserved.

The Ensembl genome database project.

PubMed

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Audio-visual affective expression recognition

NASA Astrophysics Data System (ADS)

Huang, Thomas S.; Zeng, Zhihong

2007-11-01

Automatic affective expression recognition has attracted more and more attention of researchers from different disciplines, which will significantly contribute to a new paradigm for human computer interaction (affect-sensitive interfaces, socially intelligent environments) and advance the research in the affect-related fields including psychology, psychiatry, and education. Multimodal information integration is a process that enables human to assess affective states robustly and flexibly. In order to understand the richness and subtleness of human emotion behavior, the computer should be able to integrate information from multiple sensors. We introduce in this paper our efforts toward machine understanding of audio-visual affective behavior, based on both deliberate and spontaneous displays. Some promising methods are presented to integrate information from both audio and visual modalities. Our experiments show the advantage of audio-visual fusion in affective expression recognition over audio-only or visual-only approaches.

Virtual reality systems

NASA Technical Reports Server (NTRS)

Johnson, David W.

1992-01-01

Virtual realities are a type of human-computer interface (HCI) and as such may be understood from a historical perspective. In the earliest era, the computer was a very simple, straightforward machine. Interaction was human manipulation of an inanimate object, little more than the provision of an explicit instruction set to be carried out without deviation. In short, control resided with the user. In the second era of HCI, some level of intelligence and control was imparted to the system to enable a dialogue with the user. Simple context sensitive help systems are early examples, while more sophisticated expert system designs typify this era. Control was shared more equally. In this, the third era of the HCI, the constructed system emulates a particular environment, constructed with rules and knowledge about 'reality'. Control is, in part, outside the realm of the human-computer dialogue. Virtual reality systems are discussed.

Technology-enhanced human interaction in psychotherapy.

PubMed

Imel, Zac E; Caperton, Derek D; Tanana, Michael; Atkins, David C

2017-07-01

Psychotherapy is on the verge of a technology-inspired revolution. The concurrent maturation of communication, signal processing, and machine learning technologies begs an earnest look at how these technologies may be used to improve the quality of psychotherapy. Here, we discuss 3 research domains where technology is likely to have a significant impact: (1) mechanism and process, (2) training and feedback, and (3) technology-mediated treatment modalities. For each domain, we describe current and forthcoming examples of how new technologies may change established applications. Moreover, for each domain we present research questions that touch on theoretical, systemic, and implementation issues. Ultimately, psychotherapy is a decidedly human endeavor, and thus the application of modern technology to therapy must capitalize on-and enhance-our human capacities as counselors, students, and supervisors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Tactile objects based on an amplitude disturbed diffraction pattern method

NASA Astrophysics Data System (ADS)

Liu, Yuan; Nikolovski, Jean-Pierre; Mechbal, Nazih; Hafez, Moustapha; Vergé, Michel

2009-12-01

Tactile sensing is becoming widely used in human-computer interfaces. Recent advances in acoustic approaches demonstrated the possibilities to transform ordinary solid objects into interactive interfaces. This letter proposes a static finger contact localization process using an amplitude disturbed diffraction pattern method. The localization method is based on the following physical phenomenon: a finger contact modifies the energy distribution of acoustic wave in a solid; these variations depend on the wave frequency and the contact position. The presented method first consists of exciting the object with an acoustic signal with plural frequency components. In a second step, a measured acoustic signal is compared with prerecorded values to deduce the contact position. This position is then used for human-machine interaction (e.g., finger tracking on computer screen). The selection of excitation signals is discussed and a frequency choice criterion based on contrast value is proposed. Tests on a sandwich plate (liquid crystal display screen) prove the simplicity and easiness to apply the process in various solids.

Human Cognitive Enhancement Ethical Implications for Airman-Machine Teaming

DTIC Science & Technology

2017-04-06

34 Psychological Constructs versus Neural Mechanisms: Different Perspectives for Advanced Research of Cognitive Processes and Development of Neuroadaptive...AIR WAR COLLEGE AIR UNIVERSITY HUMAN COGNITIVE ENHANCEMENT ETHICAL IMPLICATIONS FOR AIRMAN-MACHINE TEAMING by William M. Curlin...increasingly challenging adversarial threats. It is hypothesized that by the year 2030, human system operators will be “ cognitively challenged” to keep pace

Man-Machine Communication Research.

DTIC Science & Technology

1977-02-01

communication difficulty for the computer-naive; discovery of major communication structures in human communication that have been left out of man-machine...processes; creation of a new overview of how human communication functions in cooperative task-oriented activity; and assistance in ARPA policy formation on CAI equipment development.

In Praise of Robots

ERIC Educational Resources Information Center

Sagan, Carl

1975-01-01

The author of this article believes that human survival depends upon the ability to develop and work with machines of high artificial intelligence. He lists uses of such machines, including terrestrial mining, outer space exploration, and other tasks too dangerous, too expensive, or too boring for human beings. (MA)

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Foundations for a new science of learning.

PubMed

Meltzoff, Andrew N; Kuhl, Patricia K; Movellan, Javier; Sejnowski, Terrence J

2009-07-17

Human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved compared with those of other species. Homo sapiens is also the only species that has developed formal ways to enhance learning: teachers, schools, and curricula. Human infants have an intense interest in people and their behavior and possess powerful implicit learning mechanisms that are affected by social interaction. Neuroscientists are beginning to understand the brain mechanisms underlying learning and how shared brain systems for perception and action support social learning. Machine learning algorithms are being developed that allow robots and computers to learn autonomously. New insights from many different fields are converging to create a new science of learning that may transform educational practices.

Collaborative Robots and Knowledge Management - A Short Review

NASA Astrophysics Data System (ADS)

Mușat, Flaviu-Constantin; Mihu, Florin-Constantin

2017-12-01

Because the requirements of the customers are more and more high related to quality, quantity, delivery times at lowest costs possible, the industry had to come with automated solutions to improve these requirements. Starting from the automated lines developed by Ford and Toyota, we have now developed automated and self-sustained working lines, which is possible nowadays-using collaborative robots. By using the knowledge management system we can improve the development of the future of this kind of area of research. This paper shows the benefits and the smartness use of the robots that are performing the manipulation activities that increases the work place ergonomically and improve the interaction between human - machine in order to assist in parallel tasks and lowering the physically human efforts.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Mind Games: Game Engines as an Architecture for Intuitive Physics.

PubMed

Ullman, Tomer D; Spelke, Elizabeth; Battaglia, Peter; Tenenbaum, Joshua B

2017-09-01

We explore the hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games. We describe the key features of game physics engines and their parallels in human mental representation, focusing especially on the intuitive physics of young infants where the hypothesis helps to unify many classic and otherwise puzzling phenomena, and may provide the basis for a computational account of how the physical knowledge of infants develops. This hypothesis also explains several 'physics illusions', and helps to inform the development of artificial intelligence (AI) systems with more human-like common sense. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biomimetics in Intelligent Sensor and Actuator Automation Systems

NASA Astrophysics Data System (ADS)

Bruckner, Dietmar; Dietrich, Dietmar; Zucker, Gerhard; Müller, Brit

Intelligent machines are really an old mankind's dream. With increasing technological development, the requirements for intelligent devices also increased. However, up to know, artificial intelligence (AI) lacks solutions to the demands of truly intelligent machines that have no problems to integrate themselves into daily human environments. Current hardware with a processing power of billions of operations per second (but without any model of human-like intelligence) could not substantially contribute to the intelligence of machines when compared with that of the early AI times. There are great results, of course. Machines are able to find the shortest path between far apart cities on the map; algorithms let you find information described only by few key words. But no machine is able to get us a cup of coffee from the kitchen yet.

Combining human and machine processes (CHAMP)

NASA Astrophysics Data System (ADS)

Sudit, Moises; Sudit, David; Hirsch, Michael

2015-05-01

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don't push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

Combining Human and Machine Intelligence to Derive Agents' Behavioral Rules for Groundwater Irrigation

NASA Astrophysics Data System (ADS)

Hu, Y.; Quinn, C.; Cai, X.

2015-12-01

One major challenge of agent-based modeling is to derive agents' behavioral rules due to behavioral uncertainty and data scarcity. This study proposes a new approach to combine a data-driven modeling based on the directed information (i.e., machine intelligence) with expert domain knowledge (i.e., human intelligence) to derive the behavioral rules of agents considering behavioral uncertainty. A directed information graph algorithm is applied to identifying the causal relationships between agents' decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional factors. A case study is conducted for the High Plains aquifer hydrological observatory (HO) area, U.S. Preliminary results show that four factors, corn price (CP), underlying groundwater level (GWL), monthly mean temperature (T) and precipitation (P) have causal influences on agents' decisions on groundwater irrigation depth (GWID) to various extents. Based on the similarity of the directed information graph for each agent, five clusters of graphs are further identified to represent all the agents' behaviors in the study area as shown in Figure 1. Using these five representative graphs, agents' monthly optimal groundwater pumping rates are derived through the probabilistic inference. Such data-driven relationships and probabilistic quantifications are then coupled with a physically-based groundwater model to investigate the interactions between agents' pumping behaviors and the underlying groundwater system in the context of coupled human and natural systems.

Magic in the machine: a computational magician's assistant.

PubMed

Williams, Howard; McOwan, Peter W

2014-01-01

A human magician blends science, psychology, and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific, or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximize the magical effect required. The complexity is often caused by interacting and often conflicting physical and psychological constraints that need to be optimally balanced. Normally this tuning is done by trial and error, combined with human intuitions. Here we focus on applying Artificial Intelligence methods to the creation and optimization of magic tricks exploiting mathematical principles. We use experimentally derived data about particular perceptual and cognitive features, combined with a model of the underlying mathematical process to provide a psychologically valid metric to allow optimization of magical impact. In the paper we introduce our optimization methodology and describe how it can be flexibly applied to a range of different types of mathematics based tricks. We also provide two case studies as exemplars of the methodology at work: a magical jigsaw, and a mind reading card trick effect. We evaluate each trick created through testing in laboratory and public performances, and further demonstrate the real world efficacy of our approach for professional performers through sales of the tricks in a reputable magic shop in London.

Proceeding of human exoskeleton technology and discussions on future research

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xie, Hanxing; Li, Weilin; Yao, Zheng

2014-05-01

After more than half a century of intense efforts, the development of exoskeleton has seen major advances, and several remarkable achievements have been made. Reviews of developing history of exoskeleton are presented, both in active and passive categories. Major models are introduced, and typical technologies are commented on. Difficulties in control algorithm, driver system, power source, and man-machine interface are discussed. Current researching routes and major developing methods are mapped and critically analyzed, and in the process, some key problems are revealed. First, the exoskeleton is totally different from biped robot, and relative studies based on the robot technologies are considerably incorrect. Second, biomechanical studies are only used to track the motion of the human body, the interaction between human and machines are seldom studied. Third, the traditional developing ways which focused on servo-controlling have inborn deficiency from making portable systems. Research attention should be shifted to the human side of the coupling system, and the human ability to learn and adapt should play a more significant role in the control algorithms. Having summarized the major difficulties, possible future works are discussed. It is argued that, since a distinct boundary cannot be drawn in such strong-coupling human-exoskeleton system, the more complex the control system gets, the more difficult it is for the user to learn to use. It is suggested that the exoskeleton should be treated as a simple wearable tool, and downgrading its automatic level may be a change toward a brighter research outlook. This effort at simplification is definitely not easy, as it necessitates theoretical supports from fields such as biomechanics, ergonomics, and bionics.

Emerging Computer Media: On Image Interaction

NASA Astrophysics Data System (ADS)

Lippman, Andrew B.

1982-01-01

Emerging technologies such as inexpensive, powerful local computing, optical digital videodiscs, and the technologies of human-machine interaction are initiating a revolution in both image storage systems and image interaction systems. This paper will present a review of new approaches to computer media predicated upon three dimensional position sensing, speech recognition, and high density image storage. Examples will be shown such as the Spatial Data Management Systems wherein the free use of place results in intuitively clear retrieval systems and potentials for image association; the Movie-Map, wherein inherently static media generate dynamic views of data, and conferencing work-in-progress wherein joint processing is stressed. Application to medical imaging will be suggested, but the primary emphasis is on the general direction of imaging and reference systems. We are passing the age of simple possibility of computer graphics and image porcessing and entering the age of ready usability.

Human machine interface display design document.

DOT National Transportation Integrated Search

2008-01-01

The purpose of this document is to describe the design for the human machine interface : (HMI) display for the Next Generation 9-1-1 (NG9-1-1) System (or system of systems) : based on the initial Tier 1 requirements identified for the NG9-1-1 S...

Adapting human-machine interfaces to user performance.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2008-01-01

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects' high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

HUMAN ENGINEERING FOR AN EFFECTIVE AIR-NAVIGATION AND TRAFFIC-CONTROL SYSTEM, AND APPENDIXES 1 THRU 3

DTIC Science & Technology

1951-03-14

human "We have been very much occupied In perfect. engineering to the improvement of the air-navigation ing the machines and the tools which the...a man-machine system which will ever, if he were only considered as an instrument, yield optimal results in the way of efficiency and a tool , a motor...operation of machines and equipment and system development, which will permit tools , the emphasis has been upon the adjustment of an orderly and

Perspective: Interactive material property databases through aggregation of literature data

NASA Astrophysics Data System (ADS)

Seshadri, Ram; Sparks, Taylor D.

2016-05-01

Searchable, interactive, databases of material properties, particularly those relating to functional materials (magnetics, thermoelectrics, photovoltaics, etc.) are curiously missing from discussions of machine-learning and other data-driven methods for advancing new materials discovery. Here we discuss the manual aggregation of experimental data from the published literature for the creation of interactive databases that allow the original experimental data as well additional metadata to be visualized in an interactive manner. The databases described involve materials for thermoelectric energy conversion, and for the electrodes of Li-ion batteries. The data can be subject to machine-learning, accelerating the discovery of new materials.

Delivering key signals to the machine: seeking the electric signal that muscles emanate

NASA Astrophysics Data System (ADS)

Bani Hashim, A. Y.; Maslan, M. N.; Izamshah, R.; Mohamad, I. S.

2014-11-01

Due to the limitation of electric power generation in the human body, present human-machine interfaces have not been successful because of the nature of standard electronics circuit designs, which do not consider the specifications of signals that resulted from the skin. In general, the outcomes and applications of human-machine interfaces are limited to custom-designed subsystems, such as neuroprosthesis. We seek to model the bio dynamical of sub skin into equivalent mathematical definitions, descriptions, and theorems. Within the human skin, there are networks of nerves that permit the skin to function as a multi dimension transducer. We investigate the nature of structural skin. Apart from multiple networks of nerves, there are other segments within the skin such as minute muscles. We identify the segments that are active when there is an electromyography activity. When the nervous system is firing signals, the muscle is being stimulated. We evaluate the phenomena of biodynamic of the muscles that is concerned with the electromyography activity of the nervous system. In effect, we design a relationship between the human somatosensory and synthetic systems sensory as the union of a complete set of the new domain of the functional system. This classifies electromyogram waveforms linked to intent thought of an operator. The system will become the basis for delivering key signals to machine such that the machine is under operator's intent, hence slavery.

An Analysis of Methods for Maximizing the Utilization of Space in USAF Facilities.

DTIC Science & Technology

1987-09-01

vegetable . peeling machines and dish washing machines- i. Fixed barracks equipment including sinks, troughs and washing machines of all types: .4. Fixed...Prentice-Hall, 1977. 52. Spillars, W.R. and S. Al- Banna . "An Interactive Computer Graphics Space Allocation System," DAW Nine. 229-237. Association for

Chaotic behaviour of Zeeman machines at introductory course of mechanics

NASA Astrophysics Data System (ADS)

Nagy, Péter; Tasnádi, Péter

2016-05-01

Investigation of chaotic motions and cooperative systems offers a magnificent opportunity to involve modern physics into the basic course of mechanics taught to engineering students. In the present paper it will be demonstrated that Zeeman Machine can be a versatile and motivating tool for students to get introductory knowledge about chaotic motion via interactive simulations. It works in a relatively simple way and its properties can be understood very easily. Since the machine can be built easily and the simulation of its movement is also simple the experimental investigation and the theoretical description can be connected intuitively. Although Zeeman Machine is known mainly for its quasi-static and catastrophic behaviour, its dynamic properties are also of interest with its typical chaotic features. By means of a periodically driven Zeeman Machine a wide range of chaotic properties of the simple systems can be demonstrated such as bifurcation diagrams, chaotic attractors, transient chaos and so on. The main goal of this paper is the presentation of an interactive learning material for teaching the basic features of the chaotic systems through the investigation of the Zeeman Machine.

An analysis of computer-related patient safety incidents to inform the development of a classification.

PubMed

Magrabi, Farah; Ong, Mei-Sing; Runciman, William; Coiera, Enrico

2010-01-01

To analyze patient safety incidents associated with computer use to develop the basis for a classification of problems reported by health professionals. Incidents submitted to a voluntary incident reporting database across one Australian state were retrieved and a subset (25%) was analyzed to identify 'natural categories' for classification. Two coders independently classified the remaining incidents into one or more categories. Free text descriptions were analyzed to identify contributing factors. Where available medical specialty, time of day and consequences were examined. Descriptive statistics; inter-rater reliability. A search of 42,616 incidents from 2003 to 2005 yielded 123 computer related incidents. After removing duplicate and unrelated incidents, 99 incidents describing 117 problems remained. A classification with 32 types of computer use problems was developed. Problems were grouped into information input (31%), transfer (20%), output (20%) and general technical (24%). Overall, 55% of problems were machine related and 45% were attributed to human-computer interaction. Delays in initiating and completing clinical tasks were a major consequence of machine related problems (70%) whereas rework was a major consequence of human-computer interaction problems (78%). While 38% (n=26) of the incidents were reported to have a noticeable consequence but no harm, 34% (n=23) had no noticeable consequence. Only 0.2% of all incidents reported were computer related. Further work is required to expand our classification using incident reports and other sources of information about healthcare IT problems. Evidence based user interface design must focus on the safe entry and retrieval of clinical information and support users in detecting and correcting errors and malfunctions.

Classification of medication incidents associated with information technology.

PubMed

Cheung, Ka-Chun; van der Veen, Willem; Bouvy, Marcel L; Wensing, Michel; van den Bemt, Patricia M L A; de Smet, Peter A G M

2014-02-01

Information technology (IT) plays a pivotal role in improving patient safety, but can also cause new problems for patient safety. This study analyzed the nature and consequences of a large sample of IT-related medication incidents, as reported by healthcare professionals in community pharmacies and hospitals. The medication incidents submitted to the Dutch central medication incidents registration (CMR) reporting system were analyzed from the perspective of the healthcare professional with the Magrabi classification. During classification new terms were added, if necessary. The principal source of the IT-related problem, nature of error. Additional measures: consequences of incidents, IT systems, phases of the medication process. From March 2010 to February 2011 the CMR received 4161 incidents: 1643 (39.5%) from community pharmacies and 2518 (60.5%) from hospitals. Eventually one of six incidents (16.1%, n=668) were related to IT; in community pharmacies more incidents (21.5%, n=351) were related to IT than in hospitals (12.6%, n=317). In community pharmacies 41.0% (n=150) of the incidents were about choosing the wrong medicine. Most of the erroneous exchanges were associated with confusion of medicine names and poor design of screens. In hospitals 55.3% (n=187) of incidents concerned human-machine interaction-related input during the use of computerized prescriber order entry. These use problems were also a major problem in pharmacy information systems outside the hospital. A large sample of incidents shows that many of the incidents are related to IT, both in community pharmacies and hospitals. The interaction between human and machine plays a pivotal role in IT incidents in both settings.

Bio-Inspired Human-Level Machine Learning

DTIC Science & Technology

2015-10-25

extensions to high-level cognitive functions such as anagram solving problem. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...extensions to high-level cognitive functions such as anagram solving problem. We expect that the bio-inspired human-level machine learning combined with...numbers of 1011 neurons and 1014 synaptic connections in the human brain. In previous work, we experimentally demonstrated the feasibility of cognitive

Human semi-supervised learning.

PubMed

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Supplement, Appendix 4.3: Candidate ARAMIS Capabilities

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions, in the years 1985-2000, so that NASA may make informed decisions on which aspects of ARAMIS to develop. The study first identifies the specific tasks which will be required by future space projects. It then defines ARAMIS options which are candidates for those space project tasks, and evaluates the relative merits of these options. Finally, the study identifies promising applications of ARAMIS, and recommends specific areas for further research. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Long-term knowledge acquisition using contextual information in a memory-inspired robot architecture

NASA Astrophysics Data System (ADS)

Pratama, Ferdian; Mastrogiovanni, Fulvio; Lee, Soon Geul; Chong, Nak Young

2017-03-01

In this paper, we present a novel cognitive framework allowing a robot to form memories of relevant traits of its perceptions and to recall them when necessary. The framework is based on two main principles: on the one hand, we propose an architecture inspired by current knowledge in human memory organisation; on the other hand, we integrate such an architecture with the notion of context, which is used to modulate the knowledge acquisition process when consolidating memories and forming new ones, as well as with the notion of familiarity, which is employed to retrieve proper memories given relevant cues. Although much research has been carried out, which exploits Machine Learning approaches to provide robots with internal models of their environment (including objects and occurring events therein), we argue that such approaches may not be the right direction to follow if a long-term, continuous knowledge acquisition is to be achieved. As a case study scenario, we focus on both robot-environment and human-robot interaction processes. In case of robot-environment interaction, a robot performs pick and place movements using the objects in the workspace, at the same time observing their displacement on a table in front of it, and progressively forms memories defined as relevant cues (e.g. colour, shape or relative position) in a context-aware fashion. As far as human-robot interaction is concerned, the robot can recall specific snapshots representing past events using both sensory information and contextual cues upon request by humans.

Flexible Parsing.

DTIC Science & Technology

1986-06-30

Machine Studies .. 14. Minton, S. N., Hayes, P. J., and Fain, J. E. Controlling Search in Flexible Parsing. Proc. Ninth Int. Jt. Conf. on Artificial...interaction through the COUSIN command interface", International Journal of Man- Machine Studies , Vol. 19, No. 3, September 1983, pp. 285-305. 8...in a gracefully interacting user interface," "Dynamic strategy selection in flexible parsing," and "Parsing spoken language: a semantic case frame

Anthropomorphism in Human–Robot Co-evolution

PubMed Central

Damiano, Luisa; Dumouchel, Paul

2018-01-01

Social robotics entertains a particular relationship with anthropomorphism, which it neither sees as a cognitive error, nor as a sign of immaturity. Rather it considers that this common human tendency, which is hypothesized to have evolved because it favored cooperation among early humans, can be used today to facilitate social interactions between humans and a new type of cooperative and interactive agents – social robots. This approach leads social robotics to focus research on the engineering of robots that activate anthropomorphic projections in users. The objective is to give robots “social presence” and “social behaviors” that are sufficiently credible for human users to engage in comfortable and potentially long-lasting relations with these machines. This choice of ‘applied anthropomorphism’ as a research methodology exposes the artifacts produced by social robotics to ethical condemnation: social robots are judged to be a “cheating” technology, as they generate in users the illusion of reciprocal social and affective relations. This article takes position in this debate, not only developing a series of arguments relevant to philosophy of mind, cognitive sciences, and robotic AI, but also asking what social robotics can teach us about anthropomorphism. On this basis, we propose a theoretical perspective that characterizes anthropomorphism as a basic mechanism of interaction, and rebuts the ethical reflections that a priori condemns “anthropomorphism-based” social robots. To address the relevant ethical issues, we promote a critical experimentally based ethical approach to social robotics, “synthetic ethics,” which aims at allowing humans to use social robots for two main goals: self-knowledge and moral growth. PMID:29632507

Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

PubMed

Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

2015-01-01

Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

An fMRI and effective connectivity study investigating miss errors during advice utilization from human and machine agents.

PubMed

Goodyear, Kimberly; Parasuraman, Raja; Chernyak, Sergey; de Visser, Ewart; Madhavan, Poornima; Deshpande, Gopikrishna; Krueger, Frank

2017-10-01

As society becomes more reliant on machines and automation, understanding how people utilize advice is a necessary endeavor. Our objective was to reveal the underlying neural associations during advice utilization from expert human and machine agents with fMRI and multivariate Granger causality analysis. During an X-ray luggage-screening task, participants accepted or rejected good or bad advice from either the human or machine agent framed as experts with manipulated reliability (high miss rate). We showed that the machine-agent group decreased their advice utilization compared to the human-agent group and these differences in behaviors during advice utilization could be accounted for by high expectations of reliable advice and changes in attention allocation due to miss errors. Brain areas involved with the salience and mentalizing networks, as well as sensory processing involved with attention, were recruited during the task and the advice utilization network consisted of attentional modulation of sensory information with the lingual gyrus as the driver during the decision phase and the fusiform gyrus as the driver during the feedback phase. Our findings expand on the existing literature by showing that misses degrade advice utilization, which is represented in a neural network involving salience detection and self-processing with perceptual integration.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

DTIC Science & Technology

1984-06-01

other automatic machinery such as presses, molding machines , and numerically-controlled machine tools, just as people do. A-36...Robotics Technologies 3 B. Relevant AI Technologies 4 1. Expert Systems 4 2. Automatic Planning 4 3. Natural Language 5 4. Machine Vision...building machines that imitate human behavior. Artificial intelligence is concerned with the functions of the brain, whereas robotics include, in

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

NASA Astrophysics Data System (ADS)

Kalayeh, Mahdi M.; Marin, Thibault; Pretorius, P. Hendrik; Wernick, Miles N.; Yang, Yongyi; Brankov, Jovan G.

2011-03-01

In this paper, we present a numerical observer for image quality assessment, aiming to predict human observer accuracy in a cardiac perfusion defect detection task for single-photon emission computed tomography (SPECT). In medical imaging, image quality should be assessed by evaluating the human observer accuracy for a specific diagnostic task. This approach is known as task-based assessment. Such evaluations are important for optimizing and testing imaging devices and algorithms. Unfortunately, human observer studies with expert readers are costly and time-demanding. To address this problem, numerical observers have been developed as a surrogate for human readers to predict human diagnostic performance. The channelized Hotelling observer (CHO) with internal noise model has been found to predict human performance well in some situations, but does not always generalize well to unseen data. We have argued in the past that finding a model to predict human observers could be viewed as a machine learning problem. Following this approach, in this paper we propose a channelized relevance vector machine (CRVM) to predict human diagnostic scores in a detection task. We have previously used channelized support vector machines (CSVM) to predict human scores and have shown that this approach offers better and more robust predictions than the classical CHO method. The comparison of the proposed CRVM with our previously introduced CSVM method suggests that CRVM can achieve similar generalization accuracy, while dramatically reducing model complexity and computation time.

[A new machinability test machine and the machinability of composite resins for core built-up].

PubMed

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.