E-learning, dual-task, and cognitive load: The anatomy of a failed experiment.

PubMed

Van Nuland, Sonya E; Rogers, Kem A

2016-01-01

The rising popularity of commercial anatomy e-learning tools has been sustained, in part, due to increased annual enrollment and a reduction in laboratory hours across educational institutions. While e-learning tools continue to gain popularity, the research methodologies used to investigate their impact on learning remain imprecise. As new user interfaces are introduced, it is critical to understand how functionality can influence the load placed on a student's memory resources, also known as cognitive load. To study cognitive load, a dual-task paradigm wherein a learner performs two tasks simultaneously is often used, however, its application within educational research remains uncommon. Using previous paradigms as a guide, a dual-task methodology was developed to assess the cognitive load imposed by two commercial anatomical e-learning tools. Results indicate that the standard dual-task paradigm, as described in the literature, is insensitive to the cognitive load disparities across e-learning tool interfaces. Confounding variables included automation of responses, task performance tradeoff, and poor understanding of primary task cognitive load requirements, leading to unreliable quantitative results. By modifying the secondary task from a basic visual response to a more cognitively demanding task, such as a modified Stroop test, the automation of secondary task responses can be reduced. Furthermore, by recording baseline measures for the primary task as well as the secondary task, it is possible for task performance tradeoff to be detected. Lastly, it is imperative that the cognitive load of the primary task be designed such that it does not overwhelm the individual's ability to learn new material. © 2015 American Association of Anatomists.

Automation in Distance Learning: An Empirical Study of Unlearning and Academic Identity Change Linked to Automation of Student Messaging within Distance Learning

ERIC Educational Resources Information Center

Collins, Hilary; Glover, Hayley; Myers, Fran; Watson, Mor

2016-01-01

This paper explores the unlearning and learning undertaken by adjuncts (Associate Lecturers) during the introduction of automated messaging by the university as part replacement of adjunct pastoral support for students. Automated messages were introduced by the University to standardize the student experience in terms of qualification…

Image editing with Adobe Photoshop 6.0.

PubMed

Caruso, Ronald D; Postel, Gregory C

2002-01-01

The authors introduce Photoshop 6.0 for radiologists and demonstrate basic techniques of editing gray-scale cross-sectional images intended for publication and for incorporation into computerized presentations. For basic editing of gray-scale cross-sectional images, the Tools palette and the History/Actions palette pair should be displayed. The History palette may be used to undo a step or series of steps. The Actions palette is a menu of user-defined macros that save time by automating an action or series of actions. Converting an image to 8-bit gray scale is the first editing function. Cropping is the next action. Both decrease file size. Use of the smallest file size necessary for the purpose at hand is recommended. Final file size for gray-scale cross-sectional neuroradiologic images (8-bit, single-layer TIFF [tagged image file format] at 300 pixels per inch) intended for publication varies from about 700 Kbytes to 3 Mbytes. Final file size for incorporation into computerized presentations is about 10-100 Kbytes (8-bit, single-layer, gray-scale, high-quality JPEG [Joint Photographic Experts Group]), depending on source and intended use. Editing and annotating images before they are inserted into presentation software is highly recommended, both for convenience and flexibility. Radiologists should find that image editing can be carried out very rapidly once the basic steps are learned and automated. Copyright RSNA, 2002

Basic manual lensometry: a guide for measuring distance and near glasses.

PubMed

Garber, N

2000-01-01

Manual lensometry is a basic component of ophthalmic clinical care. You will find it necessary to check lens prescriptions manually when the written prescription does not match the results of an automated lensometer or when automated lensometry is not available.

Analysis of knowledge in Astronomy of the students of the technology course in industrial automation at the Federal Institute São Paulo at the Campus Cubatão

NASA Astrophysics Data System (ADS)

Moraes, Ataliba Capasso; Voelzke, Marcos Rincon

2014-05-01

This work is part of a research of the academic Masters in Science in Education in its final stages. It seeks to present the results of the survey conducted among students of the technology course in industrial automation at the Federal Institute São Paulo at the Campus Cubatão. In the first step, the students' lack of knowledge to the related primary concepts of Astronomy turned out. Correcting these deficiencies found, external to the program content, a Basic Course in Astronomy, containing dialogued or expository lectures with the aid of audiovisual resources and access to textbooks. Analysed the responses of this second step, it was found that students had a significant improvement in learning.

Automated Formative Feedback and Summative Assessment Using Individualised Spreadsheet Assignments

ERIC Educational Resources Information Center

Blayney, Paul; Freeman, Mark

2004-01-01

This paper reports on the effects of automating formative feedback at the student's discretion and automating summative assessment with individualised spreadsheet assignments. Quality learning outcomes are achieved when students adopt deep approaches to learning (Ramsden, 2003). Learning environments designed to align assessment to learning…

The Science of Home Automation

NASA Astrophysics Data System (ADS)

Thomas, Brian Louis

Smart home technologies and the concept of home automation have become more popular in recent years. This popularity has been accompanied by social acceptance of passive sensors installed throughout the home. The subsequent increase in smart homes facilitates the creation of home automation strategies. We believe that home automation strategies can be generated intelligently by utilizing smart home sensors and activity learning. In this dissertation, we hypothesize that home automation can benefit from activity awareness. To test this, we develop our activity-aware smart automation system, CARL (CASAS Activity-aware Resource Learning). CARL learns the associations between activities and device usage from historical data and utilizes the activity-aware capabilities to control the devices. To help validate CARL we deploy and test three different versions of the automation system in a real-world smart environment. To provide a foundation of activity learning, we integrate existing activity recognition and activity forecasting into CARL home automation. We also explore two alternatives to using human-labeled data to train the activity learning models. The first unsupervised method is Activity Detection, and the second is a modified DBSCAN algorithm that utilizes Dynamic Time Warping (DTW) as a distance metric. We compare the performance of activity learning with human-defined labels and with automatically-discovered activity categories. To provide evidence in support of our hypothesis, we evaluate CARL automation in a smart home testbed. Our results indicate that home automation can be boosted through activity awareness. We also find that the resulting automation has a high degree of usability and comfort for the smart home resident.

Line Pilots' Attitudes about and Experience with Flight Deck Automation: Results of an International Survey and Proposed Guidelines

NASA Technical Reports Server (NTRS)

Rudisill, Marianne

1995-01-01

A survey of line pilots' attitudes about flight deck automation was conducted by the Royal Air Force Institute of Aviation Medicine (RAF IAM, Farnborough, UK) under the sponsorship of the United Kingdom s Civil Aviation Authority and in cooperation with IATA (the International Air Transport Association). Survey freehand comments given by pilots operating 13 types of commercial transports across five manufacturers (Airbus, Boeing, British Aerospace, Lockheed, and McDonnell-Douglas) and 57 air carriers/organizations were analyzed by NASA. These data provide a "lessons learned" knowledge base which may be used for the definition of guidelines for flight deck automation and its associated crew interface within the High Speed Research Program. The aircraft chosen for analysis represented a progression of levels of automation sophistication and complexity, from "Basic" types (e.g., B727, DC9), through "Transition" types (e.g., A300, Concorde), to two levels of glass cockpits (e.g., Glass 1: e.g., A310; Glass 2: e.g., B747-400). This paper reports the results of analyses of comments from pilots flying commercial transport types having the highest level of automation sophistication (B757/B767, B747-400, and A320). Comments were decomposed into five categories relating to: (1) general observations with regard to flight deck automation; comments concerning the (2) design and (3) crew understanding of automation and the crew interface; (4) crew operations with automation; and (5) personal factors affecting crew/automation interaction. The goal of these analyses is to contribute to the definition of guidelines which may be used during design of future aircraft flight decks.

Factors to Consider When Implementing Automated Software Testing

DTIC Science & Technology

2016-11-10

programming, e.g., Java or Visual Basic.  Subject Matter Experts (SME) with firm grasp of application being automated. 2. Additional costs for setup (e.g...Abilities (KSA) required (e.g., Test and Evaluation). 2. Analyze programming skills needed (e.g., Java , C, C++, Visual Basic). 3. Compose team – testers

The effects of an online basic life support course on undergraduate nursing students' learning.

PubMed

Tobase, Lucia; Peres, Heloisa H C; Gianotto-Oliveira, Renan; Smith, Nicole; Polastri, Thatiane F; Timerman, Sergio

2017-08-25

To describe learning outcomes of undergraduate nursing students following an online basic life support course (BLS). An online BLS course was developed and administered to 94 nursing students. Pre- and post-tests were used to assess theoretical learning. Checklist simulations and feedback devices were used to assess the cardiopulmonary resuscitation (CPR) skills of the 62 students who completed the course. A paired t-test revealed a significant increase in learning [pre-test (6.4 ± 1.61), post-test (9.3 ± 0.82), p < 0.001]. The increase in the average grade after taking the online course was significant (p<0.001). No learning differences (p=0.475) had been observed between 1st and 2nd year (9.20 ± 1.60), and between 3rd and 4th year (9.67 ± 0.61) students. A CPR simulation was performed after completing the course: students checked for a response (90%), exposed the chest (98%), checked for breathing (97%), called emergency services (76%), requested for a defibrillator (92%), checked for a pulse (77%), positioned their hands properly (87%), performed 30 compressions/cycle (95%), performed compressions of at least 5 cm depth (89%), released the chest (90%), applied two breaths (97%), used the automated external defibrillator (97%), and positioned the pads (100%). The online course was an effective method for teaching and learning key BLS skills wherein students were able to accurately apply BLS procedures during the CPR simulation. This short-term online training, which likely improves learning and self-efficacy in BLS providers, can be used for the continuing education of health professionals.

Emergency skills learning on video (ESLOV): A single-blinded randomized control trial of teaching common emergency skills using self-instruction video (SIV) versus traditional face-to-face (FTF) methods.

PubMed

Mohd Saiboon, Ismail; Jaafar, Mohd Johar; Ahmad, Nurul Saadah; Nasarudin, Nazhatul Muna Ahmad; Mohamad, Nabishah; Ahmad, Mohd Radhi; Gilbert, John H V

2014-03-01

Self-instruction video (SIV) has been widely explored as a teaching mode for cardiopulmonary resuscitation (CPR) and automated external defibrillation (AED), but not with other basic emergency skills. To evaluate the effectiveness of SIV in teaching other basic emergency skill in comparison with traditional face-to-face (FTF) methods. Participants were randomized into SIV and FTF groups. Each group was assigned to learn basic airway management (BAM), cervical collar application (CCA), manual cardiac defibrillation (MCD), and emergency extremity splinting (EES) skills. Confidence level was assessed using questionnaires, and skills performances were assessed using calibrated-blinded assessors through an Objective Structured Clinical Examination (OSCE). Forty-five participants took part in the assessment exercises. There were no significant differences between both groups, on all four skill categories. The mean OSCE-score of an individual category between the FTF-group vs. the SIV-group were as follows: BAM (10.23 ± 1.04 vs. 10.04 ± 1.49; p = 0.62); CCA (7.86 ± 4.39 vs. 7.13 ± 4.12; p = 0.57); MCD (8.24 ± 0.89 vs. 7.58 ± 1.14; p = 0.39); EES (5.43 ± 2.11 vs. 4.63 ± 2.30; p = 0.23). The composite mean score for the FTF-group was 6.85, and for the SIV-group was 6.20 (p < 0.05). There was no significant different in the level of confidence for both groups. SIV is as effective as FTF in teaching and learning basic emergency skills.

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

PubMed

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

The effects of an online basic life support course on undergraduate nursing students’ learning

PubMed Central

Tobase, Lucia; Peres, Heloisa H.C.; Smith, Nicole; Polastri, Thatiane F.; Timerman, Sergio

2017-01-01

Objectives To describe learning outcomes of undergraduate nursing students following an online basic life support course (BLS). Methods An online BLS course was developed and administered to 94 nursing students. Pre- and post-tests were used to assess theoretical learning. Checklist simulations and feedback devices were used to assess the cardiopulmonary resuscitation (CPR) skills of the 62 students who completed the course. Results A paired t-test revealed a significant increase in learning [pre-test (6.4 ± 1.61), post-test (9.3 ± 0.82), p < 0.001]. The increase in the average grade after taking the online course was significant (p<0.001). No learning differences (p=0.475) had been observed between 1st and 2nd year (9.20 ± 1.60), and between 3rd and 4th year (9.67 ± 0.61) students. A CPR simulation was performed after completing the course: students checked for a response (90%), exposed the chest (98%), checked for breathing (97%), called emergency services (76%), requested for a defibrillator (92%), checked for a pulse (77%), positioned their hands properly (87%), performed 30 compressions/cycle (95%), performed compressions of at least 5 cm depth (89%), released the chest (90%), applied two breaths (97%), used the automated external defibrillator (97%), and positioned the pads (100%). Conclusions The online course was an effective method for teaching and learning key BLS skills wherein students were able to accurately apply BLS procedures during the CPR simulation. This short-term online training, which likely improves learning and self-efficacy in BLS providers, can be used for the continuing education of health professionals. PMID:28850944

Interprofessional education and social interaction: The use of automated external defibrillators in team-based basic life support.

PubMed

Onan, Arif; Simsek, Nurettin

2017-04-01

Automated external defibrillators are pervasive computing devices designed for the treatment and management of acute sudden cardiac arrest. This study aims to explain users' actual use behavior in teams formed by different professions taken after a short time span of interaction with automated external defibrillator. Before the intervention, all the participants were certified with the American Heart Association Basic Life Support for healthcare providers. A statistically significant difference was revealed in mean individual automated external defibrillator technical skills between uniprofessional and interprofessional groups. The technical automated external defibrillator team scores were greater for groups with interprofessional than for those with uniprofessional education. The nontechnical automated external defibrillator skills of interprofessional and uniprofessional teams revealed differences in advantage of interprofessional teams. Students positively accept automated external defibrillators if well-defined and validated training opportunities to use them expertly are available. Uniprofessional teams were successfully supported by their members and, thereby, used automated external defibrillator effectively. Furthermore, the interprofessional approach resulted in as much effective teamwork as the uniprofessional approach.

Designing Automated Guidance for Concept Diagrams in Inquiry Instruction

ERIC Educational Resources Information Center

Ryoo, Kihyun; Linn, Marcia C.

2016-01-01

Advances in automated scoring technologies have the potential to support student learning during inquiry instruction by providing timely and adaptive guidance on individual students' responses. To identify which forms of automated guidance can be beneficial for inquiry learning, we compared reflective guidance to directive guidance for…

Automation--down to the nuts and bolts.

PubMed

Fix, R J; Rowe, J M; McConnell, B C

2000-01-01

Laboratories that once viewed automation as an expensive luxury are now looking to automation as a solution to increase sample throughput, to help ensure data integrity and to improve laboratory safety. The question is no longer, 'Should we automate?', but 'How should we approach automation?' A laboratory may choose from three approaches when deciding to automate: (1) contract with a third party vendor to produce a turnkey system, (2) develop and fabricate the system in-house or (3) some combination of approaches (1) and (2). The best approach for a given laboratory depends upon its available resources. The first lesson to be learned in automation is that no matter how straightforward an idea appears in the beginning, the solution will not be realized until many complex problems have been resolved. Issues dealing with sample vessel manipulation, liquid handling and system control must be addressed before a final design can be developed. This requires expertise in engineering, electronics, programming and chemistry. Therefore, the team concept of automation should be employed to help ensure success. This presentation discusses the advantages and disadvantages of the three approaches to automation. The development of an automated sample handling and control system for the STAR System focused microwave will be used to illustrate the complexities encountered in a seemingly simple project, and to highlight the importance of the team concept to automation no matter which approach is taken. The STAR System focused microwave from CEM Corporation is an open vessel digestion system with six microwave cells. This system is used to prepare samples for trace metal determination. The automated sample handling was developed around a XYZ motorized gantry system. Grippers were specially designed to perform several different functions and to provide feedback to the control software. Software was written in Visual Basic 5.0 to control the movement of the samples and the operation and monitoring of the STAR microwave. This software also provides a continuous update of the system's status to the computer screen. The system provides unattended preparation of up to 59 samples per run.

An ultraviolet-visible spectrophotometer automation system. Part 3: Program documentation

NASA Astrophysics Data System (ADS)

Roth, G. S.; Teuschler, J. M.; Budde, W. L.

1982-07-01

The Ultraviolet-Visible Spectrophotometer (UVVIS) automation system accomplishes 'on-line' spectrophotometric quality assurance determinations, report generations, plot generations and data reduction for chlorophyll or color analysis. This system also has the capability to process manually entered data for the analysis of chlorophyll or color. For each program of the UVVIS system, this document contains a program description, flowchart, variable dictionary, code listing, and symbol cross-reference table. Also included are descriptions of file structures and of routines common to all automated analyses. The programs are written in Data General extended BASIC, Revision 4.3, under the RDOS operating systems, Revision 6.2. The BASIC code has been enhanced for real-time data acquisition, which is accomplished by CALLS to assembly language subroutines. Two other related publications are 'An Ultraviolet-Visible Spectrophotometer Automation System - Part I Functional Specifications,' and 'An Ultraviolet-Visible Spectrophotometer Automation System - Part II User's Guide.'

Advances in natural language processing.

PubMed

Hirschberg, Julia; Manning, Christopher D

2015-07-17

Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area. Copyright © 2015, American Association for the Advancement of Science.

TH-A-18A-01: Innovation in Clinical Breast Imaging

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

Liu, B; Yang, K; Yaffe, M

Several novel modalities have been or are on the verge of being introduced into the breast imaging clinic. These include tomosynthesis imaging, dedicated breast CT, contrast-enhanced digital mammography, and automated breast ultrasound, all of which are covered in this course. Tomosynthesis and dedicated breast CT address the problem of tissue superimposition that limits mammography screening performance, by improved or full resolution of the 3D breast morphology. Contrast-enhanced digital mammography provides functional information that allows for visualization of tumor angiogenesis. 3D breast ultrasound has high sensitivity for tumor detection in dense breasts, but the imaging exam was traditionally performed by radiologists.more » In automated breast ultrasound, the scan is performed in an automated fashion, making for a more practical imaging tool, that is now used as an adjunct to digital mammography in breast cancer screening. This course will provide medical physicists with an in-depth understanding of the imaging physics of each of these four novel imaging techniques, as well as the rationale and implementation of QC procedures. Further, basic clinical applications and work flow issues will be discussed. Learning Objectives: To be able to describe the underlying physical and physiological principles of each imaging technique, and to understand the corresponding imaging acquisition process. To be able to describe the critical system components and their performance requirements. To understand the rationale and implementation of quality control procedures, as well as regulatory requirements for systems with FDA approval. To learn about clinical applications and understand risks and benefits/strength and weakness of each modality in terms of clinical breast imaging.« less

Using Automated Scores of Student Essays to Support Teacher Guidance in Classroom Inquiry

NASA Astrophysics Data System (ADS)

Gerard, Libby F.; Linn, Marcia C.

2016-02-01

Computer scoring of student written essays about an inquiry topic can be used to diagnose student progress both to alert teachers to struggling students and to generate automated guidance. We identify promising ways for teachers to add value to automated guidance to improve student learning. Three teachers from two schools and their 386 students participated. We draw on evidence from student progress, observations of how teachers interact with students, and reactions of teachers. The findings suggest that alerts for teachers prompted rich teacher-student conversations about energy in photosynthesis. In one school, the combination of the automated guidance plus teacher guidance was more effective for student science learning than two rounds of personalized, automated guidance. In the other school, both approaches resulted in equal learning gains. These findings suggest optimal combinations of automated guidance and teacher guidance to support students to revise explanations during inquiry and build integrated understanding of science.

Decision-making and problem-solving methods in automation technology

NASA Technical Reports Server (NTRS)

Hankins, W. W.; Pennington, J. E.; Barker, L. K.

1983-01-01

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.

Analyzing Automated Instructional Systems: Metaphors from Related Design Professions.

ERIC Educational Resources Information Center

Jonassen, David H.; Wilson, Brent G.

Noting that automation has had an impact on virtually every manufacturing and information operation in the world, including instructional design (ID), this paper suggests three basic metaphors for automating instructional design activities: (1) computer-aided design and manufacturing (CAD/CAM) systems; (2) expert system advisor systems; and (3)…

Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

PubMed

Smith, Kevin; Horvath, Peter

2014-06-01

High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

Tree Classification Software

NASA Technical Reports Server (NTRS)

Buntine, Wray

1993-01-01

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

Automated external defibrillation training on the left or the right side - a randomized simulation study.

PubMed

Stærk, Mathilde; Bødtker, Henrik; Lauridsen, Kasper G; Løfgren, Bo

2017-01-01

Correct placement of the left automated external defibrillator (AED) electrode is rarely achieved. AED electrode placement is predominantly illustrated and trained with the rescuer sitting on the right side of the patient. Placement of the AED electrodes from the left side of the patient may result in a better overview of and access to the left lateral side of the thorax. This study aimed to investigate if training in automated external defibrillation on the left side compared to the right side of a manikin improves left AED electrode placement. Laypeople attending basic life support training were randomized to learn automated external defibrillation from the left or right side of a manikin. After course completion, participants used an AED and placed AED electrodes in a simulated cardiac arrest scenario. In total, 40 laypersons were randomized to AED training on the left (n=19 [missing data =1], 63% female, mean age: 47.3 years) and right (n=20, 75% female, mean age: 48.7 years) sides of a manikin. There was no difference in left AED electrode placement when trained on the left or right side: the mean (SD) distances to the recommended left AED electrode position were 5.9 (2.1) cm vs 6.9 (2.2) cm ( p =0.15) and to the recommended right AED electrode position were 2.6 (1.5) cm vs 1.8 (0.8) cm ( p =0.06), respectively. Training in automated external defibrillation on the left side of a manikin does not improve left AED electrode placement compared to training on the right side.

Deep Interactive Learning with Sharkzor

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

None

Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. Aftermore » interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.« less

Clarity: An Open Source Manager for Laboratory Automation

PubMed Central

Delaney, Nigel F.; Echenique, José Rojas; Marx, Christopher J.

2013-01-01

Software to manage automated laboratories interfaces with hardware instruments, gives users a way to specify experimental protocols, and schedules activities to avoid hardware conflicts. In addition to these basics, modern laboratories need software that can run multiple different protocols in parallel and that can be easily extended to interface with a constantly growing diversity of techniques and instruments. We present Clarity: a laboratory automation manager that is hardware agnostic, portable, extensible and open source. Clarity provides critical features including remote monitoring, robust error reporting by phone or email, and full state recovery in the event of a system crash. We discuss the basic organization of Clarity; demonstrate an example of its implementation for the automated analysis of bacterial growth; and describe how the program can be extended to manage new hardware. Clarity is mature; well documented; actively developed; written in C# for the Common Language Infrastructure; and is free and open source software. These advantages set Clarity apart from currently available laboratory automation programs. PMID:23032169

Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

PubMed

Nakanishi, Rine; Sankaran, Sethuraman; Grady, Leo; Malpeso, Jenifer; Yousfi, Razik; Osawa, Kazuhiro; Ceponiene, Indre; Nazarat, Negin; Rahmani, Sina; Kissel, Kendall; Jayawardena, Eranthi; Dailing, Christopher; Zarins, Christopher; Koo, Bon-Kwon; Min, James K; Taylor, Charles A; Budoff, Matthew J

2018-03-23

Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA). The machine learning method was trained using 75 CCTA studies by mapping features (noise, contrast, misregistration scores, and un-interpretability index) to an IQ score based on manual ground truth data. The automated method was validated on a set of 50 CCTA studies and subsequently tested on a new set of 172 CCTA studies against visual IQ scores on a 5-point Likert scale. The area under the curve in the validation set was 0.96. In the 172 CCTA studies, our method yielded a Cohen's kappa statistic for the agreement between automated and visual IQ assessment of 0.67 (p < 0.01). In the group where good to excellent (n = 163), fair (n = 6), and poor visual IQ scores (n = 3) were graded, 155, 5, and 2 of the patients received an automated IQ score > 50 %, respectively. Fully automated assessment of the IQ of CCTA data sets by machine learning was reproducible and provided similar results compared with visual analysis within the limits of inter-operator variability. • The proposed method enables automated and reproducible image quality assessment. • Machine learning and visual assessments yielded comparable estimates of image quality. • Automated assessment potentially allows for more standardised image quality. • Image quality assessment enables standardization of clinical trial results across different datasets.

A Fully Automated Drosophila Olfactory Classical Conditioning and Testing System for Behavioral Learning and Memory Assessment

PubMed Central

Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L.; Page, Terry L.; Bhuva, Bharat; Broadie, Kendal

2016-01-01

Background Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. New Method The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. Results The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24 hours) are comparable to traditional manual experiments, while minimizing experimenter involvement. Comparison with Existing Methods The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ~$500US, making it affordable to a wide range of investigators. Conclusions This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. PMID:26703418

A fully automated Drosophila olfactory classical conditioning and testing system for behavioral learning and memory assessment.

PubMed

Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L; Page, Terry L; Bhuva, Bharat; Broadie, Kendal

2016-03-01

Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24h) are comparable to traditional manual experiments, while minimizing experimenter involvement. The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ∼$500US, making it affordable to a wide range of investigators. This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. Copyright © 2015 Elsevier B.V. All rights reserved.

Competence-Based Blended Learning in Building Automation: Towards a EU Curriculum in "Domotica"

ERIC Educational Resources Information Center

Sommaruga, L.; De Angelis, E.

2007-01-01

A competence-based approach was applied to a blended learning on line distance training in the Euroinno EU project aimed at vocational training in building automation. The current paper describes the experience gathered during the learning process and the definition of the curriculum. A number of issues emerged during the sessions concerning…

Predicting automated guideway transit system station security requirements

DOT National Transportation Integrated Search

1980-03-01

This study addresses the issues of personal security on Automated Guideway Transit (AGT) Systems, as they might be deployed in typical urban residential and non-residential settings. Based upon a literature review, it outlines basic characteristics o...

Library Automation at the University for Development Studies: Challenges and Prospects

ERIC Educational Resources Information Center

Thompson, Edwin S.; Pwadura, Joana

2014-01-01

The automation of a library that basically aims at improving the management of the library's resources and increasing access to these same resources by users has caught on so well in the western world that virtually all academic libraries in that part of the world have automated most of their services. In Africa, however, several challenges are…

Automated external defibrillation training on the left or the right side – a randomized simulation study

PubMed Central

Stærk, Mathilde; Bødtker, Henrik; Lauridsen, Kasper G; Løfgren, Bo

2017-01-01

Background Correct placement of the left automated external defibrillator (AED) electrode is rarely achieved. AED electrode placement is predominantly illustrated and trained with the rescuer sitting on the right side of the patient. Placement of the AED electrodes from the left side of the patient may result in a better overview of and access to the left lateral side of the thorax. This study aimed to investigate if training in automated external defibrillation on the left side compared to the right side of a manikin improves left AED electrode placement. Methods Laypeople attending basic life support training were randomized to learn automated external defibrillation from the left or right side of a manikin. After course completion, participants used an AED and placed AED electrodes in a simulated cardiac arrest scenario. Results In total, 40 laypersons were randomized to AED training on the left (n=19 [missing data =1], 63% female, mean age: 47.3 years) and right (n=20, 75% female, mean age: 48.7 years) sides of a manikin. There was no difference in left AED electrode placement when trained on the left or right side: the mean (SD) distances to the recommended left AED electrode position were 5.9 (2.1) cm vs 6.9 (2.2) cm (p=0.15) and to the recommended right AED electrode position were 2.6 (1.5) cm vs 1.8 (0.8) cm (p=0.06), respectively. Conclusion Training in automated external defibrillation on the left side of a manikin does not improve left AED electrode placement compared to training on the right side. PMID:29066936

Project CAPABLE: Model Unit.

ERIC Educational Resources Information Center

Madawaska School District, ME.

Project CAPABLE (Classroom Action Program: Aim: Basic Learning Effectiveness) is a classroom approach which integrates the basic learning skills with content. The goal of the project is to use basic learning skills to enhance the learning of content and at the same time use the content to teach basic learning skills. This manual illustrates how…

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

PubMed Central

Burlina, Philippe; Billings, Seth; Joshi, Neil

2017-01-01

Objective To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and “engineered” features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. Results The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). Conclusions This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification. PMID:28854220

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

PubMed

Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

2017-01-01

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Microcontroller for automation application

NASA Technical Reports Server (NTRS)

Cooper, H. W.

1975-01-01

The description of a microcontroller currently being developed for automation application was given. It is basically an 8-bit microcomputer with a 40K byte random access memory/read only memory, and can control a maximum of 12 devices through standard 15-line interface ports.

Automated Assume-Guarantee Reasoning for Omega-Regular Systems and Specifications

NASA Technical Reports Server (NTRS)

Chaki, Sagar; Gurfinkel, Arie

2010-01-01

We develop a learning-based automated Assume-Guarantee (AG) reasoning framework for verifying omega-regular properties of concurrent systems. We study the applicability of non-circular (AGNC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG.We develop two learning algorithms for a class of systems, called infinite regular systems, that combine finite and infinite behaviors. We show that for infinity-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for infinite regular, and omega-regular, systems using both AG-NC and AG-C as proof rules

AUTOMATED ANALYSIS OF AQUEOUS SAMPLES CONTAINING PESTICIDES, ACIDIC/BASIC/NEUTRAL SEMIVOLATILES AND VOLATILE ORGANIC COMPOUNDS BY SOLID PHASE EXTRACTION COUPLED IN-LINE TO LARGE VOLUME INJECTION GC/MS

EPA Science Inventory

Data is presented on the development of a new automated system combining solid phase extraction (SPE) with GC/MS spectrometry for the single-run analysis of water samples containing a broad range of organic compounds. The system uses commercially available automated in-line 10-m...

Tackling the x-ray cargo inspection challenge using machine learning

NASA Astrophysics Data System (ADS)

Jaccard, Nicolas; Rogers, Thomas W.; Morton, Edward J.; Griffin, Lewis D.

2016-05-01

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Automating Expertise in Collaborative Learning Environments

ERIC Educational Resources Information Center

LaVoie, Noelle; Streeter, Lynn; Lochbaum, Karen; Wroblewski, David; Boyce, Lisa; Krupnick, Charles; Psotka, Joseph

2010-01-01

We have developed a set of tools for improving online collaborative learning including an automated expert that monitors and moderates discussions, and additional tools to evaluate contributions, semantically search all posted comments, access a library of hundreds of digital books and provide reports to instructors. The technology behind these…

Documentation: Motivation and training or automation

NASA Technical Reports Server (NTRS)

Mouton, M. L.

1970-01-01

The road blocks and mental blocks in areas where automation is not taking care of basic documentation problems are discussed. Original project documentation, documentation for project maintenance, and comparison of preliminary and final documentation are described. The use of flow charts is also mentioned.

AN ULTRAVIOLET-VISIBLE SPECTROPHOTOMETER AUTOMATION SYSTEM. PART I: FUNCTIONAL SPECIFICATIONS

EPA Science Inventory

This document contains the project definition, the functional requirements, and the functional design for a proposed computer automation system for scanning spectrophotometers. The system will be implemented on a Data General computer using the BASIC language. The system is a rea...

Stage Evolution of Office Automation Technological Change and Organizational Learning.

ERIC Educational Resources Information Center

Sumner, Mary

1985-01-01

A study was conducted to identify stage characteristics in terms of technology, applications, the role and responsibilities of the office automation organization, and planning and control strategies; and to describe the respective roles of data processing professionals, office automation analysts, and users in office automation systems development…

Automated Assessment in Massive Open Online Courses

ERIC Educational Resources Information Center

Ivaniushin, Dmitrii A.; Shtennikov, Dmitrii G.; Efimchick, Eugene A.; Lyamin, Andrey V.

2016-01-01

This paper describes an approach to use automated assessments in online courses. Open edX platform is used as the online courses platform. The new assessment type uses Scilab as learning and solution validation tool. This approach allows to use automated individual variant generation and automated solution checks without involving the course…

Improving Learning Object Quality: Moodle HEODAR Implementation

ERIC Educational Resources Information Center

Munoz, Carlos; Garcia-Penalvo, Francisco J.; Morales, Erla Mariela; Conde, Miguel Angel; Seoane, Antonio M.

2012-01-01

Automation toward efficiency is the aim of most intelligent systems in an educational context in which results calculation automation that allows experts to spend most of their time on important tasks, not on retrieving, ordering, and interpreting information. In this paper, the authors provide a tool that easily evaluates Learning Objects quality…

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.

5 CFR 293.107 - Special safeguards for automated records.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Special safeguards for automated records. 293.107 Section 293.107 Administrative Personnel OFFICE OF PERSONNEL MANAGEMENT CIVIL SERVICE REGULATIONS PERSONNEL RECORDS Basic Policies on Maintenance of Personnel Records § 293.107 Special safeguards...

Automated delineation and characterization of watersheds for more than 3,000 surface-water-quality monitoring stations active in 2010 in Texas

USGS Publications Warehouse

Archuleta, Christy-Ann M.; Gonzales, Sophia L.; Maltby, David R.

2012-01-01

The U.S. Geological Survey (USGS), in cooperation with the Texas Commission on Environmental Quality, developed computer scripts and applications to automate the delineation of watershed boundaries and compute watershed characteristics for more than 3,000 surface-water-quality monitoring stations in Texas that were active during 2010. Microsoft Visual Basic applications were developed using ArcGIS ArcObjects to format the source input data required to delineate watershed boundaries. Several automated scripts and tools were developed or used to calculate watershed characteristics using Python, Microsoft Visual Basic, and the RivEX tool. Automated methods were augmented by the use of manual methods, including those done using ArcMap software. Watershed boundaries delineated for the monitoring stations are limited to the extent of the Subbasin boundaries in the USGS Watershed Boundary Dataset, which may not include the total watershed boundary from the monitoring station to the headwaters.

Revolving back to the basics in cardiopulmonary resuscitation.

PubMed

Roppolo, L P; Wigginton, J G; Pepe, P E

2009-05-01

Since the 1970s, most of the research and debate regarding interventions for cardiopulmonary arrest have focused on advanced life support (ALS) therapies and early defibrillation strategies. During the past decade, however, international guidelines for cardiopulmonary resuscitation (CPR) have not only emphasized the concept of uninterrupted chest compressions, but also improvements in the timing, rate and quality of those compressions. In essence, it has been a ''revolution'' in resuscitation medicine in terms of ''coming full circle'' to the 1960s when basic CPR was first developed. Recent data have indicated the need for minimally-interrupted chest compressions with an accompanying emphasis toward removing rescue ventilation altogether in sudden cardiac arrest, at least in the few minutes after a sudden unheralded collapse. In other studies, transient delays in defibrillation attempts and ALS interventions are even recommended so that basic CPR can be prioritized to first restore and maintain better coronary artery perfusion. New devices have now been developed to modify, in real-time, the performance of basic CPR, during both training and an actual resuscitative effort. Several new adjuncts have been created to augment chest compressions or enhance venous return and evolving technology may now be able to identify ventricular fibrillation (VF) without interrupting chest compressions. A renewed focus on widespread CPR training for the average person has also returned to center stage with ground-breaking training initiatives including validated video-based adult learning courses that can reliably teach and enable long term retention of basic CPR skills and automated external defibrillator (AED) use.

Artificial intelligence costs, benefits, risks for selected spacecraft ground system automation scenarios

NASA Technical Reports Server (NTRS)

Truszkowski, Walter F.; Silverman, Barry G.; Kahn, Martha; Hexmoor, Henry

1988-01-01

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline), (2) standalone expert systems, (3) standardized, reusable knowledge base management systems (KBMS), and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Artificial intelligence costs, benefits, and risks for selected spacecraft ground system automation scenarios

NASA Technical Reports Server (NTRS)

Truszkowski, Walter F.; Silverman, Barry G.; Kahn, Martha; Hexmoor, Henry

1988-01-01

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline); (2) standalone expert systems; (3) standardized, reusable knowledge base management systems (KBMS); and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Cerebellum engages in automation of verb-generation skill.

PubMed

Yang, Zhi; Wu, Paula; Weng, Xuchu; Bandettini, Peter A

2014-03-01

Numerous studies have shown cerebellar involvement in item-specific association, a form of explicit learning. However, very few have demonstrated cerebellar participation in automation of non-motor cognitive tasks. Applying fMRI to a repeated verb-generation task, we sought to distinguish cerebellar involvement in learning of item-specific noun-verb association and automation of verb generation skill. The same set of nouns was repeated in six verb-generation blocks so that subjects practiced generating verbs for the nouns. The practice was followed by a novel block with a different set of nouns. The cerebellar vermis (IV/V) and the right cerebellar lobule VI showed decreased activation following practice; activation in the right cerebellar Crus I was significantly lower in the novel challenge than in the initial verb-generation task. Furthermore, activation in this region during well-practiced blocks strongly correlated with improvement of behavioral performance in both the well-practiced and the novel blocks, suggesting its role in the learning of general mental skills not specific to the practiced noun-verb pairs. Therefore, the cerebellum processes both explicit verbal associative learning and automation of cognitive tasks. Different cerebellar regions predominate in this processing: lobule VI during the acquisition of item-specific association, and Crus I during automation of verb-generation skills through practice.

Automated irrigation management with soil and canopy sensing

USDA-ARS?s Scientific Manuscript database

Automated irrigation management provides for real time feedback between crop water needs and the delivery of specific amount of irrigation water to specific locations on demand. In addition to the basic components of any irrigation system, e.g. pumps, filters, valves, pipes and tubing, sprinkler he...

Design, Development, and Commissioning of a Substation Automation Laboratory to Enhance Learning

ERIC Educational Resources Information Center

Thomas, M. S.; Kothari, D. P.; Prakash, A.

2011-01-01

Automation of power systems is gaining momentum across the world, and there is a need to expose graduate and undergraduate students to the latest developments in hardware, software, and related protocols for power automation. This paper presents the design, development, and commissioning of an automation lab to facilitate the understanding of…

English Language Learners and Automated Scoring of Essays: Critical Considerations

ERIC Educational Resources Information Center

Weigle, Sara Cushing

2013-01-01

This article presents considerations for using automated scoring systems to evaluate second language writing. A distinction is made between English language learners in English-medium educational systems and those studying English in their own countries for a variety of purposes, and between learning-to-write and writing-to-learn in a second…

Automated Analysis of Short Responses in an Interactive Synthetic Tutoring System for Introductory Physics

ERIC Educational Resources Information Center

Nakamura, Christopher M.; Murphy, Sytil K.; Christel, Michael G.; Stevens, Scott M.; Zollman, Dean A.

2016-01-01

Computer-automated assessment of students' text responses to short-answer questions represents an important enabling technology for online learning environments. We have investigated the use of machine learning to train computer models capable of automatically classifying short-answer responses and assessed the results. Our investigations are part…

Advanced, Analytic, Automated (AAA) Measurement of Engagement during Learning

ERIC Educational Resources Information Center

D'Mello, Sidney; Dieterle, Ed; Duckworth, Angela

2017-01-01

It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in…

Ability-Training-Oriented Automated Assessment in Introductory Programming Course

ERIC Educational Resources Information Center

Wang, Tiantian; Su, Xiaohong; Ma, Peijun; Wang, Yuying; Wang, Kuanquan

2011-01-01

Learning to program is a difficult process for novice programmers. AutoLEP, an automated learning and assessment system, was developed by us, to aid novice programmers to obtain programming skills. AutoLEP is ability-training-oriented. It adopts a novel assessment mechanism, which combines static analysis with dynamic testing to analyze student…

Enabling Wide-Scale Computer Science Education through Improved Automated Assessment Tools

NASA Astrophysics Data System (ADS)

Boe, Bryce A.

There is a proliferating demand for newly trained computer scientists as the number of computer science related jobs continues to increase. University programs will only be able to train enough new computer scientists to meet this demand when two things happen: when there are more primary and secondary school students interested in computer science, and when university departments have the resources to handle the resulting increase in enrollment. To meet these goals, significant effort is being made to both incorporate computational thinking into existing primary school education, and to support larger university computer science class sizes. We contribute to this effort through the creation and use of improved automated assessment tools. To enable wide-scale computer science education we do two things. First, we create a framework called Hairball to support the static analysis of Scratch programs targeted for fourth, fifth, and sixth grade students. Scratch is a popular building-block language utilized to pique interest in and teach the basics of computer science. We observe that Hairball allows for rapid curriculum alterations and thus contributes to wide-scale deployment of computer science curriculum. Second, we create a real-time feedback and assessment system utilized in university computer science classes to provide better feedback to students while reducing assessment time. Insights from our analysis of student submission data show that modifications to the system configuration support the way students learn and progress through course material, making it possible for instructors to tailor assignments to optimize learning in growing computer science classes.

Automated discovery systems and the inductivist controversy

NASA Astrophysics Data System (ADS)

Giza, Piotr

2017-09-01

The paper explores possible influences that some developments in the field of branches of AI, called automated discovery and machine learning systems, might have upon some aspects of the old debate between Francis Bacon's inductivism and Karl Popper's falsificationism. Donald Gillies facetiously calls this controversy 'the duel of two English knights', and claims, after some analysis of historical cases of discovery, that Baconian induction had been used in science very rarely, or not at all, although he argues that the situation has changed with the advent of machine learning systems. (Some clarification of terms machine learning and automated discovery is required here. The key idea of machine learning is that, given data with associated outcomes, software can be trained to make those associations in future cases which typically amounts to inducing some rules from individual cases classified by the experts. Automated discovery (also called machine discovery) deals with uncovering new knowledge that is valuable for human beings, and its key idea is that discovery is like other intellectual tasks and that the general idea of heuristic search in problem spaces applies also to discovery tasks. However, since machine learning systems discover (very low-level) regularities in data, throughout this paper I use the generic term automated discovery for both kinds of systems. I will elaborate on this later on). Gillies's line of argument can be generalised: thanks to automated discovery systems, philosophers of science have at their disposal a new tool for empirically testing their philosophical hypotheses. Accordingly, in the paper, I will address the question, which of the two philosophical conceptions of scientific method is better vindicated in view of the successes and failures of systems developed within three major research programmes in the field: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group and the programme called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett and P. Thagard.

Directory of Library Automation Software, Systems, and Services. 1998 Edition.

ERIC Educational Resources Information Center

Cibbarelli, Pamela R., Ed.; Cibbarelli, Shawn E., Ed.

This book includes basic information to locate and compare available options for library automation based on various criteria such as hardware requirements, operating systems, components and applications, and price, and provides the necessary contact information to allow further investigation. The major part of the directory lists 211 software…

Automated Essay Scoring

ERIC Educational Resources Information Center

Dikli, Semire

2006-01-01

The impacts of computers on writing have been widely studied for three decades. Even basic computers functions, i.e. word processing, have been of great assistance to writers in modifying their essays. The research on Automated Essay Scoring (AES) has revealed that computers have the capacity to function as a more effective cognitive tool (Attali,…

Library Automation: A "First Course" Teaching Syllabus.

ERIC Educational Resources Information Center

Dyson, Sam A.

This syllabus for a basic course in library automation is designed for advanced library students and practicing librarians. It is intended not to make librarians and students qualified programmers, but to give them enough background information for intelligent discussion of library problems with computer personnel. It may also stimulate the…

Automated biowaste sampling system improved feces collection, mass measurement and sampling. [by use of a breadboard model

NASA Technical Reports Server (NTRS)

Fogal, G. L.; Mangialardi, J. K.; Young, R.

1974-01-01

The capability of the basic automated Biowaste Sampling System (ABSS) hardware was extended and improved through the design, fabrication and test of breadboard hardware. A preliminary system design effort established the feasibility of integrating the breadboard concepts into the ABSS.

A 1-night operant learning task without food-restriction differentiates among mouse strains in an automated home-cage environment.

PubMed

Remmelink, Esther; Loos, Maarten; Koopmans, Bastijn; Aarts, Emmeke; van der Sluis, Sophie; Smit, August B; Verhage, Matthijs

2015-04-15

Individuals are able to change their behavior based on its consequences, a process involving instrumental learning. Studying instrumental learning in mice can provide new insights in this elementary aspect of cognition. Conventional appetitive operant learning tasks that facilitate the study of this form of learning in mice, as well as more complex operant paradigms, require labor-intensive handling and food deprivation to motivate the animals. Here, we describe a 1-night operant learning protocol that exploits the advantages of automated home-cage testing and circumvents the interfering effects of food restriction. The task builds on behavior that is part of the spontaneous exploratory repertoire during the days before the task. We compared the behavior of C57BL/6J, BALB/cJ and DBA/2J mice and found various differences in behavior during this task, but no differences in learning curves. BALB/cJ mice showed the largest instrumental learning response, providing a superior dynamic range and statistical power to study instrumental learning by using this protocol. Insights gained with this home-cage-based learning protocol without food restriction will be valuable for the development of other, more complex, cognitive tasks in automated home-cages. Copyright © 2015 Elsevier B.V. All rights reserved.

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

PubMed

Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P

2016-11-29

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p < 0.01), average early diastolic tissue velocity (e') (p < 0.01), and strain (p = 0.04). Because ATH were younger, adjusted analysis was undertaken in younger HCM patients and compared with ATH with left ventricular wall thickness >13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Production system chunking in SOAR: Case studies in automated learning

NASA Technical Reports Server (NTRS)

Allen, Robert

1989-01-01

A preliminary study of SOAR, a general intelligent architecture for automated problem solving and learning, is presented. The underlying principles of universal subgoaling and chunking were applied to a simple, yet representative, problem in artificial intelligence. A number of problem space representations were examined and compared. It is concluded that learning is an inherent and beneficial aspect of problem solving. Additional studies are suggested in domains relevant to mission planning and to SOAR itself.

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

PubMed

Lequan Yu; Hao Chen; Qi Dou; Jing Qin; Pheng Ann Heng

2017-01-01

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

Intelligent Agent Technology

NASA Technical Reports Server (NTRS)

1996-01-01

Open Sesame! is the first commercial software product that learns user's behavior, and offers automation and coaching suggestions to the user. The neural learning module looks for repetitive patterns that have not been automated; when it finds one, it creates an observation and, upon approval, automates the task. The manufacturer, Charles River Analytics, credits Langley Research Center and Johnson Space Center Small Business Innovation Research grants and the time the president and vice president spent at the two centers in the 1970s as being essential to the development of their product line.

Library Automation in Sub Saharan Africa: Case Study of the University of Botswana

ERIC Educational Resources Information Center

Mutula, Stephen Mudogo

2012-01-01

Purpose: This article aims to present experiences and the lessons learned from the University of Botswana (UB) library automation project. The implications of the project for similar libraries planning automation in sub Saharan Africa and beyond are adduced. Design/methodology/approach: The article is a case study of library automation at the…

Automated LSA Assessment of Summaries in Distance Education: Some Variables to Be Considered

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Luzón, José M.; Gómez-Veiga, Isabel; Martín-Cordero, Jesús I.

2015-01-01

A latent semantic analysis-based automated summary assessment is described; this automated system is applied to a real learning from text task in a Distance Education context. We comment on the use of automated content, plagiarism, text coherence measures, and word weights average and their impact on predicting human judges summary scoring. A…

Automated analysis of short responses in an interactive synthetic tutoring system for introductory physics

NASA Astrophysics Data System (ADS)

Nakamura, Christopher M.; Murphy, Sytil K.; Christel, Michael G.; Stevens, Scott M.; Zollman, Dean A.

2016-06-01

Computer-automated assessment of students' text responses to short-answer questions represents an important enabling technology for online learning environments. We have investigated the use of machine learning to train computer models capable of automatically classifying short-answer responses and assessed the results. Our investigations are part of a project to develop and test an interactive learning environment designed to help students learn introductory physics concepts. The system is designed around an interactive video tutoring interface. We have analyzed 9 with about 150 responses or less. We observe for 4 of the 9 automated assessment with interrater agreement of 70% or better with the human rater. This level of agreement may represent a baseline for practical utility in instruction and indicates that the method warrants further investigation for use in this type of application. Our results also suggest strategies that may be useful for writing activities and questions that are more appropriate for automated assessment. These strategies include building activities that have relatively few conceptually distinct ways of perceiving the physical behavior of relatively few physical objects. Further success in this direction may allow us to promote interactivity and better provide feedback in online learning systems. These capabilities could enable our system to function more like a real tutor.

Effect of 3basic life support training programs in future primary school teachers. A quasi-experimental design.

PubMed

Navarro-Patón, R; Freire-Tellado, M; Basanta-Camiño, S; Barcala-Furelos, R; Arufe-Giraldez, V; Rodriguez-Fernández, J E

2018-05-01

To evaluate the learning of basic life support (BLS) measures on the part of laypersons after 3different teaching programs. A quasi-experimental before-after study involving a non-probabilistic sample without a control group was carried out. Primary school teacher students from the University of Santiago (Spain). A total of 124 students (68.8% women and 31.2% men) aged 20-39 years (M=22.23; SD=3.79), with no previous knowledge of BLS, were studied. Three teaching programs were used: a traditional course, an audio-visual approach and feedback devices. Chest compressions as sole cardiopulmonary resuscitation skill evaluation: average compression depth, compression rate, chest recoil percentage and percentage of correct compressions. Automated external defibrillator: time needed to apply a shock before and after the course. There were significant differences in the results obtained after 2minutes of chest compressions, depending on the training program received, with feedback devices having a clear advantage referred to average compression depth (p<0.001), compression rate (p<0.001), chest recoil percentage (p<0.001) and percentage of correct compressions (p<0.001). Regarding automated external defibrillator, statistically significant differences were found in T after (p=0.025). The teaching course using feedback devices obtained the best results in terms of the quality of chest compressions, followed by the traditional course and audio-visual approach. These favorable results were present in both men and women. All 3teaching methods reached the goal of reducing defibrillation time. Copyright © 2017 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

Machine learning of network metrics in ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

2017-10-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

Faded-example as a Tool to Acquire and Automate Mathematics Knowledge

NASA Astrophysics Data System (ADS)

Retnowati, E.

2017-04-01

Students themselves accomplish Knowledge acquisition and automation. The teacher plays a role as the facilitator by creating mathematics tasks that assist students in building knowledge efficiently and effectively. Cognitive load caused by learning material presented by teachers should be considered as a critical factor. While the intrinsic cognitive load is related to the degree of complexity of the material learning ones can handle, the extraneous cognitive load is directly caused by how the material is presented. Strategies to present a learning material in computational learning domains like mathematics are a namely worked example (fully-guided task) or problem-solving (discovery task with no guidance). According to the empirical evidence, learning based on problem-solving may cause high-extraneous cognitive load for students who have limited prior knowledge, conversely learn based on worked example may cause high-extraneous cognitive load for students who have mastered the knowledge base. An alternative is a faded example consisting of the partly-completed task. Learning from faded-example can facilitate students who already acquire some knowledge about the to-be-learned material but still need more practice to automate the knowledge further. This instructional strategy provides a smooth transition from a fully-guided into an independent problem solver. Designs of faded examples for learning trigonometry are discussed.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

PubMed

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Principles of Automation for Patient Safety in Intensive Care: Learning From Aviation.

PubMed

Dominiczak, Jason; Khansa, Lara

2018-06-01

The transition away from written documentation and analog methods has opened up the possibility of leveraging data science and analytic techniques to improve health care. In the implementation of data science techniques and methodologies, high-acuity patients in the ICU can particularly benefit. The Principles of Automation for Patient Safety in Intensive Care (PASPIC) framework draws on Billings's principles of human-centered aviation (HCA) automation and helps in identifying the advantages, pitfalls, and unintended consequences of automation in health care. Billings's HCA principles are based on the premise that human operators must remain "in command," so that they are continuously informed and actively involved in all aspects of system operations. In addition, automated systems need to be predictable, simple to train, to learn, and to operate, and must be able to monitor the human operators, and every intelligent system element must know the intent of other intelligent system elements. In applying Billings's HCA principles to the ICU setting, PAPSIC has three key characteristics: (1) integration and better interoperability, (2) multidimensional analysis, and (3) enhanced situation awareness. PAPSIC suggests that health care professionals reduce overreliance on automation and implement "cooperative automation" and that vendors reduce mode errors and embrace interoperability. Much can be learned from the aviation industry in automating the ICU. Because it combines "smart" technology with the necessary controls to withstand unintended consequences, PAPSIC could help ensure more informed decision making in the ICU and better patient care. Copyright © 2018 The Joint Commission. Published by Elsevier Inc. All rights reserved.

IntelliCages and automated assessment of learning in group-housed mice

NASA Astrophysics Data System (ADS)

Puścian, Alicja; Knapska, Ewelina

2014-11-01

IntelliCage is a fully automated, computer controlled system, which can be used for long-term monitoring of behavior of group-housed mice. Using standardized experimental protocols we can assess cognitive abilities and behavioral flexibility in appetitively and aversively motivated tasks, as well as measure social influences on learning of the subjects. We have also identified groups of neurons specifically activated by appetitively and aversively motivated learning within the amygdala, function of which we are going to investigate optogenetically in the future.

Automated edge finishing using an active XY table

DOEpatents

Loucks, Clifford S.; Starr, Gregory P.

1993-01-01

The disclosure is directed to an apparatus and method for automated edge finishing using hybrid position/force control of an XY table. The disclosure is particularly directed to learning the trajectory of the edge of a workpiece by "guarded moves". Machining is done by controllably moving the XY table, with the workpiece mounted thereon, along the learned trajectory with feedback from a force sensor. Other similar workpieces can be mounted, without a fixture on the XY table, located and the learned trajectory adjusted

Robotic space construction

NASA Technical Reports Server (NTRS)

Mixon, Randolph W.; Hankins, Walter W., III; Wise, Marion A.

1988-01-01

Research at Langley AFB concerning automated space assembly is reviewed, including a Space Shuttle experiment to test astronaut ability to assemble a repetitive truss structure, testing the use of teleoperated manipulators to construct the Assembly Concept for Construction of Erectable Space Structures I truss, and assessment of the basic characteristics of manipulator assembly operations. Other research topics include the simultaneous coordinated control of dual-arm manipulators and the automated assembly of candidate Space Station trusses. Consideration is given to the construction of an Automated Space Assembly Laboratory to study and develop the algorithms, procedures, special purpose hardware, and processes needed for automated truss assembly.

Learning About Cockpit Automation: From Piston Trainer to Jet Transport

NASA Technical Reports Server (NTRS)

Casner, Stephen M.

2003-01-01

Two experiments explored the idea of providing cockpit automation training to airline-bound student pilots using cockpit automation equipment commonly found in small training airplanes. In a first experiment, pilots mastered a set of tasks and maneuvers using a GPS navigation computer, autopilot, and flight director system installed in a small training airplane Students were then tested on their ability to complete a similar set of tasks using the cockpit automation system found in a popular jet transport aircraft. Pilot were able to successfully complete 77% of all tasks in the jet transport on their first attempt. An analysis of a control group suggests that the pilot's success was attributable to the application of automation principles they had learned in the small airplane. A second experiment looked at two different ways of delivering small-aeroplane cockpit automation training: a self-study method, and a dual instruction method. The results showed a slight advantage for the self-study method. Overall, the results of the two studies cast a strong vote for the incorporation of cockpit automation training in curricula designed for pilot who will later transition to the jet fleet.

Changing Channels: A Guide to Functional Literacy for the Automated Workplace.

ERIC Educational Resources Information Center

Conklin, Nancy Faires; Reder, Stephen

This paper was designed to assist educators and employers as they plan curricula in language and communication skills for students and employees entering, or experiencing a transition to, automated work settings. The strategies presented in this paper may be adaptable to secondary school business skills and basic English courses, pre-employment…

Clay Tablets to Micro Chips: The Evolution of Archival Practice into the Twenty-First Century.

ERIC Educational Resources Information Center

Hannestad, Stephen E.

1991-01-01

Describes archival concepts and theories and their evolution in recent times. Basic archival functions--appraisal, arrangement, description, reference, preservation, and publication--are introduced. Early applications of automation to archives (including SPINDEX, NARS-5, NARS-A-1, MARC AMC, presNET, CTRACK, PHOTO, and DIARY) and automation trends…

Report on the CDE Examination

ERIC Educational Resources Information Center

Haga, Enoch J.

1971-01-01

The Certificate in Data Education (Basic) examination is designed to certify that successful candidates are academically proficient in those principles and concepts of automation, computing, and data processing (including social and user implications) which are usually taught in basic introductory courses at the college or university level. (CK)

What Automated Vocal Analysis Reveals about the Vocal Production and Language Learning Environment of Young Children with Autism

ERIC Educational Resources Information Center

Warren, Steven F.; Gilkerson, Jill; Richards, Jeffrey A.; Oller, D. Kimbrough; Xu, Dongxin; Yapanel, Umit; Gray, Sharmistha

2010-01-01

The study compared the vocal production and language learning environments of 26 young children with autism spectrum disorder (ASD) to 78 typically developing children using measures derived from automated vocal analysis. A digital language processor and audio-processing algorithms measured the amount of adult words to children and the amount of…

Do You Automate? Saving Time and Dollars

ERIC Educational Resources Information Center

Carmichael, Christine H.

2010-01-01

An automated workforce management strategy can help schools save jobs, improve the job satisfaction of teachers and staff, and free up precious budget dollars for investments in critical learning resources. Automated workforce management systems can help schools control labor costs, minimize compliance risk, and improve employee satisfaction.…

“Booster” training: Evaluation of instructor-led bedside cardiopulmonary resuscitation skill training and automated corrective feedback to improve cardiopulmonary resuscitation compliance of Pediatric Basic Life Support providers during simulated cardiac arrest

PubMed Central

Sutton, Robert M.; Niles, Dana; Meaney, Peter A.; Aplenc, Richard; French, Benjamin; Abella, Benjamin S.; Lengetti, Evelyn L.; Berg, Robert A.; Helfaer, Mark A.; Nadkarni, Vinay

2013-01-01

Objective To investigate the effectiveness of brief bedside “booster” cardiopulmonary resuscitation (CPR) training to improve CPR guideline compliance of hospital-based pediatric providers. Design Prospective, randomized trial. Setting General pediatric wards at Children’s Hospital of Philadelphia. Subjects Sixty-nine Basic Life Support–certified hospital-based providers. Intervention CPR recording/feedback defibrillators were used to evaluate CPR quality during simulated pediatric arrest. After a 60-sec pretraining CPR evaluation, subjects were randomly assigned to one of three instructional/feedback methods to be used during CPR booster training sessions. All sessions (training/CPR manikin practice) were of equal duration (2 mins) and differed only in the method of corrective feedback given to participants during the session. The study arms were as follows: 1) instructor-only training; 2) automated defibrillator feedback only; and 3) instructor training combined with automated feedback. Measurements and Main Results Before instruction, 57% of the care providers performed compressions within guideline rate recommendations (rate >90 min−1 and <120 min−1); 71% met minimum depth targets (depth, >38 mm); and 36% met overall CPR compliance (rate and depth within targets). After instruction, guideline compliance improved (instructor-only training: rate 52% to 87% [p .01], and overall CPR compliance, 43% to 78% [p < .02]; automated feedback only: rate, 70% to 96% [p = .02], depth, 61% to 100% [p < .01], and overall CPR compliance, 35% to 96% [p < .01]; and instructor training combined with automated feedback: rate 48% to 100% [p < .01], depth, 78% to 100% [p < .02], and overall CPR compliance, 30% to 100% [p < .01]). Conclusions Before booster CPR instruction, most certified Pediatric Basic Life Support providers did not perform guideline-compliant CPR. After a brief bedside training, CPR quality improved irrespective of training content (instructor vs. automated feedback). Future studies should investigate bedside training to improve CPR quality during actual pediatric cardiac arrests. PMID:20625336

"Booster" training: evaluation of instructor-led bedside cardiopulmonary resuscitation skill training and automated corrective feedback to improve cardiopulmonary resuscitation compliance of Pediatric Basic Life Support providers during simulated cardiac arrest.

PubMed

Sutton, Robert M; Niles, Dana; Meaney, Peter A; Aplenc, Richard; French, Benjamin; Abella, Benjamin S; Lengetti, Evelyn L; Berg, Robert A; Helfaer, Mark A; Nadkarni, Vinay

2011-05-01

To investigate the effectiveness of brief bedside "booster" cardiopulmonary resuscitation (CPR) training to improve CPR guideline compliance of hospital-based pediatric providers. Prospective, randomized trial. General pediatric wards at Children's Hospital of Philadelphia. Sixty-nine Basic Life Support-certified hospital-based providers. CPR recording/feedback defibrillators were used to evaluate CPR quality during simulated pediatric arrest. After a 60-sec pretraining CPR evaluation, subjects were randomly assigned to one of three instructional/feedback methods to be used during CPR booster training sessions. All sessions (training/CPR manikin practice) were of equal duration (2 mins) and differed only in the method of corrective feedback given to participants during the session. The study arms were as follows: 1) instructor-only training; 2) automated defibrillator feedback only; and 3) instructor training combined with automated feedback. Before instruction, 57% of the care providers performed compressions within guideline rate recommendations (rate >90 min(-1) and <120 min(-1)); 71% met minimum depth targets (depth, >38 mm); and 36% met overall CPR compliance (rate and depth within targets). After instruction, guideline compliance improved (instructor-only training: rate 52% to 87% [p .01], and overall CPR compliance, 43% to 78% [p < .02]; automated feedback only: rate, 70% to 96% [p = .02], depth, 61% to 100% [p < .01], and overall CPR compliance, 35% to 96% [p < .01]; and instructor training combined with automated feedback: rate 48% to 100% [p < .01], depth, 78% to 100% [p < .02], and overall CPR compliance, 30% to 100% [p < .01]). Before booster CPR instruction, most certified Pediatric Basic Life Support providers did not perform guideline-compliant CPR. After a brief bedside training, CPR quality improved irrespective of training content (instructor vs. automated feedback). Future studies should investigate bedside training to improve CPR quality during actual pediatric cardiac arrests.

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

PubMed

Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

2016-10-01

We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

What automated vocal analysis reveals about the vocal production and language learning environment of young children with autism.

PubMed

Warren, Steven F; Gilkerson, Jill; Richards, Jeffrey A; Oller, D Kimbrough; Xu, Dongxin; Yapanel, Umit; Gray, Sharmistha

2010-05-01

The study compared the vocal production and language learning environments of 26 young children with autism spectrum disorder (ASD) to 78 typically developing children using measures derived from automated vocal analysis. A digital language processor and audio-processing algorithms measured the amount of adult words to children and the amount of vocalizations they produced during 12-h recording periods in their natural environments. The results indicated significant differences between typically developing children and children with ASD in the characteristics of conversations, the number of conversational turns, and in child vocalizations that correlated with parent measures of various child characteristics. Automated measurement of the language learning environment of young children with ASD reveals important differences from the environments experienced by typically developing children.

FBI fingerprint identification automation study: AIDS 3 evaluation report. Volume 7: Top down functional analysis

NASA Technical Reports Server (NTRS)

Mulhall, B. D. L.

1980-01-01

The functions are identified and described in chart form as a tree in which the basic functions, to 'Provide National Identification Service,' are shown at the top. The lower levels of the tree branch out to indicate functions and sub-functions. Symbols are used to indicate whether or not a function was automated in the AIDS 1 or 2 system or is planned to be automated in the AIDS 3 system. The tree chart is shown in detail.

The Learning Basis of Automated Factories: The Case of FIAT. Training Discussion Paper No. 86.

ERIC Educational Resources Information Center

Araujo e Oliveira, Joao Batista

As part of a study on the impact of automation on training, extensive interviews were conducted at two of Fiat's plants, Termoli and Casino, Italy. Termoli, a plant built in the mid-1980s with automation in mind, production of engines and gear boxes was very much integrated by automation devices. Casino produced some individual components but was…

Investigating the Impact of Automated Feedback on Students' Scientific Argumentation

ERIC Educational Resources Information Center

Zhu, Mengxiao; Lee, Hee-Sun; Wang, Ting; Liu, Ou Lydia; Belur, Vinetha; Pallant, Amy

2017-01-01

This study investigates the role of automated scoring and feedback in supporting students' construction of written scientific arguments while learning about factors that affect climate change in the classroom. The automated scoring and feedback technology was integrated into an online module. Students' written scientific argumentation occurred…

Helping Older Adults Adjust to Automation.

ERIC Educational Resources Information Center

Sink, Clay V.; D'Abrosca, Louis A.

1985-01-01

Discusses some of the fears and anxieties of automation held by older adults. Teaching techniques that aid the older adult learning process are suggested. The article also contains an interview with Anna M. Tucker, director of the Rhode Island Department of Elderly Affairs, concerning the elder adult's fear of automation. (CT)

Cooperative Education Is a Superior Strategy for Using Basic Learning Processes.

ERIC Educational Resources Information Center

Reed, V. Gerald

Cooperative education is a learning strategy that fits very well with basic laws of learning. In fact, several basic important learning processes are far better adapted to the cooperative education strategy than to methods that lean entirely on classroom instruction. For instance, cooperative education affords more opportunities for reinforcement,…

A system-level approach to automation research

NASA Technical Reports Server (NTRS)

Harrison, F. W.; Orlando, N. E.

1984-01-01

Automation is the application of self-regulating mechanical and electronic devices to processes that can be accomplished with the human organs of perception, decision, and actuation. The successful application of automation to a system process should reduce man/system interaction and the perceived complexity of the system, or should increase affordability, productivity, quality control, and safety. The expense, time constraints, and risk factors associated with extravehicular activities have led the Automation Technology Branch (ATB), as part of the NASA Automation Research and Technology Program, to investigate the use of robots and teleoperators as automation aids in the context of space operations. The ATB program addresses three major areas: (1) basic research in autonomous operations, (2) human factors research on man-machine interfaces with remote systems, and (3) the integration and analysis of automated systems. This paper reviews the current ATB research in the area of robotics and teleoperators.

An approach toward function allocation between humans and machines in space station activities

NASA Technical Reports Server (NTRS)

Vontiesenhausen, G.

1982-01-01

Basic guidelines and data to assist in the allocation of functions between humans and automated systems in a manned permanent space station are provided. Human capabilities and limitations are described. Criteria and guidelines for various levels of automation and human participation are described. A collection of human factors data is included.

GENERAL BUSINESS UNIT, THE INFLUENCE OF AUTOMATION ON BUSINESS AND PERSONAL LIFE.

ERIC Educational Resources Information Center

SPARKS, MAVIS C.

DEVELOPED BY A SPECIALIST IN BUSINESS AND OFFICE EDUCATION, THIS 6- TO 10-CLASS PERIOD UNIT IS FOR USE IN A HIGH SCHOOL BUSINESS EDUCATION COURSE. THE TEACHING OBJECTIVE IS TO DEVELOP AN UNDERSTANDING OF THE BASIC PRINCIPLES, THE SOCIAL AND ECONOMIC IMPLICATIONS, AND THE OCCUPATIONAL OPPORTUNITIES IMPORTANT IN AUTOMATION AND TECHNOLOGICAL CHANGE.…

Substructure analysis techniques and automation. [to eliminate logistical data handling and generation chores

NASA Technical Reports Server (NTRS)

Hennrich, C. W.; Konrath, E. J., Jr.

1973-01-01

A basic automated substructure analysis capability for NASTRAN is presented which eliminates most of the logistical data handling and generation chores that are currently associated with the method. Rigid formats are proposed which will accomplish this using three new modules, all of which can be added to level 16 with a relatively small effort.

An Evaluation of the Utility and Cost of Computerized Library Catalogs. Final Report.

ERIC Educational Resources Information Center

Dolby, J.L.; And Others

This study analyzes the basic cost factors in the automation of library catalogs, with a separate examination of the influence of typography on the cost of printed catalogs and the use of efficient automatic error detection procedures in processing bibliographic records. The utility of automated catalogs is also studied, based on data from a…

Welfare to Work. JOBS Automated Systems Do Not Focus on Program's Employment Objective. Report to Congressional Requesters.

ERIC Educational Resources Information Center

General Accounting Office, Washington, DC. Accounting and Information Management Div.

A study examined states' development of automated systems for the Job Opportunities and Basic Skills (JOBS) program administered by the states, with the Administration for Children and Families (ACF) responsible for program oversight and direction. Results indicated that ACF had not provided direction and focus in its systems development guidance…

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean'). This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

PubMed Central

Uhl, Andreas; Wimmer, Georg; Häfner, Michael

2016-01-01

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results. PMID:27847543

Automation study for space station subsystems and mission ground support

NASA Technical Reports Server (NTRS)

1985-01-01

An automation concept for the autonomous operation of space station subsystems, i.e., electric power, thermal control, and communications and tracking are discussed. To assure that functions essential for autonomous operations are not neglected, an operations function (systems monitoring and control) is included in the discussion. It is recommended that automated speech recognition and synthesis be considered a basic mode of man/machine interaction for space station command and control, and that the data management system (DMS) and other systems on the space station be designed to accommodate fully automated fault detection, isolation, and recovery within the system monitoring function of the DMS.

Automating spectral measurements

NASA Astrophysics Data System (ADS)

Goldstein, Fred T.

2008-09-01

This paper discusses the architecture of software utilized in spectroscopic measurements. As optical coatings become more sophisticated, there is mounting need to automate data acquisition (DAQ) from spectrophotometers. Such need is exacerbated when 100% inspection is required, ancillary devices are utilized, cost reduction is crucial, or security is vital. While instrument manufacturers normally provide point-and-click DAQ software, an application programming interface (API) may be missing. In such cases automation is impossible or expensive. An API is typically provided in libraries (*.dll, *.ocx) which may be embedded in user-developed applications. Users can thereby implement DAQ automation in several Windows languages. Another possibility, developed by FTG as an alternative to instrument manufacturers' software, is the ActiveX application (*.exe). ActiveX, a component of many Windows applications, provides means for programming and interoperability. This architecture permits a point-and-click program to act as automation client and server. Excel, for example, can control and be controlled by DAQ applications. Most importantly, ActiveX permits ancillary devices such as barcode readers and XY-stages to be easily and economically integrated into scanning procedures. Since an ActiveX application has its own user-interface, it can be independently tested. The ActiveX application then runs (visibly or invisibly) under DAQ software control. Automation capabilities are accessed via a built-in spectro-BASIC language with industry-standard (VBA-compatible) syntax. Supplementing ActiveX, spectro-BASIC also includes auxiliary serial port commands for interfacing programmable logic controllers (PLC). A typical application is automatic filter handling.

The Effectiveness of Learning Model of Basic Education with Character-Based at Universitas Muslim Indonesia

ERIC Educational Resources Information Center

Rosmiati, Rosmiati; Mahmud, Alimuddin; Talib, Syamsul B.

2016-01-01

The purpose of this study was to determine the effectiveness of the basic education learning model with character-based through learning in the Universitas Muslim Indonesia. In addition, the research specifically examines the character of discipline, curiosity and responsibility. The specific target is to produce a basic education learning model…

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

PubMed

Brown, Andrew D; Marotta, Thomas R

2018-05-01

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

Basic Functional Capabilities for a Military Message Processing Service

DTIC Science & Technology

1974-09-01

AD-AiI1 166 BASIC FUNCTIONA’. CAPABILITIES FOR A MILITARY MESSAGE PROCESSING SERVICE Ronald Tugender, et al University of Southern California...Itte) S. TYPE OF REPORT & PERIOD COVERED BASIC FUNCTIONAL CAPABILITIES FOR A Research Report MILITARY MESSAGE PROCESSING SERVICE 6. PERFORMING ONG...WOROD (Conionwo m trevre aide If tneeoooy arm idmentify by egekA INber) automated message processing , command and control, writer-to-reader service

Effects of X-ray radiation on complex visual discrimination learning and social recognition memory in rats.

PubMed

Davis, Catherine M; Roma, Peter G; Armour, Elwood; Gooden, Virginia L; Brady, Joseph V; Weed, Michael R; Hienz, Robert D

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation.

Effects of X-Ray Radiation on Complex Visual Discrimination Learning and Social Recognition Memory in Rats

PubMed Central

Davis, Catherine M.; Roma, Peter G.; Armour, Elwood; Gooden, Virginia L.; Brady, Joseph V.; Weed, Michael R.; Hienz, Robert D.

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation. PMID:25099152

Interactive and collaborative learning in the classroom at the medical school Automated response systems and team-based learning.

PubMed

Nasr, Rihab; Antoun, Jumana; Sabra, Ramzi; Zgheib, Nathalie K

2016-01-01

There has been a pedagogic shift in higher education from the traditional teacher centered to the student centered approach in teaching, necessitating a change in the role of the teacher from a supplier of information to passive receptive students into a more facilitative role. Active learning activities are based on various learning theories such as self-directed learning, cooperative learning and adult learning. There exist many instructional activities that enhance active and collaborative learning. The aim of this manuscript is to describe two methods of interactive and collaborative learning in the classroom, automated response systems (ARS) and team-based learning (TBL), and to list some of their applications and advantages. The success of these innovative teaching and learning methods at a large scale depends on few elements, probably the most important of which is the support of the higher administration and leadership in addition to the availability of “champions” who are committed to lead the change.

Adapting for Scalability: Automating the Video Assessment of Instructional Learning

ERIC Educational Resources Information Center

Roberts , Amy M.; LoCasale-Crouch, Jennifer; Hamre, Bridget K.; Buckrop, Jordan M.

2017-01-01

Although scalable programs, such as online courses, have the potential to reach broad audiences, they may pose challenges to evaluating learners' knowledge and skills. Automated scoring offers a possible solution. In the current paper, we describe the process of creating and testing an automated means of scoring a validated measure of teachers'…

Computer-based Astronomy Labs for Non-science Majors

NASA Astrophysics Data System (ADS)

Smith, A. B. E.; Murray, S. D.; Ward, R. A.

1998-12-01

We describe and demonstrate two laboratory exercises, Kepler's Third Law and Stellar Structure, which are being developed for use in an astronomy laboratory class aimed at non-science majors. The labs run with Microsoft's Excel 98 (Macintosh) or Excel 97 (Windows). They can be run in a classroom setting or in an independent learning environment. The intent of the labs is twofold; first and foremost, students learn the subject matter through a series of informational frames. Next, students enhance their understanding by applying their knowledge in lab procedures, while also gaining familiarity with the use and power of a widely-used software package and scientific tool. No mathematical knowledge beyond basic algebra is required to complete the labs or to understand the computations in the spreadsheets, although the students are exposed to the concepts of numerical integration. The labs are contained in Excel workbook files. In the files are multiple spreadsheets, which contain either a frame with information on how to run the lab, material on the subject, or one or more procedures. Excel's VBA macro language is used to automate the labs. The macros are accessed through button interfaces positioned on the spreadsheets. This is done intentionally so that students can focus on learning the subject matter and the basic spreadsheet features without having to learn advanced Excel features all at once. Students open the file and progress through the informational frames to the procedures. After each procedure, student comments and data are automatically recorded in a preformatted Lab Report spreadsheet. Once all procedures have been completed, the student is prompted for a filename in which to save their Lab Report. The lab reports can then be printed or emailed to the instructor. The files will have full worksheet and workbook protection, and will have a "redo" feature at the end of the lab for students who want to repeat a procedure.

TU-G-303-03: Machine Learning to Improve Human Learning From Longitudinal Image Sets

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

Veeraraghavan, H.

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with othermore » biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)« less

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

PubMed

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

2015-09-22

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

PubMed Central

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P.; Zelikowsky, Moriel; Navonne, Santiago G.; Perona, Pietro; Anderson, David J.

2015-01-01

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics. PMID:26354123

Applying Machine Learning to Star Cluster Classification

NASA Astrophysics Data System (ADS)

Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

2016-01-01

Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

AED (Automated External Defibrillator) Programs: Questions and Answers

MedlinePlus

... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ...

One Year of ICTP Diploma Courses On-Line Using the Automated EyA Recording System

ERIC Educational Resources Information Center

Canessa, Enrique; Fonda, Carlo; Zennaro, Marco

2009-01-01

The 12-month pre-Ph.D. ICTP Diploma Courses in the fields of Condensed Matter Physics, High Energy Physics, Mathematics, Earth System Physics and Basics Physics have been recorded using the automated, low cost recording system called EyA developed in-house. We discuss the technical details on how these recordings were implemented, together with…

Effects of introducing a voluntary virtual patient module to a basic life support with an automated external defibrillator course: a randomised trial

PubMed Central

2012-01-01

Background The concept of virtual patients (VPs) encompasses a great variety of predominantly case-based e-learning modules with different complexity and fidelity levels. Methods for effective placement of VPs in the process of medical education are sought. The aim of this study was to determine whether the introduction of a voluntary virtual patients module into a basic life support with an automated external defibrillator (BLS-AED) course improved the knowledge and skills of students taking the course. Methods Half of the students were randomly assigned to an experimental group and given voluntary access to a virtual patient module consisting of six cases presenting BLS-AED knowledge and skills. Pre- and post-course knowledge tests and skills assessments were performed, as well as a survey of students' satisfaction with the VP usage. In addition, time spent using the virtual patient system, percentage of screen cards viewed and scores in the formative questions in the VP system throughout the course were traced and recorded. Results The study was conducted over a six week period and involved 226 first year medical students. The voluntary module was used by 61 (54%) of the 114 entitled study participants. The group that used VPs demonstrated better results in knowledge acquisition and in some key BLS-AED action skills than the group without access, or those students from the experimental group deliberately not using virtual patients. Most of the students rated the combination of VPs and corresponding teaching events positively. Conclusions The overall positive reaction of students and encouraging results in knowledge and skills acquisition suggest that the usage of virtual patients in a BLS-AED course on a voluntary basis is feasible and should be further investigated. PMID:22709278

Efficacy and retention of Basic Life Support education including Automated External Defibrillator usage during a physical education period.

PubMed

Watanabe, Kae; Lopez-Colon, Dalia; Shuster, Jonathan J; Philip, Joseph

2017-03-01

The American Heart Association (AHA) advocates for CPR education as a requirement of secondary school curriculum. Unfortunately, many states have not adopted CPR education. Our aim was to investigate a low-cost, time effective method to educate students on Basic Life Support (BLS), including reeducation. This is a prospective, randomized study. Retention was assessed at 4 months post-initial education. Education was performed by AHA-certified providers during a 45-minute physical education class in a middle school in Florida. This age provides opportunities for reinforcement through high school, with ability for efficient learning. The study included 41 Eighth grade students. Students were randomized into two groups; one group received repeat education 2 months after the first education, the second group did not. All students received BLS education limited to chest compressions and usage of an Automated External Defibrillator. Students had skills and knowledge tests administered pre- and post-education after initial education, and repeated 2 and 4 months later to assess retention. There was a significant increase in CPR skills and knowledge when comparing pre- and post-education results for all time-points ( p  < 0.001). When assessing reeducation, a significant improvement was noted in total knowledge scores but not during the actual steps of CPR. Our study indicates significant increase in CPR knowledge and skills following a one-time 45-minute session. Reeducation may be useful, but the interval needs further investigation. If schools across the United States invested one 45-60-minute period every school year, this would ensure widespread CPR knowledge with minimal cost and loss of school time.

Effects of introducing a voluntary virtual patient module to a basic life support with an automated external defibrillator course: a randomised trial.

PubMed

Kononowicz, Andrzej A; Krawczyk, Paweł; Cebula, Grzegorz; Dembkowska, Marta; Drab, Edyta; Frączek, Bartosz; Stachoń, Aleksandra J; Andres, Janusz

2012-06-18

The concept of virtual patients (VPs) encompasses a great variety of predominantly case-based e-learning modules with different complexity and fidelity levels. Methods for effective placement of VPs in the process of medical education are sought. The aim of this study was to determine whether the introduction of a voluntary virtual patients module into a basic life support with an automated external defibrillator (BLS-AED) course improved the knowledge and skills of students taking the course. Half of the students were randomly assigned to an experimental group and given voluntary access to a virtual patient module consisting of six cases presenting BLS-AED knowledge and skills. Pre- and post-course knowledge tests and skills assessments were performed, as well as a survey of students' satisfaction with the VP usage. In addition, time spent using the virtual patient system, percentage of screen cards viewed and scores in the formative questions in the VP system throughout the course were traced and recorded. The study was conducted over a six week period and involved 226 first year medical students. The voluntary module was used by 61 (54%) of the 114 entitled study participants. The group that used VPs demonstrated better results in knowledge acquisition and in some key BLS-AED action skills than the group without access, or those students from the experimental group deliberately not using virtual patients. Most of the students rated the combination of VPs and corresponding teaching events positively. The overall positive reaction of students and encouraging results in knowledge and skills acquisition suggest that the usage of virtual patients in a BLS-AED course on a voluntary basis is feasible and should be further investigated.

An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study.

PubMed

De Tobel, J; Radesh, P; Vandermeulen, D; Thevissen, P W

2017-12-01

Automated methods to evaluate growth of hand and wrist bones on radiographs and magnetic resonance imaging have been developed. They can be applied to estimate age in children and subadults. Automated methods require the software to (1) recognise the region of interest in the image(s), (2) evaluate the degree of development and (3) correlate this to the age of the subject based on a reference population. For age estimation based on third molars an automated method for step (1) has been presented for 3D magnetic resonance imaging and is currently being optimised (Unterpirker et al. 2015). To develop an automated method for step (2) based on lower third molars on panoramic radiographs. A modified Demirjian staging technique including ten developmental stages was developed. Twenty panoramic radiographs per stage per gender were retrospectively selected for FDI element 38. Two observers decided in consensus about the stages. When necessary, a third observer acted as a referee to establish the reference stage for the considered third molar. This set of radiographs was used as training data for machine learning algorithms for automated staging. First, image contrast settings were optimised to evaluate the third molar of interest and a rectangular bounding box was placed around it in a standardised way using Adobe Photoshop CC 2017 software. This bounding box indicated the region of interest for the next step. Second, several machine learning algorithms available in MATLAB R2017a software were applied for automated stage recognition. Third, the classification performance was evaluated in a 5-fold cross-validation scenario, using different validation metrics (accuracy, Rank-N recognition rate, mean absolute difference, linear kappa coefficient). Transfer Learning as a type of Deep Learning Convolutional Neural Network approach outperformed all other tested approaches. Mean accuracy equalled 0.51, mean absolute difference was 0.6 stages and mean linearly weighted kappa was 0.82. The overall performance of the presented automated pilot technique to stage lower third molar development on panoramic radiographs was similar to staging by human observers. It will be further optimised in future research, since it represents a necessary step to achieve a fully automated dental age estimation method, which to date is not available.

Analysis of astronomical concepts presented by students of the Federal Institute of São Paulo - Cubatão Campus

NASA Astrophysics Data System (ADS)

Moraes, A. C.; Voelzke, M. R.; de Macedo, J. A.

2016-04-01

This article reports the results of a survey of proficiency in astronomy, conducted among students of the Course of Technology in Industrial Automation at the São Paulo Federal Institute of Education, Science and Technology at the Cubatão campus. In order to assess the level of the students' prior knowledge, they were asked to fill out a questionnaire with twenty-five basic questions. This first step revealed the scant proficiency the students obtained both in elementary and high school. In order to correct this serious shortcoming, a course in astronomy was applied- additionally to the official content program - containing attendance lessons and videos. In a second step, the students' answers were analyzed again, and it was verified that there was a significant improvement in their learning.

In vivo robotics: the automation of neuroscience and other intact-system biological fields

PubMed Central

Kodandaramaiah, Suhasa B.; Boyden, Edward S.; Forest, Craig R.

2013-01-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to impact neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience, and present a concrete example with our recent automation of in vivo whole cell patch clamp electrophysiology of neurons in the living mouse brain. PMID:23841584

Service Learning in a Basic Writing Class: A Best Case Scenario

ERIC Educational Resources Information Center

Pine, Nancy

2008-01-01

This article explores the particular challenges and possibilities of service learning pedagogy for basic writers. Because a number of scholars of service learning and basic writing (Adler-Kassner, Arca, and Kraemer) are concerned primarily with developing underprepared students' academic literacies, I investigated how the students in a service…

Using Automated Scores of Student Essays to Support Teacher Guidance in Classroom Inquiry

ERIC Educational Resources Information Center

Gerard, Libby F.; Linn, Marcia C.

2016-01-01

Computer scoring of student written essays about an inquiry topic can be used to diagnose student progress both to alert teachers to struggling students and to generate automated guidance. We identify promising ways for teachers to add value to automated guidance to improve student learning. Three teachers from two schools and their 386 students…

PROGRAMED LEARNING--A COMPARATIVE EVALUATION OF STUDENT PERFORMANCE VARIABLES UNDER COMBINATIONS OF CONVENTIONAL AND AUTOMATED INSTRUCTION.

ERIC Educational Resources Information Center

FLINT, LANNING L.; HATCH, RICHARD S.

STUDENT PERFORMANCE VARIABLES UNDER AUTOMATED, CONVENTIONAL, AND A COMBINATION OF AUTOMATED AND CONVENTIONAL CONDITIONS OF INSTRUCTION WERE INVESTIGATED. RECOMMENDATIONS FOR THE INTEGRATION OF PROGRAMED MATERIAL INTO THE CLASSROOM WERE SOUGHT. THREE GROUPS OF JUNIOR COLLEGE STUDENTS WERE USED IN THE EXPERIMENT. THE GROUPS WERE CHOSEN AT RANDOM.…

Is Respiration-Induced Variation in the Photoplethysmogram Associated with Major Hypovolemia in Patients with Acute Traumatic Injuries?

DTIC Science & Technology

2010-11-01

hypovolemia in the prehospital environment. Photoplethysmogram waveforms and basic vital signs were recorded in trauma patients during prehospital...transport. Retrospectively, we used automated algorithms to select patient records with all five basic vital signs and 45 s or longer continuous, clean PPG... basic vital signs by applying multivariate regression. In 344 patients, RIWV max-min yielded areas under the ROC curves (AUCs) not significantly better

Combining Offline and Online Computation for Solving Partially Observable Markov Decision Process

DTIC Science & Technology

2015-03-06

David Hsu and Wee Sun Lee, Monte Carlo Bayesian Reinforcement Learning, International Conference on Machine Learning (ICML), 2012. • Haoyu Bai, David...and Automation (ICRA), 2015. • Zhan Wei Lim, David Hsu, and Wee Sun Lee, Adaptive Informative Path Planning in Metric Spaces. Submitted to Int. J... Automation (ICRA), 2015. 2. Bai, H., Hsu, D., Kochenderfer, M. J., and Lee, W. S., Unmanned aircraft collision avoidance using continuous state POMDPs

Man-Robot Symbiosis: A Framework For Cooperative Intelligence And Control

NASA Astrophysics Data System (ADS)

Parker, Lynne E.; Pin, Francois G.

1988-10-01

The man-robot symbiosis concept has the fundamental objective of bridging the gap between fully human-controlled and fully autonomous systems to achieve true man-robot cooperative control and intelligence. Such a system would allow improved speed, accuracy, and efficiency of task execution, while retaining the man in the loop for innovative reasoning and decision-making. The symbiont would have capabilities for supervised and unsupervised learning, allowing an increase of expertise in a wide task domain. This paper describes a robotic system architecture facilitating the symbiotic integration of teleoperative and automated modes of task execution. The architecture reflects a unique blend of many disciplines of artificial intelligence into a working system, including job or mission planning, dynamic task allocation, man-robot communication, automated monitoring, and machine learning. These disciplines are embodied in five major components of the symbiotic framework: the Job Planner, the Dynamic Task Allocator, the Presenter/Interpreter, the Automated Monitor, and the Learning System.

Robot graphic simulation testbed

NASA Technical Reports Server (NTRS)

Cook, George E.; Sztipanovits, Janos; Biegl, Csaba; Karsai, Gabor; Springfield, James F.

1991-01-01

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts.

A review of human-automation interaction and lessons learned

DOT National Transportation Integrated Search

2006-10-01

This report reviews 37 accidents in aviation, other vehicles, process control and other complex systems where human-automation interaction is involved. Implications about causality with respect to design, procedures, management and training are drawn...

An Automated Procedure for Evaluating Song Imitation

PubMed Central

Mandelblat-Cerf, Yael; Fee, Michale S.

2014-01-01

Songbirds have emerged as an excellent model system to understand the neural basis of vocal and motor learning. Like humans, songbirds learn to imitate the vocalizations of their parents or other conspecific “tutors.” Young songbirds learn by comparing their own vocalizations to the memory of their tutor song, slowly improving until over the course of several weeks they can achieve an excellent imitation of the tutor. Because of the slow progression of vocal learning, and the large amounts of singing generated, automated algorithms for quantifying vocal imitation have become increasingly important for studying the mechanisms underlying this process. However, methodologies for quantifying song imitation are complicated by the highly variable songs of either juvenile birds or those that learn poorly because of experimental manipulations. Here we present a method for the evaluation of song imitation that incorporates two innovations: First, an automated procedure for selecting pupil song segments, and, second, a new algorithm, implemented in Matlab, for computing both song acoustic and sequence similarity. We tested our procedure using zebra finch song and determined a set of acoustic features for which the algorithm optimally differentiates between similar and non-similar songs. PMID:24809510

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

Community College Basic Skills Math Instructors' Experiences with Universal Design for Learning

ERIC Educational Resources Information Center

Greene, Sunny

2016-01-01

Multiple approaches have been used in U.S. community colleges to address the learning needs of postsecondary students who are underprepared in basic skills math. The purpose of this exploratory interview study was to gain a deeper understanding of community college basic skills math learning through instructors' lived experiences using the…

Learning Genetics with Paper Pets

ERIC Educational Resources Information Center

Finnerty, Valerie Raunig

2006-01-01

By the end of the eighth grade, students are expected to have a basic understanding of the mechanism of basic genetic inheritance. However, these concepts can be difficult to teach. In this article, the author introduces a new learning tool that will help facilitate student learning and enthusiasm to the basic concepts of genetic inheritance. This…

Analyzing the Relationship between Learning Styles and Basic Concept Knowledge Level of Kindergarten Children

ERIC Educational Resources Information Center

Balat, Gülden Uyanik

2014-01-01

Most basic concepts are acquired during preschool period. There are studies indicating that the basic concept knowledge of children is related to language development, cognitive development, academic achievement and intelligence. The relationship between learning behaviors (sometime called learning or cognitive styles) and a child academic success…

The Complexity Analysis Tool

DTIC Science & Technology

1988-10-01

overview of the complexity analysis tool ( CAT ), an automated tool which will analyze mission critical computer resources (MCCR) software. CAT is based...84 MAR UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE 19. ABSTRACT: (cont) CAT automates the metric for BASIC (HP-71), ATLAS (EQUATE), Ada (subset...UNIX 5.2). CAT analyzes source code and computes complexity on a module basis. CAT also generates graphic representations of the logic flow paths and

OR automation systems.

PubMed

2002-12-01

An operating room (OR) automation system is a combination of hardware and software designed to address efficiency issues in the OR by controling multiple devices via a common interface. Systems range from the relatively basic--allowing control of a few devices within a single OR--to advanced designs that are capable of not only controlling a wide range of devices within the OR but also exchanging information with remote locations.

Airborne electronics for automated flight systems

NASA Technical Reports Server (NTRS)

Graves, G. B., Jr.

1975-01-01

The increasing importance of airborne electronics for use in automated flight systems is briefly reviewed with attention to both basic aircraft control functions and flight management systems for operational use. The requirements for high levels of systems reliability are recognized. Design techniques are discussed and the areas of control systems, computing and communications are considered in terms of key technical problems and trends for their solution.

Effects of obligatory training and prior training experience on attitudes towards performing basic life support: a questionnaire survey.

PubMed

Matsubara, Hiroki; Enami, Miki; Hirose, Keiko; Kamikura, Takahisa; Nishi, Taiki; Takei, Yutaka; Inaba, Hideo

2015-04-01

To determine the effect of Japanese obligatory basic life support training for new driver's license applicants on their willingness to carry out basic life support. We distributed a questionnaire to 9,807 participants of basic life support courses in authorized driving schools from May 2007 to April 2008 after the release of the 2006 Japanese guidelines. The questionnaire explored the participants' willingness to perform basic life support in four hypothetical scenarios: cardiopulmonary resuscitation on one's own initiative; compression-only cardiopulmonary resuscitation following telephone cardiopulmonary resuscitation; early emergency call; and use of an automated external defibrillator. The questionnaire was given at the beginning of the basic life support course in the first 6-month term and at the end in the second 6-month term. The 9,011 fully completed answer sheets were analyzed. The training significantly increased the proportion of respondents willing to use an automated external defibrillator and to perform cardiopulmonary resuscitation on their own initiative in those with and without prior basic life support training experience. It significantly increased the proportion of respondents willing to carry out favorable actions in all four scenarios. In multiple logistic regression analysis, basic life support training and prior training experiences within 3 years were associated with the attitude. The analysis of reasons for unwillingness suggested that the training reduced the lack of confidence in their skill but did not attenuate the lack of confidence in detection of arrest or clinical judgment to initiate a basic life support action. Obligatory basic life support training should be carried out periodically and modified to ensure that participants gain confidence in judging and detecting cardiac arrest.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

FTMP (Fault Tolerant Multiprocessor) programmer's manual

NASA Technical Reports Server (NTRS)

Feather, F. E.; Liceaga, C. A.; Padilla, P. A.

1986-01-01

The Fault Tolerant Multiprocessor (FTMP) computer system was constructed using the Rockwell/Collins CAPS-6 processor. It is installed in the Avionics Integration Research Laboratory (AIRLAB) of NASA Langley Research Center. It is hosted by AIRLAB's System 10, a VAX 11/750, for the loading of programs and experimentation. The FTMP support software includes a cross compiler for a high level language called Automated Engineering Design (AED) System, an assembler for the CAPS-6 processor assembly language, and a linker. Access to this support software is through an automated remote access facility on the VAX which relieves the user of the burden of learning how to use the IBM 4381. This manual is a compilation of information about the FTMP support environment. It explains the FTMP software and support environment along many of the finer points of running programs on FTMP. This will be helpful to the researcher trying to run an experiment on FTMP and even to the person probing FTMP with fault injections. Much of the information in this manual can be found in other sources; we are only attempting to bring together the basic points in a single source. If the reader should need points clarified, there is a list of support documentation in the back of this manual.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

PubMed

Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

2016-01-01

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

NMRNet: A deep learning approach to automated peak picking of protein NMR spectra.

PubMed

Klukowski, Piotr; Augoff, Michal; Zieba, Maciej; Drwal, Maciej; Gonczarek, Adam; Walczak, Michal J

2018-03-14

Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the structure calculation, and analysis of dynamics and interactions of macromolecules. Recent advancement in handling big data, together with an outburst of machine learning techniques, offer an opportunity to tackle the peak picking problem substantially faster than manual picking and on par with human accuracy. In particular, deep learning has proven to systematically achieve human-level performance in various recognition tasks, and thus emerges as an ideal tool to address automated identification of NMR signals. We have applied a convolutional neural network for visual analysis of multidimensional NMR spectra. A comprehensive test on 31 manually-annotated spectra has demonstrated top-tier average precision (AP) of 0.9596, 0.9058 and 0.8271 for backbone, side-chain and NOESY spectra, respectively. Furthermore, a combination of extracted peak lists with automated assignment routine, FLYA, outperformed other methods, including the manual one, and led to correct resonance assignment at the levels of 90.40%, 89.90% and 90.20% for three benchmark proteins. The proposed model is a part of a Dumpling software (platform for protein NMR data analysis), and is available at https://dumpling.bio/. michaljerzywalczak@gmail.compiotr.klukowski@pwr.edu.pl. Supplementary data are available at Bioinformatics online.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

PubMed Central

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

Framework for Evaluating Loop Invariant Detection Games in Relation to Automated Dynamic Invariant Detectors

DTIC Science & Technology

2015-09-01

Detectability ...............................................................................................37 Figure 20. Excel VBA Codes for Checker...National Vulnerability Database OS Operating System SQL Structured Query Language VC Verification Condition VBA Visual Basic for Applications...checks each of these assertions for detectability by Daikon. The checker is an Excel Visual Basic for Applications ( VBA ) script that checks the

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

In vivo robotics: the automation of neuroscience and other intact-system biological fields.

PubMed

Kodandaramaiah, Suhasa B; Boyden, Edward S; Forest, Craig R

2013-12-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to influence neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience and present a concrete example with our recent automation of in vivo whole-cell patch clamp electrophysiology of neurons in the living mouse brain. © 2013 New York Academy of Sciences.

Automated Decomposition of Model-based Learning Problems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Millar, Bill

1996-01-01

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Learning Computers, Speaking English: Cooperative Activities for Learning English and Basic Word Processing.

ERIC Educational Resources Information Center

Quann, Steve; Satin, Diana

This textbook leads high-beginning and intermediate English-as-a-Second-Language (ESL) students through cooperative computer-based activities that combine language learning with training in basic computer skills and word processing. Each unit concentrates on a basic concept of word processing while also focusing on a grammar topic. Skills are…

Students' perspectives on basic nursing care education.

PubMed

Huisman-de Waal, Getty; Feo, Rebecca; Vermeulen, Hester; Heinen, Maud

2018-02-05

The aim of the study is to explore the perspectives of nursing students on their education concerning basic nursing care, learned either during theoretical education or clinical placement, with a specific focus on nutrition and communication. Basic care activities lie at the core of nursing, but are ill-informed by evidence and often poorly delivered. Nursing students' education on basic care might be lacking, and the question remains how they learn to deliver basic care in clinical practice. Descriptive study, using an online questionnaire. Nursing students at the vocational and bachelor level of six nursing schools in the Netherlands were invited to complete an online questionnaire regarding their perception of basic nursing care education in general (both theoretical education and clinical placement), and specifically in relation to nutrition and communication. Nursing students (n=226 bachelor students, n=30 vocational students) completed the questionnaire. Most students reported that they learned more about basic nursing care during clinical placement than during theoretical education. Vocational students also reported learning more about basic nursing care in both theoretical education and clinical practice than bachelor students. In terms of nutrition, low numbers of students from both education levels reported learning about nutrition protocols and guidelines during theoretical education. In terms of communication, vocational students indicated that they learned more about different aspects of communication during clinical practice than theoretical education, and were also more likely to learn about communication (in both theoretical education and clinical practice) than were bachelor students. Basic nursing care seems to be largely invisible in nursing education, especially at the bachelor level and during theoretical education. Improved basic nursing care will enhance nurse sensitive outcomes and patient satisfaction and will contribute to lower healthcare costs. This study shows that there is scope within current nurse education in the Netherlands to focus more systematically and explicitly on basic nursing care. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Focus on Basics: Connecting Research & Practice. Volume 7, Issue D

ERIC Educational Resources Information Center

Garner, Barbara, Ed.

2005-01-01

"Focus on Basics" is the quarterly publication of the National Center for the Study of Adult Learning and Literacy. It presents best practices, current research on adult learning and literacy, and how research is used by adult basic education teachers, counselors, program administrators, and policymakers. "Focus on Basics" is…

Focus on Basics: Connecting Research & Practice. Volume 8, Issue B

ERIC Educational Resources Information Center

Garner, Barbara, Ed.

2006-01-01

"Focus on Basics" is the quarterly publication of the National Center for the Study of Adult Learning and Literacy. It presents best practices, current research on adult learning and literacy, and how research is used by adult basic education teachers, counselors, program administrators, and policymakers. "Focus on Basics" is…

Focus on Basics: Connecting Research & Practice. Volume 6, Issue A

ERIC Educational Resources Information Center

Garner, Barbara, Ed.

2002-01-01

"Focus on Basics" is the quarterly publication of the National Center for the Study of Adult Learning and Literacy. It presents best practices, current research on adult learning and literacy, and how research is used by adult basic education teachers, counselors, program administrators, and policymakers. "Focus on Basics" is…

Automated measurement of zebrafish larval movement

PubMed Central

Cario, Clinton L; Farrell, Thomas C; Milanese, Chiara; Burton, Edward A

2011-01-01

Abstract The zebrafish is a powerful vertebrate model that is readily amenable to genetic, pharmacological and environmental manipulations to elucidate the molecular and cellular basis of movement and behaviour. We report software enabling automated analysis of zebrafish movement from video recordings captured with cameras ranging from a basic camcorder to more specialized equipment. The software, which is provided as open-source MATLAB functions, can be freely modified and distributed, and is compatible with multiwell plates under a wide range of experimental conditions. Automated measurement of zebrafish movement using this technique will be useful for multiple applications in neuroscience, pharmacology and neuropsychiatry. PMID:21646414

Automated measurement of zebrafish larval movement.

PubMed

Cario, Clinton L; Farrell, Thomas C; Milanese, Chiara; Burton, Edward A

2011-08-01

The zebrafish is a powerful vertebrate model that is readily amenable to genetic, pharmacological and environmental manipulations to elucidate the molecular and cellular basis of movement and behaviour. We report software enabling automated analysis of zebrafish movement from video recordings captured with cameras ranging from a basic camcorder to more specialized equipment. The software, which is provided as open-source MATLAB functions, can be freely modified and distributed, and is compatible with multiwell plates under a wide range of experimental conditions. Automated measurement of zebrafish movement using this technique will be useful for multiple applications in neuroscience, pharmacology and neuropsychiatry.

Electronic prototyping

NASA Technical Reports Server (NTRS)

Hopcroft, J.

1987-01-01

The potential benefits of automation in space are significant. The science base needed to support this automation not only will help control costs and reduce lead-time in the earth-based design and construction of space stations, but also will advance the nation's capability for computer design, simulation, testing, and debugging of sophisticated objects electronically. Progress in automation will require the ability to electronically represent, reason about, and manipulate objects. Discussed here is the development of representations, languages, editors, and model-driven simulation systems to support electronic prototyping. In particular, it identifies areas where basic research is needed before further progress can be made.

Industrial Automation Mechanic Model Curriculum Project. Final Report.

ERIC Educational Resources Information Center

Toledo Public Schools, OH.

This document describes a demonstration program that developed secondary level competency-based instructional materials for industrial automation mechanics. Program activities included task list compilation, instructional materials research, learning activity packet (LAP) development, construction of lab elements, system implementation,…

Automation of experiments at Dubna Gas-Filled Recoil Separator

NASA Astrophysics Data System (ADS)

Tsyganov, Yu. S.

2016-01-01

Approaches to solving the problems of automation of basic processes in long-term experiments in heavy ion beams of the Dubna Gas-Filled Recoil Separator (DGFRS) facility are considered. Approaches in the field of spectrometry, both of rare α decays of superheavy nuclei and those for constructing monitoring systems to provide accident-free experiment running with highly radioactive targets and recording basic parameters of experiment, are described. The specific features of Double Side Silicon Strip Detectors (DSSSDs) are considered, special attention is paid to the role of boundary effects of neighboring p-n transitions in the "active correlations" method. An example of an off-beam experiment attempting to observe Zeno effect is briefly considered. Basic examples for nuclear reactions of complete fusion at 48Ca ion beams of U-400 cyclotron (LNR, JINR) are given. A scenario of development of the "active correlations" method for the case of very high intensity beams of heavy ions at promising accelerators of LNR, JINR, is presented.

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.

Blueprint for Incorporating Service Learning: A Basic, Developmental, K-12 Service Learning Typology

ERIC Educational Resources Information Center

Terry, Alice W.; Bohnenberger, Jann E.

2004-01-01

Citing the need for a basic, K-12 developmental framework for service learning, this article describes such a model. This model, an inclusive typology of service learning, distinguishes three levels of service learning: Community Service, Community Exploration, and Community Action. The authors correlate this typology to Piaget's cognitive…

FORESEE™ User-Centric Energy Automation

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

FORESEE™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, FORESEE intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, FORESEE provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. FORESEE improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highlymore » reliable demand response services are likely to be incentivized by utility companies, making FORESEE economically viable for most homes.« less

Automated Blazar Light Curves Using Machine Learning

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

Johnson, Spencer James

2017-07-27

This presentation describes a problem and methodology pertaining to automated blazar light curves. Namely, optical variability patterns for blazars require the construction of light curves and in order to generate the light curves, data must be filtered before processing to ensure quality.

Focus on Basics: Connecting Research & Practice. Volume 6, Issue B

ERIC Educational Resources Information Center

Garner, Barbara, Ed.

2003-01-01

"Focus on Basics" is the quarterly publication of the National Center for the Study of Adult Learning and Literacy. It presents best practices, current research on adult learning and literacy, and how research is used by adult basic education teachers, counselors, program administrators, and policymakers. "Focus on Basics" is dedicated to…

Focus on Basics: Connecting Research and Practice. Volume 6, Issue D

ERIC Educational Resources Information Center

National Center for the Study of Adult Learning and Literacy (NCSALL), Harvard University, 2004

2004-01-01

"Focus on Basics" is the quarterly publication of the National Center for the Study of Adult Learning and Literacy. It presents best practices, current research on adult learning and literacy, and how research is used by adult basic education teachers, counselors, program administrators, and policymakers. "Focus on Basics" is dedicated to…

Teaching Basic Life Support to Students of Public and Private High Schools

PubMed Central

Fernandes, José Maria Gonçalves; Leite, Amanda Lira dos Santos; Auto, Bruna de Sá Duarte; de Lima, José Elson Gama; Rivera, Ivan Romero; Mendonça, Maria Alayde

2014-01-01

Background Despite being recommended as a compulsory part of the school curriculum, the teaching of basic life support (BLS) has yet to be implemented in high schools in most countries. Objectives To compare prior knowledge and degree of immediate and delayed learning between students of one public and one private high school after these students received BLS training. Methods Thirty students from each school initially answered a questionnaire on cardiopulmonary resuscitation (CPR) and use of the automated external defibrillator (AED). They then received theoretical-practical BLS training, after which they were given two theory assessments: one immediately after the course and the other six months later. Results The overall success rates in the prior, immediate, and delayed assessments were significantly different between groups, with better performance shown overall by private school students than by public school students: 42% ± 14% vs. 30.2% ± 12.2%, p = 0.001; 86% ± 7.8% vs. 62.4% ± 19.6%, p < 0.001; and 65% ± 12.4% vs. 45.6% ± 16%, p < 0.001, respectively. The total odds ratio of the questions showed that the private school students performed the best on all three assessments, respectively: 1.66 (CI95% 1.26-2.18), p < 0.001; 3.56 (CI95% 2.57-4.93), p < 0.001; and 2.21 (CI95% 1.69-2.89), p < 0.001. Conclusions Before training, most students had insufficient knowledge about CPR and AED; after BLS training a significant immediate and delayed improvement in learning was observed in students, especially in private school students. PMID:25004421

Creation of an Integrated Environment to Supply e-Learning Platforms with Office Automation Features

ERIC Educational Resources Information Center

Palumbo, Emilio; Verga, Francesca

2015-01-01

Over the last years great efforts have been made within the University environment to implement e-learning technologies in the standard educational practice. These learning technologies distribute online educational multimedia contents through technological platforms. Even though specific e-learning tools for technical disciplines were already…

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

Predicting Robust Vocabulary Growth from Measures of Incremental Learning

ERIC Educational Resources Information Center

Frishkoff, Gwen A.; Perfetti, Charles A.; Collins-Thompson, Kevyn

2011-01-01

We report a study of incremental learning of new word meanings over multiple episodes. A new method called MESA (Markov Estimation of Semantic Association) tracked this learning through the automated assessment of learner-generated definitions. The multiple word learning episodes varied in the strength of contextual constraint provided by…

OpenControl: a free opensource software for video tracking and automated control of behavioral mazes.

PubMed

Aguiar, Paulo; Mendonça, Luís; Galhardo, Vasco

2007-10-15

Operant animal behavioral tests require the interaction of the subject with sensors and actuators distributed in the experimental environment of the arena. In order to provide user independent reliable results and versatile control of these devices it is vital to use an automated control system. Commercial systems for control of animal mazes are usually based in software implementations that restrict their application to the proprietary hardware of the vendor. In this paper we present OpenControl: an opensource Visual Basic software that permits a Windows-based computer to function as a system to run fully automated behavioral experiments. OpenControl integrates video-tracking of the animal, definition of zones from the video signal for real-time assignment of animal position in the maze, control of the maze actuators from either hardware sensors or from the online video tracking, and recording of experimental data. Bidirectional communication with the maze hardware is achieved through the parallel-port interface, without the need for expensive AD-DA cards, while video tracking is attained using an inexpensive Firewire digital camera. OpenControl Visual Basic code is structurally general and versatile allowing it to be easily modified or extended to fulfill specific experimental protocols and custom hardware configurations. The Visual Basic environment was chosen in order to allow experimenters to easily adapt the code and expand it at their own needs.

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

PubMed

Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

2018-01-01

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Simplified programming and control of automated radiosynthesizers through unit operations.

PubMed

Claggett, Shane B; Quinn, Kevin M; Lazari, Mark; Moore, Melissa D; van Dam, R Michael

2013-07-15

Many automated radiosynthesizers for producing positron emission tomography (PET) probes provide a means for the operator to create custom synthesis programs. The programming interfaces are typically designed with the engineer rather than the radiochemist in mind, requiring lengthy programs to be created from sequences of low-level, non-intuitive hardware operations. In some cases, the user is even responsible for adding steps to update the graphical representation of the system. In light of these unnecessarily complex approaches, we have created software to perform radiochemistry on the ELIXYS radiosynthesizer with the goal of being intuitive and easy to use. Radiochemists were consulted, and a wide range of radiosyntheses were analyzed to determine a comprehensive set of basic chemistry unit operations. Based around these operations, we created a software control system with a client-server architecture. In an attempt to maximize flexibility, the client software was designed to run on a variety of portable multi-touch devices. The software was used to create programs for the synthesis of several 18F-labeled probes on the ELIXYS radiosynthesizer, with [18F]FDG detailed here. To gauge the user-friendliness of the software, program lengths were compared to those from other systems. A small sample group with no prior radiosynthesizer experience was tasked with creating and running a simple protocol. The software was successfully used to synthesize several 18F-labeled PET probes, including [18F]FDG, with synthesis times and yields comparable to literature reports. The resulting programs were significantly shorter and easier to debug than programs from other systems. The sample group of naive users created and ran a simple protocol within a couple of hours, revealing a very short learning curve. The client-server architecture provided reliability, enabling continuity of the synthesis run even if the computer running the client software failed. The architecture enabled a single user to control the hardware while others observed the run in progress or created programs for other probes. We developed a novel unit operation-based software interface to control automated radiosynthesizers that reduced the program length and complexity and also exhibited a short learning curve. The client-server architecture provided robustness and flexibility.

Simplified programming and control of automated radiosynthesizers through unit operations

PubMed Central

2013-01-01

Background Many automated radiosynthesizers for producing positron emission tomography (PET) probes provide a means for the operator to create custom synthesis programs. The programming interfaces are typically designed with the engineer rather than the radiochemist in mind, requiring lengthy programs to be created from sequences of low-level, non-intuitive hardware operations. In some cases, the user is even responsible for adding steps to update the graphical representation of the system. In light of these unnecessarily complex approaches, we have created software to perform radiochemistry on the ELIXYS radiosynthesizer with the goal of being intuitive and easy to use. Methods Radiochemists were consulted, and a wide range of radiosyntheses were analyzed to determine a comprehensive set of basic chemistry unit operations. Based around these operations, we created a software control system with a client–server architecture. In an attempt to maximize flexibility, the client software was designed to run on a variety of portable multi-touch devices. The software was used to create programs for the synthesis of several 18F-labeled probes on the ELIXYS radiosynthesizer, with [18F]FDG detailed here. To gauge the user-friendliness of the software, program lengths were compared to those from other systems. A small sample group with no prior radiosynthesizer experience was tasked with creating and running a simple protocol. Results The software was successfully used to synthesize several 18F-labeled PET probes, including [18F]FDG, with synthesis times and yields comparable to literature reports. The resulting programs were significantly shorter and easier to debug than programs from other systems. The sample group of naive users created and ran a simple protocol within a couple of hours, revealing a very short learning curve. The client–server architecture provided reliability, enabling continuity of the synthesis run even if the computer running the client software failed. The architecture enabled a single user to control the hardware while others observed the run in progress or created programs for other probes. Conclusions We developed a novel unit operation-based software interface to control automated radiosynthesizers that reduced the program length and complexity and also exhibited a short learning curve. The client–server architecture provided robustness and flexibility. PMID:23855995

Reports and Testimony: March 1992

DTIC Science & Technology

1992-03-01

pesticide data needs, it is essential that an interaigency strategy guide the progrmn. Otherwise, USDA may be jeopardizing a significant federal...assess the effectiveness of Job Opportunities and Basic Skills Training (JoBs) programs run by Indian Tribes and Alaska Native groups or determine...contractor reporting requirements. irr’s efforts to develop automation systems continue to disappoint. Major systems are plagued by basic problems, including

TRAINING IN INDUSTRY--THE MANAGEMENT OF LEARNING.

ERIC Educational Resources Information Center

BASS, BERNARD M.; VAUGHAN, JAMES A.

THE PRINCIPLES OF LEARNING BEHAVIOR DERIVED THROUGH LABORATORY STUDY CAN BE EXTENDED TO EXPLAIN MUCH OF THE COMPLEX LEARNING REQUIRED IN INDUSTRIAL TRAINING PROGRAMS. A REVIEW OF THE BASIC PRINCIPLES OF HUMAN LEARNING INTRODUCES FOUR BASIC CONCEPTS--DRIVE, STIMULUS, RESPONSE, AND REINFORCER--AND DISCUSSES CLASSICAL AND INSTRUMENTAL CONDITIONING…

Screening for Learning Disabilities in Adult Basic Education Students

ERIC Educational Resources Information Center

Reynolds, Sharon L.; Johnson, Jerry D.; Salzman, James A.

2012-01-01

The extant literature offers little to describe the processes for screening students in adult basic education (ABE) programs for potential learning disabilities, referring adult students for diagnostic assessment, or barriers to obtaining diagnostic assessment for a learning disability. Without current documentation of a learning disability, ABE…

Optical Disk Technology and Information.

ERIC Educational Resources Information Center

Goldstein, Charles M.

1982-01-01

Provides basic information on videodisks and potential applications, including inexpensive online storage, random access graphics to complement online information systems, hybrid network architectures, office automation systems, and archival storage. (JN)

LOW-COST PERSONNEL DOSIMETER.

DTIC Science & Technology

specification was achieved by simplifying and improving the basic Bendix dosimeter design, using plastics for component parts, minimizing direct labor, and making the instrument suitable for automated processing and assembly. (Author)

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

GeoDeepDive: Towards a Machine Reading-Ready Digital Library and Information Integration Resource

NASA Astrophysics Data System (ADS)

Husson, J. M.; Peters, S. E.; Livny, M.; Ross, I.

2015-12-01

Recent developments in machine reading and learning approaches to text and data mining hold considerable promise for accelerating the pace and quality of literature-based data synthesis, but these advances have outpaced even basic levels of access to the published literature. For many geoscience domains, particularly those based on physical samples and field-based descriptions, this limitation is significant. Here we describe a general infrastructure to support published literature-based machine reading and learning approaches to information integration and knowledge base creation. This infrastructure supports rate-controlled automated fetching of original documents, along with full bibliographic citation metadata, from remote servers, the secure storage of original documents, and the utilization of considerable high-throughput computing resources for the pre-processing of these documents by optical character recognition, natural language parsing, and other document annotation and parsing software tools. New tools and versions of existing tools can be automatically deployed against original documents when they are made available. The products of these tools (text/XML files) are managed by MongoDB and are available for use in data extraction applications. Basic search and discovery functionality is provided by ElasticSearch, which is used to identify documents of potential relevance to a given data extraction task. Relevant files derived from the original documents are then combined into basic starting points for application building; these starting points are kept up-to-date as new relevant documents are incorporated into the digital library. Currently, our digital library stores contains more than 360K documents supplied by Elsevier and the USGS and we are actively seeking additional content providers. By focusing on building a dependable infrastructure to support the retrieval, storage, and pre-processing of published content, we are establishing a foundation for complex, and continually improving, information integration and data extraction applications. We have developed one such application, which we present as an example, and invite new collaborations to develop other such applications.

Basic practical skills teaching and learning in undergraduate medical education - a review on methodological evidence.

PubMed

Vogel, Daniela; Harendza, Sigrid

2016-01-01

Practical skills are an essential part of physicians' daily routine. Nevertheless, medical graduates' performance of basic skills is often below the expected level. This review aims to identify and summarize teaching approaches of basic practical skills in undergraduate medical education which provide evidence with respect to effective students' learning of these skills. Basic practical skills were defined as basic physical examination skills, routine skills which get better with practice, and skills which are also performed by nurses. We searched PubMed with different terms describing these basic practical skills. In total, 3467 identified publications were screened and 205 articles were eventually reviewed for eligibility. 43 studies that included at least one basic practical skill, a comparison of two groups of undergraduate medical students and effects on students' performance were analyzed. Seven basic practical skills and 15 different teaching methods could be identified. The most consistent results with respect to effective teaching and acquisition of basic practical skills were found for structured skills training, feedback, and self-directed learning. Simulation was effective with specific teaching methods and in several studies no differences in teaching effects were detected between expert or peer instructors. Multimedia instruction, when used in the right setting, also showed beneficial effects for basic practical skills learning. A combination of voluntary or obligatory self-study with multimedia applications like video clips in combination with a structured program including the possibility for individual exercise with personal feedback by peers or teachers might provide a good learning opportunity for basic practical skills.

Basic practical skills teaching and learning in undergraduate medical education – a review on methodological evidence

PubMed Central

Vogel, Daniela; Harendza, Sigrid

2016-01-01

Objective: Practical skills are an essential part of physicians’ daily routine. Nevertheless, medical graduates’ performance of basic skills is often below the expected level. This review aims to identify and summarize teaching approaches of basic practical skills in undergraduate medical education which provide evidence with respect to effective students’ learning of these skills. Methods: Basic practical skills were defined as basic physical examination skills, routine skills which get better with practice, and skills which are also performed by nurses. We searched PubMed with different terms describing these basic practical skills. In total, 3467 identified publications were screened and 205 articles were eventually reviewed for eligibility. Results: 43 studies that included at least one basic practical skill, a comparison of two groups of undergraduate medical students and effects on students’ performance were analyzed. Seven basic practical skills and 15 different teaching methods could be identified. The most consistent results with respect to effective teaching and acquisition of basic practical skills were found for structured skills training, feedback, and self-directed learning. Simulation was effective with specific teaching methods and in several studies no differences in teaching effects were detected between expert or peer instructors. Multimedia instruction, when used in the right setting, also showed beneficial effects for basic practical skills learning. Conclusion: A combination of voluntary or obligatory self-study with multimedia applications like video clips in combination with a structured program including the possibility for individual exercise with personal feedback by peers or teachers might provide a good learning opportunity for basic practical skills. PMID:27579364

Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance.

PubMed

Mori, Kensaku; Ota, Shunsuke; Deguchi, Daisuke; Kitasaka, Takayuki; Suenaga, Yasuhito; Iwano, Shingo; Hasegawa, Yosihnori; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

2009-01-01

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.

National Forum on the Future of Automated Materials Processing in US Industry: The Role of Sensors. Report of a workshop (1st) held at Santa Barbara, California on December 16-17, 1985

NASA Astrophysics Data System (ADS)

Yolken, H. T.; Mehrabian, R.

1985-12-01

These are the proceedings of the workshop A National Forum on the Future of Automated Materials Processing in U.S. Industry - The Role of Sensors. This is the first of two workshops to be sponsored by the Industrial Research Institute and the White House Office of Science and Technology Policy, Committee on Materials Working Group on Automation of Materials Processing. The second workshop will address the other two key components required for automated materials processing, process models and artificial intelligence coupled with computer integration of the system. The objective of these workshops is to identify and assess important issues afecting the competitive position of U.S. industry related to its ability to automate production processes for basic and advanced materials and to develop approaches for improved capability through cooperative R&D and associated efforts.

Project MAPP. The Vocabulary Booklet # 1. Implementing the Instructional Program: A Competency-Based Adult Education Learning System.

ERIC Educational Resources Information Center

Maryland State Dept. of Education, Baltimore.

This booklet is intended to help adults master the basic and life skill vocabulary needed to meet the simple communication demands of daily life. It is designed to assist adult basic education (ABE) teachers in the implementation of a competency-based learning system that emphasizes the integration of basic and life skills learning. The booklet…

Schools Inc.: An Administrator's Guide to the Business of Education.

ERIC Educational Resources Information Center

McCarthy, Bob; And Others

1989-01-01

This theme issue describes ways in which educational administrators are successfully automating many of their administrative tasks. Articles focus on student management; office automation, including word processing, databases, and spreadsheets; human resources; support services, including supplies, textbooks, and learning resources; financial…

a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

2016-06-01

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

What Makes Cooperative Learning Work.

ERIC Educational Resources Information Center

Johnson, David W.; Johnson, Roger T.

This paper gives an introduction to cooperative learning (CL), providing a definition of what it is and is not (pseudo-learning groups, traditional classroom learning groups), discussing basic principles, describing two basic types of CL (formal and informal), and listing the benefits of CL suggested by previous research. In order to understand…

Automation of Tooling Backup and Cutter Selection for Engineering Production

NASA Astrophysics Data System (ADS)

Terekhov, M. V.; Averchenkov, V. I.; Reutov, A. A.; Handozhko, A. V.

2017-01-01

This paper reports the analysis of a tool support procedure for mechanical engineering and basic trends in the automation of this field are revealed. The system of technical-organizational measures directed at the formation, management and development of the tool stock and a high degree of technological readiness of manufacturing are described. The problems of an automated optimum cutter selection are considered. A mathematical support for a choice of cutters with through-away tips is described. A simulator for the description of combined cutters is presented. Basic criteria defining cutter choice are established. The problem of a multi-criterion fuzzy estimation of alternatives at different significance of choice criteria is solved. The criterion significance ranking at the parameter choice of cutter plates and tool supports is carried out. A set of estimations of cutter plate forms and other cutter parameters taking into account a relative significance of criteria is defined. The application of a decisive rule in the choice of an alternative required is described, which consists in the definition of the intersection of sets of alternative estimations.

Design of multiple representations e-learning resources based on a contextual approach for the basic physics course

NASA Astrophysics Data System (ADS)

Bakri, F.; Muliyati, D.

2018-05-01

This research aims to design e-learning resources with multiple representations based on a contextual approach for the Basic Physics Course. The research uses the research and development methods accordance Dick & Carey strategy. The development carried out in the digital laboratory of Physics Education Department, Mathematics and Science Faculty, Universitas Negeri Jakarta. The result of the process of product development with Dick & Carey strategy, have produced e-learning design of the Basic Physics Course is presented in multiple representations in contextual learning syntax. The appropriate of representation used in the design of learning basic physics include: concept map, video, figures, data tables of experiment results, charts of data tables, the verbal explanations, mathematical equations, problem and solutions example, and exercise. Multiple representations are presented in the form of contextual learning by stages: relating, experiencing, applying, transferring, and cooperating.

Automation in the Teaching of Descriptive Geometry and CAD. High-Level CAD Templates Using Script Languages

NASA Astrophysics Data System (ADS)

Moreno, R.; Bazán, A. M.

2017-10-01

The main purpose of this work is to study improvements to the learning method of technical drawing and descriptive geometry through exercises with traditional techniques that are usually solved manually by applying automated processes assisted by high-level CAD templates (HLCts). Given that an exercise with traditional procedures can be solved, detailed step by step in technical drawing and descriptive geometry manuals, CAD applications allow us to do the same and generalize it later, incorporating references. Traditional teachings have become obsolete and current curricula have been relegated. However, they can be applied in certain automation processes. The use of geometric references (using variables in script languages) and their incorporation into HLCts allows the automation of drawing processes. Instead of repeatedly creating similar exercises or modifying data in the same exercises, users should be able to use HLCts to generate future modifications of these exercises. This paper introduces the automation process when generating exercises based on CAD script files, aided by parametric geometry calculation tools. The proposed method allows us to design new exercises without user intervention. The integration of CAD, mathematics, and descriptive geometry facilitates their joint learning. Automation in the generation of exercises not only saves time but also increases the quality of the statements and reduces the possibility of human error.

A METHOD FOR AUTOMATED ANALYSIS OF 10 ML WATER SAMPLES CONTAINING ACIDIC, BASIC, AND NEUTRAL SEMIVOLATILE COMPOUNDS LISTED IN USEPA METHOD 8270 BY SOLID PHASE EXTRACTION COUPLED IN-LINE TO LARGE VOLUME INJECTION GAS CHROMATOGRAPHY/MASS SPECTROMETRY

EPA Science Inventory

Data is presented showing the progress made towards the development of a new automated system combining solid phase extraction (SPE) with gas chromatography/mass spectrometry for the single run analysis of water samples containing a broad range of acid, base and neutral compounds...

Neurorehabilitation applied to specific learning disability: Study of a single case.

PubMed

Bilancia, Giovanni; Marazzi, Moreno; Filippi, Davide

2015-01-01

Specific Learning Disorders (SLD) therefore represent chronic, not temporary disorders with varying degrees of expression throughout life. The beginning of imaging, anatomy and genetics studies have made it possible to investigate the brain organization of individuals suffering from SLD (Deheane, 2009). The purpose of this paper is to describe a treatment method for reading and writing disorders through an intervention based on the integration of a sublexical method and a neuropsychological approach, with assistive technologies in the study of a single case. The protocol is based on the modularization theory (Karmiloff-Smith, 1990). The data presented in this paper with a A-B-A basic experimental drawing. This study confirms the degree of effectiveness of the treatments based on the automated identification of syllables and words together with the integrated enhancement of neuropsychological aspects such as visual attention and phonological loop (Benso, 2008), although in the follow-up condition only some abilities maintain the progress achieved. As previously mentioned, the SLD represents a chronic disorder, consequently the treatment does not solve the root cause of the problem, but can grant a use of the process decidedly more instrumental to everyday life.

Key steps for integrating a basic science throughout a medical school curriculum using an e-learning approach.

PubMed

Dubois, Eline Agnès; Franson, Kari Lanette

2009-09-01

Basic sciences can be integrated into the medical school curriculum via e-learning. The process of integrating a basic science in this manner resembles a curricular change. The change usually begins with an idea for using e-learning to teach a basic science and establishing the need for the innovation. In the planning phase, learning outcomes are formulated and a prototype of the program is developed based on the desired requirements. A realistic concept is formed after considering the limitations of the current institute. Next, a project team is assembled to develop the program and plan its integration. Incorporation of the e-learning program is facilitated by a well-developed and communicated integration plan. Various course coordinators are contacted to determine content of the e-learning program as well as establish assessment. Linking the e-learning program to existing course activities and thereby applying the basic science into the clinical context enhances the degree of integration. The success of the integration is demonstrated by a positive assessment of the program including favourable cost-benefit analysis and improved student performance. Lastly, when the program becomes institutionalised, continuously updating content and technology (when appropriate), and evaluating the integration contribute to the prolonged survival of the e-learning program.

Fully automated, deep learning segmentation of oxygen-induced retinopathy images

PubMed Central

Xiao, Sa; Bucher, Felicitas; Wu, Yue; Rokem, Ariel; Lee, Cecilia S.; Marra, Kyle V.; Fallon, Regis; Diaz-Aguilar, Sophia; Aguilar, Edith; Friedlander, Martin; Lee, Aaron Y.

2017-01-01

Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary parameters that are analyzed in this mouse model include the percentage of retina with vaso-obliteration (VO) and NV areas. However, quantification of these two key variables comes with a great challenge due to the requirement of human experts to read the images. Human readers are costly, time-consuming, and subject to bias. Using recent advances in machine learning and computer vision, we trained deep learning neural networks using over a thousand segmentations to fully automate segmentation in OIR images. While determining the percentage area of VO, our algorithm achieved a similar range of correlation coefficients to that of expert inter-human correlation coefficients. In addition, our algorithm achieved a higher range of correlation coefficients compared with inter-expert correlation coefficients for quantification of the percentage area of neovascular tufts. In summary, we have created an open-source, fully automated pipeline for the quantification of key values of OIR images using deep learning neural networks. PMID:29263301

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

PubMed Central

Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

2015-01-01

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age. PMID:26402795

Automated Categorization Scheme for Digital Libraries in Distance Learning: A Pattern Recognition Approach

ERIC Educational Resources Information Center

Gunal, Serkan

2008-01-01

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is…

Generalizing Automated Detection of the Robustness of Student Learning in an Intelligent Tutor for Genetics

ERIC Educational Resources Information Center

Baker, Ryan S. J. d.; Corbett, Albert T.; Gowda, Sujith M.

2013-01-01

Recently, there has been growing emphasis on supporting robust learning within intelligent tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer term retention. It has been shown that different pedagogical strategies promote robust learning to different degrees. However, the student…

PleurAlert: an augmented chest drainage system with electronic sensing, automated alerts and internet connectivity.

PubMed

Leeson, Cory E; Weaver, Robert A; Bissell, Taylor; Hoyer, Rachel; McClain, Corinne; Nelson, Douglas A; Samosky, Joseph T

2012-01-01

We have enhanced a common medical device, the chest tube drainage container, with electronic sensing of fluid volume, automated detection of critical alarm conditions and the ability to automatically send alert text messages to a nurse's cell phone. The PleurAlert system provides a simple touch-screen interface and can graphically display chest tube output over time. Our design augments a device whose basic function dates back 50 years by adding technology to automate and optimize a monitoring process that can be time consuming and inconvenient for nurses. The system may also enhance detection of emergency conditions and speed response time.

The application of artificial intelligence technology to aeronautical system design

NASA Technical Reports Server (NTRS)

Bouchard, E. E.; Kidwell, G. H.; Rogan, J. E.

1988-01-01

This paper describes the automation of one class of aeronautical design activity using artificial intelligence and advanced software techniques. Its purpose is to suggest concepts, terminology, and approaches that may be useful in enhancing design automation. By understanding the basic concepts and tasks in design, and the technologies that are available, it will be possible to produce, in the future, systems whose capabilities far exceed those of today's methods. Some of the tasks that will be discussed have already been automated and are in production use, resulting in significant productivity benefits. The concepts and techniques discussed are applicable to all design activity, though aeronautical applications are specifically presented.

Automated Student Model Improvement

ERIC Educational Resources Information Center

Koedinger, Kenneth R.; McLaughlin, Elizabeth A.; Stamper, John C.

2012-01-01

Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational…

Nomenclature and basic concepts in automation in the clinical laboratory setting: a practical glossary.

PubMed

Evangelopoulos, Angelos A; Dalamaga, Maria; Panoutsopoulos, Konstantinos; Dima, Kleanthi

2013-01-01

In the early 80s, the word automation was used in the clinical laboratory setting referring only to analyzers. But in late 80s and afterwards, automation found its way into all aspects of the diagnostic process, embracing not only the analytical but also the pre- and post-analytical phase. While laboratories in the eastern world, mainly Japan, paved the way for laboratory automation, US and European laboratories soon realized the benefits and were quick to follow. Clearly, automation and robotics will be a key survival tool in a very competitive and cost-concious healthcare market. What sets automation technology apart from so many other efficiency solutions are the dramatic savings that it brings to the clinical laboratory. Further standardization will assure the success of this revolutionary new technology. One of the main difficulties laboratory managers and personnel must deal with when studying solutions to reengineer a laboratory is familiarizing themselves with the multidisciplinary and technical terminology of this new and exciting field. The present review/glossary aims at giving an overview of the most frequently used terms within the scope of laboratory automation and to put laboratory automation on a sounder linguistic basis.

Promoting Teaching and Learning in Ghanaian Basic Schools through ICT

ERIC Educational Resources Information Center

Natia, James Adam; Al-hassan, Seidu

2015-01-01

The Basic School Computerization policy was created in 2011 to introduce computers and e-learning into the entire educational system to promote training and life-long learning. Using data obtained by Connect for Change Education Ghana Alliance, this paper investigates the extent to which school administration, and teaching and learning are…

Utilizing a Micro in the Accounting Classroom.

ERIC Educational Resources Information Center

Wolverton, L. Craig

1982-01-01

The author discusses how to select microcomputer software for an accounting program and what types of instructional modes to use. The following modes are examined: problem solving, decision making, automated accounting functions, learning new accounting concepts, reinforcing concepts already learned, developing independent learning skills, and…

TARDIS: An Automation Framework for JPL Mission Design and Navigation

NASA Technical Reports Server (NTRS)

Roundhill, Ian M.; Kelly, Richard M.

2014-01-01

Mission Design and Navigation at the Jet Propulsion Laboratory has implemented an automation framework tool to assist in orbit determination and maneuver design analysis. This paper describes the lessons learned from previous automation tools and how they have been implemented in this tool. In addition this tool has revealed challenges in software implementation, testing, and user education. This paper describes some of these challenges and invites others to share their experiences.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

PubMed

Atkinson, Jonathan A; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E; Griffiths, Marcus; Wells, Darren M

2017-10-01

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. © The Authors 2017. Published by Oxford University Press.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

PubMed Central

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus

2017-01-01

Abstract Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. PMID:29020748

Automated inspection of bread and loaves

NASA Astrophysics Data System (ADS)

Batchelor, Bruce G.

1993-08-01

The prospects for building practical automated inspection machines, capable of detecting the following faults in ordinary, everyday loaves are reviewed: (1) foreign bodies, using X-rays, (2) texture changes, using glancing illumination, mathematical morphology and Neural Net learning techniques, and (3) shape deformations, using structured lighting and simple geometry.

The ORT Open Tech Robotics and Automation Literacy Course.

ERIC Educational Resources Information Center

Sharon, Dan; And Others

1987-01-01

Presents an overview of a course on robotics and automation developed by the Organization for Rehabilitation through Training (ORT) to be offered through an open learning environment in the United Kingdom. Highlights include hardware and software requirements, an educational model, design principles, and future developments. (LRW)

Automated Database Schema Design Using Mined Data Dependencies.

ERIC Educational Resources Information Center

Wong, S. K. M.; Butz, C. J.; Xiang, Y.

1998-01-01

Describes a bottom-up procedure for discovering multivalued dependencies in observed data without knowing a priori the relationships among the attributes. The proposed algorithm is an application of technique designed for learning conditional independencies in probabilistic reasoning; a prototype system for automated database schema design has…

Fluency with Basic Addition

ERIC Educational Resources Information Center

Garza-Kling, Gina

2011-01-01

Traditionally, learning basic facts has focused on rote memorization of isolated facts, typically through the use of flash cards, repeated drilling, and timed testing. However, as many experienced teachers have seen, "drill alone does not develop mastery of single-digit combinations." In contrast, a fluency approach to learning basic addition…

Train the Trainer. Facilitator Guide Sample. Basic Blueprint Reading (Chapter One).

ERIC Educational Resources Information Center

Saint Louis Community Coll., MO.

This publication consists of three sections: facilitator's guide--train the trainer, facilitator's guide sample--Basic Blueprint Reading (Chapter 1), and participant's guide sample--basic blueprint reading (chapter 1). Section I addresses why the trainer should learn new classroom techniques; lecturing versus facilitating; learning styles…

29 CFR 825.500 - Recordkeeping requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... maintained and preserved on microfilm or other basic source document of an automated data processing memory... this section with respect to any primary employees, and must keep the records required by paragraph (c...

29 CFR 825.500 - Recordkeeping requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... maintained and preserved on microfilm or other basic source document of an automated data processing memory... this section with respect to any primary employees, and must keep the records required by paragraph (c...

Preparing medical students for future learning using basic science instruction.

PubMed

Mylopoulos, Maria; Woods, Nicole

2014-07-01

The construct of 'preparation for future learning' (PFL) is understood as the ability to learn new information from available resources, relate new learning to past experiences and demonstrate innovation and flexibility in problem solving. Preparation for future learning has been proposed as a key competence of adaptive expertise. There is a need for educators to ensure that opportunities are provided for students to develop PFL ability and that assessments accurately measure the development of this form of competence. The objective of this research was to compare the relative impacts of basic science instruction and clinically focused instruction on performance on a PFL assessment (PFLA). This study employed a 'double transfer' design. Fifty-one pre-clerkship students were randomly assigned to either basic science instruction or clinically focused instruction to learn four categories of disease. After completing an initial assessment on the learned material, all participants received clinically focused instruction for four novel diseases and completed a PFLA. The data from the initial assessment and the PFLA were submitted to independent-sample t-tests. Mean ± standard deviation [SD] scores on the diagnostic cases in the initial assessment were similar for participants in the basic science (0.65 ± 0.11) and clinical learning (0.62 ± 0.11) conditions. The difference was not significant (t[42] = 0.90, p = 0.37, d = 0.27). Analysis of the diagnostic cases on the PFLA revealed significantly higher mean ± SD scores for participants in the basic science learning condition (0.72 ± 0.14) compared with those in the clinical learning condition (0.63 ± 0.15) (t[42] = 2.02, p = 0.05, d = 0.62). Our results show that the inclusion of basic science instruction enhanced the learning of novel related content. We discuss this finding within the broader context of research on basic science instruction, development of adaptive expertise and assessment in medical education. © 2014 John Wiley & Sons Ltd.

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving.

PubMed

Hergeth, Sebastian; Lorenz, Lutz; Vilimek, Roman; Krems, Josef F

2016-05-01

The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency. The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior. Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving. © 2016, Human Factors and Ergonomics Society.

Visual and Auditory Learning Processes in Normal Children and Children with Specific Learning Disabilities. Final Report.

ERIC Educational Resources Information Center

McGrady, Harold J.; Olson, Don A.

To describe and compare the psychosensory functioning of normal children and children with specific learning disabilities, 62 learning disabled and 68 normal children were studied. Each child was given a battery of thirteen subtests on an automated psychosensory system representing various combinations of auditory and visual intra- and…

Experience in Education Environment Virtualization within the Automated Information System "Platonus" (Kazakhstan)

ERIC Educational Resources Information Center

Abeldina, Zhaidary; Moldumarova, Zhibek; Abeldina, Rauza; Makysh, Gulmira; Moldumarova, Zhuldyz Ilibaevna

2016-01-01

This work reports on the use of virtual tools as means of learning process activation. A good result can be achieved by combining the classical learning with modern computer technology. By creating a virtual learning environment and using multimedia learning tools one can obtain a significant result while facilitating the development of students'…

Basic Skills in Asian Studies: India.

ERIC Educational Resources Information Center

Hantula, James

Designed for an Asian studies program at the secondary level and using learning activities centering on India, the guide develops four basic skills: reading, applying critical thinking, interpreting the geography, and understanding history. Five learning activities are provided for each basic skill and each unit is introduced with a description…

Basic Education: Reflections on Participatory Curriculum Development and Planning.

ERIC Educational Resources Information Center

Sachsenmeier, Peter, Ed.; And Others

Basic education as the first stage of lifelong education is emerging as a significant alternative to traditional education, especially for rural populations in Third World countries. Basic education is a set of interrelated ideas: community orientation of education, integration of formal, nonformal, and informal learning into lifelong learning,…

Basic Visual Processes and Learning Disability.

ERIC Educational Resources Information Center

Leisman, Gerald

Representatives of a variety of disciplines concerned with either clinical or research problems in vision and learning disabilities present reviews and reports of relevant research and clinical approaches. Contributions are organized into four broad sections: basic processes, specific disorders, diagnosis of visually based problems in learning,…

Research relative to automated multisensor image registration

NASA Technical Reports Server (NTRS)

Kanal, L. N.

1983-01-01

The basic aproaches to image registration are surveyed. Three image models are presented as models of the subpixel problem. A variety of approaches to the analysis of subpixel analysis are presented using these models.

29 CFR 825.500 - Recordkeeping requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... maintained and preserved on microfilm or other basic source document of an automated data processing memory... paragraph (c) of this section with respect to any primary employees, and must keep the records required by...

29 CFR 825.500 - Recordkeeping requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... maintained and preserved on microfilm or other basic source document of an automated data processing memory... paragraph (c) of this section with respect to any primary employees, and must keep the records required by...

Automated Assessment in a Programming Tools Course

ERIC Educational Resources Information Center

Fernandez Aleman, J. L.

2011-01-01

Automated assessment systems can be useful for both students and instructors. Ranking and immediate feedback can have a strongly positive effect on student learning. This paper presents an experience using automatic assessment in a programming tools course. The proposal aims at extending the traditional use of an online judging system with a…

A Handbook for Programmers of Automated Instruction.

ERIC Educational Resources Information Center

Melching, William H.; And Others

U.S. Army service schools have been directed (USONARC Directive 350-34, Education and Training, New Instructional Techniques) to initiate development of automated courses of instruction in their curriculum. Programers and training supervisors are provided with procedural guidelines for the derivation of learning objectives and for the use of…

Improving the basic skills of teaching mathematics through learning with search-solve-create-share strategy

NASA Astrophysics Data System (ADS)

Rahayu, D. V.; Kusumah, Y. S.; Darhim

2018-05-01

This study examined to see the improvement of prospective teachers’ basic skills of teaching mathematics through search-solve-create-share learning strategy based on overall and Mathematical Prior Knowledge (MPK) and interaction of both. Quasi experiments with the design of this experimental-non-equivalent control group design involved 67 students at the mathematics program of STKIP Garut. The instrument used in this study included pre-test and post-test. The result of this study showed that: (1) The improvement and achievement of the basic skills of teaching mathematics of the prospective teachers who get the learning of search-solve-create-share strategy is better than the improvement and achievement of the prospective teachers who get the conventional learning as a whole and based on MPK; (2) There is no interaction between the learning used and MPK on improving and achieving basic skills of teaching mathematics.

Ontogeny of Classical and Operant Learning Behaviors in Zebrafish

ERIC Educational Resources Information Center

Valente, Andre; Huang, Kuo-Hua; Portugues, Ruben; Engert, Florian

2012-01-01

The performance of developing zebrafish in both classical and operant conditioning assays was tested with a particular focus on the emergence of these learning behaviors during development. Strategically positioned visual cues paired with electroshocks were used in two fully automated assays to investigate both learning paradigms. These allow the…

Anesthesiology, automation, and artificial intelligence.

PubMed

Alexander, John C; Joshi, Girish P

2018-01-01

There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized.

Effect of descriptive information and experience on automation reliance.

PubMed

Yuviler-Gavish, Nirit; Gopher, Daniel

2011-06-01

The present research addresses the issue of reliance on decision support systems for the long-term (DSSLT), which help users develop decision-making strategies and long-term planning. It is argued that providing information about a system's future performance in an experiential manner, as compared with a descriptive manner, encourages users to increase their reliance level. Establishing appropriate reliance on DSSLT is contingent on the system developer's ability to provide users with information about the system's future performance. A sequence of three studies contrasts the effect on automation reliance of providing descriptive information versus experience for DSSLT with two different positive expected values of recommendations. Study I demonstrated that when automation reliance was determined solely on the basis of description, it was relatively low, but it increased significantly when a decision was made after experience with 50 training simulations. Participants were able to learn to increase their automation reliance levels when they encountered the same type of recommendation again. Study 2 showed that the absence of preliminary descriptive information did not affect the automation reliance levels obtained after experience. Study 3 demonstrated that participants were able to generalize their learning about increasing reliance levels to new recommendations. Using experience rather than description to give users information about future performance in DSSLT can help increase automation reliance levels. Implications for designing DSSLT and decision support systems in general are discussed.

34 CFR 668.142 - Special definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

.... General learned abilities: Cognitive operations, such as deductive reasoning, reading comprehension, or translation from graphic to numerical representation, that may be learned in both school and non-school...,” “curricula,” or “basic verbal and quantitative skills,” the basic knowledge or skills generally learned in...

34 CFR 668.142 - Special definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

.... General learned abilities: Cognitive operations, such as deductive reasoning, reading comprehension, or translation from graphic to numerical representation, that may be learned in both school and non-school...,” “curricula,” or “basic verbal and quantitative skills,” the basic knowledge or skills generally learned in...

34 CFR 668.142 - Special definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... General learned abilities: Cognitive operations, such as deductive reasoning, reading comprehension, or translation from graphic to numerical representation, that may be learned in both school and non-school...,” “curricula,” or “basic verbal and quantitative skills,” the basic knowledge or skills generally learned in...

34 CFR 668.142 - Special definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

.... General learned abilities: Cognitive operations, such as deductive reasoning, reading comprehension, or translation from graphic to numerical representation, that may be learned in both school and non-school...,” “curricula,” or “basic verbal and quantitative skills,” the basic knowledge or skills generally learned in...

An Introduction to Research and the Computer: A Self-Instructional Package.

ERIC Educational Resources Information Center

Vasu, Ellen Storey; Palmer, Richard I.

This self-instructional package includes learning objectives, definitions, exercises, and feedback for learning some basic concepts and skills involved in using computers for analyzing data and understanding basic research terminology. Learning activities are divided into four sections: research and research hypotheses; variables, cases, and…

Basic Math Facts: Guidelines for Teaching and Learning.

ERIC Educational Resources Information Center

Thornton, Carol A.; Toohey, Margaret A.

1985-01-01

Research and curriculum development projects have investigated ways to make teaching and learning basic facts easier. Reseach results and implications from four major projects are presented. Ten specific guidelines are then given and illustrated by examples from addition. Modifying instructional sequence and matching learning tasks with learning…

Automated Subsystem Control for Life Support System (ASCLSS)

NASA Technical Reports Server (NTRS)

Block, Roger F.

1987-01-01

The Automated Subsystem Control for Life Support Systems (ASCLSS) program has successfully developed and demonstrated a generic approach to the automation and control of space station subsystems. The automation system features a hierarchical and distributed real-time control architecture which places maximum controls authority at the lowest or process control level which enhances system autonomy. The ASCLSS demonstration system pioneered many automation and control concepts currently being considered in the space station data management system (DMS). Heavy emphasis is placed on controls hardware and software commonality implemented in accepted standards. The approach demonstrates successfully the application of real-time process and accountability with the subsystem or process developer. The ASCLSS system completely automates a space station subsystem (air revitalization group of the ASCLSS) which moves the crew/operator into a role of supervisory control authority. The ASCLSS program developed over 50 lessons learned which will aide future space station developers in the area of automation and controls..

Pharmacy students' retention of knowledge and skills following training in automated external defibrillator use.

PubMed

Kopacek, Karen Birckelbaw; Dopp, Anna Legreid; Dopp, John M; Vardeny, Orly; Sims, J Jason

2010-08-10

To assess pharmacy students' retention of knowledge about appropriate automated external defibrillator use and counseling points following didactic training and simulated experience. Following a lecture on sudden cardiac arrest and automated external defibrillator use, second-year doctor of pharmacy (PharmD) students were assessed on their ability to perform basic life support and deliver a shock at baseline, 3 weeks, and 4 months. Students completed a questionnaire to evaluate recall of counseling points for laypeople/the public. Mean time to shock delivery at baseline was 74 ± 25 seconds, which improved significantly at 3 weeks (50 ± 17 seconds, p < 0.001) and was maintained at 4 months (47 ± 18 seconds, p < 0.001). Recall of all signs and symptoms of sudden cardiac arrest and automated external defibrillator counseling points was diminished after 4 months. Pharmacy students can use automated external defibrillators to quickly deliver a shock and are able to retain this ability after 4 months. Refresher training/courses will be required to improve students' retention of automated external defibrillator counseling points to ensure their ability to deliver appropriate patient education.

Semi-Automated Identification of Rocks in Images

NASA Technical Reports Server (NTRS)

Bornstein, Benjamin; Castano, Andres; Anderson, Robert

2006-01-01

Rock Identification Toolkit Suite is a computer program that assists users in identifying and characterizing rocks shown in images returned by the Mars Explorer Rover mission. Included in the program are components for automated finding of rocks, interactive adjustments of outlines of rocks, active contouring of rocks, and automated analysis of shapes in two dimensions. The program assists users in evaluating the surface properties of rocks and soil and reports basic properties of rocks. The program requires either the Mac OS X operating system running on a G4 (or more capable) processor or a Linux operating system running on a Pentium (or more capable) processor, plus at least 128MB of random-access memory.

Effect of between-category similarity on basic-level superiority in pigeons

PubMed Central

Lazareva, Olga F.; Soto, Fabián A.; Wasserman, Edward A.

2010-01-01

Children categorize stimuli at the basic level faster than at the superordinate level. We hypothesized that between-category similarity may affect this basic-level superiority effect. Dissimilar categories may be easy to distinguish at the basic level but be difficult to group at the superordinate level, whereas similar categories may be easy to group at the superordinate level but be difficult to distinguish at the basic level. Consequently, similar basic-level categories may produce a superordinate-before-basic learning trend, whereas dissimilar basic-level categories may result in a basic-before-superordinate learning trend. We tested this hypothesis in pigeons by constructing superordinate-level categories out of basic-level categories with known similarity. In Experiment 1, we experimentally evaluated the between-category similarity of four basic-level photographic categories using multiple fixed interval-extinction training (Astley & Wasserman, 1992). We used the resultant similarity matrices in Experiment 2 to construct two superordinate-level categories from basic-level categories with high between-category similarity (cars and persons; chairs and flowers). We then trained pigeons to concurrently classify those photographs into either the proper basic-level category or the proper superordinate-level category. Under these conditions, the pigeons learned the superordinate-level discrimination faster than the basic-level discrimination, confirming our hypothesis that basic-level superiority is affected by between-category similarity. PMID:20600696

ARES (Automated Residential Energy Standard) 1.2: User`s guide, in support of proposed interim energy conservation voluntary performance standards for new non-federal residential buildings: Volume 1

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

NONE

The ARES (Automated Residential Energy Standard) User`s Guide is designed to the user successfully operate the ARES computer program. This guide assumes that the user is familiar with basic PC skills such as using a keyboard and loading a disk drive. The ARES computer program was designed to assist building code officials in creating a residential energy standard based on local climate and costs.

Artificial Intelligence and Spacecraft Power Systems

NASA Technical Reports Server (NTRS)

Dugel-Whitehead, Norma R.

1997-01-01

This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.

Improving Mastery of Basic Mathematics Facts in Elementary School through Various Learning Strategies.

ERIC Educational Resources Information Center

Haught, Laurie; Kunce, Christine; Pratt, Phyllis; Werneske, Roberta; Zemel, Susan

This report describes the intervention programs used to improve student proficiency in learning, recalling, and retaining basic mathematics facts. The targeted population consisted of first, second, third, and fifth grades in four suburban midwestern schools. The problems of recalling basic mathematics facts is documented through teacher surveys,…

75 FR 26286 - Agency Information Collection Activities: Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-11

.... The Foundation accounts for about one-fourth of Federal support to academic institutions for basic... (NSF 428A) is used by managers to maintain an automated database of reviewers for the many disciplines...

Using a Self-Administered Visual Basic Software Tool To Teach Psychological Concepts.

ERIC Educational Resources Information Center

Strang, Harold R.; Sullivan, Amie K.; Schoeny, Zahrl G.

2002-01-01

Introduces LearningLinks, a Visual Basic software tool that allows teachers to create individualized learning modules that use constructivist and behavioral learning principles. Describes field testing of undergraduates at the University of Virginia that tested a module designed to improve understanding of the psychological concepts of…

[The use of virtual learning environment in teaching basic and advanced life support].

PubMed

Cogo, Ana Luísa Petersen; Silveira, Denise Tolfo; Lírio, Aline de Morais; Severo, Carolina Lopes

2003-12-01

The present paper is the result of an experiment conducted as part of the Nursing: basic and advanced life support course, which was offered as a semi-online course using the virtual learning environment called Learning Space. The virtual learning environment optimizes classroom dynamics, since in the classroom setting, practical activities may be privileged; besides, learning is customized as students may access the environment whenever and wherever they wish.

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

Delay Assessment Framework for Automated Question-Answering System: An Approach for eLearning Paradigm

ERIC Educational Resources Information Center

Iqbal, Muhammad Munwar; Saleem, Yasir

2017-01-01

Adoption of Electronic Learning (eLearning) for the dissemination of higher education is rapidly increasing day by day. A large number of universities offering hundreds of course and a large number of the students are taking advantage from this type of learning paradigm. The purpose of this study is to investigate the delay factor in answering the…

Utilization of Intelligent Software Agent Features for Improving E-Learning Efforts: A Comprehensive Investigation

ERIC Educational Resources Information Center

Farzaneh, Mandana; Vanani, Iman Raeesi; Sohrabi, Babak

2012-01-01

E-learning is one of the most important learning approaches within which intelligent software agents can be efficiently used so as to automate and facilitate the process of learning. The aim of this paper is to illustrate a comprehensive categorization of intelligent software agent features, which is valuable for being deployed in the virtual…

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

PubMed

Ngo, Tuan Anh; Lu, Zhi; Carneiro, Gustavo

2017-01-01

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

PubMed Central

D’Mello, Sidney; Dieterle, Ed; Duckworth, Angela

2017-01-01

It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research. PMID:29038607

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning.

PubMed

D'Mello, Sidney; Dieterle, Ed; Duckworth, Angela

2017-01-01

It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Designing Anticancer Peptides by Constructive Machine Learning.

PubMed

Grisoni, Francesca; Neuhaus, Claudia S; Gabernet, Gisela; Müller, Alex T; Hiss, Jan A; Schneider, Gisbert

2018-04-21

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learn About Stem Cells

MedlinePlus

... Handbook Stem Cell Glossary Search Toggle Nav Stem Cell Basics Stem cells are the foundation from which ... Home > Learn About Stem Cells > Stem Cell Basics Cells in the human body The human body comprises ...

Proof Rules for Automated Compositional Verification through Learning

NASA Technical Reports Server (NTRS)

Barringer, Howard; Giannakopoulou, Dimitra; Pasareanu, Corina S.

2003-01-01

Compositional proof systems not only enable the stepwise development of concurrent processes but also provide a basis to alleviate the state explosion problem associated with model checking. An assume-guarantee style of specification and reasoning has long been advocated to achieve compositionality. However, this style of reasoning is often non-trivial, typically requiring human input to determine appropriate assumptions. In this paper, we present novel assume- guarantee rules in the setting of finite labelled transition systems with blocking communication. We show how these rules can be applied in an iterative and fully automated fashion within a framework based on learning.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Anesthesiology, automation, and artificial intelligence

PubMed Central

Alexander, John C.; Joshi, Girish P.

2018-01-01

ABSTRACT There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized. PMID:29686578

The Effect of Stages and Levels of Automation and Reliability on Workload and Performance for Remotely Piloted Aircraft Operations

DTIC Science & Technology

2015-03-26

participant, it is assumed that no learning effects affected the data. Preview This chapter began with the background of RPAs and described a...for alarm- style automation systems; however, these attributes may be less relevant for other types of automation implementation. For example, with...and tactile and the speech channel was added for a total of seven channels that are being used in the DES software tool IMPRINT. This updated

The Implementation of an Automated Assessment Feedback and Quality Assurance System for ICT Courses

ERIC Educational Resources Information Center

Debuse, J.; Lawley, M.; Shibl, R.

2007-01-01

Providing detailed, constructive and helpful feedback is an important contribution to effective student learning. Quality assurance is also required to ensure consistency across all students and reduce error rates. However, with increasing workloads and student numbers these goals are becoming more difficult to achieve. An automated feedback…

Adult Students' Perceptions of Automated Writing Assessment Software: Does It Foster Engagement?

ERIC Educational Resources Information Center

LaGuerre, Joselle L.

2013-01-01

Generally, this descriptive study endeavored to include the voice of adult learners to the scholarly body of research regarding automated writing assessment tools (AWATs). Specifically, the study sought to determine the extent to which students perceive that the AWAT named Criterion fosters learning and if students' opinions differ depending on…

Readerbench: Automated Evaluation of Collaboration Based on Cohesion and Dialogism

ERIC Educational Resources Information Center

Dascalu, Mihai; Trausan-Matu, Stefan; McNamara, Danielle S.; Dessus, Philippe

2015-01-01

As Computer-Supported Collaborative Learning (CSCL) gains a broader usage, the need for automated tools capable of supporting tutors in the time-consuming process of analyzing conversations becomes more pressing. Moreover, collaboration, which presumes the intertwining of ideas or points of view among participants, is a central element of dialogue…

Students' Experiences with an Automated Essay Scorer

ERIC Educational Resources Information Center

Scharber, Cassandra; Dexter, Sara; Riedel, Eric

2008-01-01

The purpose of this research is to analyze preservice teachers' use of and reactions to an automated essay scorer used within an online, case-based learning environment called ETIPS. Data analyzed include post-assignment surveys, a user log of students' actions within the cases, instructor-assigned scores on final essays, and interviews with four…

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

PubMed

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

NASA Astrophysics Data System (ADS)

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

A critical narrative review of transfer of basic science knowledge in health professions education.

PubMed

Castillo, Jean-Marie; Park, Yoon Soo; Harris, Ilene; Cheung, Jeffrey J H; Sood, Lonika; Clark, Maureen D; Kulasegaram, Kulamakan; Brydges, Ryan; Norman, Geoffrey; Woods, Nicole

2018-06-01

'Transfer' is the application of a previously learned concept to solve a new problem in another context. Transfer is essential for basic science education because, to be valuable, basic science knowledge must be transferred to clinical problem solving. Therefore, better understanding of interventions that enhance the transfer of basic science knowledge to clinical reasoning is essential. This review systematically identifies interventions described in the health professions education (HPE) literature that document the transfer of basic science knowledge to clinical reasoning, and considers teaching and assessment strategies. A systematic search of the literature was conducted. Articles related to basic science teaching at the undergraduate level in HPE were analysed using a 'transfer out'/'transfer in' conceptual framework. 'Transfer out' refers to the application of knowledge developed in one learning situation to the solving of a new problem. 'Transfer in' refers to the use of previously acquired knowledge to learn from new problems or learning situations. Of 9803 articles initially identified, 627 studies were retrieved for full text evaluation; 15 were included in the literature review. A total of 93% explored 'transfer out' to clinical reasoning and 7% (one article) explored 'transfer in'. Measures of 'transfer out' fostered by basic science knowledge included diagnostic accuracy over time and in new clinical cases. Basic science knowledge supported learning - 'transfer in' - of new related content and ultimately the 'transfer out' to diagnostic reasoning. Successful teaching strategies included the making of connections between basic and clinical sciences, the use of commonsense analogies, and the study of multiple clinical problems in multiple contexts. Performance on recall tests did not reflect the transfer of basic science knowledge to clinical reasoning. Transfer of basic science knowledge to clinical reasoning is an essential component of HPE that requires further development for implementation and scholarship. © 2018 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Work-based Project Overcomes Basic Skills Stigma.

ERIC Educational Resources Information Center

Wallis, Emma

2002-01-01

A project to provide steel workers in North Wales with guidance about learning opportunities and to promote lifelong learning in the workplace focused on the development of basic information technology skills. (JOW)

Crew/Automation Interaction in Space Transportation Systems: Lessons Learned from the Glass Cockpit

NASA Technical Reports Server (NTRS)

Rudisill, Marianne

2000-01-01

The progressive integration of automation technologies in commercial transport aircraft flight decks - the 'glass cockpit' - has had a major, and generally positive, impact on flight crew operations. Flight deck automation has provided significant benefits, such as economic efficiency, increased precision and safety, and enhanced functionality within the crew interface. These enhancements, however, may have been accrued at a price, such as complexity added to crew/automation interaction that has been implicated in a number of aircraft incidents and accidents. This report briefly describes 'glass cockpit' evolution. Some relevant aircraft accidents and incidents are described, followed by a more detailed description of human/automation issues and problems (e.g., crew error, monitoring, modes, command authority, crew coordination, workload, and training). This paper concludes with example principles and guidelines for considering 'glass cockpit' human/automation integration within space transportation systems.

Physics Laboratory in UEC

NASA Astrophysics Data System (ADS)

Takada, Tohru; Nakamura, Jin; Suzuki, Masaru

All the first-year students in the University of Electro-Communications (UEC) take "Basic Physics I", "Basic Physics II" and "Physics Laboratory" as required subjects; Basic Physics I and Basic Physics II are calculus-based physics of mechanics, wave and oscillation, thermal physics and electromagnetics. Physics Laboratory is designed mainly aiming at learning the skill of basic experimental technique and technical writing. Although 95% students have taken physics in the senior high school, they poorly understand it by connecting with experience, and it is difficult to learn Physics Laboratory in the university. For this reason, we introduced two ICT (Information and Communication Technology) systems of Physics Laboratory to support students'learning and staff's teaching. By using quantitative data obtained from the ICT systems, we can easily check understanding of physics contents in students, and can improve physics education.

The equivalence of learning paths in early science instruction: effect of direct instruction and discovery learning.

PubMed

Klahr, David; Nigam, Milena

2004-10-01

In a study with 112 third- and fourth-grade children, we measured the relative effectiveness of discovery learning and direct instruction at two points in the learning process: (a) during the initial acquisition of the basic cognitive objective (a procedure for designing and interpreting simple, unconfounded experiments) and (b) during the subsequent transfer and application of this basic skill to more diffuse and authentic reasoning associated with the evaluation of science-fair posters. We found not only that many more children learned from direct instruction than from discovery learning, but also that when asked to make broader, richer scientific judgments, the many children who learned about experimental design from direct instruction performed as well as those few children who discovered the method on their own. These results challenge predictions derived from the presumed superiority of discovery approaches in teaching young children basic procedures for early scientific investigations.

Video and accelerometer-based motion analysis for automated surgical skills assessment.

PubMed

Zia, Aneeq; Sharma, Yachna; Bettadapura, Vinay; Sarin, Eric L; Essa, Irfan

2018-03-01

Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy-based" features-approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment. We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment. Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.

Assessment and E-Learning: Current Issues and Future Trends

ERIC Educational Resources Information Center

Cowie, Neil; Sakui, Keiko

2015-01-01

This paper describes different ways in which digital technology can be used for language learning. It then identifies some key trends connecting assessment and technology in language learning and higher education: the use of automated systems to enhance traditional assessment practices; the use of Web 2.0 tools to facilitate new assessment…

Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

ERIC Educational Resources Information Center

Blikstein, Paulo; Worsley, Marcelo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne

2014-01-01

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…

Automated Problem Generation in Learning Management Systems: A Tutorial

ERIC Educational Resources Information Center

Romero, Jaime; Rozano, Mercedes

2016-01-01

The benefits of solving problems have been widely acknowledged by literature. Its implementation in e-learning platforms can make easier its management and the learning process itself. However, its implementation can also become a very time-consuming task, particularly when the number of problems to generate is high. In this tutorial we describe a…

A Method for Automated Program Code Testing

ERIC Educational Resources Information Center

Drasutis, Sigitas; Motekaityte, Vida; Noreika, Algirdas

2010-01-01

The Internet has recently encouraged the society to convert almost all its needs to electronic resources such as e-libraries, e-cultures, e-entertainment as well as e-learning, which has become a radical idea to increase the effectiveness of learning services in most schools, colleges and universities. E-learning can not be completely featured and…

Analyzing Educators' Online Interactions: A Framework of Online Learning Support Roles

ERIC Educational Resources Information Center

Nacu, Denise C.; Martin, Caitlin K.; Pinkard, Nichole; Gray, Tené

2016-01-01

While the potential benefits of participating in online learning communities are documented, so too are inequities in terms of how different populations access and use them. We present the online learning support roles (OLSR) framework, an approach using both automated analytics and qualitative interpretation to identify and explore online…

Teaching High School Students Machine Learning Algorithms to Analyze Flood Risk Factors in River Deltas

NASA Astrophysics Data System (ADS)

Rose, R.; Aizenman, H.; Mei, E.; Choudhury, N.

2013-12-01

High School students interested in the STEM fields benefit most when actively participating, so I created a series of learning modules on how to analyze complex systems using machine-learning that give automated feedback to students. The automated feedbacks give timely responses that will encourage the students to continue testing and enhancing their programs. I have designed my modules to take the tactical learning approach in conveying the concepts behind correlation, linear regression, and vector distance based classification and clustering. On successful completion of these modules, students will learn how to calculate linear regression, Pearson's correlation, and apply classification and clustering techniques to a dataset. Working on these modules will allow the students to take back to the classroom what they've learned and then apply it to the Earth Science curriculum. During my research this summer, we applied these lessons to analyzing river deltas; we looked at trends in the different variables over time, looked for similarities in NDVI, precipitation, inundation, runoff and discharge, and attempted to predict floods based on the precipitation, waves mean, area of discharge, NDVI, and inundation.

Automated structural classification of lipids by machine learning.

PubMed

Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T

2015-03-01

Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

NASA Astrophysics Data System (ADS)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Meeting Basic Learning Needs through Programmes of Early Childhood Care and Development.

ERIC Educational Resources Information Center

Consultative Group on Early Childhood Care and Development, Haydenville, MA.

Noting that early childhood development is the foundation for basic education across the life span, the first chapter of this report discusses the benefits of early interventions for individuals and society and justifies the basis for programs which aim at meeting the basic learning needs of young children. It also suggests several questions which…

The Octet Rules: A Dating Game for Atoms

ERIC Educational Resources Information Center

Welborn, Jennifer

2004-01-01

To develop student interest in the periodic table, the author developed a simple, but fun, role-playing activity. This play is used after students have learned the basic structure of atoms and the general layout of the periodic table. It also comes after students have learned the basics of ionic and covalent bonding. The basic idea of bonding is…

Writing Partners: Service Learning as a Route to Authority for Basic Writers

ERIC Educational Resources Information Center

Gabor, Catherine

2009-01-01

This article looks at best practices in basic writing instruction in terms of non-traditional audiences and writerly authority. Much conventional wisdom discourages participation in service-learning projects for basic writers because of the assumption that their writing is not yet ready to "go public." Countering this line of thinking, the author…

Beyond Passive Learning: Problem-Based Learning and Concept Maps to Promote Basic and Higher-Order Thinking in Basic Skills Instruction

ERIC Educational Resources Information Center

Smith, Regina O.

2014-01-01

Research into the best practices for basic skills education, national bridge programs, the new GED® assessment, and accelerated developmental education indicated that contextualized instruction was most effective when preparing adult literacy students for college and work. Nevertheless, "remedial pedagogy" with a sole focus on the…

The Effects of Computer Games on the Achievement of Basic Mathematical Skills

ERIC Educational Resources Information Center

Sayan, Hamiyet

2015-01-01

This study aims to analyze the relationship between playing computer games and learning basic mathematics skills. It shows the role computer games play in the learning and achievement of basic mathematical skills by students. Nowadays it is clear that individuals, especially young persons are very fond of computer and computer games. Since…

Intelligent transportation systems for work zones : deployment benefits and lessons learned

DOT National Transportation Integrated Search

2000-12-01

This paper presents what has been learned in four principal areas of arterial management: 1) adaptive control strategies; 2) advanced traveler information systems; 3) automated enforcement; and 4) integration. The levels of deployment, benefits, depl...

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part C: Basic AI topics

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1983-01-01

Readily understandable overviews of search oriented problem solving, knowledge representation, and computational logic are provided. Mechanization, automation and artificial intelligence are discussed as well as how they interrelate.

Cluster: Drafting. Course: Basic Technical Drafting. Research Project.

ERIC Educational Resources Information Center

Sanford - Lee County Schools, NC.

The set of six units is designed for use with an instructor in basic technical drafting and is also keyed to other texts. Each unit contains several task packages specifying prerequisites, rationale for learning, objectives, learning activities to be supervised by the instructor, and learning practice. The units cover: pictorial drawing; screw…

Project BEST-PAL (Basic Education Skills Through-Parenting Affective Learning): Level I Modules.

ERIC Educational Resources Information Center

Brevard Community Coll., Cocoa, FL.

These eight learning modules were prepared for parents participating in Brevard Community College's Project BEST-PAL (Basic Education Skills Through-Parenting Affective Learning), which was designed for low socioeconomic parents who are in need of an opportunity to explore effective parenting. First, materials for the BEST-PAL volunteer sponsors…

Project BEST-PAL (Basic Education Skills Through-Parenting Affective Learning): Process Manual for Program Implementation.

ERIC Educational Resources Information Center

Brevard Community Coll., Cocoa, FL.

This manual describes and evaluates the implementation of Project BEST-PAL (Basic Education Skills Through-Parent Affective Learning), Brevard Community College's special demonstration training project intended to return adults who have dropped out of the educational system back into the learning environment by bringing them to parenting classes…

Project BEST-PAL (Basic Education Skills Through-Parenting Affective Learning): Level II Modules.

ERIC Educational Resources Information Center

Brevard Community Coll., Cocoa, FL.

These eight learning modules were prepared for parents participating in Brevard Community College's Project BEST-PAL (Basic Education Skills Through-Parenting Affective Learning), which was designed for low socioeconomic parents who are in need of an opportunity to explore effective parenting. First, materials for the BEST-PAL volunteer sponsors…

Basics of the "Learning Organization" at Jordanian Schools: A Case Study

ERIC Educational Resources Information Center

Hawamdeh, Basem; Jaradat, Mohammed H.

2012-01-01

The study aims at identifying the extent to which the basics of the "learning organization" (LO) principles are available at Jordanian schools (Pilot TQA schools in Jersah); to this effect, a specially customized questionnaire was developed--it was made of 19 items across three areas: a leadership that supports learning, an environment…

Improvement of Word Problem Solving and Basic Mathematics Competencies in Students with Attention Deficit/Hyperactivity Disorder and Mathematical Learning Difficulties

ERIC Educational Resources Information Center

González-Castro, Paloma; Cueli, Marisol; Areces, Débora; Rodríguez, Celestino; Sideridis, Georgios

2016-01-01

Problem solving represents a salient deficit in students with mathematical learning difficulties (MLD) primarily caused by difficulties with informal and formal mathematical competencies. This study proposes a computerized intervention tool, the integrated dynamic representation (IDR), for enhancing the early learning of basic mathematical…

Learning about Computer-Based Education in Adult Basic Education.

ERIC Educational Resources Information Center

Fahy, Patrick J.

In 1979 the adult basic education department at the Alberta Vocational Centre (AVC), Edmonton, began to use the Control Data PLATO system. Results of the first PLATO project showed students using PLATO learned at least as much as students in regular classes. Students learned faster and reported great satisfaction with PLATO experiences. Staff and…

Adventures with Cell Phones

ERIC Educational Resources Information Center

Kolb, Liz

2011-01-01

Teachers are finding creative ways to turn the basic cell phone from a digital distraction into a versatile learning tool. In this article, the author explains why cell phones are important in learning and suggests rather than banning them that they be integrated into learning. She presents activities that can be done on a basic cell phone with a…

Personality Characteristics and Learning Style Preferences of Adult Basic Education Students. Research Monograph.

ERIC Educational Resources Information Center

Manzo, Anthony V.; And Others

The study described in the report identifies personality characteristics and learning styles of adult basic education (ABE) students on the basis of three instruments: the Luscher Color Test, the Manzo Bestiary Inventory, and the Learning Preference Inventory. The volunteer sample consisted of 83 ABE students. Subsample comparison groups consisted…

A Concept Transformation Learning Model for Architectural Design Learning Process

ERIC Educational Resources Information Center

Wu, Yun-Wu; Weng, Kuo-Hua; Young, Li-Ming

2016-01-01

Generally, in the foundation course of architectural design, much emphasis is placed on teaching of the basic design skills without focusing on teaching students to apply the basic design concepts in their architectural designs or promoting students' own creativity. Therefore, this study aims to propose a concept transformation learning model to…

Integrated Curricular Approaches in Reaching Adult Students

ERIC Educational Resources Information Center

Emerick-Brown, Dylan

2013-01-01

In the field of adult basic education, there are two strategies that have been found to be of particular value to student learning: multiple intelligences and purpose-based learning. However, putting these learning theories into practice is not always as easy as an educator might at first believe. Adult basic education teacher Dylan Emerick-Brown…

Pilots' monitoring strategies and performance on automated flight decks: an empirical study combining behavioral and eye-tracking data.

PubMed

Sarter, Nadine B; Mumaw, Randall J; Wickens, Christopher D

2007-06-01

The objective of the study was to examine pilots' automation monitoring strategies and performance on highly automated commercial flight decks. A considerable body of research and operational experience has documented breakdowns in pilot-automation coordination on modern flight decks. These breakdowns are often considered symptoms of monitoring failures even though, to date, only limited and mostly anecdotal data exist concerning pilots' monitoring strategies and performance. Twenty experienced B-747-400 airline pilots flew a 1-hr scenario involving challenging automation-related events on a full-mission simulator. Behavioral, mental model, and eye-tracking data were collected. The findings from this study confirm that pilots monitor basic flight parameters to a much greater extent than visual indications of the automation configuration. More specifically, they frequently fail to verify manual mode selections or notice automatic mode changes. In other cases, they do not process mode annunciations in sufficient depth to understand their implications for aircraft behavior. Low system observability and gaps in pilots' understanding of complex automation modes were shown to contribute to these problems. Our findings describe and explain shortcomings in pilot's automation monitoring strategies and performance based on converging behavioral, eye-tracking, and mental model data. They confirm that monitoring failures are one major contributor to breakdowns in pilot-automation interaction. The findings from this research can inform the design of improved training programs and automation interfaces that support more effective system monitoring.

A machine-learning apprentice for the completion of repetitive forms

NASA Technical Reports Server (NTRS)

Hermens, Leonard A.; Schlimmer, Jeffrey C.

1994-01-01

Forms of all types are used in businesses and government agencies, and most of them are filled in by hand. Yet much time and effort has been expended to automate form-filling by programming specific systems or computers. The high cost of programmers and other resources prohibits many organizations from benefiting from efficient office automation. A learning apprentice can be used for such repetitious form-filling tasks. In this paper, we establish the need for learning apprentices, describe a framework for such a system, explain the difficulties of form-filling, and present empirical results of a form-filling system used in our department from September 1991 to April 1992. The form-filling apprentice saves up to 87 percent in keystroke effort and correctly predicts nearly 90 percent of the values on the form.

Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen

2014-01-01

One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments. PMID:25530925

Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

2013-11-01

One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments.

CRIE: An automated analyzer for Chinese texts.

PubMed

Sung, Yao-Ting; Chang, Tao-Hsing; Lin, Wei-Chun; Hsieh, Kuan-Sheng; Chang, Kuo-En

2016-12-01

Textual analysis has been applied to various fields, such as discourse analysis, corpus studies, text leveling, and automated essay evaluation. Several tools have been developed for analyzing texts written in alphabetic languages such as English and Spanish. However, currently there is no tool available for analyzing Chinese-language texts. This article introduces a tool for the automated analysis of simplified and traditional Chinese texts, called the Chinese Readability Index Explorer (CRIE). Composed of four subsystems and incorporating 82 multilevel linguistic features, CRIE is able to conduct the major tasks of segmentation, syntactic parsing, and feature extraction. Furthermore, the integration of linguistic features with machine learning models enables CRIE to provide leveling and diagnostic information for texts in language arts, texts for learning Chinese as a foreign language, and texts with domain knowledge. The usage and validation of the functions provided by CRIE are also introduced.

Computational Analysis of Behavior.

PubMed

Egnor, S E Roian; Branson, Kristin

2016-07-08

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with.

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

PubMed

Kline, Timothy L; Korfiatis, Panagiotis; Edwards, Marie E; Blais, Jaime D; Czerwiec, Frank S; Harris, Peter C; King, Bernard F; Torres, Vicente E; Erickson, Bradley J

2017-08-01

Deep learning techniques are being rapidly applied to medical imaging tasks-from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.

Active learning of neuron morphology for accurate automated tracing of neurites

PubMed Central

Gala, Rohan; Chapeton, Julio; Jitesh, Jayant; Bhavsar, Chintan; Stepanyants, Armen

2014-01-01

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users. PMID:24904306

Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

NASA Technical Reports Server (NTRS)

Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

2013-01-01

Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

PubMed

Niemeijer, Meindert; van Ginneken, Bram; Russell, Stephen R; Suttorp-Schulten, Maria S A; Abràmoff, Michael D

2007-05-01

To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy. Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined. The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type. A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Comparing Core-Image-Based Basic Verb Learning in an EFL Junior High School: Learner-Centered and Teacher-Centered Approaches

ERIC Educational Resources Information Center

Yamagata, Satoshi

2018-01-01

The present study investigated the effects of two types of core-image-based basic verb learning approaches: the learner-centered and the teacher-centered approaches. The learner-centered approach was an activity in which participants found semantic relationships among several definitions of each basic target verb through a picture-elucidated card…

Computer aided fixture design - A case based approach

NASA Astrophysics Data System (ADS)

Tanji, Shekhar; Raiker, Saiesh; Mathew, Arun Tom

2017-11-01

Automated fixture design plays important role in process planning and integration of CAD and CAM. An automated fixture setup design system is developed where when fixturing surfaces and points are described allowing modular fixture components to get automatically select for generating fixture units and placed into position with satisfying assembled conditions. In past, various knowledge based system have been developed to implement CAFD in practice. In this paper, to obtain an acceptable automated machining fixture design, a case-based reasoning method with developed retrieval system is proposed. Visual Basic (VB) programming language is used in integrating with SolidWorks API (Application programming interface) module for better retrieval procedure reducing computational time. These properties are incorporated in numerical simulation to determine the best fit for practical use.

Collected Papers Prepared Under Work Unit TEXTRUCT: Methods of Instruction in Technical Training.

ERIC Educational Resources Information Center

Human Resources Research Organization, Alexandria, VA.

Although the concept of automated instruction is not new, it has gained major support only in the past 3 years. These 12 papers describe research in the area of instructional methods for technical training. The scientific principles of learning and their applicability to automated instruction are discussed, with emphasis on the role of automated…

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

ERIC Educational Resources Information Center

Wind, Stefanie A.; Wolfe, Edward W.; Engelhard, George, Jr.; Foltz, Peter; Rosenstein, Mark

2018-01-01

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be "trained" using machine-learning techniques that incorporate human ratings. However, the…

Developing an Automated Writing Placement System for ESL Learners

ERIC Educational Resources Information Center

Yannakoudakis, Helen; Andersen, Øistein E.; Geranpayeh, Ardeshir; Briscoe, Ted; Nicholls, Diane

2018-01-01

There are quite a few challenges in the development of an automated writing placement model for non-native English learners, among them the fact that exams that encompass the full range of language proficiency exhibited at different stages of learning are hard to design. However, acquisition of appropriate training data that are relevant to the…

Discovering Indicators of Successful Collaboration Using Tense: Automated Extraction of Patterns in Discourse

ERIC Educational Resources Information Center

Thompson, Kate; Kennedy-Clark, Shannon; Wheeler, Penny; Kelly, Nick

2014-01-01

This paper describes a technique for locating indicators of success within the data collected from complex learning environments, proposing an application of e-research to access learner processes and measure and track group progress. The technique combines automated extraction of tense and modality via parts-of-speech tagging with a visualisation…

Automation Technology in Elementary Technology Education.

ERIC Educational Resources Information Center

Hiltunen, Jukka; Jarvinen, Esa-Matti

2000-01-01

Finnish fifth-graders (n=20) and sixth-graders (n=23) worked in teams in a Lego/Logo-Control Lab to complete Lego design activities. Observations showed that they became familiar with automation technology but their skills were not always up to their ideas. Activities based on real-life situations gave them ownership and engaged them in learning.…

Automated Writing Evaluation for Non-Native Speaker English Academic Writing: The Case of IADE and Its Formative Feedback

ERIC Educational Resources Information Center

Cotos, Elena

2010-01-01

This dissertation presents an innovative approach to the development and empirical evaluation of Automated Writing Evaluation (AWE) technology used for teaching and learning. It introduces IADE (Intelligent Academic Discourse Evaluator), a new web-based AWE program that analyzes research article Introduction sections and generates immediate,…

A Multidisciplinary PBL Robot Control Project in Automation and Electronic Engineering

ERIC Educational Resources Information Center

Hassan, Houcine; Domínguez, Carlos; Martínez, Juan-Miguel; Perles, Angel; Capella, Juan-Vicente; Albaladejo, José

2015-01-01

This paper presents a multidisciplinary problem-based learning (PBL) project consisting of the development of a robot arm prototype and the implementation of its control system. The project is carried out as part of Industrial Informatics (II), a compulsory third-year course in the Automation and Electronic Engineering (AEE) degree program at the…

Automation in Vocational Training of the Mentally Retarded. Final Report.

ERIC Educational Resources Information Center

Platt, Henry; And Others

Various uses of automation in teaching were studied with mentally retarded (IQ 70 to 90) and/or emotionally disturbed (IQ 80 to 90) youth aged 16 to 20. Programed instruction was presented by six audiovisual devices and techniques: the Devereux Model 50 Teaching Aid, the Learn-Ease Teaching Device, the Mast Teaching Machine, the Graflex…

How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust

DTIC Science & Technology

2014-10-01

155–156 5. Jian, J.Y., Bisantz, A.M., Drury , C.G.: Foundations for an empirically determined scale of trust in automated systems. International...517–527 8. Carlson, M.S., Desai, M., Drury , J.L., Kwak, H., Yanco, H.A.: Identifying factors that influence trust in automated cars and medical

Summary of Downtown People Mover (DPM) Winterization Test Activities

DOT National Transportation Integrated Search

1982-01-01

This report describes and summarizes the test activities and presents the results of a two-year winter operation test program for three technologically-different Automated Guideway Transit (AGT) systems. The basic objective of the program was to dete...

Downtown People Mover (DPM) Winterization Test Demonstration : UMI

DOT National Transportation Integrated Search

1982-01-01

This report describes and summarizes the test activities and presents the results of a two-year winter operation test program for three technologically-different Automated Guideway Transit (AGT) systems. The basic objective of the program was to dete...

Extending Basic Learning Opportunities: Challenge and Response. UNESCO-UNICEF Co-operative Programme Digest No. 16.

ERIC Educational Resources Information Center

Prakasha, Veda; And Others

This digest focuses on problems encountered in the expansion of facilities for universal primary education and responses being developed to overcome these problems. The central message of the document is that nonformal structures of learning and community involvement play a key role in the expansion of basic learning opportunities in the…

Instructional Strategies and Resource Utility in Language Teaching among Basic Educators in 21st Century Nigeria

ERIC Educational Resources Information Center

Ofodu, Graceful Onovughe

2012-01-01

Learning in the twenty-first century demands learning skills, strategies and utilizing resources which learners can deploy when they leave the school environment. The paper investigates the instructional strategies and resources employed by teachers in teaching and learning English Studies at the basic level of Nigeria's educational system. It…

Learning and Motivation in Thailand: A Comparative Regional Study on Basic Education Ninth Graders

ERIC Educational Resources Information Center

Loima, Jyrki; Vibulphol, Jutarat

2016-01-01

This qualitative research studied regional motivation and learning of the basic education 9th graders in Thailand. Second topic was the school size and its possible effect on motivation. Furthermore, the data gave an opportunity to discuss, whether international research on motivation and learning was valid in Thai classrooms. The informants were…

Increasing Access to Learning for the Adult Basic Education Learner with Learning Disabilities: Evidence-Based Accommodation Research

ERIC Educational Resources Information Center

Gregg, Noel

2012-01-01

Accommodating adult basic education (ABE) learners with learning disabilities (LD) is common practice across many instructional, testing, and work settings. However, the results from this literature search indicate that very few empirically based studies are available to support or reject the effectiveness of a great deal of accommodation…

Learning basic programming using CLIS through gamification

NASA Astrophysics Data System (ADS)

Prabawa, H. W.; Sutarno, H.; Kusnendar, J.; Rahmah, F.

2018-05-01

The difficulty of understanding programming concept is a major problem in basic programming lessons. Based on the results of preliminary studies, 60% of students reveal the monotonous of learning process caused by the limited number of media. Children Learning in Science (CLIS) method was chosen as solution because CLIS has facilitated students’ initial knowledge to be optimized into conceptual knowledge. Technological involvement in CLIS (gamification) helped students to understand basic programming concept. This research developed a media using CLIS method with gamification elements to increase the excitement of learning process. This research declared that multimedia is considered good by students, especially regarding the mechanical aspects of multimedia, multimedia elements and aspects of multimedia information structure. Multimedia gamification learning with the CLIS model showed increased number of students’ concept understanding.

Parameterized examination in econometrics

NASA Astrophysics Data System (ADS)

Malinova, Anna; Kyurkchiev, Vesselin; Spasov, Georgi

2018-01-01

The paper presents a parameterization of basic types of exam questions in Econometrics. This algorithm is used to automate and facilitate the process of examination, assessment and self-preparation of a large number of students. The proposed parameterization of testing questions reduces the time required to author tests and course assignments. It enables tutors to generate a large number of different but equivalent dynamic questions (with dynamic answers) on a certain topic, which are automatically assessed. The presented methods are implemented in DisPeL (Distributed Platform for e-Learning) and provide questions in the areas of filtering and smoothing of time-series data, forecasting, building and analysis of single-equation econometric models. Questions also cover elasticity, average and marginal characteristics, product and cost functions, measurement of monopoly power, supply, demand and equilibrium price, consumer and product surplus, etc. Several approaches are used to enable the required numerical computations in DisPeL - integration of third-party mathematical libraries, developing our own procedures from scratch, and wrapping our legacy math codes in order to modernize and reuse them.

Thermal Model Development for Ares I-X

NASA Technical Reports Server (NTRS)

Amundsen, Ruth M.; DelCorso, Joe

2008-01-01

Thermal analysis for the Ares I-X vehicle has involved extensive thermal model integration, since thermal models of vehicle elements came from several different NASA and industry organizations. Many valuable lessons were learned in terms of model integration and validation. Modeling practices such as submodel, analysis group and symbol naming were standardized to facilitate the later model integration. Upfront coordination of coordinate systems, timelines, units, symbols and case scenarios was very helpful in minimizing integration rework. A process for model integration was developed that included pre-integration runs and basic checks of both models, and a step-by-step process to efficiently integrate one model into another. Extensive use of model logic was used to create scenarios and timelines for avionics and air flow activation. Efficient methods of model restart between case scenarios were developed. Standardization of software version and even compiler version between organizations was found to be essential. An automated method for applying aeroheating to the full integrated vehicle model, including submodels developed by other organizations, was developed.

Expedition Memory: Towards Agent-based Web Services for Creating and Using Mars Exploration Data.

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten; Briggs, Geoff; Sims, Mike

2005-01-01

Explorers ranging over kilometers of rugged, sometimes "feature-less" terrain for over a year could be overwhelmed by tracking and sharing what they have done and learned. An automated system based on the existing Mobile Agents design [ I ] and Mars Exploration Rover experience [2], could serve as an "expedition memory" that would be indexed by voice as wel1 as a web interface, linking people, places, activities, records (voice notes, photographs, samples). and a descriptive scientific ontology. This database would be accessible during EVAs by astronauts, annotated by the remote science team, linked to EVA plans, and allow cross indexing between sites and expeditions. We consider the basic problem, our philosophical approach, technical methods, and uses of the expedition memory for facilitating long-term collaboration between Mars crews and Earth support teams. We emphasize that a "memory" does not mean a database per se, but an interactive service that combines different resources, and ultimately could be like a helpful librarian.

Architecture Views Illustrating the Service Automation Aspect of SOA

NASA Astrophysics Data System (ADS)

Gu, Qing; Cuadrado, Félix; Lago, Patricia; Duenãs, Juan C.

Earlier in this book, Chapter 8 provided a detailed analysis of service engineering, including a review of service engineering techniques and methodologies. This chapter is closely related to Chapter 8 as shows how such approaches can be used to develop a service, with particular emphasis on the identification of three views (the automation decision view, degree of service automation view and service automation related data view) that structure and ease elicitation and documentation of stakeholders' concerns. This is carried out through two large case studies to learn the industrial needs in illustrating services deployment and configuration automation. This set of views adds to the more traditional notations like UML, the visual power of attracting the attention of their users to the addressed concerns, and assist them in their work. This is especially crucial in service oriented architecting where service automation is highly demanded.

Automated Inattention and Fatigue Detection System in Distance Education for Elementary School Students

ERIC Educational Resources Information Center

Hwang, Kuo-An; Yang, Chia-Hao

2009-01-01

Most courses based on distance learning focus on the cognitive domain of learning. Because students are sometimes inattentive or tired, they may neglect the attention goal of learning. This study proposes an auto-detection and reinforcement mechanism for the distance-education system based on the reinforcement teaching strategy. If a student is…

Towards the Development of an Automated Learning Assistant for Vector Calculus: Integration over Planar Regions

ERIC Educational Resources Information Center

Yaacob, Yuzita; Wester, Michael; Steinberg, Stanly

2010-01-01

This paper presents a prototype of a computer learning assistant ILMEV (Interactive Learning-Mathematica Enhanced Vector calculus) package with the purpose of helping students to understand the theory and applications of integration in vector calculus. The main problem for students using Mathematica is to convert a textbook description of a…

The automated system for technological process of spacecraft's waveguide paths soldering

NASA Astrophysics Data System (ADS)

Tynchenko, V. S.; Murygin, A. V.; Emilova, O. A.; Bocharov, A. N.; Laptenok, V. D.

2016-11-01

The paper solves the problem of automated process control of space vehicles waveguide paths soldering by means of induction heating. The peculiarities of the induction soldering process are analyzed and necessity of information-control system automation is identified. The developed automated system makes the control of the product heating process, by varying the power supplied to the inductor, on the basis of information about the soldering zone temperature, and stabilizing the temperature in a narrow range above the melting point of the solder but below the melting point of the waveguide. This allows the soldering process automating to improve the quality of the waveguides and eliminate burn-troughs. The article shows a block diagram of a software system consisting of five modules, and describes the main algorithm of its work. Also there is a description of the waveguide paths automated soldering system operation, for explaining the basic functions and limitations of the system. The developed software allows setting of the measurement equipment, setting and changing parameters of the soldering process, as well as view graphs of temperatures recorded by the system. There is shown the results of experimental studies that prove high quality of soldering process control and the system applicability to the tasks of automation.

InPRO: Automated Indoor Construction Progress Monitoring Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Hamledari, Hesam

In this research, an envisioned automated intelligent robotic solution for automated indoor data collection and inspection that employs a series of unmanned aerial vehicles (UAV), entitled "InPRO", is presented. InPRO consists of four stages, namely: 1) automated path planning; 2) autonomous UAV-based indoor inspection; 3) automated computer vision-based assessment of progress; and, 4) automated updating of 4D building information models (BIM). The works presented in this thesis address the third stage of InPRO. A series of computer vision-based methods that automate the assessment of construction progress using images captured at indoor sites are introduced. The proposed methods employ computer vision and machine learning techniques to detect the components of under-construction indoor partitions. In particular, framing (studs), insulation, electrical outlets, and different states of drywall sheets (installing, plastering, and painting) are automatically detected using digital images. High accuracy rates, real-time performance, and operation without a priori information are indicators of the methods' promising performance.

Control task substitution in semiautomated driving: does it matter what aspects are automated?

PubMed

Carsten, Oliver; Lai, Frank C H; Barnard, Yvonne; Jamson, A Hamish; Merat, Natasha

2012-10-01

The study was designed to show how driver attention to the road scene and engagement of a choice of secondary tasks are affected by the level of automation provided to assist or take over the basic task of vehicle control. It was also designed to investigate the difference between support in longitudinal control and support in lateral control. There is comparatively little literature on the implications of automation for drivers' engagement in the driving task and for their willingness to engage in non-driving-related activities. A study was carried out on a high-level driving simulator in which drivers experienced three levels of automation: manual driving, semiautomated driving with either longitudinal or lateral control provided, and highly automated driving with both longitudinal and lateral control provided. Drivers were free to pay attention to the roadway and traffic or to engage in a range of entertainment and grooming tasks. Engagement in the nondriving tasks increased from manual to semiautomated driving and increased further with highly automated driving. There were substantial differences in attention to the road and traffic between the two types of semiautomated driving. The literature on automation and the various task analyses of driving do not currently help to explain the effects that were found. Lateral support and longitudinal support may be the same in terms of levels of automation but appear to be regarded rather differently by drivers.

Satellite Ground Operations Automation: Lessons Learned and Future Approaches

NASA Technical Reports Server (NTRS)

Catena, John; Frank, Lou; Saylor, Rick; Weikel, Craig; Obenschain, Arthur F. (Technical Monitor)

2001-01-01

Reducing spacecraft ground system operations costs is a major goal in all missions. The Fast Auroral Snapshot (FAST) flight operations team at the NASA/Goddard Spacecraft Flight Center developed in-house scripts and procedures to automate monitoring of critical spacecraft functions. The initial staffing profile of 16x7 was reduced first to 8x5 and then to 'lights out'. Operations functions became an offline review of system performance and the generation of future science plans for subsequent upload to the spacecraft. Lessons learned will be applied to the challenging Triana mission, where 24x7 contact with the spacecraft will be necessary at all times.

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

PubMed

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

Recent advances in automated protein design and its future challenges.

PubMed

Setiawan, Dani; Brender, Jeffrey; Zhang, Yang

2018-04-25

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

Pharmacy Students' Retention of Knowledge and Skills Following Training in Automated External Defibrillator Use

PubMed Central

Dopp, Anna Legreid; Dopp, John M.; Vardeny, Orly; Sims, J. Jason

2010-01-01

Objectives To assess pharmacy students' retention of knowledge about appropriate automated external defibrillator use and counseling points following didactic training and simulated experience. Design Following a lecture on sudden cardiac arrest and automated external defibrillator use, second-year doctor of pharmacy (PharmD) students were assessed on their ability to perform basic life support and deliver a shock at baseline, 3 weeks, and 4 months. Students completed a questionnaire to evaluate recall of counseling points for laypeople/the public. Assessment Mean time to shock delivery at baseline was 74 ± 25 seconds, which improved significantly at 3 weeks (50 ± 17 seconds, p < 0.001) and was maintained at 4 months (47 ± 18 seconds, p < 0.001). Recall of all signs and symptoms of sudden cardiac arrest and automated external defibrillator counseling points was diminished after 4 months. Conclusion Pharmacy students can use automated external defibrillators to quickly deliver a shock and are able to retain this ability after 4 months. Refresher training/courses will be required to improve students' retention of automated external defibrillator counseling points to ensure their ability to deliver appropriate patient education. PMID:21045951

Microcomputers: Tools for Developing Technological Literacy.

ERIC Educational Resources Information Center

Liao, Thomas T.

1983-01-01

Describes a course in which undergraduate students learn to program microcomputers while learning about its applications and ramifications. Descriptions of software developed for the course are also provided. These include yellow light (traffic flow), domestic electrical energy use/cost, water pollution, and supermarket automation. (CN)

[Automated anesthesia record system].

PubMed

Zhu, Tao; Liu, Jin

2005-12-01

Based on Client/Server architecture, a software of automated anesthesia record system running under Windows operation system and networks has been developed and programmed with Microsoft Visual C++ 6.0, Visual Basic 6.0 and SQL Server. The system can deal with patient's information throughout the anesthesia. It can collect and integrate the data from several kinds of medical equipment such as monitor, infusion pump and anesthesia machine automatically and real-time. After that, the system presents the anesthesia sheets automatically. The record system makes the anesthesia record more accurate and integral and can raise the anesthesiologist's working efficiency.

Interactive Classification Technology

NASA Technical Reports Server (NTRS)

deBessonet, Cary

2000-01-01

The investigators upgraded a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the more effective use of the technologies in automated reasoning and interactive classification systems. The overall goals of the project were: 1) the enhancement of the representation language SL to accommodate a wider range of meaning; 2) the development of a default inference scheme to operate over SL notation as it is encoded; and 3) the development of an interpreter for SL that would handle representations of some basic cognitive acts and perspectives.

Theory-Led Design of Instruments and Representations in Learning Analytics: Developing a Novel Tool for Orchestration of Online Collaborative Learning

ERIC Educational Resources Information Center

Kelly, Nick; Thompson, Kate; Yeoman, Pippa

2015-01-01

This paper describes theory-led design as a way of developing novel tools for learning analytics (LA). It focuses upon the domain of automated discourse analysis (ADA) of group learning activities to help an instructor to orchestrate online groups in real-time. The paper outlines the literature on the development of LA tools within the domain of…

Automated model integration at source code level: An approach for implementing models into the NASA Land Information System

NASA Astrophysics Data System (ADS)

Wang, S.; Peters-Lidard, C. D.; Mocko, D. M.; Kumar, S.; Nearing, G. S.; Arsenault, K. R.; Geiger, J. V.

2014-12-01

Model integration bridges the data flow between modeling frameworks and models. However, models usually do not fit directly into a particular modeling environment, if not designed for it. An example includes implementing different types of models into the NASA Land Information System (LIS), a software framework for land-surface modeling and data assimilation. Model implementation requires scientific knowledge and software expertise and may take a developer months to learn LIS and model software structure. Debugging and testing of the model implementation is also time-consuming due to not fully understanding LIS or the model. This time spent is costly for research and operational projects. To address this issue, an approach has been developed to automate model integration into LIS. With this in mind, a general model interface was designed to retrieve forcing inputs, parameters, and state variables needed by the model and to provide as state variables and outputs to LIS. Every model can be wrapped to comply with the interface, usually with a FORTRAN 90 subroutine. Development efforts need only knowledge of the model and basic programming skills. With such wrappers, the logic is the same for implementing all models. Code templates defined for this general model interface could be re-used with any specific model. Therefore, the model implementation can be done automatically. An automated model implementation toolkit was developed with Microsoft Excel and its built-in VBA language. It allows model specifications in three worksheets and contains FORTRAN 90 code templates in VBA programs. According to the model specification, the toolkit generates data structures and procedures within FORTRAN modules and subroutines, which transfer data between LIS and the model wrapper. Model implementation is standardized, and about 80 - 90% of the development load is reduced. In this presentation, the automated model implementation approach is described along with LIS programming interfaces, the general model interface and five case studies, including a regression model, Noah-MP, FASST, SAC-HTET/SNOW-17, and FLake. These different models vary in complexity with software structure. Also, we will describe how these complexities were overcome through using this approach and results of model benchmarks within LIS.

The NIE Conference on Basic Mathematical Skills and Learning (Euclid, Ohio, October 4-6, 1975). Volume I: Contributed Position Papers.

ERIC Educational Resources Information Center

National Inst. of Education (DHEW), Washington, DC.

In October 1975 a conference was convened in Euclid, Ohio, by the Basic Skills Group of the National Institute of Education (NIE). Thirty-three participants presented position papers addressing two major questions: (1) What are basic mathematical skills and learning? (2) What are the major problems related to children's acquisition of basic…

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

PubMed

Abràmoff, Michael David; Lou, Yiyue; Erginay, Ali; Clarida, Warren; Amelon, Ryan; Folk, James C; Niemeijer, Meindert

2016-10-01

To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.

Automated Geospatial Watershed Assessment (AGWA) Tool for hydrologic modeling and watershed assessment

EPA Pesticide Factsheets

Using basic, easily attainable GIS data, AGWA provides a simple, direct, and repeatable methodology for hydrologic model setup, execution, and visualization. AGWA experiences activity from over 170 countries. It l has been downloaded over 11,000 times.

76 FR 14442 - 60-Day Notice of Proposed Information Collection: DS 6561 Pre-Assignment for Overseas Duty for...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-16

... automated collection techniques or other forms of technology. Abstract of proposed collection: The DS 6561 form provides a concise summary of basic medical history, lab tests and physical examination. Since...

Radio Frequency Interference Detection using Machine Learning.

NASA Astrophysics Data System (ADS)

Mosiane, Olorato; Oozeer, Nadeem; Aniyan, Arun; Bassett, Bruce A.

2017-05-01

Radio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man. With the huge amount of data that will be available with up coming radio telescopes, an automated aproach will be required to detect RFI. In this paper to try automate this process we present the result of applying machine learning techniques to cross match RFI from the Karoo Array Telescope (KAT-7) data. We found that not all the features selected to characterise RFI are always important. We further investigated 3 machine learning techniques and conclude that the Random forest classifier performs with a 98% Area Under Curve and 91% recall in detecting RFI.

Machine learning for micro-tomography

NASA Astrophysics Data System (ADS)

Parkinson, Dilworth Y.; Pelt, Daniël. M.; Perciano, Talita; Ushizima, Daniela; Krishnan, Harinarayan; Barnard, Harold S.; MacDowell, Alastair A.; Sethian, James

2017-09-01

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab-from workstations to supercomputers-and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

The Predictive Validity of the Assessment of Basic Learning Abilities versus Parents' Predictions with Children with Autism

ERIC Educational Resources Information Center

Murphy, Colleen; Martin, Garry L.; Yu, C. T.

2014-01-01

The Assessment of Basic Learning Abilities (ABLA) is an empirically validated clinical tool for assessing the learning ability of persons with intellectual disabilities and children with autism. An ABLA tester uses standardized prompting and reinforcement procedures to attempt to teach, individually, each of six tasks, called levels, to a testee,…

Teaching Two Basic Nanotechnology Concepts in Secondary School by Using a Variety of Teaching Methods

ERIC Educational Resources Information Center

Blonder, Ron; Sakhnini, Sohair

2012-01-01

A nanotechnology module was developed for ninth grade students in the context of teaching chemistry. Two basic concepts in nanotechnology were chosen: (1) size and scale and (2) surface-area-to-volume ratio (SA/V). A wide spectrum of instructional methods (e.g., game-based learning, learning with multimedia, learning with models, project based…

Internal Interest or External Performing? A Qualitative Study on Motivation and Learning of 9th Graders in Thailand Basic Education

ERIC Educational Resources Information Center

Loima, Jyrki; Vibulphol, Jutarat

2014-01-01

This qualitative research was the first academic attempt to study and discuss the internal and external motivation in learning of students in basic education schools in Thailand. The study addressed two research questions to analyze similarities and differences in learning motivation or interest and teachers' enhancement or discouragement. 1) What…

Does Physical Environment Contribute to Basic Psychological Needs? A Self-Determination Theory Perspective on Learning in the Chemistry Laboratory

ERIC Educational Resources Information Center

Sjöblom, Kirsi; Mälkki, Kaisu; Sandström, Niclas; Lonka, Kirsti

2016-01-01

The role of motivation and emotions in learning has been extensively studied in recent years; however, research on the role of the physical environment still remains scarce. This study examined the role of the physical environment in the learning process from the perspective of basic psychological needs. Although self-determination theory stresses…

Pilot Project in Computer Assisted Instruction for Adult Basic Education Students. Adult Learning Centers, the Adult Program, 1982-83.

ERIC Educational Resources Information Center

Buckley, Elizabeth; Johnston, Peter

In February 1977, computer assisted instruction (CAI) was introducted to the Great Neck Adult Learning Centers (GNALC) to promote greater cognitive and affective growth of educationally disadvantaged adults. The project expanded to include not only adult basic education (ABE) students studying in the learning laboratory, but also ABE students…

Learnings from an Entrepreneur: How to Start a Consulting Practice

NASA Astrophysics Data System (ADS)

Bowes, Debra

2013-03-01

There are important basic learnings I have experienced in starting my own consulting practice over 7 years ago. These learnings will help you maximize your value, reduce competition and build your reputation and business income. I believe these can apply to many fields but certainly for the Life Sciences. A few of the basic I will cover are

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow.

PubMed

Stockton, David B; Santamaria, Fidel

2017-10-01

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

Reading Guided by Automated Graphical Representations: How Model-Based Text Visualizations Facilitate Learning in Reading Comprehension Tasks

ERIC Educational Resources Information Center

Pirnay-Dummer, Pablo; Ifenthaler, Dirk

2011-01-01

Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The "text to graph" feature of the software is based on…

Liberated Learning: Analysis of University Students' Perceptions and Experiences with Continuous Automated Speech Recognition

ERIC Educational Resources Information Center

Ryba, Ken; McIvor, Tom; Shakir, Maha; Paez, Di

2006-01-01

This study examined continuous automated speech recognition in the university lecture theatre. The participants were both native speakers of English (L1) and English as a second language students (L2) enrolled in an information systems course (Total N=160). After an initial training period, an L2 lecturer in information systems delivered three…

Evaluating the Validity and Applicability of Automated Essay Scoring in Two Massive Open Online Courses

ERIC Educational Resources Information Center

Reilly, Erin Dawna; Stafford, Rose Eleanore; Williams, Kyle Marie; Corliss, Stephanie Brooks

2014-01-01

The use of massive open online courses (MOOCs) to expand students' access to higher education has raised questions regarding the extent to which this course model can provide and assess authentic, higher level student learning. In response to this need, MOOC platforms have begun utilizing automated essay scoring (AES) systems that allow…

Comparing Human and Automated Essay Scoring for Prospective Graduate Students with Learning Disabilities and/or ADHD

ERIC Educational Resources Information Center

Buzick, Heather; Oliveri, Maria Elena; Attali, Yigal; Flor, Michael

2016-01-01

Automated essay scoring is a developing technology that can provide efficient scoring of large numbers of written responses. Its use in higher education admissions testing provides an opportunity to collect validity and fairness evidence to support current uses and inform its emergence in other areas such as K-12 large-scale assessment. In this…

An Automated Sample Processing System for Planetary Exploration

NASA Technical Reports Server (NTRS)

Soto, Juancarlos; Lasnik, James; Roark, Shane; Beegle, Luther

2012-01-01

An Automated Sample Processing System (ASPS) for wet chemistry processing of organic materials on the surface of Mars has been jointly developed by Ball Aerospace and the Jet Propulsion Laboratory. The mechanism has been built and tested to demonstrate TRL level 4. This paper describes the function of the system, mechanism design, lessons learned, and several challenges that were overcome.

Effects of Automated Tier 2 Storybook Intervention on Vocabulary and Comprehension Learning in Preschool Children with Limited Oral Language Skills

ERIC Educational Resources Information Center

Kelley, Elizabeth Spencer; Goldstein, Howard; Spencer, Trina D.; Sherman, Amber

2015-01-01

This early efficacy study examined the effects of an automated storybook intervention designed to promote school readiness among at-risk prekindergarten children. Story Friends is a small-group intervention in which vocabulary and question-answering lessons are embedded in a series of storybooks.A randomized group design with an embedded…

A Cognitive Approach to the Education of Retarded Children

ERIC Educational Resources Information Center

Haywood, H. Carl

1977-01-01

Moderately mentally retarded children can acquire the necessary basic mental operations through a proper progression of mediated learning experiences; once the basic mental operations have been acquired, complex learning can occur because the necessary cognitive tools are present. (JD)

Gesture Recognition for Educational Games: Magic Touch Math

NASA Astrophysics Data System (ADS)

Kye, Neo Wen; Mustapha, Aida; Azah Samsudin, Noor

2017-08-01

Children nowadays are having problem learning and understanding basic mathematical operations because they are not interested in studying or learning mathematics. This project proposes an educational game called Magic Touch Math that focuses on basic mathematical operations targeted to children between the age of three to five years old using gesture recognition to interact with the game. Magic Touch Math was developed in accordance to the Game Development Life Cycle (GDLC) methodology. The prototype developed has helped children to learn basic mathematical operations via intuitive gestures. It is hoped that the application is able to get the children motivated and interested in mathematics.

Mathematics Content Coverage and Student Learning in Kindergarten

PubMed Central

Engel, Mimi; Claessens, Amy; Watts, Tyler; Farkas, George

2017-01-01

Analyzing data from two nationally representative kindergarten cohorts, we examine the mathematics content teachers cover in kindergarten. We expand upon prior research, finding that kindergarten teachers report emphasizing basic mathematics content. Although teachers reported increased coverage of advanced content between the 1998–99 and 2010–11 school years, they continued to place more emphasis on basic content. We find that time on advanced content is positively associated with student learning, whereas time on basic content has a negative association with learning. We argue that increased exposure to more advanced mathematics content could benefit the vast majority of kindergartners. PMID:29353913

Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior

PubMed Central

Gris, Katsiaryna V.; Coutu, Jean-Philippe; Gris, Denis

2017-01-01

Quantifying behavior is a challenge for scientists studying neuroscience, ethology, psychology, pathology, etc. Until now, behavior was mostly considered as qualitative descriptions of postures or labor intensive counting of bouts of individual movements. Many prominent behavioral scientists conducted studies describing postures of mice and rats, depicting step by step eating, grooming, courting, and other behaviors. Automated video assessment technologies permit scientists to quantify daily behavioral patterns/routines, social interactions, and postural changes in an unbiased manner. Here, we extensively reviewed published research on the topic of the structural blocks of behavior and proposed a structure of behavior based on the latest publications. We discuss the importance of defining a clear structure of behavior to allow professionals to write viable algorithms. We presented a discussion of technologies that are used in automated video assessment of behavior in mice and rats. We considered advantages and limitations of supervised and unsupervised learning. We presented the latest scientific discoveries that were made using automated video assessment. In conclusion, we proposed that the automated quantitative approach to evaluating animal behavior is the future of understanding the effect of brain signaling, pathologies, genetic content, and environment on behavior. PMID:28804452

Application of the Golden Software Surfer mapping software for automation of visualisation of meteorological and oceanographic data in IMGW Maritime Branch.

NASA Astrophysics Data System (ADS)

Piliczewski, B.

2003-04-01

The Golden Software Surfer has been used in IMGW Maritime Branch for more than ten years. This tool provides ActiveX Automation objects, which allow scripts to control practically every feature of Surfer. These objects can be accessed from any Automation-enabled environment, such as Visual Basic or Excel. Several applications based on Surfer has been developed in IMGW. The first example is an on-line oceanographic service, which presents forecasts of the water temperature, sea level and currents originating from the HIROMB model and is automatically updated every day. Surfer was also utilised in MERMAID, an international project supported by EC under the 5th Framework Programme. The main aim of this project was to create a prototype of the Internet-based data brokerage system, which would enable to search, extract, buy and download datasets containing meteorological or oceanographic data. During the project IMGW developed an online application, called Mermaid Viewer, which enables communication with the data broker and automatic visualisation of the downloaded data using Surfer. Both the above mentioned applications were developed in Visual Basic. Currently it is considered to adopt Surfer for the monitoring service, which provides access to the data collected in the monitoring of the Baltic Sea environment.

Towards Careful Practices for Automated Linguistic Analysis of Group Learning

ERIC Educational Resources Information Center

Howley, Iris; Rosé, Carolyn Penstein

2016-01-01

The multifaceted nature of collaborative learning environments necessitates theory to investigate the cognitive, motivational, and relational dimensions of collaboration. Several existing frameworks include aspects related to each of these three. This article explores the capability of multi-dimensional frameworks for analysis of collaborative…

Academic Vocabulary Learning in First Through Third Grade in Low-Income Schools: Effects of Automated Supplemental Instruction.

PubMed

Goldstein, Howard; Ziolkowski, Robyn A; Bojczyk, Kathryn E; Marty, Ana; Schneider, Naomi; Harpring, Jayme; Haring, Christa D

2017-11-09

This study investigated cumulative effects of language learning, specifically whether prior vocabulary knowledge or special education status moderated the effects of academic vocabulary instruction in high-poverty schools. Effects of a supplemental intervention targeting academic vocabulary in first through third grades were evaluated with 241 students (6-9 years old) from low-income families, 48% of whom were retained for the 3-year study duration. Students were randomly assigned to vocabulary instruction or comparison groups. Curriculum-based measures of word recognition, receptive identification, expressive labeling, and decontextualized definitions showed large effects for multiple levels of word learning. Hierarchical linear modeling revealed that students with higher initial Peabody Picture Vocabulary Test-Fourth Edition scores (Dunn & Dunn, 2007) demonstrated greater word learning, whereas students with special needs demonstrated less growth in vocabulary. This model of vocabulary instruction can be applied efficiently in high-poverty schools through an automated, easily implemented adjunct to reading instruction in the early grades and holds promise for reducing gaps in vocabulary development.

Advanced interdisciplinary undergraduate program: light engineering

NASA Astrophysics Data System (ADS)

Bakholdin, Alexey; Bougrov, Vladislav; Voznesenskaya, Anna; Ezhova, Kseniia

2016-09-01

The undergraduate educational program "Light Engineering" of an advanced level of studies is focused on development of scientific learning outcomes and training of professionals, whose activities are in the interdisciplinary fields of Optical engineering and Technical physics. The program gives practical experience in transmission, reception, storage, processing and displaying information using opto-electronic devices, automation of optical systems design, computer image modeling, automated quality control and characterization of optical devices. The program is implemented in accordance with Educational standards of the ITMO University. The specific features of the Program is practice- and problem-based learning implemented by engaging students to perform research and projects, internships at the enterprises and in leading Russian and international research educational centers. The modular structure of the Program and a significant proportion of variable disciplines provide the concept of individual learning for each student. Learning outcomes of the program's graduates include theoretical knowledge and skills in natural science and core professional disciplines, deep knowledge of modern computer technologies, research expertise, design skills, optical and optoelectronic systems and devices.

Anomaly detection using temporal data mining in a smart home environment.

PubMed

Jakkula, V; Cook, D J

2008-01-01

To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

ERIC Educational Resources Information Center

Liu, Ran; Koedinger, Kenneth R.

2017-01-01

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…

Virtual laboratory learning media development to improve science literacy skills of mechanical engineering students on basic physics concept of material measurement

NASA Astrophysics Data System (ADS)

Jannati, E. D.; Setiawan, A.; Siahaan, P.; Rochman, C.

2018-05-01

This study aims to determine the description of virtual laboratory learning media development to improve science literacy skills of Mechanical Engineering students on the concept of basic Physics. Quasi experimental method was employed in this research. The participants of this research were first semester students of mechanical engineering in Majalengka University. The research instrument was readability test of instructional media. The results of virtual laboratory learning media readability test show that the average score is 78.5%. It indicates that virtual laboratory learning media development are feasible to be used in improving science literacy skill of Mechanical Engineering students in Majalengka University, specifically on basic Physics concepts of material measurement.

Mobilization and Defense Management Technical Reports Series. Acquisition of ADP (Automated Data Processing) by the Army during Mobilization.

DTIC Science & Technology

1983-03-01

have both a Federal and a State mission. The State mission is to provide protection of life and property and to preserve peace and public safety. The...logistics system is basically the same and will be integrated into the active system in wartime. Financial man- agement support consists of financial...the entire system is security. ErS is on contract to furnish basic security as well as a higher type of security known as the enhanced version

Intelligent Help in the LOCATE Workspace Layout Tool

DTIC Science & Technology

1999-06-01

LOCATE’s basic design and analysis features; • commercialising the application; • expanding the groundwork for tracking actions and goals at the interface...Muraida, D.J. (Eds.) (1993). Automating instructional design: Concepts and issues. Englewood Cliffs, N.J.: Educational Technology Publications

Mining the Galaxy Zoo Database: Machine Learning Applications

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Wallin, J.; Vedachalam, A.; Baehr, S.; Lintott, C.; Darg, D.; Smith, A.; Fortson, L.

2010-01-01

The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

Improving the Quality of Teaching and Learning through Leadership for Learning: Changing Scenarios in Basic Schools of Ghana

ERIC Educational Resources Information Center

Malakolunthu, Suseela; McBeath, John; Swaffield, Sue

2014-01-01

This article emerged as a case study from a fact-finding mission of a joint programme between the Centre for Commonwealth Education (CCE) in Cambridge University and the Institute for Educational Planning and Administration (IEPA) in University of Cape Coast, Ghana, to embed innovative approaches to teaching and learning in the basic schools of…

A Comparison between Flash and Second Life Programs as Aids in the Learning of Basic Laboratory Procedures

ERIC Educational Resources Information Center

Booth, Paula; Henderson-Begg, Stephanie

2011-01-01

Invited as a paper from E-Learn 2009 This study compared two programmes developed as a learning tool for students to practise basic laboratory procedures. One was a Flash simulation programme, the other a Second Life virtual reality programme. A cohort of 93 bioscience students participated in the between trial. A control group was used to…

More than the Sum of the Parts: Using Small Group Learning in Adult Basic and Literacy Education. A 353 Special Demonstration Project.

ERIC Educational Resources Information Center

Imel, Susan; And Others

This guide provides practical information that teachers and administrators can use to initiate the small group learning approach in adult basic and literacy education (ABLE). A locator index directs readers to specific topics. Chapter 1 describes small group learning, provides a rationale, and points out advantages and disadvantages for learners,…

Faster Teaching via POMDP Planning

ERIC Educational Resources Information Center

Rafferty, Anna N.; Brunskill, Emma; Griffiths, Thomas L.; Shafto, Patrick

2016-01-01

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model…

Semantic Services in e-Learning: An Argumentation Case Study

ERIC Educational Resources Information Center

Moreale, Emanuela; Vargas-Vera, Maria

2004-01-01

This paper outlines an e-Learning services architecture offering semantic-based services to students and tutors, in particular ways to browse and obtain information through web services. Services could include registration, authentication, tutoring systems, smart question answering for students' queries, automated marking systems and a student…

Educators' Perceptions of Automated Feedback Systems

ERIC Educational Resources Information Center

Debuse, Justin C. W.; Lawley, Meredith; Shibl, Rania

2008-01-01

Assessment of student learning is a core function of educators. Ideally students should be provided with timely, constructive feedback to facilitate learning. However, provision of high quality feedback becomes more complex as class sizes increase, modes of study expand and academic workloads increase. ICT solutions are being developed to…

Prototyping with Application Generators: Lessons Learned from the Naval Aviation Logistics Command Management Information System Case

DTIC Science & Technology

1992-10-01

Prototyping with Application Generators: Lessons Learned from the Naval Aviation Logistics Command Management Information System Case. This study... management information system to automate manual Naval aviation maintenance tasks-NALCOMIS. With the use of a fourth-generation programming language

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

PubMed

Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

2018-01-01

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

Automated Essay Grading using Machine Learning Algorithm

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

2018-04-01

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

Deep Learning and Image Processing for Automated Crack Detection and Defect Measurement in Underground Structures

NASA Astrophysics Data System (ADS)

Panella, F.; Boehm, J.; Loo, Y.; Kaushik, A.; Gonzalez, D.

2018-05-01

This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information). As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

"The Dilemma That Still Counts": Basic Writing at a Political Crossroads.

ERIC Educational Resources Information Center

Harrington, Susanmarie; Adler-Kassner, Linda

1998-01-01

Reviews definitions of basic writers and basic writing over the last 20 years. Argues that basic writers are not defined only in terms of institutional convenience. Offers future directions for basic writing research, suggesting that to learn more about basic writers, researchers must return to studies of error informed by basic writing's rich…

Automated Assume-Guarantee Reasoning by Abstraction Refinement

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Giannakopoulous, Dimitra; Glannakopoulou, Dimitra

2008-01-01

Current automated approaches for compositional model checking in the assume-guarantee style are based on learning of assumptions as deterministic automata. We propose an alternative approach based on abstraction refinement. Our new method computes the assumptions for the assume-guarantee rules as conservative and not necessarily deterministic abstractions of some of the components, and refines those abstractions using counter-examples obtained from model checking them together with the other components. Our approach also exploits the alphabets of the interfaces between components and performs iterative refinement of those alphabets as well as of the abstractions. We show experimentally that our preliminary implementation of the proposed alternative achieves similar or better performance than a previous learning-based implementation.

Overcoming Barriers to Technology Adoption in Small Manufacturing Enterprises (SMEs)

DTIC Science & Technology

2003-06-01

automates quote-generation, order - processing workflow management, perform- ance analysis, and accounting functions. Ultimately, it will enable Magdic...that Magdic imple- ment an MES instead. The MES, in addition to solving the problem of document manage- ment, would automate quote-generation, order ... processing , workflow management, perform- ance analysis, and accounting functions. To help Magdic personnel learn about the MES, TIDE personnel provided

Training-Based Requirements for Semi-Automated Forces

DTIC Science & Technology

1999-03-01

ARI Research Note 99-18 Training-based Requirements for Semi-Automated Forces Jim Kornell Syukhtun Research, Inc. Research and Advanced Concepts ... Concepts , Inc.; Dr. Susan Fischer, of Anacapa Sciences, Inc.; and Lt. Col. Ken Bell, ret., of THETA Technologies, Inc. All conclusions and...construed) in terms of symbols and concepts , and/or the learning of procedural knowledge. • Cognitive strategies. Skills for governing thinking. This

Effectiveness of Active Learning Strategy in Improving the Acoustic Awareness Skills and Understanding What Is Heard by the Basic Stage Students in Jordan

ERIC Educational Resources Information Center

Al-Odwan, Yaser

2016-01-01

This research aims to get acquainted with the effectiveness of the active learning strategy in improving the acoustic awareness skills and understanding what is heard by the basic stage students in Jordan by answering the two following questions: This research has been applied to a sample of 60 students from the basic third grade in Al-Ahnaf Ben…

A robust automated system elucidates mouse home cage behavioral structure

PubMed Central

Goulding, Evan H.; Schenk, A. Katrin; Juneja, Punita; MacKay, Adrienne W.; Wade, Jennifer M.; Tecott, Laurence H.

2008-01-01

Patterns of behavior exhibited by mice in their home cages reflect the function and interaction of numerous behavioral and physiological systems. Detailed assessment of these patterns thus has the potential to provide a powerful tool for understanding basic aspects of behavioral regulation and their perturbation by disease processes. However, the capacity to identify and examine these patterns in terms of their discrete levels of organization across diverse behaviors has been difficult to achieve and automate. Here, we describe an automated approach for the quantitative characterization of fundamental behavioral elements and their patterns in the freely behaving mouse. We demonstrate the utility of this approach by identifying unique features of home cage behavioral structure and changes in distinct levels of behavioral organization in mice with single gene mutations altering energy balance. The robust, automated, reproducible quantification of mouse home cage behavioral structure detailed here should have wide applicability for the study of mammalian physiology, behavior, and disease. PMID:19106295

An anatomy of industrial robots and their controls

NASA Astrophysics Data System (ADS)

Luh, J. Y. S.

1983-02-01

The modernization of manufacturing facilities by means of automation represents an approach for increasing productivity in industry. The three existing types of automation are related to continuous process controls, the use of transfer conveyor methods, and the employment of programmable automation for the low-volume batch production of discrete parts. The industrial robots, which are defined as computer controlled mechanics manipulators, belong to the area of programmable automation. Typically, the robots perform tasks of arc welding, paint spraying, or foundary operation. One may assign a robot to perform a variety of job assignments simply by changing the appropriate computer program. The present investigation is concerned with an evaluation of the potential of the robot on the basis of its basic structure and controls. It is found that robots function well in limited areas of industry. If the range of tasks which robots can perform is to be expanded, it is necessary to provide multiple-task sensors, or special tooling, or even automatic tooling.

Automated space processing payloads study. Volume 2, book 1: Technical report. [instrument packages and space shuttles

NASA Technical Reports Server (NTRS)

1975-01-01

The extent was investigated to which experiment hardware and operational requirements can be met by automatic control and material handling devices; payload and system concepts that make extensive use of automation technology are defined. Hardware requirements for each experiment were established and tabulated, and investigations of applicable existing hardware were documented. The capabilities and characteristics of industrial automation equipment, controls, and techniques are presented in the form of a summary of applicable equipment characteristics in three basic mutually-supporting formats. Facilities for performing groups of experiments are defined along with four levitation groups and three furnace groups; major hardware elements required to implement them were identified. A conceptual design definition of ten different automated processing facilities is presented along with the specific equipment to implement each facility and the design layouts of the different units. Constraints and packaging, weight, and power requirements for six payloads postulated for shuttle missions in the 1979 to 1982 time period were examined.

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

A computerized procedure for teaching the relationship between graphic symbols and their referents.

PubMed

Isaacson, Mick; Lloyd, Lyle L

2013-01-01

Many individuals with little or no functional speech communicate through graphic symbols. Communication is enhanced when the relationship between symbols and their referents are learned to such a degree that retrieval is effortless, resulting in fluent communication. Developing fluency is a time consuming endeavor for special educators and speech-language pathologists (SLPs). It would be beneficial for these professionals to have an automated procedure based on the most efficacious method for teaching the relationship between symbols and referent. Hence, this study investigated whether a procedure based on the generation effect would promote learning the association between symbols and their referents. Results show that referent generation produces the best long-term retention of this relationship. These findings provide evidence that software based on referent generation would provide special educators and SLPs with an efficacious automated procedure, requiring minimal direct supervision, to facilitate symbol/referent learning and the development of communicative fluency.

Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

ERIC Educational Resources Information Center

Matthews, Kevin; Janicki, Thomas; He, Ling; Patterson, Laurie

2012-01-01

This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains…

Commentary on "Theory-Led Design of Instruments and Representations in Learning Analytics: Developing a Novel Tool for Orchestration of Online Collaborative Learning"

ERIC Educational Resources Information Center

Teplovs, Chris

2015-01-01

This commentary reflects on the contributions to learning analytics and theory by a paper that describes how multiple theoretical frameworks were woven together to inform the creation of a new, automated discourse analysis tool. The commentary highlights the contributions of the original paper, provides some alternative approaches, and touches on…

Will the future of knowledge work automation transform personalized medicine?

PubMed

Naik, Gauri; Bhide, Sanika S

2014-09-01

Today, we live in a world of 'information overload' which demands high level of knowledge-based work. However, advances in computer hardware and software have opened possibilities to automate 'routine cognitive tasks' for knowledge processing. Engineering intelligent software systems that can process large data sets using unstructured commands and subtle judgments and have the ability to learn 'on the fly' are a significant step towards automation of knowledge work. The applications of this technology for high throughput genomic analysis, database updating, reporting clinically significant variants, and diagnostic imaging purposes are explored using case studies.

Automated activity-aware prompting for activity initiation.

PubMed

Holder, Lawrence B; Cook, Diane J

2013-01-01

Performing daily activities without assistance is important to maintaining an independent functional lifestyle. As a result, automated activity prompting systems can potentially extend the period of time that adults can age in place. In this paper we introduce AP, an algorithm to automate activity prompting based on smart home technology. AP learns prompt rules based on the time when activities are typically performed as well as the relationship between activities that normally occur in a sequence. We evaluate the AP algorithm based on smart home datasets and demonstrate its ability to operate within a physical smart environment.

Exploring cognitive integration of basic science and its effect on diagnostic reasoning in novices.

PubMed

Lisk, Kristina; Agur, Anne M R; Woods, Nicole N

2016-06-01

Integration of basic and clinical science knowledge is increasingly being recognized as important for practice in the health professions. The concept of 'cognitive integration' places emphasis on the value of basic science in providing critical connections to clinical signs and symptoms while accounting for the fact that clinicians may not spontaneously articulate their use of basic science knowledge in clinical reasoning. In this study we used a diagnostic justification test to explore the impact of integrated basic science instruction on novices' diagnostic reasoning process. Participants were allocated to an integrated basic science or clinical science training group. The integrated basic science group was taught the clinical features along with the underlying causal mechanisms of four musculoskeletal pathologies while the clinical science group was taught only the clinical features. Participants completed a diagnostic accuracy test immediately after initial learning, and one week later a diagnostic accuracy and justification test. The results showed that novices who learned the integrated causal mechanisms had superior diagnostic accuracy and better understanding of the relative importance of key clinical features. These findings further our understanding of cognitive integration by providing evidence of the specific changes in clinical reasoning when basic and clinical sciences are integrated during learning.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

NASA Astrophysics Data System (ADS)

Brown, James M.; Campbell, J. Peter; Beers, Andrew; Chang, Ken; Donohue, Kyra; Ostmo, Susan; Chan, R. V. Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Chiang, Michael F.; Kalpathy-Cramer, Jayashree

2018-03-01

Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CNNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeNet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts' consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool.

Selected Instrumentation Films, 1969-1970.

ERIC Educational Resources Information Center

Simmons, Raymond L., Ed.

This list of currently available films and filmstrips pertinent to instrumentation has been compiled from information solicited from many government and private sources. The 1969 compilation has been organized into the following eight categories: (1) principles of measurement and basic measurements; (2) analysis instrumentation; (3) automation and…

21 CFR 352.77 - Test modifications.

Code of Federal Regulations, 2010 CFR

2010-04-01

... modification of the testing procedures in this subpart. In addition, alternative methods (including automated or in vitro procedures) employing the same basic procedures as those described in this subpart may be used. Any proposed modification or alternative procedure shall be submitted as a petition in accord...

21 CFR 352.77 - Test modifications.

Code of Federal Regulations, 2011 CFR

2011-04-01

... modification of the testing procedures in this subpart. In addition, alternative methods (including automated or in vitro procedures) employing the same basic procedures as those described in this subpart may be used. Any proposed modification or alternative procedure shall be submitted as a petition in accord...

The Ins and Outs of Access Control.

ERIC Educational Resources Information Center

Longworth, David

1999-01-01

Presents basic considerations when school districts plan to acquire an access-control system for their education facilities. Topics cover cards and readers, controllers, software, automation, card technology, expandability, price, specification of needs beyond the canned specifications already supplied, and proper usage training to cardholders.…

A little anthropomorphism goes a long way: Effects of oxytocin on trust, compliance and team performance with automated agents

PubMed Central

de Visser, Ewart J.; Monfort, Samuel S.; Goodyear, Kimberly; Lu, Li; O’Hara, Martin; Lee, Mary R.; Parasuraman, Raja; Krueger, Frank

2017-01-01

Objective We investigated the effects of exogenous oxytocin on trust, compliance, and team decision making with agents varying in anthropomorphism (computer, avatar, human) and reliability (100%, 50%). Background Recent work has explored psychological similarities in how we trust human-like automation compared to how we trust other humans. Exogenous administration of oxytocin, a neuropeptide associated with trust among humans, offers a unique opportunity to probe the anthropomorphism continuum of automation to infer when agents are trusted like another human or merely a machine. Method Eighty-four healthy male participants collaborated with automated agents varying in anthropomorphism that provided recommendations in a pattern recognition task. Results Under placebo, participants exhibited less trust and compliance with automated aids as the anthropomorphism of those aids increased. Under oxytocin, participants interacted with aids on the extremes of the anthropomorphism continuum similarly to placebos, but increased their trust, compliance, and performance with the avatar, an agent on the midpoint of the anthropomorphism continuum. Conclusion This study provided the first evidence that administration of exogenous oxytocin affected trust, compliance, and team decision making with automated agents. These effects provide support for the premise that oxytocin increases affinity for social stimuli in automated aids. Application Designing automation to mimic basic human characteristics is sufficient to elicit behavioral trust outcomes that are driven by neurological processes typically observed in human-human interactions. Designers of automated systems should consider the task, the individual, and the level of anthropomorphism to achieve the desired outcome. PMID:28146673

The Role of Affective and Motivational Factors in Designing Personalized Learning Environments

ERIC Educational Resources Information Center

Kim, ChanMin

2012-01-01

In this paper, guidelines for designing virtual change agents (VCAs) are proposed to support students' affective and motivational needs in order to promote personalized learning in online remedial mathematics courses. Automated, dynamic, and personalized support is emphasized in the guidelines through maximizing "interactions" between VCAs and…

Automated Agent Ontology Creation for Distributed Databases

DTIC Science & Technology

2004-03-01

relationships between themselves if one exists. For example, if one agent’s ontology was ‘ NBA ’ and the second agent’s ontology was ‘College Hoops...the two agents should discover their relationship ‘ basketball ’ [28]. The authors’ agents use supervised inductive learning to learn their individual

Catalyzing Collaborative Learning: How Automated Task Distribution May Prompt Students to Collaborate

ERIC Educational Resources Information Center

Armstrong, Chandler

2010-01-01

Collaborative learning must prompt collaborative behavior among students. Once initiated, collaboration then must facilitate awareness between students of each other's activities and knowledge. Collaborative scripts provide explicit framework and guidance for roles and activities within student interactions, and are one method of fulfilling the…

Using Reading as an Automated Learning Tool

ERIC Educational Resources Information Center

Ruiz Fodor, Ana

2017-01-01

The problem addressed in this quantitative experimental study was that students were having more difficulty learning from audiovisual lessons than necessary because educators had eliminated textual references, based on early findings from CLT research. In more recent studies, CLT researchers estimated that long-term memory schemas may be used by…

Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations

ERIC Educational Resources Information Center

Nehm, Ross H.; Ha, Minsu; Mayfield, Elijah

2012-01-01

This study explored the use of machine learning to automatically evaluate the accuracy of students' written explanations of evolutionary change. Performance of the Summarization Integrated Development Environment (SIDE) program was compared to human expert scoring using a corpus of 2,260 evolutionary explanations written by 565 undergraduate…

Assessment of learning and memory using the autoshaping of operant responding in mice.

PubMed

Barrett, James E; Vanover, K E

2004-02-01

This unit describes the use of an automated procedure for developing an operant response ("autoshaping") in the mouse. The method has applications in the study of the acquisition of behavior (learning) as well as for the assessment of memory or retention of that task.

The Evolution of Instrument Flying in the U.S. Army.

DTIC Science & Technology

1988-04-01

had learned to fly in World War One without instruments. They either distrusted instruments and avoided clouds, or were "seat-of- the-pants" flyers...hooded flight training, Basic students received six hours, and Advanced students received fifteen. Primary and Basic students learned aircraft control and...instrument maneuvers while Advanced students learned radio-navigation.;’ The twenty-seven hours of instrument 23 flying represented 11 percent of the

Adult Basic Skills Instructor Training and Experiential Learning Theory.

ERIC Educational Resources Information Center

Marlowe, Mike; And Others

1991-01-01

Competency-based training workshops based on Kolb's experiential learning theory were held for North Carolina adult basic education teachers; 251 attended 1-day sessions and 91 a week-long summer institute. Topics included interpersonal communication, reading, numeracy, language arts, math, assessment, and program evaluation. (SK)

Environmental Education: Back to Basics.

ERIC Educational Resources Information Center

Warpinski, Robert

1984-01-01

Describes an instructional framework based on concepts of energy, ecosystems, carrying capacity, change, and stewardship. Stresses the importance of determining what is really important (basic) for each student to experience or learn in relation to each concept and grade level. Student-centered learning activities and sample lesson on energy…

Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

PubMed

Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

2016-01-01

Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

Teaching pathology in the 21st century. An experimental automated curriculum delivery system for basic pathology.

PubMed

Woods, J W; Jones, R R; Schoultz, T W; Kuenz, M; Moore, R L

1988-08-01

In late 1984, the "General Professional Education of the Physician" (GPEP) report recommended, among other things, that medical curricula be revised to rely less on lectures and more on independent study and problem solving. We seem to have anticipated, in 1980, the findings of the GPEP panel by formulating and starting to test the hypothesis that certain "core" information in medical curricula can be as effectively delivered by technology-based self-study means as by lecture or formal laboratory. We began, at that time, to prepare a series of self-study materials using, at first, videotape and then computer-controlled optical videodiscs. The content area selected for study was basic microscopic pathology. The series was planned to cover the following areas of study: cellular alterations and adaptations, cell injury, acute inflammation, chronic inflammation and wound healing, cellular accumulations, circulatory disturbances, necrosis, and neoplasia. All are intended to provide learning experiences in basic pathology. The first two programs were released for testing in 1983 as a two-sided videodisc accompanied by computer-driven pretests, study modules, and posttests that used Apple computers and Pioneer (DiscoVision) videodisc players. An MS DOS (eg, IBM) version of the computer programs was released in 1984. The first two programs are now used in 57 US, Canadian, European, and Philippine health professions schools, and over 1300 student and faculty evaluations have been received. Student and faculty evaluations of these first two programs were very positive, and, as a result, the others are in production and will be completed in 1988. Only when a critical mass of curriculum is available can we really test our stated hypothesis. In the meantime, it is worthwhile to report the evaluation of the first two programs.

Using machine learning techniques to automate sky survey catalog generation

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Roden, J. C.; Doyle, R. J.; Weir, Nicholas; Djorgovski, S. G.

1993-01-01

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

NASA Astrophysics Data System (ADS)

Ross, Z. E.; Meier, M. A.; Hauksson, E.

2017-12-01

Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

Robust System for Automated Identification of Martian Impact Craters

NASA Astrophysics Data System (ADS)

Stepinski, T. F.; Mendenhall, M. P.

2006-12-01

Detailed analysis of the number and morphology of impact craters on Mars provides the worth of information about the geologic history of its surface. Global catalogs of Martian craters have been compiled (for example, the Barlow catalog) but they are not comprehensive, especially for small craters. Existing methods for machine detection of craters from images suffer from low efficiency and are not practical for global surveys. We have developed a robust two-stage system for an automated cataloging of craters from digital topography data (DEM). In the first stage an innovative crater-finding transform is performed on a DEM to identify centers of potential craters, their extents, and their basic characteristics. This stage produces a preliminary catalog. In the second stage a machine learning methods are employed to eliminate false positives. Using the MOLA derived DEMs with resolution of 1/128 degrees/pixel, we have applied our system to six ~ 106 km2 sites. The system has identified 3217 craters, 43% more than are present in the Barlow catalog. The extra finds are predominantly small craters that are most difficult to account for in manual surveys. Because our automated survey is DEM-based, the resulting catalog lists craters' depths in addition to their positions, sizes, and measures of shape. This feature significantly increases the scientific utility of any catalog generated using our system. Our initial calculations yield a training set that will be used to identify craters over the entire Martian surface with estimated accuracy of 95%. Moreover, because our method is pixel-based and scale- independent, the present training set may be used to identify craters in higher resolution DEMs derived from Mars Express HRSC images. It also can be applied to future topography data from Mars and other planets. For example, it may be utilized to catalog craters on Mercury and the Moon using altimetry data to be gathered by Messenger and Lunar Reconnaissance Orbiter spacecrafts.

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

PubMed Central

Gallistel, C. R.; Balci, Fuat; Freestone, David; Kheifets, Aaron; King, Adam

2014-01-01

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer. PMID:24637442

Automated, quantitative cognitive/behavioral screening of mice: for genetics, pharmacology, animal cognition and undergraduate instruction.

PubMed

Gallistel, C R; Balci, Fuat; Freestone, David; Kheifets, Aaron; King, Adam

2014-02-26

We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.

Virtual reality for intelligent and interactive operating, training, and visualization systems

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Rossmann, Juergen; Schluse, Michael

2000-10-01

Virtual Reality Methods allow a new and intuitive way of communication between man and machine. The basic idea of Virtual Reality (VR) is the generation of artificial computer simulated worlds, which the user not only can look at but also can interact with actively using data glove and data helmet. The main emphasis for the use of such techniques at the IRF is the development of a new generation of operator interfaces for the control of robots and other automation components and for intelligent training systems for complex tasks. The basic idea of the methods developed at the IRF for the realization of Projective Virtual Reality is to let the user work in the virtual world as he would act in reality. The user actions are recognized by the Virtual reality System and by means of new and intelligent control software projected onto the automation components like robots which afterwards perform the necessary actions in reality to execute the users task. In this operation mode the user no longer has to be a robot expert to generate tasks for robots or to program them, because intelligent control software recognizes the users intention and generated automatically the commands for nearly every automation component. Now, Virtual Reality Methods are ideally suited for universal man-machine-interfaces for the control and supervision of a big class of automation components, interactive training and visualization systems. The Virtual Reality System of the IRF-COSIMIR/VR- forms the basis for different projects starting with the control of space automation systems in the projects CIROS, VITAL and GETEX, the realization of a comprehensive development tool for the International Space Station and last but not least with the realistic simulation fire extinguishing, forest machines and excavators which will be presented in the final paper in addition to the key ideas of this Virtual Reality System.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

PubMed

Burlina, Philippe M; Joshi, Neil; Pekala, Michael; Pacheco, Katia D; Freund, David E; Bressler, Neil M

2017-11-01

Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Accuracy, receiver operating characteristics and area under the curve, and kappa score. The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

The Effect of Instructional Method on Cardiopulmonary Resuscitation Skill Performance: A Comparison Between Instructor-Led Basic Life Support and Computer-Based Basic Life Support With Voice-Activated Manikin.

PubMed

Wilson-Sands, Cathy; Brahn, Pamela; Graves, Kristal

2015-01-01

Validating participants' ability to correctly perform cardiopulmonary resuscitation (CPR) skills during basic life support courses can be a challenge for nursing professional development specialists. This study compares two methods of basic life support training, instructor-led and computer-based learning with voice-activated manikins, to identify if one method is more effective for performance of CPR skills. The findings suggest that a computer-based learning course with voice-activated manikins is a more effective method of training for improved CPR performance.

Characterization of available automated external defibrillators in the market based on the product manuals in 2014

PubMed Central

Ho, Chik Leung; Cheng, Ka Wai; Ma, Tze Hang; Wong, Yau Hang; Cheng, Ka Lok; Kam, Chak Wah

2016-01-01

BACKGROUND: To popularize the wide-spread use of automated external defibrillator (AED) to save life in sudden cardiac arrest, we compared the strength and weakness of different types of AEDs to enable a sound selection based on regional requirement. METHODS: This was a retrospective descriptive study. Different types of AEDs were compared according to the information of AEDs from manuals and brochures provided by the manufacturers. Fifteen types of AEDs were divided into 3 groups, basic, intermediate and advanced. RESULTS: Lifeline™ AUTO AED had the best performance in price, portability and user-friendly among AEDs of basic level. It required less time for shock charging. Samaritan PAD defibrillator was superior in price, portability, durability and characteristic among AEDs of intermediate level. It had the longest warranty and highest protection against water and dust. Lifeline™ PRO AED had the best performance in most of the criteria among AEDs of advanced level and offered CPR video and manual mode for laypersons and clinicians respectively. CONCLUSION: Lifeline™ AUTO AED, Samaritan PAD defibrillator, Lifeline™ PRO AED are superior in AEDs of basic, intermediate and advanced levels, respectively. A feasible AED may be chosen by users according to the regional requirement and the current information about the best available products. PMID:27313810

Reinforcing Basic Skills Through Social Studies. Grades 4-7.

ERIC Educational Resources Information Center

Lewis, Teresa Marie

Arranged into seven parts, this document provides a variety of games and activities, bulletin board ideas, overhead transparencies, student handouts, and learning station ideas to help reinforce basic social studies skills in the intermediate grades. In part 1, students learn about timelines, first constructing their own life timeline, then a…

Lifelong Learning Research Conference Proceedings (4th, College Park, Maryland, February 12-13, 1982).

ERIC Educational Resources Information Center

Whaples, Gene C., Comp.; Rivera, William M., Comp.

These conference proceedings contain 55 papers and symposia presented at the conference whose focus was on nonformal adult education. Papers deal with adult/continuing education concerns such as participatory research, ABLE (Adult Basic Level Education) parenting, army basic skills educational development, learning contracts, volunteerism,…

Application of Number. Teaching and Learning.

ERIC Educational Resources Information Center

Bove, Francis

This basic math skills teaching and learning guide contains practical advice and resources for British vocational teachers who have little formal mathematics education training and for beginning teachers. The document has five sections on these topics dealing with numeracy instruction: (1) overview of the basic skill and its application to other…

Job-Related Basic Skills. ERIC Digest No. 94.

ERIC Educational Resources Information Center

Kerka, Sandra

Seven job-related basic skills identified as skills employers want are as follows: (1) learning to learn; (2) reading, writing, and computation; (3) oral communication and listening; (4) creative thinking and problem solving; (5) personal management, including self-esteem, goal setting, motivation, and personal and career development; (6) group…

Feasibility of Explicit Instruction in Adult Basic Education: Instructor-Learner Interaction Patterns

ERIC Educational Resources Information Center

Mellard, Daryl; Scanlon, David

2006-01-01

A strategic instruction model introduced into adult basic education classrooms yields insight into the feasibility of using direct and explicit instruction with adults with learning disabilities or other cognitive barriers to learning. Ecobehavioral assessment was used to describe and compare instructor-learner interaction patterns during learning…

Dispositional Factors Affecting Motivation during Learning in Adult Basic and Secondary Education Programs

ERIC Educational Resources Information Center

Mellard, Daryl F.; Krieshok, Thomas; Fall, Emily; Woods, Kari

2013-01-01

Research indicates that about a quarter of adult students separate from formal adult basic and secondary education (ABE/ASE) programs before completing one educational level. This retrospective study explores individual dispositional factors that affect motivation during learning, particularly students' goals, goal-directed thinking and action…

Comparison of automated home-cage monitoring systems: emphasis on feeding behaviour, activity and spatial learning following pharmacological interventions.

PubMed

Robinson, Lianne; Riedel, Gernot

2014-08-30

Different automated systems have been developed to facilitate long-term and continuous assessment of behaviours including locomotor activity, feeding behaviour and circadian activity. This study assessed the effectiveness of three different observation systems as methods for determining strain and pharmacological induced differences in locomotor activity, feeding behaviour and spatial learning. The effect of the CB1 antagonist AM251 on feeding behaviour was determined in the PhenoMaster and PhenoTyper. Next, effects of cholinergic (scopolamine) and glutamatergic (Phenylcyclidine, PCP) receptor antagonism and dopaminergic agonism (apomorphine) on activity were assessed in the PhenoTyper and IntelliCage. Finally, the IntelliCage was utilised to determine differences in activity and spatial learning of C57BL/6 and DBA/2 mouse strains following pharmacological intervention. AM251 induced a suppression of food intake, feeding behaviour and a reduction in body weight in both the PhenoTyper and PhenoMaster. Apomorphine reduced activity in both the PhenoTyper and IntelliCage. Whereas, decreased activity was evident with PCP in the PhenoTyper, but not IntelliCage and Scopolamine induced a trend towards elevated levels of activity in the IntelliCage but not PhenoTyper. Strain differences in activity and spatial learning were also evident, with increased corner visits and drug induced impairments only observed with C57BL/6 mice. The automated home cage observation systems determined similar drug and strain effects on behaviour to those observed using traditional methods. All three observation systems reported drug-induced changes in behaviour however, they differ in their application of spatial learning tasks and utilisation of single versus group housed recordings. Copyright © 2014 Elsevier B.V. All rights reserved.

Going deeper in the automated identification of Herbarium specimens.

PubMed

Carranza-Rojas, Jose; Goeau, Herve; Bonnet, Pierre; Mata-Montero, Erick; Joly, Alexis

2017-08-11

Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.

Classification and Subject Cataloguing Section. Bibliographic Control Division. Papers.

ERIC Educational Resources Information Center

International Federation of Library Associations, The Hague (Netherlands).

Papers on classification and subject cataloging which were presented at the 1983 International Federation of Library Associations (IFLA) conference include: (1) "PRECIS: Basic Principles, Function, and Use," in which Derek Austin (United Kingdom) describes the automated subject indexing system developed for use in the "British…

An Automated System for Comprehensive Assessment of Visual Field Sensitivity.

DTIC Science & Technology

1985-04-01

act to degrade this basic configuration; e.g., pathology, such as glaucoma and retinitis pigmentosa ; environmental extremes, such as hypoxia...and B. Appleton. 1971. Effects of hypoxia on visual performance and retinal vascular state. Journal of Applied Physiology. 31: 357蘺. Kobrick, J. L

Master control data handling program uses automatic data input

NASA Technical Reports Server (NTRS)

Alliston, W.; Daniel, J.

1967-01-01

General purpose digital computer program is applicable for use with analysis programs that require basic data and calculated parameters as input. It is designed to automate input data preparation for flight control computer programs, but it is general enough to permit application in other areas.

Exploring the Issues: Humans and Computers.

ERIC Educational Resources Information Center

Walsh, Huber M.

This presentation addresses three basic social issues generated by the computer revolution. The first section, "Money Matters," focuses on the economic effects of computer technology. These include the replacement of workers by fully automated machines, the threat to professionals posed by expanded access to specialized information, and the…

Learning and Coping Strategies Used by Learning Disabled Students Participating in Adult Basic Education and Literacy Programs. A Final Report of the 310 Special Project 87-98-7014.

ERIC Educational Resources Information Center

Ross, Jovita M.

Interviews with 19 adults participating in adult basic education or literacy programs were conducted to ascertain the strategies they used to compensate for reading and writing difficulties. Although the project intended to secure this information from adults diagnosed as learning disabled, it had to rely on self-reports and educational history to…

Rote Learning in the Age of Technology: A Quantitative Study of a Career and Technical High School and the Practical Use of Basic Skills

ERIC Educational Resources Information Center

Cotreau Berube, Elyse A.

2011-01-01

The purpose of this quantitative research study was to investigate the use of rote learning in basic skills of mathematics and spelling of 12 high school students, from a career and technical high school, in an effort to learn if the pedagogy of rote fits in the frameworks of today's education. The study compared the accuracy of…

Learning class descriptions from a data base of spectral reflectance with multiple view angles

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Harrison, Patrick R.; Harrison, P. A.

1992-01-01

A learning program has been developed which combines 'learning by example' with the generate-and-test paradigm to furnish a robust learning environment capable of handling error-prone data. The problem is shown to be capable of learning class descriptions from positive and negative training examples of spectral and directional reflectance data taken from soil and vegetation. The program, which used AI techniques to automate very tedious processes, found the sequence of relationships that contained the most important information which could distinguish the classes.

A structural SVM approach for reference parsing.

PubMed

Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

Automated expert modeling for automated student evaluation.

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

Abbott, Robert G.

The 8th International Conference on Intelligent Tutoring Systems provides a leading international forum for the dissemination of original results in the design, implementation, and evaluation of intelligent tutoring systems and related areas. The conference draws researchers from a broad spectrum of disciplines ranging from artificial intelligence and cognitive science to pedagogy and educational psychology. The conference explores intelligent tutoring systems increasing real world impact on an increasingly global scale. Improved authoring tools and learning object standards enable fielding systems and curricula in real world settings on an unprecedented scale. Researchers deploy ITS's in ever larger studies and increasingly use datamore » from real students, tasks, and settings to guide new research. With high volumes of student interaction data, data mining, and machine learning, tutoring systems can learn from experience and improve their teaching performance. The increasing number of realistic evaluation studies also broaden researchers knowledge about the educational contexts for which ITS's are best suited. At the same time, researchers explore how to expand and improve ITS/student communications, for example, how to achieve more flexible and responsive discourse with students, help students integrate Web resources into learning, use mobile technologies and games to enhance student motivation and learning, and address multicultural perspectives.« less

A digital peer-to-peer learning platform for clinical skills development.

PubMed

Basnak, Jesse; Ortynski, Jennifer; Chow, Meghan; Nzekwu, Emeka

2017-02-01

Due to constraints in time and resources, medical curricula may not provide adequate opportunities for pre-clerkship students to practice clinical skills. To address this, medical students at the University of Alberta developed a digital peer-to-peer learning initiative. The initiative assessed if students can learn clinical skills from their peers in co-curricular practice objective structured clinical exams (OSCEs). A total of 144 first-year medical students participated. Students wrote case scenarios that were reviewed by physicians. Students enacted the cases in practice OSCEs, acting as the patient, physician, and evaluator. Verbal and electronic evaluations were completed. A digital platform was used to automate the process. Surveys were disseminated to assess student perceptions of their experience. Seventy-five percent of participants said they needed opportunities to practice patient histories and physical exams in addition to those provided in the medical school curriculum. All participants agreed that the co-curricular practice OSCEs met this need. The majority of participants also agreed that the digital platform was efficient and easy to use. Students found the practice OSCEs and digital platform effective for learning clinical skills. Thus, peer-to-peer learning and computer automation can be useful adjuncts to traditional medical curricula.

A Generalized Timeline Representation, Services, and Interface for Automating Space Mission Operations

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Johnston, Mark; Frank, Jeremy; Giuliano, Mark; Kavelaars, Alicia; Lenzen, Christoph; Policella, Nicola

2012-01-01

Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

NASA Automated Fiber Placement Capabilities: Similar Systems, Complementary Purposes

NASA Technical Reports Server (NTRS)

Wu, K. Chauncey; Jackson, Justin R.; Pelham, Larry I.; Stewart, Brian K.

2015-01-01

New automated fiber placement systems at the NASA Langley Research Center and NASA Marshall Space Flight Center provide state-of-art composites capabilities to these organizations. These systems support basic and applied research at Langley, complementing large-scale manufacturing and technology development at Marshall. These systems each consist of a multi-degree of freedom mobility platform including a commercial robot, a commercial tool changer mechanism, a bespoke automated fiber placement end effector, a linear track, and a rotational tool support structure. In addition, new end effectors with advanced capabilities may be either bought or developed with partners in industry and academia to extend the functionality of these systems. These systems will be used to build large and small composite parts in support of the ongoing NASA Composites for Exploration Upper Stage Project later this year.

Utility deregulation and AMR technology. [Automated Meter Reading

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

Moore, G.

1991-06-15

This article reviews the effects of deregulation on other utilities and services and examines how the electric utilities can avoid the worst of these effects and capitalize of the best aspects of competition in achieving marketing excellence. The article presents deregulation as a customer service and underscores the need for utilities to learn to compete aggressively and intelligently and provide additional services available through technology such as automated meter reading.

Preliminary Evaluation of an Aviation Safety Thesaurus' Utility for Enhancing Automated Processing of Incident Reports

NASA Technical Reports Server (NTRS)

Barrientos, Francesca; Castle, Joseph; McIntosh, Dawn; Srivastava, Ashok

2007-01-01

This document presents a preliminary evaluation the utility of the FAA Safety Analytics Thesaurus (SAT) utility in enhancing automated document processing applications under development at NASA Ames Research Center (ARC). Current development efforts at ARC are described, including overviews of the statistical machine learning techniques that have been investigated. An analysis of opportunities for applying thesaurus knowledge to improving algorithm performance is then presented.

Instructive Video Retrieval for Surgical Skill Coaching Using Attribute Learning

DTIC Science & Technology

2015-06-28

dance, sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited automated...including dance, sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited...sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited automated feed- back to a

Understanding social collaboration between actors and technology in an automated and digitised deep mining environment.

PubMed

Sanda, M-A; Johansson, J; Johansson, B; Abrahamsson, L

2011-10-01

The purpose of this article is to develop knowledge and learning on the best way to automate organisational activities in deep mines that could lead to the creation of harmony between the human, technical and the social system, towards increased productivity. The findings showed that though the introduction of high-level technological tools in the work environment disrupted the social relations developed over time amongst the employees in most situations, the technological tools themselves became substitute social collaborative partners to the employees. It is concluded that, in developing a digitised mining production system, knowledge of the social collaboration between the humans (miners) and the technology they use for their work must be developed. By implication, knowledge of the human's subject-oriented and object-oriented activities should be considered as an important integral resource for developing a better technological, organisational and human interactive subsystem when designing the intelligent automation and digitisation systems for deep mines. STATEMENT OF RELEVANCE: This study focused on understanding the social collaboration between humans and the technologies they use to work in underground mines. The learning provides an added knowledge in designing technologies and work organisations that could better enhance the human-technology interactive and collaborative system in the automation and digitisation of underground mines.

ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

PubMed

Gandola, Emanuele; Antonioli, Manuela; Traficante, Alessio; Franceschini, Simone; Scardi, Michele; Congestri, Roberta

2016-05-01

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments. Copyright © 2016 Elsevier B.V. All rights reserved.

Working Notes of the 1990 Spring Symposium on Automated Abduction

DTIC Science & Technology

1990-09-27

possibilities for abstracting the leaf nodes in using apprenticeship learning techniques. In LTCAI.E the proof tree. Morgan Kaufmann, 1987. A detailed...ibm.com Abstract planation process and compute particular operational A major limitation of explanation-based learn - descriptions of the target...for the learning that would be difficult or impos- 3n educated, somewhat abstract guess at why the pro- sible using abduction. I position is likely to

Deep learning applications in ophthalmology.

PubMed

Rahimy, Ehsan

2018-05-01

To describe the emerging applications of deep learning in ophthalmology. Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma. Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.

PASCAL vs BASIC

ERIC Educational Resources Information Center

Mundie, David A.

1978-01-01

A comparison between PASCAL and BASIC as general purpose microprocessor languages rates PASCAL above BASIC in such points as program structure, data types, structuring methods, control structures, procedures and functions, and ease in learning. (CMV)

Interprofessional education and the basic sciences: Rationale and outcomes.

PubMed

Thistlethwaite, Jill E

2015-01-01

Interprofessional education (IPE) aims to improve patient outcomes and the quality of care. Interprofessional learning outcomes and interprofessional competencies are now included in many countries' health and social care professions' accreditation standards. While IPE may take place at any time in health professions curricula it tends to focus on professionalism and clinical topics rather than basic science activities. However generic interprofessional competencies could be included in basic science courses that are offered to at least two different professional groups. In developing interprofessional activities at the preclinical level, it is important to define explicit interprofessional learning outcomes plus the content and process of the learning. Interprofessional education must involve interactive learning processes and integration of theory and practice. This paper provides examples of IPE in anatomy and makes recommendations for course development and evaluation. © 2015 American Association of Anatomists.

Managing laboratory automation in a changing pharmaceutical industry

PubMed Central

Rutherford, Michael L.

1995-01-01

The health care reform movement in the USA and increased requirements by regulatory agencies continue to have a major impact on the pharmaceutical industry and the laboratory. Laboratory management is expected to improve effciency by providing more analytical results at a lower cost, increasing customer service, reducing cycle time, while ensuring accurate results and more effective use of their staff. To achieve these expectations, many laboratories are using robotics and automated work stations. Establishing automated systems presents many challenges for laboratory management, including project and hardware selection, budget justification, implementation, validation, training, and support. To address these management challenges, the rationale for project selection and implementation, the obstacles encountered, project outcome, and learning points for several automated systems recently implemented in the Quality Control Laboratories at Eli Lilly are presented. PMID:18925014

LaboREM--A Remote Laboratory for Game-Like Training in Electronics

ERIC Educational Resources Information Center

Luthon, Franck; Larroque, Benoît

2015-01-01

The advances in communication networks and web technologies, in conjunction with the improved connectivity of test and measurement devices make it possible to implement e-learning applications that encompass the whole learning process. In the field of electrical engineering, automation or mechatronics, it means not only lectures, tutorials, demos…

Automated vehicle identification tags in San Antonio : lessons learned from the metropolitan model deployment initiative : unique method for collecting arterial travel speed information

DOT National Transportation Integrated Search

2000-10-01

This report demonstrates a unique solution to the challenge of providing accurate, timely estimates of arterial travel times to the motoring public. In particular, it discusses the lessons learned in deploying the Vehicle Tag Project in San Antonio, ...

The Impact of an Automated Learning Component against a Traditional Lecturing Environment

ERIC Educational Resources Information Center

Maycock, Keith W.; Keating, J. G.

2017-01-01

This experimental study investigates the effect on the examination performance of a cohort of first-year undergraduate learners undertaking a Unified Modelling Language (UML) course using an adaptive learning system against a control group of learners undertaking the same UML course through a traditional lecturing environment. The adaptive…

77 FR 24667 - TANF Assistance and Electronic Benefit Transfer Transactions; Request for Public Comment

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-25

... Family Assistance (OFA) is interested in learning about how States deliver Temporary Assistance to Needy... types of restrictions on assistance usage. OFA also is interested in learning about States' current... as ``the use of a credit or debit card service, automated teller machine, point-of-sale terminal, or...

Competency-Based Education. Innovations in Teaching and Learning. Research Brief 1

ERIC Educational Resources Information Center

Katz, Philip M.

2015-01-01

Competency-based education (CBE) is an approach to pedagogy that emphasizes the mastery of skills and concepts rather than credit hours or seat time. The assessment of mastery can take several forms, including formal assessments of prior learning (such as portfolio reviews or examinations) and automated evaluations of online coursework. Advocates…

Tutoring Bilingual Students with an Automated Reading Tutor that Listens

ERIC Educational Resources Information Center

Poulsen, Robert; Hastings, Peter; Allbritton, David

2007-01-01

Children from non-English-speaking homes are doubly disadvantaged when learning English in school. They enter school with less prior knowledge of English sounds, word meanings, and sentence structure, and they get little or no reinforcement of their learning outside of the classroom. This article compares the classroom standard practice of…

The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement

ERIC Educational Resources Information Center

Bodily, Robert; Nyland, Rob; Wiley, David

2017-01-01

The RISE (Resource Inspection, Selection, and Enhancement) Framework is a framework supporting the continuous improvement of open educational resources (OER). The framework is an automated process that identifies learning resources that should be evaluated and either eliminated or improved. This is particularly useful in OER contexts where the…

An e-Learning System with MR for Experiments Involving Circuit Construction to Control a Robot

ERIC Educational Resources Information Center

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system for technological experiments involving electronic circuit-construction and controlling robot motion that are necessary in the field of technology. The proposed system performs automated recognition of circuit images transmitted from individual learners and automatically supplies the learner with…

The "Intelligent Classroom": Changing Teaching and Learning with an Evolving Technological Environment.

ERIC Educational Resources Information Center

Winer, Laura R.; Cooperstock, Jeremy

2002-01-01

Describes the development and use of the Intelligent Classroom collaborative project at McGill University that explored technology use to improve teaching and learning. Explains the hardware and software installation that allows for the automated capture of audio, video, slides, and handwritten annotations during a live lecture, with subsequent…

Academic Vocabulary Learning in First through Third Grade in Low-Income Schools: Effects of Automated Supplemental Instruction

ERIC Educational Resources Information Center

Goldstein, Howard; Ziolkowski, Robyn A.; Bojczyk, Kathryn E.; Marty, Ana; Schneider, Naomi; Harpring, Jayme; Haring, Christa D.

2017-01-01

Purpose: This study investigated cumulative effects of language learning, specifically whether prior vocabulary knowledge or special education status moderated the effects of academic vocabulary instruction in high-poverty schools. Method: Effects of a supplemental intervention targeting academic vocabulary in first through third grades were…