The Effectiveness of the Thesaurus Method in Automatic Information Retrieval. Technical Report No. 75-261.

ERIC Educational Resources Information Center

Yu, C. T.; Salton, G.

Formal proofs are given of the effectiveness under well-defined conditions of the thesaurus method in information retrieval. It is shown, in particular, that when certain semantically related terms are added to the information queries originally submitted by the user population, a superior retrieval system is obtained in the sense that for every…

Foreign Language Analysis and Recognition (FLARE) Progress

DTIC Science & Technology

2015-02-01

Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil). AFRL- RH -WP-TR-2015-0007 HAS BEEN REVIEWED AND IS... retrieval (IR). 15. SUBJECT TERMS Automatic speech recognition (ASR), information retrieval (IR). 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...to the Haystack Multilingual Multimedia Information Extraction and Retrieval (MMIER) system that was initially developed under a prior work unit

Information Storage and Retrieval. Reports on Analysis, Search, and Iterative Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard

As the fourteenth report in a series describing research in automatic information storage and retrieval, this document covers work carried out on the SMART project for approximately one year (summer 1967 to summer 1968). The document is divided into four main parts: (1) SMART systems design, (2) analysis and search experiments, (3) user feedback…

Science information systems: Archive, access, and retrieval

NASA Technical Reports Server (NTRS)

Campbell, William J.

1991-01-01

The objective of this research is to develop technology for the automated characterization and interactive retrieval and visualization of very large, complex scientific data sets. Technologies will be developed for the following specific areas: (1) rapidly archiving data sets; (2) automatically characterizing and labeling data in near real-time; (3) providing users with the ability to browse contents of databases efficiently and effectively; (4) providing users with the ability to access and retrieve system independent data sets electronically; and (5) automatically alerting scientists to anomalies detected in data.

JANE, A new information retrieval system for the Radiation Shielding Information Center

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

Trubey, D.K.

A new information storage and retrieval system has been developed for the Radiation Shielding Information Center (RSIC) at Oak Ridge National Laboratory to replace mainframe systems that have become obsolete. The database contains citations and abstracts of literature which were selected by RSIC analysts and indexed with terms from a controlled vocabulary. The database, begun in 1963, has been maintained continuously since that time. The new system, called JANE, incorporates automatic indexing techniques and on-line retrieval using the RSIC Data General Eclipse MV/4000 minicomputer, Automatic indexing and retrieval techniques based on fuzzy-set theory allow the presentation of results in ordermore » of Retrieval Status Value. The fuzzy-set membership function depends on term frequency in the titles and abstracts and on Term Discrimination Values which indicate the resolving power of the individual terms. These values are determined by the Cover Coefficient method. The use of a commercial database base to store and retrieve the indexing information permits rapid retrieval of the stored documents. Comparisons of the new and presently-used systems for actual searches of the literature indicate that it is practical to replace the mainframe systems with a minicomputer system similar to the present version of JANE. 18 refs., 10 figs.« less

Automatic Processing of Metallurgical Abstracts for the Purpose of Information Retrieval. Final Report.

ERIC Educational Resources Information Center

Melton, Jessica S.

Objectives of this project were to develop and test a method for automatically processing the text of abstracts for a document retrieval system. The test corpus consisted of 768 abstracts from the metallurgical section of Chemical Abstracts (CA). The system, based on a subject indexing rational, had two components: (1) a stored dictionary of words…

Automatic indexing in a drug information portal.

PubMed

Sakji, Saoussen; Letord, Catherine; Dahamna, Badisse; Kergourlay, Ivan; Pereira, Suzanne; Joubert, Michel; Darmoni, Stéfan

2009-01-01

The objective of this work is to create a bilingual (French/English) Drug Information Portal (DIP), in a multi-terminological context and to emphasize its exploitation by an ATC automatic indexing allowing having more pertinent information about substances, organs or systems on which drugs act and their therapeutic and chemical characteristics. The development of the DIP was based on the CISMeF portal, which catalogues and indexes the most important and quality-controlled sources of institutional health information in French. DIP has created specific functionalities and uses specific drugs terminologies such as the ATC classification which used to automatic index the DIP resources. DIP is the result of collaboration between the CISMeF team and the VIDAL Company, specialized in drug information. DIP is conceived to facilitate the user information retrieval. The ATC automatic indexing provided relevant results in 76% of cases. Using multi-terminological context and in the framework of the drug field, indexing drugs with the appropriate codes or/and terms revealed to be very important to have the appropriate information storage and retrieval. The main challenge in the coming year is to increase the accuracy of the approach.

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

PubMed

Khader, Patrick H; Pachur, Thorsten; Weber, Lilian A E; Jost, Kerstin

2016-01-01

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

Automatic Query Formulations in Information Retrieval.

ERIC Educational Resources Information Center

Salton, G.; And Others

1983-01-01

Introduces methods designed to reduce role of search intermediaries by generating Boolean search formulations automatically using term frequency considerations from natural language statements provided by system patrons. Experimental results are supplied and methods are described for applying automatic query formulation process in practice.…

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

On-Line Retrieval System Design; Part V of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

On-line retrieval system design is discussed in the two papers which make up Part Five of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Prototype On-Line Document Retrieval System" by D. Williamson and R. Williamson outlines a design for a SMART on-line document retrieval system…

Documents Similarity Measurement Using Field Association Terms.

ERIC Educational Resources Information Center

Atlam, El-Sayed; Fuketa, M.; Morita, K.; Aoe, Jun-ichi

2003-01-01

Discussion of text analysis and information retrieval and measurement of document similarity focuses on a new text manipulation system called FA (field association)-Sim that is useful for retrieving information in large heterogeneous texts and for recognizing content similarity in text excerpts. Discusses recall and precision, automatic indexing…

Image/text automatic indexing and retrieval system using context vector approach

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Caid, William R.; Ren, Clara Z.; McCabe, Patrick

1995-11-01

Thousands of documents and images are generated daily both on and off line on the information superhighway and other media. Storage technology has improved rapidly to handle these data but indexing this information is becoming very costly. HNC Software Inc. has developed a technology for automatic indexing and retrieval of free text and images. This technique is demonstrated and is based on the concept of `context vectors' which encode a succinct representation of the associated text and features of sub-image. In this paper, we will describe the Automated Librarian System which was designed for free text indexing and the Image Content Addressable Retrieval System (ICARS) which extends the technique from the text domain into the image domain. Both systems have the ability to automatically assign indices for a new document and/or image based on the content similarities in the database. ICARS also has the capability to retrieve images based on similarity of content using index terms, text description, and user-generated images as a query without performing segmentation or object recognition.

The State of Retrieval System Evaluation.

ERIC Educational Resources Information Center

Salton, Gerald

1992-01-01

The current state of information retrieval (IR) evaluation is reviewed with criticisms directed at the available test collections and the research and evaluation methodologies used, including precision and recall rates for online searches and laboratory tests not including real users. Automatic text retrieval systems are also discussed. (32…

TRECVID: the utility of a content-based video retrieval evaluation

NASA Astrophysics Data System (ADS)

Hauptmann, Alexander G.

2006-01-01

TRECVID, an annual retrieval evaluation benchmark organized by NIST, encourages research in information retrieval from digital video. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of semantic features, and the automatic segmentation of TV news broadcasts. Evaluations done in the context of the TRECVID benchmarks show that generally, speech transcripts and annotations provide the single most important clue for successful retrieval. However, automatically finding the individual images is still a tremendous and unsolved challenge. The evaluations repeatedly found that none of the multimedia analysis and retrieval techniques provide a significant benefit over retrieval using only textual information such as from automatic speech recognition transcripts or closed captions. In interactive systems, we do find significant differences among the top systems, indicating that interfaces can make a huge difference for effective video/image search. For interactive tasks efficient interfaces require few key clicks, but display large numbers of images for visual inspection by the user. The text search finds the right context region in the video in general, but to select specific relevant images we need good interfaces to easily browse the storyboard pictures. In general, TRECVID has motivated the video retrieval community to be honest about what we don't know how to do well (sometimes through painful failures), and has focused us to work on the actual task of video retrieval, as opposed to flashy demos based on technological capabilities.

Cross-Language Information Retrieval: An Analysis of Errors.

ERIC Educational Resources Information Center

Ruiz, Miguel E.; Srinivasan, Padmini

1998-01-01

Investigates an automatic method for Cross Language Information Retrieval (CLIR) that utilizes the multilingual Unified Medical Language System (UMLS) Metathesaurus to translate Spanish natural-language queries into English. Results indicate that for Spanish, the UMLS Metathesaurus-based CLIR method is at least equivalent to if not better than…

Computer-Assisted Search Of Large Textual Data Bases

NASA Technical Reports Server (NTRS)

Driscoll, James R.

1995-01-01

"QA" denotes high-speed computer system for searching diverse collections of documents including (but not limited to) technical reference manuals, legal documents, medical documents, news releases, and patents. Incorporates previously available and emerging information-retrieval technology to help user intelligently and rapidly locate information found in large textual data bases. Technology includes provision for inquiries in natural language; statistical ranking of retrieved information; artificial-intelligence implementation of semantics, in which "surface level" knowledge found in text used to improve ranking of retrieved information; and relevance feedback, in which user's judgements of relevance of some retrieved documents used automatically to modify search for further information.

Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

ERIC Educational Resources Information Center

Khribi, Mohamed Koutheair; Jemni, Mohamed; Nasraoui, Olfa

2009-01-01

In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among…

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

Information Storage and Retrieval Scientific Report No. ISR-22.

ERIC Educational Resources Information Center

Salton, Gerard

The twenty-second in a series, this report describes research in information organization and retrieval conducted by the Department of Computer Science at Cornell University. The report covers work carried out during the period summer 1972 through summer 1974 and is divided into four parts: indexing theory, automatic content analysis, feedback…

A Study of Adaptive Relevance Feedback - UIUC TREC-2008 Relevance Feedback Experiments

DTIC Science & Technology

2008-11-01

terms. Journal of the American Society for Information Science, 27(3):129–146, 1976. [7] J . J . Rocchio. Relevance feedback in information retrieval. In...In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313–323. Prentice-Hall Inc., 1971. [8] Gerard Salton and Chris

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras.

PubMed

Jung, Jaehoon; Yoon, Inhye; Lee, Seungwon; Paik, Joonki

2016-06-24

Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system.

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Lee, Seungwon; Paik, Joonki

2016-01-01

Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system. PMID:27347961

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

PubMed

Kropf, Stefan; Krücken, Peter; Mueller, Wolf; Denecke, Kerstin

2017-05-18

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

INFORMATION STORAGE AND RETRIEVAL, REPORTS ON EVALUATION, CLUSTERING, AND FEEDBACK.

ERIC Educational Resources Information Center

SALTON, GERALD

THE TWELFTH IN A SERIES COVERING RESEARCH IN AUTOMATIC STORAGE AND RETRIEVAL, THIS REPORT IS DIVIDED INTO THREE PARTS TITLED EVALUATION, CLUSTER SEARCHING, AND USER FEEDBACK METHODS, RESPECTIVELY. THE FIRST PART, EVALUATION, CONTAINS A COMPLETE SUMMARY OF THE RETRIEVAL RESULTS DERIVED FROM SOME SIXTY DIFFERENT TEXT ANALYSIS EXPERIMENTS. IN EACH…

Retrieval Practice Makes Procedure from Remembering: An Automatization Account of the Testing Effect

ERIC Educational Resources Information Center

Racsmány, Mihály; Szollosi, Ágnes; Bencze, Dorottya

2018-01-01

The "testing effect" refers to the striking phenomenon that repeated retrieval practice is one of the most effective learning strategies, and certainly more advantageous for long-term learning, than additional restudying of the same information. How retrieval can boost the retention of memories is still without unanimous explanation. In…

Experiments in automatic word class and word sense identification for information retrieval

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

Gauch, S.; Futrelle, R.P.

Automatic identification of related words and automatic detection of word senses are two long-standing goals of researchers in natural language processing. Word class information and word sense identification may enhance the performance of information retrieval system4ms. Large online corpora and increased computational capabilities make new techniques based on corpus linguisitics feasible. Corpus-based analysis is especially needed for corpora from specialized fields for which no electronic dictionaries or thesauri exist. The methods described here use a combination of mutual information and word context to establish word similarities. Then, unsupervised classification is done using clustering in the word space, identifying word classesmore » without pretagging. We also describe an extension of the method to handle the difficult problems of disambiguation and of determining part-of-speech and semantic information for low-frequency words. The method is powerful enough to produce high-quality results on a small corpus of 200,000 words from abstracts in a field of molecular biology.« less

Microsoft Research at TREC 2009. Web and Relevance Feedback Tracks

DTIC Science & Technology

2009-11-01

Information Processing Systems, pages 193–200, 2006. [2] J . M. Kleinberg. Authoritative sources in a hyperlinked environment. In Proc. of the 9th...Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at TREC-3. In Proc. of the 3rd Text REtrieval Conference, 1994. [8] J . J . Rocchio. Relevance...feedback in information retrieval. In Gerard Salton , editor, The SMART Retrieval System - Experiments in Automatic Document Processing. Prentice Hall

Resolving Quasi-Synonym Relationships in Automatic Thesaurus Construction Using Fuzzy Rough Sets and an Inverse Term Frequency Similarity Function

ERIC Educational Resources Information Center

Davault, Julius M., III.

2009-01-01

One of the problems associated with automatic thesaurus construction is with determining the semantic relationship between word pairs. Quasi-synonyms provide a type of equivalence relationship: words are similar only for purposes of information retrieval. Determining such relationships in a thesaurus is hard to achieve automatically. The term…

Rapid automatic keyword extraction for information retrieval and analysis

DOEpatents

Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

2012-03-06

Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

Automatic generation of stop word lists for information retrieval and analysis

DOEpatents

Rose, Stuart J

2013-01-08

Methods and systems for automatically generating lists of stop words for information retrieval and analysis. Generation of the stop words can include providing a corpus of documents and a plurality of keywords. From the corpus of documents, a term list of all terms is constructed and both a keyword adjacency frequency and a keyword frequency are determined. If a ratio of the keyword adjacency frequency to the keyword frequency for a particular term on the term list is less than a predetermined value, then that term is excluded from the term list. The resulting term list is truncated based on predetermined criteria to form a stop word list.

Improving Disambiguation in FASIT.

ERIC Educational Resources Information Center

Burgin, Robert; Dillon, Martin

1992-01-01

Discussion of automatic indexing in information retrieval systems focuses on attempts to improve the indexing representation produced by the FASIT system. Concept selection and concept grouping are explained; improving disambiguation is discussed; and a retrieval experiment to test the effectiveness of the disambiguation using the cystic fibrosis…

Effects of ongoing task context and target typicality on prospective memory performance: the importance of associative cueing

NASA Technical Reports Server (NTRS)

Nowinski, Jessica Lang; Dismukes, Key R.

2005-01-01

Two experiments examined whether prospective memory performance is influenced by contextual cues. In our automatic activation model, any information available at encoding and retrieval should aid recall of the prospective task. The first experiment demonstrated an effect of the ongoing task context; performance was better when information about the ongoing task present at retrieval was available at encoding. Performance was also improved by a strong association between the prospective memory target as it was presented at retrieval and the intention as it was encoded. Experiment 2 demonstrated boundary conditions of the ongoing task context effect, which implicate the association between the ongoing and prospective tasks formed at encoding as the source of the context effect. The results of this study are consistent with predictions based on automatic activation of intentions.

Information Storage and Retrieval...Reports on Text Analysis, Dynamic Indexing, Feedback Searches, Dictionary Construction and File Organization.

ERIC Educational Resources Information Center

Salton, Gerald; And Others

The present report is the twenty-first in a series describing research in information storage and retrieval conducted by the Department of Computer Science at Cornell University. The report covering work carried out by the SMART project for approximately two years (summer 1970 to summer 1972) is separated into five parts: automatic content…

Information retrieval and terminology extraction in online resources for patients with diabetes.

PubMed

Seljan, Sanja; Baretić, Maja; Kucis, Vlasta

2014-06-01

Terminology use, as a mean for information retrieval or document indexing, plays an important role in health literacy. Specific types of users, i.e. patients with diabetes need access to various online resources (on foreign and/or native language) searching for information on self-education of basic diabetic knowledge, on self-care activities regarding importance of dietetic food, medications, physical exercises and on self-management of insulin pumps. Automatic extraction of corpus-based terminology from online texts, manuals or professional papers, can help in building terminology lists or list of "browsing phrases" useful in information retrieval or in document indexing. Specific terminology lists represent an intermediate step between free text search and controlled vocabulary, between user's demands and existing online resources in native and foreign language. The research aiming to detect the role of terminology in online resources, is conducted on English and Croatian manuals and Croatian online texts, and divided into three interrelated parts: i) comparison of professional and popular terminology use ii) evaluation of automatic statistically-based terminology extraction on English and Croatian texts iii) comparison and evaluation of extracted terminology performed on English manual using statistical and hybrid approaches. Extracted terminology candidates are evaluated by comparison with three types of reference lists: list created by professional medical person, list of highly professional vocabulary contained in MeSH and list created by non-medical persons, made as intersection of 15 lists. Results report on use of popular and professional terminology in online diabetes resources, on evaluation of automatically extracted terminology candidates in English and Croatian texts and on comparison of statistical and hybrid extraction methods in English text. Evaluation of automatic and semi-automatic terminology extraction methods is performed by recall, precision and f-measure.

Interoperability Policy Roadmap

DTIC Science & Technology

2010-01-01

Retrieval – SMART The technique developed by Dr. Gerard Salton for automated information retrieval and text analysis is called the vector-space... Salton , G., Wong, A., Yang, C.S., “A Vector Space Model for Automatic Indexing”, Commu- nications of the ACM, 18, 613-620. [10] Salton , G., McGill

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

PubMed

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Multi-view information fusion for automatic BI-RADS description of mammographic masses

NASA Astrophysics Data System (ADS)

Narvaez, Fabián; Díaz, Gloria; Romero, Eduardo

2011-03-01

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.

Information Retrieval and Text Mining Technologies for Chemistry.

PubMed

Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália; Oyarzabal, Julen; Valencia, Alfonso

2017-06-28

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

NASA automatic subject analysis technique for extracting retrievable multi-terms (NASA TERM) system

NASA Technical Reports Server (NTRS)

Kirschbaum, J.; Williamson, R. E.

1978-01-01

Current methods for information processing and retrieval used at the NASA Scientific and Technical Information Facility are reviewed. A more cost effective computer aided indexing system is proposed which automatically generates print terms (phrases) from the natural text. Satisfactory print terms can be generated in a primarily automatic manner to produce a thesaurus (NASA TERMS) which extends all the mappings presently applied by indexers, specifies the worth of each posting term in the thesaurus, and indicates the areas of use of the thesaurus entry phrase. These print terms enable the computer to determine which of several terms in a hierarchy is desirable and to differentiate ambiguous terms. Steps in the NASA TERMS algorithm are discussed and the processing of surrogate entry phrases is demonstrated using four previously manually indexed STAR abstracts for comparison. The simulation shows phrase isolation, text phrase reduction, NASA terms selection, and RECON display.

The effect of scene context on episodic object recognition: parahippocampal cortex mediates memory encoding and retrieval success.

PubMed

Hayes, Scott M; Nadel, Lynn; Ryan, Lee

2007-01-01

Previous research has investigated intentional retrieval of contextual information and contextual influences on object identification and word recognition, yet few studies have investigated context effects in episodic memory for objects. To address this issue, unique objects embedded in a visually rich scene or on a white background were presented to participants. At test, objects were presented either in the original scene or on a white background. A series of behavioral studies with young adults demonstrated a context shift decrement (CSD)-decreased recognition performance when context is changed between encoding and retrieval. The CSD was not attenuated by encoding or retrieval manipulations, suggesting that binding of object and context may be automatic. A final experiment explored the neural correlates of the CSD, using functional Magnetic Resonance Imaging. Parahippocampal cortex (PHC) activation (right greater than left) during incidental encoding was associated with subsequent memory of objects in the context shift condition. Greater activity in right PHC was also observed during successful recognition of objects previously presented in a scene. Finally, a subset of regions activated during scene encoding, such as bilateral PHC, was reactivated when the object was presented on a white background at retrieval. Although participants were not required to intentionally retrieve contextual information, the results suggest that PHC may reinstate visual context to mediate successful episodic memory retrieval. The CSD is attributed to automatic and obligatory binding of object and context. The results suggest that PHC is important not only for processing of scene information, but also plays a role in successful episodic memory encoding and retrieval. These findings are consistent with the view that spatial information is stored in the hippocampal complex, one of the central tenets of Multiple Trace Theory. (c) 2007 Wiley-Liss, Inc.

The TREC Interactive Track: An Annotated Bibliography.

ERIC Educational Resources Information Center

Over, Paul

2001-01-01

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

Automatic Construction of English/Chinese Parallel Corpora.

ERIC Educational Resources Information Center

Yang, Christopher C.; Li, Kar Wing

2003-01-01

Discussion of multilingual corpora and cross-lingual information retrieval focuses on research that constructed English/Chinese parallel corpus automatically from the World Wide Web. Presents an alignment method which is based on dynamic programming to identify one-to-one Chinese and English title pairs and discusses results of experiments…

17 CFR 256.307 - Equipment.

Code of Federal Regulations, 2010 CFR

2010-04-01

..., communications and dispatching, automatic data processing, information storage and retrieval, research and laboratory testing, construction, meter repairing, and printing and stationery. Subaccounts shall be...

A novel architecture for information retrieval system based on semantic web

NASA Astrophysics Data System (ADS)

Zhang, Hui

2011-12-01

Nowadays, the web has enabled an explosive growth of information sharing (there are currently over 4 billion pages covering most areas of human endeavor) so that the web has faced a new challenge of information overhead. The challenge that is now before us is not only to help people locating relevant information precisely but also to access and aggregate a variety of information from different resources automatically. Current web document are in human-oriented formats and they are suitable for the presentation, but machines cannot understand the meaning of document. To address this issue, Berners-Lee proposed a concept of semantic web. With semantic web technology, web information can be understood and processed by machine. It provides new possibilities for automatic web information processing. A main problem of semantic web information retrieval is that when these is not enough knowledge to such information retrieval system, the system will return to a large of no sense result to uses due to a huge amount of information results. In this paper, we present the architecture of information based on semantic web. In addiction, our systems employ the inference Engine to check whether the query should pose to Keyword-based Search Engine or should pose to the Semantic Search Engine.

Measuring automatic retrieval: a comparison of implicit memory, process dissociation, and speeded response procedures.

PubMed

Horton, Keith D; Wilson, Daryl E; Vonk, Jennifer; Kirby, Sarah L; Nielsen, Tina

2005-07-01

Using the stem completion task, we compared estimates of automatic retrieval from an implicit memory task, the process dissociation procedure, and the speeded response procedure. Two standard manipulations were employed. In Experiment 1, a depth of processing effect was found on automatic retrieval using the speeded response procedure although this effect was substantially reduced in Experiment 2 when lexical processing was required of all words. In Experiment 3, the speeded response procedure showed an advantage of full versus divided attention at study on automatic retrieval. An implicit condition showed parallel effects in each study, suggesting that implicit stem completion may normally provide a good estimate of automatic retrieval. Also, we replicated earlier findings from the process dissociation procedure, but estimates of automatic retrieval from this procedure were consistently lower than those from the speeded response procedure, except when conscious retrieval was relatively low. We discuss several factors that may contribute to the conflicting outcomes, including the evidence for theoretical assumptions and criterial task differences between implicit and explicit tests.

World Key Information Service System Designed For EPCOT Center

NASA Astrophysics Data System (ADS)

Kelsey, J. A.

1984-03-01

An advanced Bell Laboratories and Western Electric designed electronic information retrieval system utilizing the latest Information Age technologies, and a fiber optic transmission system is featured at the Walt Disney World Resort's newest theme park - The Experimental Prototype Community of Tomorrow (EPCOT Center). The project is an interactive audio, video and text information system that is deployed at key locations within the park. The touch sensitive terminals utilizing the ARIEL (Automatic Retrieval of Information Electronically) System is interconnected by a Western Electric designed and manufactured lightwave transmission system.

Information Storage and Retrieval, Scientific Report No. ISR-15.

ERIC Educational Resources Information Center

Salton, Gerard

Several algorithms were investigated which would allow a user to interact with an automatic document retrieval system by requesting relevance judgments on selected sets of documents. Two viewpoints were taken in evaluation. One measured the movement of queries toward the optimum query as defined by Rocchio; the other measured the retrieval…

17 CFR 242.600 - NMS security designation and definitions.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Interrogation device means any securities information retrieval system capable of displaying transaction reports... with respect to such order; and (v) Immediately and automatically displays information that updates the... security; and (ii) Consolidated last sale information for a security. (14) Consolidated last sale...

17 CFR 242.600 - NMS security designation and definitions.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Interrogation device means any securities information retrieval system capable of displaying transaction reports... with respect to such order; and (v) Immediately and automatically displays information that updates the... security; and (ii) Consolidated last sale information for a security. (14) Consolidated last sale...

17 CFR 242.600 - NMS security designation and definitions.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Interrogation device means any securities information retrieval system capable of displaying transaction reports... with respect to such order; and (v) Immediately and automatically displays information that updates the... security; and (ii) Consolidated last sale information for a security. (14) Consolidated last sale...

17 CFR 242.600 - NMS security designation and definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Interrogation device means any securities information retrieval system capable of displaying transaction reports... with respect to such order; and (v) Immediately and automatically displays information that updates the... security; and (ii) Consolidated last sale information for a security. (14) Consolidated last sale...

Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library.

PubMed

Chen, Yang; Ren, Xiaofeng; Zhang, Guo-Qiang; Xu, Rong

2013-01-01

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.

Using Stream Features for Instant Document Filtering

DTIC Science & Technology

2012-11-01

expansion and qual- ity indicators in searching microblog posts. Advances in Information Retrieval, pages 362–367, 2011. [12] N. Naveed, T. Gottron, J ...16] G Salton and C Buckley. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513–523, 1988. [17...Overview of the TREC-2012 Microblog Track. In trec.nist.gov. NIST. [19] Michael J Welch, Uri Schonfeld, Dan He, and Junghoo Cho. Topical semantics of

45 CFR 205.37 - Responsibilities of the Administration for Children and Families (ACF).

Code of Federal Regulations, 2013 CFR

2013-10-01

... Processing and Information Retrieval System Guide. The initial advance automatic data processing planning... description of the proposed statewide management system, including the description of information flows, input..., review, assess, and inspect the planning, design, and operation of, statewide management information...

45 CFR 205.37 - Responsibilities of the Administration for Children and Families (ACF).

Code of Federal Regulations, 2012 CFR

2012-10-01

... Application Processing and Information Retrieval System Guide. The initial advance automatic data processing... description of the proposed statewide management system, including the description of information flows, input..., review, assess, and inspect the planning, design, and operation of, statewide management information...

45 CFR 205.37 - Responsibilities of the Administration for Children and Families (ACF).

Code of Federal Regulations, 2011 CFR

2011-10-01

... Application Processing and Information Retrieval System Guide. The initial advance automatic data processing... description of the proposed statewide management system, including the description of information flows, input..., review, assess, and inspect the planning, design, and operation of, statewide management information...

A PROPOSED CHEMICAL INFORMATION AND DATA SYSTEM. VOLUME I.

DTIC Science & Technology

CHEMICAL COMPOUNDS, *DATA PROCESSING, *INFORMATION RETRIEVAL, * CHEMICAL ANALYSIS, INPUT OUTPUT DEVICES, COMPUTER PROGRAMMING, CLASSIFICATION...CONFIGURATIONS, DATA STORAGE SYSTEMS, ATOMS, MOLECULES, PERFORMANCE( ENGINEERING ), MAINTENANCE, SUBJECT INDEXING, MAGNETIC TAPE, AUTOMATIC, MILITARY REQUIREMENTS, TYPEWRITERS, OPTICS, TOPOLOGY, STATISTICAL ANALYSIS, FLOW CHARTING.

Dynamic Information and Library Processing.

ERIC Educational Resources Information Center

Salton, Gerard

This book provides an introduction to automated information services: collection, analysis, classification, storage, retrieval, transmission, and dissemination. An introductory chapter is followed by an overview of mechanized processes for acquisitions, cataloging, and circulation. Automatic indexing and abstracting methods are covered, followed…

A humming retrieval system based on music fingerprint

NASA Astrophysics Data System (ADS)

Han, Xingkai; Cao, Baiyu

2011-10-01

In this paper, we proposed an improved music information retrieval method utilizing the music fingerprint. The goal of this method is to represent the music with compressed musical information. Based on the selected MIDI files, which are generated automatically as our music target database, we evaluate the accuracy, effectiveness, and efficiency of this method. In this research we not only extract the feature sequence, which can represent the file effectively, from the query and melody database, but also make it possible for retrieving the results in an innovative way. We investigate on the influence of noise to the performance of our system. As experimental result shows, the retrieval accuracy arriving at up to91% without noise is pretty well

Clustering Methods; Part IV of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…

User Feedback Procedures; Part III of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Part Three of this five part report on Salton's Magical Automatic Retriever of Texts (SMART) project contains four papers. The first: "Variations on the Query Splitting Technique with Relevance Feedback" by T. P. Baker discusses some experiments in relevance feedback performed with variations on the technique of query splitting. The…

A content-based news video retrieval system: NVRS

NASA Astrophysics Data System (ADS)

Liu, Huayong; He, Tingting

2009-10-01

This paper focus on TV news programs and design a content-based news video browsing and retrieval system, NVRS, which is convenient for users to fast browsing and retrieving news video by different categories such as political, finance, amusement, etc. Combining audiovisual features and caption text information, the system automatically segments a complete news program into separate news stories. NVRS supports keyword-based news story retrieval, category-based news story browsing and generates key-frame-based video abstract for each story. Experiments show that the method of story segmentation is effective and the retrieval is also efficient.

Preparing a collection of radiology examinations for distribution and retrieval.

PubMed

Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B; Shooshan, Sonya E; Rodriguez, Laritza; Antani, Sameer; Thoma, George R; McDonald, Clement J

2016-03-01

Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

Applying Hypertext Structures to Software Documentation.

ERIC Educational Resources Information Center

French, James C.; And Others

1997-01-01

Describes a prototype system for software documentation management called SLEUTH (Software Literacy Enhancing Usefulness to Humans) being developed at the University of Virginia. Highlights include information retrieval techniques, hypertext links that are installed automatically, a WAIS (Wide Area Information Server) search engine, user…

Beyond Information Retrieval—Medical Question Answering

PubMed Central

Lee, Minsuk; Cimino, James; Zhu, Hai Ran; Sable, Carl; Shanker, Vijay; Ely, John; Yu, Hong

2006-01-01

Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user’s query. Frequently the number of returned documents is large and makes physicians’ information seeking “practical only ‘after hours’ and not in the clinical settings”. Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians’ information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we currently implement MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., “What is X?”). MedQA can be accessed at http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html. PMID:17238385

User's operating procedures. Volume 1: Scout project information programs

NASA Technical Reports Server (NTRS)

Harris, C. G.; Harris, D. K.

1985-01-01

A review of the user's operating procedures for the Scout Project Automatic Data System, called SPADS is given. SPADS is the result of the past seven years of software development on a Prime minicomputer located at the Scout Project Office. SPADS was developed as a single entry, multiple cross reference data management and information retrieval system for the automation of Project office tasks, including engineering, financial, managerial, and clerical support. The instructions to operate the Scout Project Information programs in data retrieval and file maintenance via the user friendly menu drivers is presented.

User's operating procedures. Volume 3: Projects directorate information programs

NASA Technical Reports Server (NTRS)

Haris, C. G.; Harris, D. K.

1985-01-01

A review of the user's operating procedures for the scout project automatic data system, called SPADS is presented. SPADS is the results of the past seven years of software development on a prime mini-computer. SPADS was developed as a single entry, multiple cross-reference data management and information retrieval system for the automation of Project office tasks, including engineering, financial, managerial, and clerical support. This volume, three of three, provides the instructions to operate the projects directorate information programs in data retrieval and file maintenance via the user friendly menu drivers.

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

Crain, Steven P.; Yang, Shuang-Hong; Zha, Hongyuan

Access to health information by consumers is ham- pered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, com- mon and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations us- ing consumermore » questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.« less

On the Application of Syntactic Methodologies in Automatic Text Analysis.

ERIC Educational Resources Information Center

Salton, Gerard; And Others

1990-01-01

Summarizes various linguistic approaches proposed for document analysis in information retrieval environments. Topics discussed include syntactic analysis; use of machine-readable dictionary information; knowledge base construction; the PLNLP English Grammar (PEG) system; phrase normalization; and statistical and syntactic phrase evaluation used…

Automated MeSH indexing of the World-Wide Web.

PubMed Central

Fowler, J.; Kouramajian, V.; Maram, S.; Devadhar, V.

1995-01-01

To facilitate networked discovery and information retrieval in the biomedical domain, we have designed a system for automatic assignment of Medical Subject Headings to documents retrieved from the World-Wide Web. Our prototype implementations show significant promise. We describe our methods and discuss the further development of a completely automated indexing tool called the "Web-MeSH Medibot." PMID:8563421

Speech Processing and Recognition (SPaRe)

DTIC Science & Technology

2011-01-01

results in the areas of automatic speech recognition (ASR), speech processing, machine translation (MT), natural language processing ( NLP ), and...Processing ( NLP ), Information Retrieval (IR) 16. SECURITY CLASSIFICATION OF: UNCLASSIFED 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME...Figure 9, the IOC was only expected to provide document submission and search; automatic speech recognition (ASR) for English, Spanish, Arabic , and

FUB at TREC 2008 Relevance Feedback Track: Extending Rocchio with Distributional Term Analysis

DTIC Science & Technology

2008-11-01

starting point is the improved version [ Salton and Buckley 1990] of the original Rocchio’s formula [Rocchio 1971]: newQ = α ⋅ origQ + β R r r∈R ∑ − γR...earlier studies about the low effect of the main relevance feedback parameters on retrieval performance (e.g., Salton and Buckley 1990), while they seem...Relevance feedback in information retrieval. In The SMART retrieval system - experiments in automatic document processing, Salton , G., Ed., Prentice Hall

Instance-based categorization: automatic versus intentional forms of retrieval.

PubMed

Neal, A; Hesketh, B; Andrews, S

1995-03-01

Two experiments are reported which attempt to disentangle the relative contribution of intentional and automatic forms of retrieval to instance-based categorization. A financial decision-making task was used in which subjects had to decide whether a bank would approve loans for a series of applicants. Experiment 1 found that categorization was sensitive to instance-specific knowledge, even when subjects had practiced using a simple rule. L. L. Jacoby's (1991) process-dissociation procedure was adapted for use in Experiment 2 to infer the relative contribution of intentional and automatic retrieval processes to categorization decisions. The results provided (1) strong evidence that intentional retrieval processes influence categorization, and (2) some preliminary evidence suggesting that automatic retrieval processes may also contribute to categorization decisions.

A Proposed Operational Concept for the Defense Communications Operations Support System.

DTIC Science & Technology

1986-01-01

Artificial Intelligence AMA Automatic Message Accounting AMIE AUTODIN Management Index System AMPE Automated Message Processing Exchange ANCS AUTOVON Network...Support IMPRESS Inpact/Restoral System INFORM Information Retrieval System 1OC Initial Operational Capability IRU Intellegent Remote Unit I-S/A AMPE

RDFBuilder: a tool to automatically build RDF-based interfaces for MAGE-OM microarray data sources.

PubMed

Anguita, Alberto; Martin, Luis; Garcia-Remesal, Miguel; Maojo, Victor

2013-07-01

This paper presents RDFBuilder, a tool that enables RDF-based access to MAGE-ML-compliant microarray databases. We have developed a system that automatically transforms the MAGE-OM model and microarray data stored in the ArrayExpress database into RDF format. Additionally, the system automatically enables a SPARQL endpoint. This allows users to execute SPARQL queries for retrieving microarray data, either from specific experiments or from more than one experiment at a time. Our system optimizes response times by caching and reusing information from previous queries. In this paper, we describe our methods for achieving this transformation. We show that our approach is complementary to other existing initiatives, such as Bio2RDF, for accessing and retrieving data from the ArrayExpress database. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Distributed Patterns of Brain Activity that Lead to Forgetting

PubMed Central

Öztekin, Ilke; Badre, David

2011-01-01

Proactive interference (PI), in which irrelevant information from prior learning disrupts memory performance, is widely viewed as a major cause of forgetting. However, the hypothesized spontaneous recovery (i.e., automatic retrieval) of interfering information presumed to be at the base of PI remains to be demonstrated directly. Moreover, it remains unclear at what point during learning and/or retrieval interference impacts memory performance. In order to resolve these open questions, we employed a machine-learning algorithm to identify distributed patterns of brain activity associated with retrieval of interfering information that engenders PI and causes forgetting. Participants were scanned using functional magnetic resonance imaging during an item recognition task. We induced PI by constructing sets of three consecutive study lists from the same semantic category. The classifier quantified the magnitude of category-related activity at encoding and retrieval. Category-specific activity during retrieval increased across lists, consistent with the category information becoming increasingly available and producing interference. Critically, this increase was correlated with individual differences in forgetting and the deployment of frontal lobe mechanisms that resolve interference. Collectively, these findings suggest that distributed patterns of brain activity pertaining to the interfering information during retrieval contribute to forgetting. The prefrontal cortex mediates the relationship between the spontaneous recovery of interfering information at retrieval and individual differences in memory performance. PMID:21897814

Memory loss versus memory distortion: the role of encoding and retrieval deficits in Korsakoff patients' false memories.

PubMed

Van Damme, Ilse; d'Ydewalle, Gery

2009-05-01

Recent studies with the Deese/Roediger-McDermott (DRM) paradigm have revealed that Korsakoff patients show reduced levels of false recognition and different patterns of false recall compared to controls. The present experiment examined whether this could be attributed to an encoding deficit, or rather to problems with explicitly retrieving thematic information at test. In a variation on the DRM paradigm, both patients and controls were presented with associative as well as categorised word lists, with the order of recall and recognition tests manipulated between-subjects. The results point to an important role for the automatic/controlled retrieval distinction: Korsakoff patients' false memory was only diminished compared to controls' when automatic or short-term memory processes could not be used to fulfil the task at hand. Hence, the patients' explicit retrieval deficit appears to be crucial in explaining past and present data. Results are discussed in terms of fuzzy-trace and activation-monitoring theories.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

High-speed data search

NASA Technical Reports Server (NTRS)

Driscoll, James N.

1994-01-01

The high-speed data search system developed for KSC incorporates existing and emerging information retrieval technology to help a user intelligently and rapidly locate information found in large textual databases. This technology includes: natural language input; statistical ranking of retrieved information; an artificial intelligence concept called semantics, where 'surface level' knowledge found in text is used to improve the ranking of retrieved information; and relevance feedback, where user judgements about viewed information are used to automatically modify the search for further information. Semantics and relevance feedback are features of the system which are not available commercially. The system further demonstrates focus on paragraphs of information to decide relevance; and it can be used (without modification) to intelligently search all kinds of document collections, such as collections of legal documents medical documents, news stories, patents, and so forth. The purpose of this paper is to demonstrate the usefulness of statistical ranking, our semantic improvement, and relevance feedback.

Automatic indexing of compound words based on mutual information for Korean text retrieval

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

Pan Koo Kim; Yoo Kun Cho

In this paper, we present an automatic indexing technique for compound words suitable to an aggulutinative language, specifically Korean. Firstly, we present the construction conditions to compose compound words as indexing terms. Also we present the decomposition rules applicable to consecutive nouns to extract all contents of text. Finally we propose a measure to estimate the usefulness of a term, mutual information, to calculate the degree of word association of compound words, based on the information theoretic notion. By applying this method, our system has raised the precision rate of compound words from 72% to 87%.

Incorporating Non-Relevance Information in the Estimation of Query Models

DTIC Science & Technology

2008-11-01

experiments in relevance feedback. In Salton , G., editor, The SMART Retrieval System – Exper- iments in Automatic Document Processing, pages 337– 354...W. (2001). Relevance based lan- guage models. In SIGIR ’01. Rocchio, J. (1971). Relevance feedback in information re- trieval. In Salton , G., editor

Content-based TV sports video retrieval using multimodal analysis

NASA Astrophysics Data System (ADS)

Yu, Yiqing; Liu, Huayong; Wang, Hongbin; Zhou, Dongru

2003-09-01

In this paper, we propose content-based video retrieval, which is a kind of retrieval by its semantical contents. Because video data is composed of multimodal information streams such as video, auditory and textual streams, we describe a strategy of using multimodal analysis for automatic parsing sports video. The paper first defines the basic structure of sports video database system, and then introduces a new approach that integrates visual stream analysis, speech recognition, speech signal processing and text extraction to realize video retrieval. The experimental results for TV sports video of football games indicate that the multimodal analysis is effective for video retrieval by quickly browsing tree-like video clips or inputting keywords within predefined domain.

New Term Weighting Formulas for the Vector Space Method in Information Retrieval

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

Chisholm, E.; Kolda, T.G.

The goal in information retrieval is to enable users to automatically and accurately find data relevant to their queries. One possible approach to this problem i use the vector space model, which models documents and queries as vectors in the term space. The components of the vectors are determined by the term weighting scheme, a function of the frequencies of the terms in the document or query as well as throughout the collection. We discuss popular term weighting schemes and present several new schemes that offer improved performance.

The Development of Automatic and Controlled Inhibitory Retrieval Processes in True and False Recall

ERIC Educational Resources Information Center

Knott, Lauren M.; Howe, Mark L.; Wimmer, Marina C.; Dewhurst, Stephen A.

2011-01-01

In three experiments, we investigated the role of automatic and controlled inhibitory retrieval processes in true and false memory development in children and adults. Experiment 1 incorporated a directed forgetting task to examine controlled retrieval inhibition. Experiments 2 and 3 used a part-set cue and retrieval practice task to examine…

An interactive program on digitizing historical seismograms

NASA Astrophysics Data System (ADS)

Xu, Yihe; Xu, Tao

2014-02-01

Retrieving information from analog seismograms is of great importance since they are considered as the unique sources that provide quantitative information of historical earthquakes. We present an algorithm for automatic digitization of the seismograms as an inversion problem that forms an interactive program using Matlab® GUI. The program integrates automatic digitization with manual digitization and users can easily switch between the two modalities and carry out different combinations for the optimal results. Several examples about applying the interactive program are given to illustrate the merits of the method.

Interactions among emotional attention, encoding, and retrieval of ambiguous information: An eye-tracking study.

PubMed

Everaert, Jonas; Koster, Ernst H W

2015-10-01

Emotional biases in attention modulate encoding of emotional material into long-term memory, but little is known about the role of such attentional biases during emotional memory retrieval. The present study investigated how emotional biases in memory are related to attentional allocation during retrieval. Forty-nine individuals encoded emotionally positive and negative meanings derived from ambiguous information and then searched their memory for encoded meanings in response to a set of retrieval cues. The remember/know/new procedure was used to classify memories as recollection-based or familiarity-based, and gaze behavior was monitored throughout the task to measure attentional allocation. We found that a bias in sustained attention during recollection-based, but not familiarity-based, retrieval predicted subsequent memory bias toward positive versus negative material following encoding. Thus, during emotional memory retrieval, attention affects controlled forms of retrieval (i.e., recollection) but does not modulate relatively automatic, familiarity-based retrieval. These findings enhance understanding of how distinct components of attention regulate the emotional content of memories. Implications for theoretical models and emotion regulation are discussed. (c) 2015 APA, all rights reserved).

Automatic and Controlled Semantic Retrieval: TMS Reveals Distinct Contributions of Posterior Middle Temporal Gyrus and Angular Gyrus

PubMed Central

Davey, James; Cornelissen, Piers L.; Thompson, Hannah E.; Sonkusare, Saurabh; Hallam, Glyn; Smallwood, Jonathan

2015-01-01

Semantic retrieval involves both (1) automatic spreading activation between highly related concepts and (2) executive control processes that tailor this activation to suit the current context or goals. Two structures in left temporoparietal cortex, angular gyrus (AG) and posterior middle temporal gyrus (pMTG), are thought to be crucial to semantic retrieval and are often recruited together during semantic tasks; however, they show strikingly different patterns of functional connectivity at rest (coupling with the “default mode network” and “frontoparietal control system,” respectively). Here, transcranial magnetic stimulation (TMS) was used to establish a causal yet dissociable role for these sites in semantic cognition in human volunteers. TMS to AG disrupted thematic judgments particularly when the link between probe and target was strong (e.g., a picture of an Alsatian with a bone), and impaired the identification of objects at a specific but not a superordinate level (for the verbal label “Alsatian” not “animal”). In contrast, TMS to pMTG disrupted thematic judgments for weak but not strong associations (e.g., a picture of an Alsatian with razor wire), and impaired identity matching for both superordinate and specific-level labels. Thus, stimulation to AG interfered with the automatic retrieval of specific concepts from the semantic store while stimulation of pMTG impaired semantic cognition when there was a requirement to flexibly shape conceptual activation in line with the task requirements. These results demonstrate that AG and pMTG make a dissociable contribution to automatic and controlled aspects of semantic retrieval. SIGNIFICANCE STATEMENT We demonstrate a novel functional dissociation between the angular gyrus (AG) and posterior middle temporal gyrus (pMTG) in conceptual processing. These sites are often coactivated during neuroimaging studies using semantic tasks, but their individual contributions are unclear. Using transcranial magnetic stimulation and tasks designed to assess different aspects of semantics (item identity and thematic matching), we tested two alternative theoretical accounts. Neither site showed the pattern expected for a “thematic hub” (i.e., a site storing associations between concepts) since stimulation disrupted both tasks. Instead, the data indicated that pMTG contributes to the controlled retrieval of conceptual knowledge, while AG is critical for the efficient automatic retrieval of specific semantic information. PMID:26586812

Web information retrieval for health professionals.

PubMed

Ting, S L; See-To, Eric W K; Tse, Y K

2013-06-01

This paper presents a Web Information Retrieval System (WebIRS), which is designed to assist the healthcare professionals to obtain up-to-date medical knowledge and information via the World Wide Web (WWW). The system leverages the document classification and text summarization techniques to deliver the highly correlated medical information to the physicians. The system architecture of the proposed WebIRS is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and a questionnaire is administrated to collect feedback on the operation and performance of WebIRS in comparison with conventional information retrieval in the WWW. A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in classification and summarizing the medical information that the physicians needed and interested. The results of the case study show that with the use of the proposed WebIRS, significant reduction of searching time and effort, with retrieval of highly relevant materials can be attained.

Top-down and bottom-up attention to memory: a hypothesis (AtoM) on the role of the posterior parietal cortex in memory retrieval.

PubMed

Ciaramelli, Elisa; Grady, Cheryl L; Moscovitch, Morris

2008-01-01

Recent neuroimaging studies have implicated the posterior parietal cortex in episodic memory retrieval, but there is uncertainty about its specific role. Research in the attentional domain has shown that superior parietal lobe (SPL) regions along the intraparietal sulcus are implicated in the voluntary orienting of attention to relevant aspects of the environment, whereas inferior parietal lobe (IPL) regions at the temporo-parietal junction mediate the automatic allocation of attention to task-relevant information. Here we propose that the SPL and the IPL play conceptually similar roles in episodic memory retrieval. We hypothesize that the SPL allocates top-down attention to memory retrieval, whereas the IPL mediates the automatic, bottom-up attentional capture by retrieved memory contents. By reviewing the existing fMRI literature, we show that the posterior intraparietal sulcus of SPL is consistently active when the need for top-down assistance to memory retrieval is supposedly maximal, e.g., for memories retrieved with low vs. high confidence, for familiar vs. recollected memories, for recognition of high vs. low frequency words. On the other hand, the supramarginal gyrus of IPL is consistently active when the attentional capture by memory contents is supposedly maximal, i.e., for strong vs. weak memories, for vividly recollected vs. familiar memories, for memories retrieved with high vs. low confidence. We introduce a model of episodic memory retrieval that characterizes contributions of posterior parietal cortex.

WRIS: a resource information system for wildland management

Treesearch

Robert M. Russell; David A. Sharpnack; Elliot Amidon

1975-01-01

WRIS (Wildland Resource Information System) is a computer system for processing, storing, retrieving, updating, and displaying geographic data. The polygon, representing a land area boundary, forms the building block of WRIS. Polygons form a map. Maps are digitized manually or by automatic scanning. Computer programs can extract and produce polygon maps and can overlay...

On search guide phrase compilation for recommending home medical products.

PubMed

Luo, Gang

2010-01-01

To help people find desired home medical products (HMPs), we developed an intelligent personal health record (iPHR) system that can automatically recommend HMPs based on users' health issues. Using nursing knowledge, we pre-compile a set of "search guide" phrases that provides semantic translation from words describing health issues to their underlying medical meanings. Then iPHR automatically generates queries from those phrases and uses them and a search engine to retrieve HMPs. To avoid missing relevant HMPs during retrieval, the compiled search guide phrases need to be comprehensive. Such compilation is a challenging task because nursing knowledge updates frequently and contains numerous details scattered in many sources. This paper presents a semi-automatic tool facilitating such compilation. Our idea is to formulate the phrase compilation task as a multi-label classification problem. For each newly obtained search guide phrase, we first use nursing knowledge and information retrieval techniques to identify a small set of potentially relevant classes with corresponding hints. Then a nurse makes the final decision on assigning this phrase to proper classes based on those hints. We demonstrate the effectiveness of our techniques by compiling search guide phrases from an occupational therapy textbook.

A comparison of automatic and intentional instructions when using the method of vanishing cues in acquired brain injury.

PubMed

Riley, Gerard A; Venn, Paul

2015-01-01

Thirty-four participants with acquired brain injury learned word lists under two forms of vanishing cues - one in which the learning trial instructions encouraged intentional retrieval (i.e., explicit memory) and one in which they encouraged automatic retrieval (which encompasses implicit memory). The automatic instructions represented a novel approach in which the cooperation of participants was actively sought to avoid intentional retrieval. Intentional instructions resulted in fewer errors during the learning trials and better performance on immediate and delayed retrieval tests. The advantage of intentional over automatic instructions was generally less for those who had more severe memory and/or executive impairments. Most participants performed better under intentional instructions on both the immediate and the delayed tests. Although those who were more severely impaired in both memory and executive function also did better with intentional instructions on the immediate retrieval test, they were significantly more likely to show an advantage for automatic instructions on the delayed test. It is suggested that this pattern of results may reflect impairments in the consolidation of intentional memories in this group. When using vanishing cues, automatic instructions may be better for those with severe consolidation impairments, but otherwise intentional instructions may be better.

Rumination impairs the control of stimulus-induced retrieval of irrelevant information, but not attention, control, or response selection in general.

PubMed

Colzato, Lorenza S; Steenbergen, Laura; Hommel, Bernhard

2018-01-23

The aim of the study was to throw more light on the relationship between rumination and cognitive-control processes. Seventy-eight adults were assessed with respect to rumination tendencies by means of the LEIDS-r before performing a Stroop task, an event-file task assessing the automatic retrieval of irrelevant information, an attentional set-shifting task, and the Attentional Network Task, which provided scores for alerting, orienting, and executive control functioning. The size of the Stroop effect and irrelevant retrieval in the event-five task were positively correlated with the tendency to ruminate, while all other scores did not correlate with any rumination scale. Controlling for depressive tendencies eliminated the Stroop-related finding (an observation that may account for previous failures to replicate), but not the event-file finding. Taken altogether, our results suggest that rumination does not affect attention, executive control, or response selection in general, but rather selectively impairs the control of stimulus-induced retrieval of irrelevant information.

Enriching Formal Language Learning with an Informal Social Component

ERIC Educational Resources Information Center

Dettori, Giuliana; Torsani, Simone

2013-01-01

This paper describes an informal component that we added to an online formal language learning environment in order to help the learners reach relevant Internet pages they can freely use to complement their learning activity. Thanks to this facility, each lesson is enriched, at run time, with a number of links automatically retrieved from social…

User Evaluation of Automatically Generated Semantic Hypertext Links in a Heavily Used Procedural Manual.

ERIC Educational Resources Information Center

Tebbutt, John

1999-01-01

Discusses efforts at National Institute of Standards and Technology (NIST) to construct an information discovery tool through the fusion of hypertext and information retrieval that works by parsing a contiguous document base into smaller documents and inserting semantic links between them. Also presents a case study that evaluated user reactions.…

Fluency Heuristic: A Model of How the Mind Exploits a By-Product of Information Retrieval

ERIC Educational Resources Information Center

Hertwig, Ralph; Herzog, Stefan M.; Schooler, Lael J.; Reimer, Torsten

2008-01-01

Boundedly rational heuristics for inference can be surprisingly accurate and frugal for several reasons. They can exploit environmental structures, co-opt complex capacities, and elude effortful search by exploiting information that automatically arrives on the mental stage. The fluency heuristic is a prime example of a heuristic that makes the…

Polyphonic Music Information Retrieval Based on Multi-Label Cascade Classification System

ERIC Educational Resources Information Center

Jiang, Wenxin

2009-01-01

Recognition and separation of sounds played by various instruments is very useful in labeling audio files with semantic information. This is a non-trivial task requiring sound analysis, but the results can aid automatic indexing and browsing music data when searching for melodies played by user specified instruments. Melody match based on pitch…

Improving information retrieval using Medical Subject Headings Concepts: a test case on rare and chronic diseases.

PubMed

Darmoni, Stéfan J; Soualmia, Lina F; Letord, Catherine; Jaulent, Marie-Christine; Griffon, Nicolas; Thirion, Benoît; Névéol, Aurélie

2012-07-01

As more scientific work is published, it is important to improve access to the biomedical literature. Since 2000, when Medical Subject Headings (MeSH) Concepts were introduced, the MeSH Thesaurus has been concept based. Nevertheless, information retrieval is still performed at the MeSH Descriptor or Supplementary Concept level. The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. Three sets of queries were built for thirty-two rare diseases and twenty-two chronic diseases: (1) using PubMed Automatic Term Mapping (ATM), (2) using Catalog and Index of French-language Health Internet (CISMeF) ATM, and (3) extrapolating the MEDLINE citations that should be indexed with a MeSH Concept. Type 3 queries retrieve significantly fewer results than type 1 or type 2 queries (about 18,000 citations versus 200,000 for rare diseases; about 300,000 citations versus 2,000,000 for chronic diseases). CISMeF ATM also provides better precision than PubMed ATM for both disease categories. Using MeSH Concept indexing instead of ATM is theoretically possible to improve retrieval performance with the current indexing policy. However, using MeSH Concept information retrieval and indexing rules would be a fundamentally better approach. These modifications have already been implemented in the CISMeF search engine.

Text-mining and information-retrieval services for molecular biology

PubMed Central

Krallinger, Martin; Valencia, Alfonso

2005-01-01

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators. PMID:15998455

Intelligent web image retrieval system

NASA Astrophysics Data System (ADS)

Hong, Sungyong; Lee, Chungwoo; Nah, Yunmook

2001-07-01

Recently, the web sites such as e-business sites and shopping mall sites deal with lots of image information. To find a specific image from these image sources, we usually use web search engines or image database engines which rely on keyword only retrievals or color based retrievals with limited search capabilities. This paper presents an intelligent web image retrieval system. We propose the system architecture, the texture and color based image classification and indexing techniques, and representation schemes of user usage patterns. The query can be given by providing keywords, by selecting one or more sample texture patterns, by assigning color values within positional color blocks, or by combining some or all of these factors. The system keeps track of user's preferences by generating user query logs and automatically add more search information to subsequent user queries. To show the usefulness of the proposed system, some experimental results showing recall and precision are also explained.

Overview of the INEX 2008 Book Track

NASA Astrophysics Data System (ADS)

Kazai, Gabriella; Doucet, Antoine; Landoni, Monica

This paper provides an overview of the INEX 2008 Book Track. Now in its second year, the track aimed at broadening its scope by investigating topics of interest in the fields of information retrieval, human computer interaction, digital libraries, and eBooks. The main topics of investigation were defined around challenges for supporting users in reading, searching, and navigating the full texts of digitized books. Based on these themes, four tasks were defined: 1) The Book Retrieval task aimed at comparing traditional and book-specific retrieval approaches, 2) the Page in Context task aimed at evaluating the value of focused retrieval approaches for searching books, 3) the Structure Extraction task aimed to test automatic techniques for deriving structure from OCR and layout information, and 4) the Active Reading task aimed to explore suitable user interfaces for eBooks enabling reading, annotation, review, and summary across multiple books. We report on the setup and results of each of these tasks.

A hypertext system that learns from user feedback

NASA Technical Reports Server (NTRS)

Mathe, Nathalie

1994-01-01

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation). Besides providing an hypertext interface for browsing large documents, the CID system automatically acquires and reuses the context in which previous searches were appropriate. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. Thus, the user continually augments and refines the intelligence of the retrieval system. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time. We successfully tested the CID system with users of the Space Station Freedom requirements documents. We are currently extending CID to other application domains (Space Shuttle operations documents, airplane maintenance manuals, and on-line training). We are also exploring the potential commercialization of this technique.

Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

NASA Astrophysics Data System (ADS)

Felder, Martin; Sehnke, Frank; Kaifel, Anton

2013-12-01

The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the method can be applied to almost any kind of retrieval or, more generally, regression problem.

Semantic Annotation of Complex Text Structures in Problem Reports

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Throop, David R.; Fleming, Land D.

2011-01-01

Text analysis is important for effective information retrieval from databases where the critical information is embedded in text fields. Aerospace safety depends on effective retrieval of relevant and related problem reports for the purpose of trend analysis. The complex text syntax in problem descriptions has limited statistical text mining of problem reports. The presentation describes an intelligent tagging approach that applies syntactic and then semantic analysis to overcome this problem. The tags identify types of problems and equipment that are embedded in the text descriptions. The power of these tags is illustrated in a faceted searching and browsing interface for problem report trending that combines automatically generated tags with database code fields and temporal information.

Towards organizing health knowledge on community-based health services.

PubMed

Akbari, Mohammad; Hu, Xia; Nie, Liqiang; Chua, Tat-Seng

2016-12-01

Online community-based health services accumulate a huge amount of unstructured health question answering (QA) records at a continuously increasing pace. The ability to organize these health QA records has been found to be effective for data access. The existing approaches for organizing information are often not applicable to health domain due to its domain nature as characterized by complex relation among entities, large vocabulary gap, and heterogeneity of users. To tackle these challenges, we propose a top-down organization scheme, which can automatically assign the unstructured health-related records into a hierarchy with prior domain knowledge. Besides automatic hierarchy prototype generation, it also enables each data instance to be associated with multiple leaf nodes and profiles each node with terminologies. Based on this scheme, we design a hierarchy-based health information retrieval system. Experiments on a real-world dataset demonstrate the effectiveness of our scheme in organizing health QA into a topic hierarchy and retrieving health QA records from the topic hierarchy.

Evolutionary Computing Methods for Spectral Retrieval

NASA Technical Reports Server (NTRS)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Automated search and retrieval of information from imaged documents using optical correlation techniques

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-10-01

Litton PRC and Litton Data Systems Division are developing a system, the Imaged Document Optical Correlation and Conversion System (IDOCCS), to provide a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provides the search and retrieval of information from imaged documents. IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited; e.g., imaged documents containing an agency's seal or logo can be singled out. In this paper, we present a description of IDOCCS as well as preliminary performance results and theoretical projections.

Early Visual Word Processing Is Flexible: Evidence from Spatiotemporal Brain Dynamics.

PubMed

Chen, Yuanyuan; Davis, Matthew H; Pulvermüller, Friedemann; Hauk, Olaf

2015-09-01

Visual word recognition is often described as automatic, but the functional locus of top-down effects is still a matter of debate. Do task demands modulate how information is retrieved, or only how it is used? We used EEG/MEG recordings to assess whether, when, and how task contexts modify early retrieval of specific psycholinguistic information in occipitotemporal cortex, an area likely to contribute to early stages of visual word processing. Using a parametric approach, we analyzed the spatiotemporal response patterns of occipitotemporal cortex for orthographic, lexical, and semantic variables in three psycholinguistic tasks: silent reading, lexical decision, and semantic decision. Task modulation of word frequency and imageability effects occurred simultaneously in ventral occipitotemporal regions-in the vicinity of the putative visual word form area-around 160 msec, following task effects on orthographic typicality around 100 msec. Frequency and typicality also produced task-independent effects in anterior temporal lobe regions after 200 msec. The early task modulation for several specific psycholinguistic variables indicates that occipitotemporal areas integrate perceptual input with prior knowledge in a task-dependent manner. Still, later task-independent effects in anterior temporal lobes suggest that word recognition eventually leads to retrieval of semantic information irrespective of task demands. We conclude that even a highly overlearned visual task like word recognition should be described as flexible rather than automatic.

Automatic visibility retrieval from thermal camera images

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Ott, Beat; Wellig, Peter; Wunderle, Stefan

2017-10-01

This study presents an automatic visibility retrieval of a FLIR A320 Stationary Thermal Imager installed on a measurement tower on the mountain Lagern located in the Swiss Jura Mountains. Our visibility retrieval makes use of edges that are automatically detected from thermal camera images. Predefined target regions, such as mountain silhouettes or buildings with high thermal differences to the surroundings, are used to derive the maximum visibility distance that is detectable in the image. To allow a stable, automatic processing, our procedure additionally removes noise in the image and includes automatic image alignment to correct small shifts of the camera. We present a detailed analysis of visibility derived from more than 24000 thermal images of the years 2015 and 2016 by comparing them to (1) visibility derived from a panoramic camera image (VISrange), (2) measurements of a forward-scatter visibility meter (Vaisala FD12 working in the NIR spectra), and (3) modeled visibility values using the Thermal Range Model TRM4. Atmospheric conditions, mainly water vapor from European Center for Medium Weather Forecast (ECMWF), were considered to calculate the extinction coefficients using MODTRAN. The automatic visibility retrieval based on FLIR A320 images is often in good agreement with the retrieval from the systems working in different spectral ranges. However, some significant differences were detected as well, depending on weather conditions, thermal differences of the monitored landscape, and defined target size.

Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

ERIC Educational Resources Information Center

Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

2001-01-01

Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

Video-assisted segmentation of speech and audio track

NASA Astrophysics Data System (ADS)

Pandit, Medha; Yusoff, Yusseri; Kittler, Josef; Christmas, William J.; Chilton, E. H. S.

1999-08-01

Video database research is commonly concerned with the storage and retrieval of visual information invovling sequence segmentation, shot representation and video clip retrieval. In multimedia applications, video sequences are usually accompanied by a sound track. The sound track contains potential cues to aid shot segmentation such as different speakers, background music, singing and distinctive sounds. These different acoustic categories can be modeled to allow for an effective database retrieval. In this paper, we address the problem of automatic segmentation of audio track of multimedia material. This audio based segmentation can be combined with video scene shot detection in order to achieve partitioning of the multimedia material into semantically significant segments.

The Future of Access Technology for Blind and Visually Impaired People.

ERIC Educational Resources Information Center

Schreier, E. M.

1990-01-01

This article describes potential use of new technological products and services by blind/visually impaired people. Items discussed include computer input devices, public telephones, automatic teller machines, airline and rail arrival/departure displays, ticketing machines, information retrieval systems, order-entry terminals, optical character…

How the mind shapes action: Offline contexts modulate involuntary episodic retrieval.

PubMed

Frings, Christian; Koch, Iring; Moeller, Birte

2017-11-01

Involuntary retrieval of previous stimulus-response episodes is a centerpiece of many theories of priming, episodic binding, and action control. Typically it is assumed that by repeating a stimulus from trial n-1 to trial n, involuntary retrieval is triggered in a nearly automatic fashion, facilitating (or interfering with) the to-be-executed action. Here we argue that changes in the offline context weaken the involuntary retrieval of previous episodes (the offline context is defined to be the information presented before or after the focal stimulus). In four conditions differing in cue modality and target modality, retrieval was diminished if participants changed the target selection criterion (as indicated by a cue presented before the selection took place) while they still performed the same task. Thus, solely through changes in the offline context (cue or selection criterion), involuntary retrieval can be weakened in an effective way.

Exploration of use of SenseCam to support autobiographical memory retrieval within a cognitive-behavioural therapeutic intervention following acquired brain injury.

PubMed

Brindley, Rob; Bateman, Andrew; Gracey, Fergus

2011-10-01

Delivering effective psychotherapy to address the significant emotional consequences of acquired brain injury (ABI) is challenged by the presence of acquired cognitive impairments, especially retrieval of detailed autobiographical memories of emotional trigger events. Initial studies using a wearable camera (SenseCam) suggest long-term improvements in autobiographical retrieval of recorded events. In this study a single-case experimental design was implemented to explore the use of SenseCam as a memory aid for a man with a specific anxiety disorder and memory and executive difficulties following ABI. We predicted that SenseCam supported rehearsal of memories of events that trigger high levels of anxiety would yield improved retrieval of both factual detail and internal state information (thoughts and feelings) compared with a conventional psychotherapy aid (automatic thought record sheets, ATRs) and no strategy. The findings indicated SenseCam supported retrieval of anxiety trigger events was superior to ATRs or no strategy in terms of both detail and internal state information, with 94% of the information being recalled in the SenseCam condition, compared to 39% for the "no strategy" and 22% for the ATR conditions. It is concluded that SenseCam may be of use as a compensatory aid in psychotherapies relying on retrieval of emotionally salient trigger events.

A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text

ERIC Educational Resources Information Center

Nguyen, Bao-An; Yang, Don-Lin

2012-01-01

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…

Report on Information Retrieval and Library Automation Studies.

ERIC Educational Resources Information Center

Alberta Univ., Edmonton. Dept. of Computing Science.

Short abstracts of works in progress or completed in the Department of Computing Science at the University of Alberta are presented under five major headings. The five categories are: Storage and search techniques for document data bases, Automatic classification, Study of indexing and classification languages through computer manipulation of data…

Enabling the Interoperability of Large-Scale Legacy Systems

DTIC Science & Technology

2008-01-01

information retrieval systems ( Salton and McGill 1983). We use this method because, in the schema mapping task, only one instance per class is...2001). A survey of approaches to automatic schema matching. The VLDB Journal, 10, 334-350. Salton , G., & McGill, M.J. (1983). Introduction to

Web image retrieval using an effective topic and content-based technique

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Prabhakara, Rashmi

2005-03-01

There has been an exponential growth in the amount of image data that is available on the World Wide Web since the early development of Internet. With such a large amount of information and image available and its usefulness, an effective image retrieval system is thus greatly needed. In this paper, we present an effective approach with both image matching and indexing techniques that improvise on existing integrated image retrieval methods. This technique follows a two-phase approach, integrating query by topic and query by example specification methods. In the first phase, The topic-based image retrieval is performed by using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. This technique consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. In the second phase, we use query by example specification to perform a low-level content-based image match in order to retrieve smaller and relatively closer results of the example image. From this, information related to the image feature is automatically extracted from the query image. The main objective of our approach is to develop a functional image search and indexing technique and to demonstrate that better retrieval results can be achieved.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Techniques for Soundscape Retrieval and Synthesis

NASA Astrophysics Data System (ADS)

Mechtley, Brandon Michael

The study of acoustic ecology is concerned with the manner in which life interacts with its environment as mediated through sound. As such, a central focus is that of the soundscape: the acoustic environment as perceived by a listener. This dissertation examines the application of several computational tools in the realms of digital signal processing, multimedia information retrieval, and computer music synthesis to the analysis of the soundscape. Namely, these tools include a) an open source software library, Sirens, which can be used for the segmentation of long environmental field recordings into individual sonic events and compare these events in terms of acoustic content, b) a graph-based retrieval system that can use these measures of acoustic similarity and measures of semantic similarity using the lexical database WordNet to perform both text-based retrieval and automatic annotation of environmental sounds, and c) new techniques for the dynamic, realtime parametric morphing of multiple field recordings, informed by the geographic paths along which they were recorded.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

PubMed

Perez-Rey, David; Jimenez-Castellanos, Ana; Garcia-Remesal, Miguel; Crespo, Jose; Maojo, Victor

2012-04-05

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

PubMed Central

2012-01-01

Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems. PMID:22480327

Automatically identifying health outcome information in MEDLINE records.

PubMed

Demner-Fushman, Dina; Few, Barbara; Hauser, Susan E; Thoma, George

2006-01-01

Understanding the effect of a given intervention on the patient's health outcome is one of the key elements in providing optimal patient care. This study presents a methodology for automatic identification of outcomes-related information in medical text and evaluates its potential in satisfying clinical information needs related to health care outcomes. An annotation scheme based on an evidence-based medicine model for critical appraisal of evidence was developed and used to annotate 633 MEDLINE citations. Textual, structural, and meta-information features essential to outcome identification were learned from the created collection and used to develop an automatic system. Accuracy of automatic outcome identification was assessed in an intrinsic evaluation and in an extrinsic evaluation, in which ranking of MEDLINE search results obtained using PubMed Clinical Queries relied on identified outcome statements. The accuracy and positive predictive value of outcome identification were calculated. Effectiveness of the outcome-based ranking was measured using mean average precision and precision at rank 10. Automatic outcome identification achieved 88% to 93% accuracy. The positive predictive value of individual sentences identified as outcomes ranged from 30% to 37%. Outcome-based ranking improved retrieval accuracy, tripling mean average precision and achieving 389% improvement in precision at rank 10. Preliminary results in outcome-based document ranking show potential validity of the evidence-based medicine-model approach in timely delivery of information critical to clinical decision support at the point of service.

Enhancing the MeSH thesaurus to retrieve French online health resources in a quality-controlled gateway.

PubMed

Douyère, Magaly; Soualmia, Lina F; Névéol, Aurélie; Rogozan, Alexandrina; Dahamna, Badisse; Leroy, Jean-Philippe; Thirion, Benoît; Darmoni, Stefan J

2004-12-01

The amount of health information available on the Internet is considerable. In this context, several health gateways have been developed. Among them, CISMeF (Catalogue and Index of Health Resources in French) was designed to catalogue and index health resources in French. The goal of this article is to describe the various enhancements to the MeSH thesaurus developed by the CISMeF team to adapt this terminology to the broader field of health Internet resources instead of scientific articles for the medline bibliographic database. CISMeF uses two standard tools for organizing information: the MeSH thesaurus and several metadata element sets, in particular the Dublin Core metadata format. The heterogeneity of Internet health resources led the CISMeF team to enhance the MeSH thesaurus with the introduction of two new concepts, respectively, resource types and metaterms. CISMeF resource types are a generalization of the publication types of medline. A resource type describes the nature of the resource and MeSH keyword/qualifier pairs describe the subject of the resource. A metaterm is generally a medical specialty or a biological science, which has semantic links with one or more MeSH keywords, qualifiers and resource types. The CISMeF terminology is exploited for several tasks: resource indexing performed manually, resource categorization performed automatically, visualization and navigation through the concept hierarchies and information retrieval using the Doc'CISMeF search engine. The CISMeF health gateway uses several MeSH thesaurus enhancements to optimize information retrieval, hierarchy navigation and automatic indexing.

Validation of MODIS Dust Aerosol Retrieval and Development Ambient Dust Phase Function using PRIDE Data

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Lau, William (Technical Monitor)

2002-01-01

The PRIDE data set of MODIS aerosol retrievals co-located with sunphotometer measurements provides the basis of MODIS validation in a dust environment. The sunphotometer measurements include AERONET automatic instruments, land-based Microtops instruments, ship-board Microtops instruments and the AATS-6 aboard the Navajo aircraft. Analysis of these data indicate that the MODIS retrieval is within pre-launch estimates of uncertainty within the spectral range of 600-900 nm. However, the MODIS algorithm consistently retrieves smaller particles than reality thus leading to incorrect spectral response outside of the 600-900 nm range and improper size information. Further analysis of MODIS retrievals in other dust environments shows the inconsistencies are due to nonspherical effects in the phase function. These data are used to develop an ambient phase function for dust aerosol to be used for remote sensing purposes.

A Simple Blueprint for Automatic Boolean Query Processing.

ERIC Educational Resources Information Center

Salton, G.

1988-01-01

Describes a new Boolean retrieval environment in which an extended soft Boolean logic is used to automatically construct queries from original natural language formulations provided by users. Experimental results that compare the retrieval effectiveness of this method to conventional Boolean and vector processing are discussed. (27 references)…

Phase structure rewrite systems in information retrieval

NASA Technical Reports Server (NTRS)

Klingbiel, P. H.

1985-01-01

Operational level automatic indexing requires an efficient means of normalizing natural language phrases. Subject switching requires an efficient means of translating one set of authorized terms to another. A phrase structure rewrite system called a Lexical Dictionary is explained that performs these functions. Background, operational use, other applications and ongoing research are explained.

intelligentCAPTURE 1.0 Adds Tables of Content to Library Catalogues and Improves Retrieval.

ERIC Educational Resources Information Center

Hauer, Manfred; Simedy, Walton

2002-01-01

Describes an online library catalog that was developed for an Austrian scientific library that includes table of contents in addition to the standard bibliographic information in order to increase relevance for searchers. Discusses the technology involved, including OCR (Optical Character Recognition) and automatic indexing techniques; weighted…

Information retrieval system utilizing wavelet transform

DOEpatents

Brewster, Mary E.; Miller, Nancy E.

2000-01-01

A method for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.

Survey of Learning Experiences and Influence of Learning Style Preferences on User Intentions Regarding MOOCs

ERIC Educational Resources Information Center

Chang, Ray I.; Hung, Yu Hsin; Lin, Chun Fu

2015-01-01

With the rapid development of web techniques, information and communication technology is being increasingly used in curricula, and learning portfolios can be automatically retrieved and maintained as learners interact through e-learning platforms. Further, massive open online courses (MOOCs), which apply such technology to provide open access to…

An Algorithm for Suffix Stripping

ERIC Educational Resources Information Center

Porter, M. F.

2006-01-01

Purpose: The automatic removal of suffixes from words in English is of particular interest in the field of information retrieval. This work was originally published in Program in 1980 and is republished as part of a series of articles commemorating the 40th anniversary of the journal. Design/methodology/approach: An algorithm for suffix stripping…

Automatic evidence retrieval for systematic reviews.

PubMed

Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G; Tsafnat, Guy

2014-10-01

Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Our goal was to evaluate an automatic method for citation snowballing's capacity to identify and retrieve the full text and/or abstracts of cited articles. Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.

Retrieval, automaticity, vocabulary elaboration, orthography (RAVE-O): a comprehensive, fluency-based reading intervention program.

PubMed

Wolf, M; Miller, L; Donnelly, K

2000-01-01

The most important implication of the double-deficit hypothesis (Wolf & Bowers, in this issue) concerns a new emphasis on fluency and automaticity in intervention for children with developmental reading disabilities. The RAVE-O (Retrieval, Automaticity, Vocabulary Elaboration, Orthography) program is an experimental, fluency-based approach to reading intervention that is designed to accompany a phonological analysis program. In an effort to address multiple possible sources of dysfluency in readers with disabilities, the program involves comprehensive emphases both on fluency in word attack, word identification, and comprehension and on automaticity in underlying componential processes (e.g., phonological, orthographic, semantic, and lexical retrieval skills). The goals, theoretical principles, and applied activities of the RAVE-O curriculum are described with particular stress on facilitating the development of rapid orthographic pattern recognition and on changing children's attitudes toward language.

The application of similar image retrieval in electronic commerce.

PubMed

Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

2014-01-01

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

Facilitating access to information in large documents with an intelligent hypertext system

NASA Technical Reports Server (NTRS)

Mathe, Nathalie

1993-01-01

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation) and tested it on the Space Station Freedom requirement documents. The CID system enables integration of various technical documents in a hypertext framework and includes an intelligent context-sensitive indexing and retrieval mechanism. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time.

The Application of Similar Image Retrieval in Electronic Commerce

PubMed Central

Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

2014-01-01

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system. PMID:24883411

Effects of single cortisol administrations on human affect reviewed: Coping with stress through adaptive regulation of automatic cognitive processing.

PubMed

Putman, Peter; Roelofs, Karin

2011-05-01

The human stress hormone cortisol may facilitate effective coping after psychological stress. In apparent agreement, administration of cortisol has been demonstrated to reduce fear in response to stressors. For anxious patients with phobias or posttraumatic stress disorder this has been ascribed to hypothetical inhibition of retrieval of traumatic memories. However, such stress-protective effects may also work via adaptive regulation of early cognitive processing of threatening information from the environment. This paper selectively reviews the available literature on effects of single cortisol administrations on affect and early cognitive processing of affectively significant information. The concluded working hypothesis is that immediate effects of high concentration of cortisol may facilitate stress-coping via inhibition of automatic processing of goal-irrelevant threatening information and through increased automatic approach-avoidance responses in early emotional processing. Limitations in the existing literature and suggestions for future directions are briefly discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Episodic Memory Retrieval Functionally Relies on Very Rapid Reactivation of Sensory Information.

PubMed

Waldhauser, Gerd T; Braun, Verena; Hanslmayr, Simon

2016-01-06

Episodic memory retrieval is assumed to rely on the rapid reactivation of sensory information that was present during encoding, a process termed "ecphory." We investigated the functional relevance of this scarcely understood process in two experiments in human participants. We presented stimuli to the left or right of fixation at encoding, followed by an episodic memory test with centrally presented retrieval cues. This allowed us to track the reactivation of lateralized sensory memory traces during retrieval. Successful episodic retrieval led to a very early (∼100-200 ms) reactivation of lateralized alpha/beta (10-25 Hz) electroencephalographic (EEG) power decreases in the visual cortex contralateral to the visual field at encoding. Applying rhythmic transcranial magnetic stimulation to interfere with early retrieval processing in the visual cortex led to decreased episodic memory performance specifically for items encoded in the visual field contralateral to the site of stimulation. These results demonstrate, for the first time, that episodic memory functionally relies on very rapid reactivation of sensory information. Remembering personal experiences requires a "mental time travel" to revisit sensory information perceived in the past. This process is typically described as a controlled, relatively slow process. However, by using electroencephalography to measure neural activity with a high time resolution, we show that such episodic retrieval entails a very rapid reactivation of sensory brain areas. Using transcranial magnetic stimulation to alter brain function during retrieval revealed that this early sensory reactivation is causally relevant for conscious remembering. These results give first neural evidence for a functional, preconscious component of episodic remembering. This provides new insight into the nature of human memory and may help in the understanding of psychiatric conditions that involve the automatic intrusion of unwanted memories. Copyright © 2016 the authors 0270-6474/16/360251-10$15.00/0.

Evaluation of a simple method for the automatic assignment of MeSH descriptors to health resources in a French online catalogue.

PubMed

Névéol, Aurélie; Pereira, Suzanne; Kerdelhué, Gaetan; Dahamna, Badisse; Joubert, Michel; Darmoni, Stéfan J

2007-01-01

The growing number of resources to be indexed in the catalogue of online health resources in French (CISMeF) calls for curating strategies involving automatic indexing tools while maintaining the catalogue's high indexing quality standards. To develop a simple automatic tool that retrieves MeSH descriptors from documents titles. In parallel to research on advanced indexing methods, a bag-of-words tool was developed for timely inclusion in CISMeF's maintenance system. An evaluation was carried out on a corpus of 99 documents. The indexing sets retrieved by the automatic tool were compared to manual indexing based on the title and on the full text of resources. 58% of the major main headings were retrieved by the bag-of-words algorithm and the precision on main heading retrieval was 69%. Bag-of-words indexing has effectively been used on selected resources to be included in CISMeF since August 2006. Meanwhile, on going work aims at improving the current version of the tool.

Implicit proactive interference, age, and automatic versus controlled retrieval strategies.

PubMed

Ikier, Simay; Yang, Lixia; Hasher, Lynn

2008-05-01

We assessed the extent to which implicit proactive interference results from automatic versus controlled retrieval among younger and older adults. During a study phase, targets (e.g., "ALLERGY") either were or were not preceded by nontarget competitors (e.g., "ANALOGY"). After a filled interval, the participants were asked to complete word fragments, some of which cued studied words (e.g., "A_L_ _GY"). Retrieval strategies were identified by the difference in response speed between a phase containing fragments that cued only new words and a phase that included a mix of fragments cuing old and new words. Previous results were replicated: Proactive interference was found in implicit memory, and the negative effects were greater for older than for younger adults. Novel findings demonstrate two retrieval processes that contribute to interference: an automatic one that is age invariant and a controlled process that can reduce the magnitude of the automatic interference effects. The controlled process, however, is used effectively only by younger adults. This pattern of findings potentially explains age differences in susceptibility to proactive interference.

Automatic Evidence Retrieval for Systematic Reviews

PubMed Central

Choong, Miew Keen; Galgani, Filippo; Dunn, Adam G

2014-01-01

Background Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. Results The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F1 score=87.3%) of the 633 correctly retrieved citations. Conclusions The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. PMID:25274020

Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts.

PubMed

Ng; Wong

1999-01-01

We are entering a new era of research where the latest scientific discoveries are often first reported online and are readily accessible by scientists worldwide. This rapid electronic dissemination of research breakthroughs has greatly accelerated the current pace in genomics and proteomics research. The race to the discovery of a gene or a drug has now become increasingly dependent on how quickly a scientist can scan through voluminous amount of information available online to construct the relevant picture (such as protein-protein interaction pathways) as it takes shape amongst the rapidly expanding pool of globally accessible biological data (e.g. GENBANK) and scientific literature (e.g. MEDLINE). We describe a prototype system for automatic pathway discovery from on-line text abstracts, combining technologies that (1) retrieve research abstracts from online sources, (2) extract relevant information from the free texts, and (3) present the extracted information graphically and intuitively. Our work demonstrates that this framework allows us to routinely scan online scientific literature for automatic discovery of knowledge, giving modern scientists the necessary competitive edge in managing the information explosion in this electronic age.

Information retrieval system utilizing wavelet transform

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

Brewster, M.E.; Miller, N.E.

A method is disclosed for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.

A framework for biomedical figure segmentation towards image-based document retrieval

PubMed Central

2013-01-01

The figures included in many of the biomedical publications play an important role in understanding the biological experiments and facts described within. Recent studies have shown that it is possible to integrate the information that is extracted from figures in classical document classification and retrieval tasks in order to improve their accuracy. One important observation about the figures included in biomedical publications is that they are often composed of multiple subfigures or panels, each describing different methodologies or results. The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures. Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels. This is a challenging task, however, as different panels can contain similar objects (i.e., barcharts and linecharts) with multiple layouts. Also, certain types of biomedical figures are text-heavy (e.g., DNA sequences and protein sequences images) and they differ from traditional images. As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels. In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure. Our framework starts by robustly harvesting figure-caption pairs from biomedical articles. We base our approach on the observation that the document layout can be used to identify encoded figures and figure boundaries within PDF files. Taking into consideration the document layout allows us to correctly extract figures from the PDF document and associate their corresponding caption. We combine pixel-level representations of the extracted images with information gathered from their corresponding captions to estimate the number of panels in the figure. Thus, our approach simultaneously identifies the number of panels and the layout of figures. In order to evaluate the approach described here, we applied our system on documents containing protein-protein interactions (PPIs) and compared the results against a gold standard that was annotated by biologists. Experimental results showed that our automatic figure segmentation approach surpasses pure caption-based and image-based approaches, achieving a 96.64% accuracy. To allow for efficient retrieval of information, as well as to provide the basis for integration into document classification and retrieval systems among other, we further developed a web-based interface that lets users easily retrieve panels containing the terms specified in the user queries. PMID:24565394

Automatic indexing and retrieval of encounter-specific evidence for point-of-care support.

PubMed

O'Sullivan, Dympna M; Wilk, Szymon A; Michalowski, Wojtek J; Farion, Ken J

2010-08-01

Evidence-based medicine relies on repositories of empirical research evidence that can be used to support clinical decision making for improved patient care. However, retrieving evidence from such repositories at local sites presents many challenges. This paper describes a methodological framework for automatically indexing and retrieving empirical research evidence in the form of the systematic reviews and associated studies from The Cochrane Library, where retrieved documents are specific to a patient-physician encounter and thus can be used to support evidence-based decision making at the point of care. Such an encounter is defined by three pertinent groups of concepts - diagnosis, treatment, and patient, and the framework relies on these three groups to steer indexing and retrieval of reviews and associated studies. An evaluation of the indexing and retrieval components of the proposed framework was performed using documents relevant for the pediatric asthma domain. Precision and recall values for automatic indexing of systematic reviews and associated studies were 0.93 and 0.87, and 0.81 and 0.56, respectively. Moreover, precision and recall for the retrieval of relevant systematic reviews and associated studies were 0.89 and 0.81, and 0.92 and 0.89, respectively. With minor modifications, the proposed methodological framework can be customized for other evidence repositories. Copyright 2010 Elsevier Inc. All rights reserved.

A novel 3D shape descriptor for automatic retrieval of anatomical structures from medical images

NASA Astrophysics Data System (ADS)

Nunes, Fátima L. S.; Bergamasco, Leila C. C.; Delmondes, Pedro H.; Valverde, Miguel A. G.; Jackowski, Marcel P.

2017-03-01

Content-based image retrieval (CBIR) aims at retrieving from a database objects that are similar to an object provided by a query, by taking into consideration a set of extracted features. While CBIR has been widely applied in the two-dimensional image domain, the retrieval of3D objects from medical image datasets using CBIR remains to be explored. In this context, the development of descriptors that can capture information specific to organs or structures is desirable. In this work, we focus on the retrieval of two anatomical structures commonly imaged by Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques, the left ventricle of the heart and blood vessels. Towards this aim, we developed the Area-Distance Local Descriptor (ADLD), a novel 3D local shape descriptor that employs mesh geometry information, namely facet area and distance from centroid to surface, to identify shape changes. Because ADLD only considers surface meshes extracted from volumetric medical images, it substantially diminishes the amount of data to be analyzed. A 90% precision rate was obtained when retrieving both convex (left ventricle) and non-convex structures (blood vessels), allowing for detection of abnormalities associated with changes in shape. Thus, ADLD has the potential to aid in the diagnosis of a wide range of vascular and cardiac diseases.

High-level specification of a proposed information architecture for support of a bioterrorism early-warning system.

PubMed

Berkowitz, Murray R

2013-01-01

Current information systems for use in detecting bioterrorist attacks lack a consistent, overarching information architecture. An overview of the use of biological agents as weapons during a bioterrorist attack is presented. Proposed are the design, development, and implementation of a medical informatics system to mine pertinent databases, retrieve relevant data, invoke appropriate biostatistical and epidemiological software packages, and automatically analyze these data. The top-level information architecture is presented. Systems requirements and functional specifications for this level are presented. Finally, future studies are identified.

Automatic recognition of seismic intensity based on RS and GIS: a case study in Wenchuan Ms8.0 earthquake of China.

PubMed

Zhang, Qiuwen; Zhang, Yan; Yang, Xiaohong; Su, Bin

2014-01-01

In recent years, earthquakes have frequently occurred all over the world, which caused huge casualties and economic losses. It is very necessary and urgent to obtain the seismic intensity map timely so as to master the distribution of the disaster and provide supports for quick earthquake relief. Compared with traditional methods of drawing seismic intensity map, which require many investigations in the field of earthquake area or are too dependent on the empirical formulas, spatial information technologies such as Remote Sensing (RS) and Geographical Information System (GIS) can provide fast and economical way to automatically recognize the seismic intensity. With the integrated application of RS and GIS, this paper proposes a RS/GIS-based approach for automatic recognition of seismic intensity, in which RS is used to retrieve and extract the information on damages caused by earthquake, and GIS is applied to manage and display the data of seismic intensity. The case study in Wenchuan Ms8.0 earthquake in China shows that the information on seismic intensity can be automatically extracted from remotely sensed images as quickly as possible after earthquake occurrence, and the Digital Intensity Model (DIM) can be used to visually query and display the distribution of seismic intensity.

Automatic generation of Web mining environments

NASA Astrophysics Data System (ADS)

Cibelli, Maurizio; Costagliola, Gennaro

1999-02-01

The main problem related to the retrieval of information from the world wide web is the enormous number of unstructured documents and resources, i.e., the difficulty of locating and tracking appropriate sources. This paper presents a web mining environment (WME), which is capable of finding, extracting and structuring information related to a particular domain from web documents, using general purpose indices. The WME architecture includes a web engine filter (WEF), to sort and reduce the answer set returned by a web engine, a data source pre-processor (DSP), which processes html layout cues in order to collect and qualify page segments, and a heuristic-based information extraction system (HIES), to finally retrieve the required data. Furthermore, we present a web mining environment generator, WMEG, that allows naive users to generate a WME specific to a given domain by providing a set of specifications.

Automatic information timeliness assessment of diabetes web sites by evidence based medicine.

PubMed

Sağlam, Rahime Belen; Taşkaya Temizel, Tuğba

2014-11-01

Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites. The proposed method is based on automatic term recognition, relevance feedback and information retrieval techniques in order to generate time-aware structured queries. We tested the framework on diabetes health web sites which were archived between 2006 and 2013 by Archive-it using American Diabetes Association's (ADA) guidelines. The results showed that the proposed framework achieves 65% and 77% accuracy in detecting the timeliness of the web content according to years and pre-determined time intervals respectively. Information seekers and web site owners may benefit from the proposed framework in finding relevant and up-to-date diabetes web sites. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

Automatic Summarization of MEDLINE Citations for Evidence–Based Medical Treatment: A Topic-Oriented Evaluation

PubMed Central

Fiszman, Marcelo; Demner-Fushman, Dina; Kilicoglu, Halil; Rindflesch, Thomas C.

2009-01-01

As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for fifty-three diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p < 0.01) and the increase in the overall score of clinical usefulness was 0.39 (p < 0.05). PMID:19022398

Automatic inference of indexing rules for MEDLINE

PubMed Central

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-01-01

Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI. PMID:19025687

Automatic inference of indexing rules for MEDLINE.

PubMed

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-11-19

Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.

Semantic-based surveillance video retrieval.

PubMed

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques.

PubMed

Gandy, Lisa M; Gumm, Jordan; Fertig, Benjamin; Thessen, Anne; Kennish, Michael J; Chavan, Sameer; Marchionni, Luigi; Xia, Xiaoxin; Shankrit, Shambhavi; Fertig, Elana J

2017-01-01

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.

Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques

PubMed Central

Gumm, Jordan; Fertig, Benjamin; Thessen, Anne; Kennish, Michael J.; Chavan, Sameer; Marchionni, Luigi; Xia, Xiaoxin; Shankrit, Shambhavi; Fertig, Elana J.

2017-01-01

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85–100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases. PMID:28437440

Expert system for automatically correcting OCR output

NASA Astrophysics Data System (ADS)

Taghva, Kazem; Borsack, Julie; Condit, Allen

1994-03-01

This paper describes a new expert system for automatically correcting errors made by optical character recognition (OCR) devices. The system, which we call the post-processing system, is designed to improve the quality of text produced by an OCR device in preparation for subsequent retrieval from an information system. The system is composed of numerous parts: an information retrieval system, an English dictionary, a domain-specific dictionary, and a collection of algorithms and heuristics designed to correct as many OCR errors as possible. For the remaining errors that cannot be corrected, the system passes them on to a user-level editing program. This post-processing system can be viewed as part of a larger system that would streamline the steps of taking a document from its hard copy form to its usable electronic form, or it can be considered a stand alone system for OCR error correction. An earlier version of this system has been used to process approximately 10,000 pages of OCR generated text. Among the OCR errors discovered by this version, about 87% were corrected. We implement numerous new parts of the system, test this new version, and present the results.

Modeling and mining term association for improving biomedical information retrieval performance.

PubMed

Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

2012-06-11

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

Modeling and mining term association for improving biomedical information retrieval performance

PubMed Central

2012-01-01

Background The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. Results We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. Conclusions First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages. PMID:22901087

CLARET user's manual: Mainframe Logs. Revision 1

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

Frobose, R.H.

1984-11-12

CLARET (Computer Logging and RETrieval) is a stand-alone PDP 11/23 system that can support 16 terminals. It provides a forms-oriented front end by which operators enter online activity logs for the Lawrence Livermore National Laboratory's OCTOPUS computer network. The logs are stored on the PDP 11/23 disks for later retrieval, and hardcopy reports are generated both automatically and upon request. Online viewing of the current logs is provided to management. As each day's logs are completed, the information is automatically sent to a CRAY and included in an online database system. The terminal used for the CLARET system is amore » dual-port Hewlett Packard 2626 terminal that can be used as either the CLARET logging station or as an independent OCTOPUS terminal. Because this is a stand-alone system, it does not depend on the availability of the OCTOPUS network to run and, in the event of a power failure, can be brought up independently.« less

Biomedical information retrieval across languages.

PubMed

Daumke, Philipp; Markü, Kornél; Poprat, Michael; Schulz, Stefan; Klar, Rüdiger

2007-06-01

This work presents a new dictionary-based approach to biomedical cross-language information retrieval (CLIR) that addresses many of the general and domain-specific challenges in current CLIR research. Our method is based on a multilingual lexicon that was generated partly manually and partly automatically, and currently covers six European languages. It contains morphologically meaningful word fragments, termed subwords. Using subwords instead of entire words significantly reduces the number of lexical entries necessary to sufficiently cover a specific language and domain. Mediation between queries and documents is based on these subwords as well as on lists of word-n-grams that are generated from large monolingual corpora and constitute possible translation units. The translations are then sent to a standard Internet search engine. This process makes our approach an effective tool for searching the biomedical content of the World Wide Web in different languages. We evaluate this approach using the OHSUMED corpus, a large medical document collection, within a cross-language retrieval setting.

Event-related potential evidence for separable automatic and controlled retrieval processes in proactive interference.

PubMed

Bergström, Zara M; O'Connor, Richard J; Li, Martin K-H; Simons, Jon S

2012-05-21

Interference between competing memories is a major source of retrieval failure, yet, surprisingly little is known about how competitive memory activation arises in the brain. One possibility is that interference during episodic retrieval might be produced by relatively automatic conceptual priming mechanisms that are independent of strategic retrieval processes. Such priming-driven interference might occur when the competing memories have strong pre-existing associations to the retrieval cue. We used ERPs to measure the neural dynamics of retrieval competition, and investigated whether the ERP correlates of interference were affected by varying task demands for selective retrieval. Participants encoded cue words that were presented either two or four times, paired either with the same or different strongly associated words across repetitions. In a subsequent test, participants either selectively recalled each cue's most recent associate, or simply judged how many times a cue had been presented, without requiring selective recall. Interference effects on test performance were only seen in the recall task. In contrast, ERPs during test revealed an early posterior positivity for high interference items that was present in both retrieval tasks. This early ERP effect likely reflects a conceptual priming-related N400 reduction when many associations to a cue were pre-activated. A later parietal positivity resembling the ERP correlate of conscious recollection was found only in the recall task. The results suggest that early effects of proactive interference are relatively automatic and independent of intentional retrieval processes, consistent with suggestions that interference can arise through conceptual priming. Copyright © 2012 Elsevier B.V. All rights reserved.

Visual content highlighting via automatic extraction of embedded captions on MPEG compressed video

NASA Astrophysics Data System (ADS)

Yeo, Boon-Lock; Liu, Bede

1996-03-01

Embedded captions in TV programs such as news broadcasts, documentaries and coverage of sports events provide important information on the underlying events. In digital video libraries, such captions represent a highly condensed form of key information on the contents of the video. In this paper we propose a scheme to automatically detect the presence of captions embedded in video frames. The proposed method operates on reduced image sequences which are efficiently reconstructed from compressed MPEG video and thus does not require full frame decompression. The detection, extraction and analysis of embedded captions help to capture the highlights of visual contents in video documents for better organization of video, to present succinctly the important messages embedded in the images, and to facilitate browsing, searching and retrieval of relevant clips.

User's operating procedures. Volume 2: Scout project financial analysis program

NASA Technical Reports Server (NTRS)

Harris, C. G.; Haris, D. K.

1985-01-01

A review is presented of the user's operating procedures for the Scout Project Automatic Data system, called SPADS. SPADS is the result of the past seven years of software development on a Prime mini-computer located at the Scout Project Office, NASA Langley Research Center, Hampton, Virginia. SPADS was developed as a single entry, multiple cross-reference data management and information retrieval system for the automation of Project office tasks, including engineering, financial, managerial, and clerical support. This volume, two (2) of three (3), provides the instructions to operate the Scout Project Financial Analysis program in data retrieval and file maintenance via the user friendly menu drivers.

Preservation of memory-based automaticity in reading for older adults.

PubMed

Rawson, Katherine A; Touron, Dayna R

2015-12-01

Concerning age-related effects on cognitive skill acquisition, the modal finding is that older adults do not benefit from practice to the same extent as younger adults in tasks that afford a shift from slower algorithmic processing to faster memory-based processing. In contrast, Rawson and Touron (2009) demonstrated a relatively rapid shift to memory-based processing in the context of a reading task. The current research extended beyond this initial study to provide more definitive evidence for relative preservation of memory-based automaticity in reading tasks for older adults. Younger and older adults read short stories containing unfamiliar noun phrases (e.g., skunk mud) followed by disambiguating information indicating the combination's meaning (either the normatively dominant meaning or an alternative subordinate meaning). Stories were repeated across practice blocks, and then the noun phrases were presented in novel sentence frames in a transfer task. Both age groups shifted from computation to retrieval after relatively few practice trials (as evidenced by convergence of reading times for dominant and subordinate items). Most important, both age groups showed strong evidence for memory-based processing of the noun phrases in the transfer task. In contrast, older adults showed minimal shifting to retrieval in an alphabet arithmetic task, indicating that the preservation of memory-based automaticity in reading was task-specific. Discussion focuses on important implications for theories of memory-based automaticity in general and for specific theoretical accounts of age effects on memory-based automaticity, as well as fruitful directions for future research. (c) 2015 APA, all rights reserved).

Data-Base Software For Tracking Technological Developments

NASA Technical Reports Server (NTRS)

Aliberti, James A.; Wright, Simon; Monteith, Steve K.

1996-01-01

Technology Tracking System (TechTracS) computer program developed for use in storing and retrieving information on technology and related patent information developed under auspices of NASA Headquarters and NASA's field centers. Contents of data base include multiple scanned still images and quick-time movies as well as text. TechTracS includes word-processing, report-editing, chart-and-graph-editing, and search-editing subprograms. Extensive keyword searching capabilities enable rapid location of technologies, innovators, and companies. System performs routine functions automatically and serves multiple users.

Structuring Broadcast Audio for Information Access

NASA Astrophysics Data System (ADS)

Gauvain, Jean-Luc; Lamel, Lori

2003-12-01

One rapidly expanding application area for state-of-the-art speech recognition technology is the automatic processing of broadcast audiovisual data for information access. Since much of the linguistic information is found in the audio channel, speech recognition is a key enabling technology which, when combined with information retrieval techniques, can be used for searching large audiovisual document collections. Audio indexing must take into account the specificities of audio data such as needing to deal with the continuous data stream and an imperfect word transcription. Other important considerations are dealing with language specificities and facilitating language portability. At Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), broadcast news transcription systems have been developed for seven languages: English, French, German, Mandarin, Portuguese, Spanish, and Arabic. The transcription systems have been integrated into prototype demonstrators for several application areas such as audio data mining, structuring audiovisual archives, selective dissemination of information, and topic tracking for media monitoring. As examples, this paper addresses the spoken document retrieval and topic tracking tasks.

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Integrated approach to multimodal media content analysis

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1999-12-01

In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textural content analysis. The video stream is demultiplexed into audio, image and caption components. Then, a semantic segmentation of the audio signal based on audio content analysis is conducted, and each segment is indexed as one of the basic audio types. The image sequence is segmented into shots based on visual information analysis, and keyframes are extracted from each shot. Meanwhile, keywords are detected from the closed caption. Index tables are designed for both linear and non-linear access to the video. It is shown by experiments that the proposed methods for multimodal media content analysis are effective. And that the integrated framework achieves satisfactory results for video information filtering and retrieval.

MPEG-7 based video annotation and browsing

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Wellhausen, Jens

2003-11-01

The huge amount of multimedia data produced worldwide requires annotation in order to enable universal content access and to provide content-based search-and-retrieval functionalities. Since manual video annotation can be time consuming, automatic annotation systems are required. We review recent approaches to content-based indexing and annotation of videos for different kind of sports and describe our approach to automatic annotation of equestrian sports videos. We especially concentrate on MPEG-7 based feature extraction and content description, where we apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information. Having determined single shot positions as well as the visual highlights, the information is jointly stored with meta-textual information in an MPEG-7 description scheme. Based on this information, we generate content summaries which can be utilized in a user-interface in order to provide content-based access to the video stream, but further for media browsing on a streaming server.

A boost of confidence: The role of the ventromedial prefrontal cortex in memory, decision-making, and schemas.

PubMed

Hebscher, Melissa; Gilboa, Asaf

2016-09-01

The ventromedial prefrontal cortex (vmPFC) has been implicated in a wide array of functions across multiple domains. In this review, we focus on the vmPFC's involvement in mediating strategic aspects of memory retrieval, memory-related schema functions, and decision-making. We suggest that vmPFC generates a confidence signal that informs decisions and memory-guided behaviour. Confidence is central to these seemingly diverse functions: (1) Strategic retrieval: lesions to the vmPFC impair an early, automatic, and intuitive monitoring process ("feeling of rightness"; FOR) often associated with confabulation (spontaneous reporting of erroneous memories). Critically, confabulators typically demonstrate high levels of confidence in their false memories, suggesting that faulty monitoring following vmPFC damage may lead to indiscriminate confidence signals. (2) Memory schemas: the vmPFC is critically involved in instantiating and maintaining contextually relevant schemas, broadly defined as higher level knowledge structures that encapsulate lower level representational elements. The correspondence between memory retrieval cues and these activated schemas leads to FOR monitoring. Stronger, more elaborate schemas produce stronger FOR and influence confidence in the veracity of memory candidates. (3) Finally, we review evidence on the vmPFC's role in decision-making, extending this role to decision-making during memory retrieval. During non-mnemonic and mnemonic decision-making the vmPFC automatically encodes confidence. Confidence signal in the vmPFC is revealed as a non-linear relationship between a first-order monitoring assessment and second-order action or choice. Attempting to integrate the multiple functions of the vmPFC, we propose a posterior-anterior organizational principle for this region. More posterior vmPFC regions are involved in earlier, automatic, subjective, and contextually sensitive functions, while more anterior regions are involved in controlled actions based on these earlier functions. Confidence signals reflect the non-linear relationship between first-order, posterior-mediated and second-order, anterior-mediated processes and are represented along the entire axis. Copyright © 2016 Elsevier Ltd. All rights reserved.

A knowledge engineering approach to recognizing and extracting sequences of nucleic acids from scientific literature.

PubMed

García-Remesal, Miguel; Maojo, Victor; Crespo, José

2010-01-01

In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences.

Executive Functions Are Employed to Process Episodic and Relational Memories in Children With Autism Spectrum Disorders

PubMed Central

2013-01-01

Objective: Long-term memory functioning in autism spectrum disorders (ASDs) is marked by a characteristic pattern of impairments and strengths. Individuals with ASD show impairment in memory tasks that require the processing of relational and contextual information, but spared performance on tasks requiring more item-based, acontextual processing. Two experiments investigated the cognitive mechanisms underlying this memory profile. Method: A sample of 14 children with a diagnosis of high-functioning ASD (age: M = 12.2 years), and a matched control group of 14 typically developing (TD) children (age: M = 12.1 years), participated in a range of behavioral memory tasks in which we measured both relational and item-based memory abilities. They also completed a battery of executive function measures. Results: The ASD group showed specific deficits in relational memory, but spared or superior performance in item-based memory, across all tasks. Importantly, for ASD children, executive ability was significantly correlated with relational memory but not with item-based memory. No such relationship was present in the control group. This suggests that children with ASD atypically employed effortful, executive strategies to retrieve relational (but not item-specific) information, whereas TD children appeared to use more automatic processes. Conclusions: The relational memory impairment in ASD may result from a specific impairment in automatic associative retrieval processes with an increased reliance on effortful and strategic retrieval processes. Our findings allow specific neural predictions to be made regarding the interactive functioning of the hippocampus, prefrontal cortex, and posterior parietal cortex in ASD as a neural network supporting relational memory processing. PMID:24245930

Distinct neural substrates for semantic knowledge and naming in the temporoparietal network.

PubMed

Gesierich, Benno; Jovicich, Jorge; Riello, Marianna; Adriani, Michela; Monti, Alessia; Brentari, Valentina; Robinson, Simon D; Wilson, Stephen M; Fairhall, Scott L; Gorno-Tempini, Maria Luisa

2012-10-01

Patients with anterior temporal lobe (ATL) lesions show semantic and lexical retrieval deficits, and the differential role of this area in the 2 processes is debated. Functional neuroimaging in healthy individuals has not clarified the matter because semantic and lexical processes usually occur simultaneously and automatically. Furthermore, the ATL is a region challenging for functional magnetic resonance imaging (fMRI) due to susceptibility artifacts, especially at high fields. In this study, we established an optimized ATL-sensitive fMRI acquisition protocol at 4 T and applied an event-related paradigm to study the identification (i.e., association of semantic biographical information) of celebrities, with and without the ability to retrieve their proper names. While semantic processing reliably activated the ATL, only more posterior areas in the left temporal and temporal-parietal junction were significantly modulated by covert lexical retrieval. These results suggest that within a temporoparietal network, the ATL is relatively more important for semantic processing, and posterior language regions are relatively more important for lexical retrieval.

Advanced instrumentation for the collection, retrieval, and processing of urban stormwater data

USGS Publications Warehouse

Robinson, Jerald B.; Bales, Jerad D.; Young, Wendi S.; ,

1995-01-01

The U.S. Geological Survey, in cooperation with the City of Charlotte and Mecklenburg County, North Carolina, has developed a data-collection network that uses advanced instrumentation to automatically collect, retrieve, and process urban stormwater data. Precipitation measurement and water-quality networks provide data for (1) planned watershed simulation models, (2) early warning of possible flooding, (3) computation of material export, and (4) characterization of water quality in relation to basin conditions. Advantages of advanced instrumentation include remote access to real-time data, reduced demands on and more efficient use of limited human resources, and direct importation of data into a geographical information system for display and graphic analysis.

MACSIMS : multiple alignment of complete sequences information management system

PubMed Central

Thompson, Julie D; Muller, Arnaud; Waterhouse, Andrew; Procter, Jim; Barton, Geoffrey J; Plewniak, Frédéric; Poch, Olivier

2006-01-01

Background In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family. Results MACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis. Conclusion MACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at . PMID:16792820

How automatic is the musical stroop effect? Commentary on “the musical stroop effect: opening a new avenue to research on automatisms” by l. Grégoire, P. Perruchet, and B. Poulin-Charronnat (Experimental Psychology, 2013, vol. 60, pp. 269–278).

PubMed

Moeller, Birte; Frings, Christian

2014-01-01

Grégoire, Perruchet, and Poulin-Charronnat (2013) investigated a musical variant of the reversed Stroop effect. According to the authors, one big advantage of this variant is that the automaticity of note naming can be better controlled than in other Stroop variants as musicians are very practiced in note reading whereas non-musicians are not. In this comment we argue that at present the exact impact of automaticity in this Stroop variant remains somewhat unclear for at least three reasons, namely due to the type of information that is automatically retrieved when notes are encountered, due to the possible influence of object-based attention, and finally due to the fact that the exact influence of expertise on interference cannot be pinpointed with an extreme group design.

Popular song and lyrics synchronization and its application to music information retrieval

NASA Astrophysics Data System (ADS)

Chen, Kai; Gao, Sheng; Zhu, Yongwei; Sun, Qibin

2006-01-01

An automatic synchronization system of the popular song and its lyrics is presented in the paper. The system includes two main components: a) automatically detecting vocal/non-vocal in the audio signal and b) automatically aligning the acoustic signal of the song with its lyric using speech recognition techniques and positioning the boundaries of the lyrics in its acoustic realization at the multiple levels simultaneously (e.g. the word / syllable level and phrase level). The GMM models and a set of HMM-based acoustic model units are carefully designed and trained for the detection and alignment. To eliminate the severe mismatch due to the diversity of musical signal and sparse training data available, the unsupervised adaptation technique such as maximum likelihood linear regression (MLLR) is exploited for tailoring the models to the real environment, which improves robustness of the synchronization system. To further reduce the effect of the missed non-vocal music on alignment, a novel grammar net is build to direct the alignment. As we know, this is the first automatic synchronization system only based on the low-level acoustic feature such as MFCC. We evaluate the system on a Chinese song dataset collecting from 3 popular singers. We obtain 76.1% for the boundary accuracy at the syllable level (BAS) and 81.5% for the boundary accuracy at the phrase level (BAP) using fully automatic vocal/non-vocal detection and alignment. The synchronization system has many applications such as multi-modality (audio and textual) content-based popular song browsing and retrieval. Through the study, we would like to open up the discussion of some challenging problems when developing a robust synchronization system for largescale database.

Neural Bases of Automaticity

ERIC Educational Resources Information Center

Servant, Mathieu; Cassey, Peter; Woodman, Geoffrey F.; Logan, Gordon D.

2018-01-01

Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this…

Generating Models of Surgical Procedures using UMLS Concepts and Multiple Sequence Alignment

PubMed Central

Meng, Frank; D’Avolio, Leonard W.; Chen, Andrew A.; Taira, Ricky K.; Kangarloo, Hooshang

2005-01-01

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient’s anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation. PMID:16779094

Event-Related Potential Evidence that Automatic Recollection Can Be Voluntarily Avoided

ERIC Educational Resources Information Center

Bergstrom, Zara M.; de Fockert, Jan; Richardson-Klavehn, Alan

2009-01-01

Voluntary control processes can be recruited to facilitate recollection in situations where a retrieval cue fails to automatically bring to mind a desired episodic memory. We investigated whether voluntary control processes can also stop recollection of unwanted memories that would otherwise have been automatically recollected. Participants were…

Effects of divided attention and speeded responding on implicit and explicit retrieval of artificial grammar knowledge.

PubMed

Helman, Shaun; Berry, Dianne C

2003-07-01

The artificial grammar (AG) learning literature (see, e.g., Mathews et al., 1989; Reber, 1967) has relied heavily on a single measure of implicitly acquired knowledge. Recent work comparing this measure (string classification) with a more indirect measure in which participants make liking ratings of novel stimuli (e.g., Manza & Bornstein, 1995; Newell & Bright, 2001) has shown that string classification (which we argue can be thought of as an explicit, rather than an implicit, measure of memory) gives rise to more explicit knowledge of the grammatical structure in learning strings and is more resilient to changes in surface features and processing between encoding and retrieval. We report data from two experiments that extend these findings. In Experiment 1, we showed that a divided attention manipulation (at retrieval) interfered with explicit retrieval of AG knowledge but did not interfere with implicit retrieval. In Experiment 2, we showed that forcing participants to respond within a very tight deadline resulted in the same asymmetric interference pattern between the tasks. In both experiments, we also showed that the type of information being retrieved influenced whether interference was observed. The results are discussed in terms of the relatively automatic nature of implicit retrieval and also with respect to the differences between analytic and nonanalytic processing (Whittlesea & Price, 2001).

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

PubMed Central

2013-01-01

Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

Current Development at the Southern California Earthquake Data Center (SCEDC)

NASA Astrophysics Data System (ADS)

Appel, V. L.; Clayton, R. W.

2005-12-01

Over the past year, the SCEDC completed or is near completion of three featured projects: Station Information System (SIS) Development: The SIS will provide users with an interface into complete and accurate station metadata for all current and historic data at the SCEDC. The goal of this project is to develop a system that can interact with a single database source to enter, update and retrieve station metadata easily and efficiently. The system will provide accurate station/channel information for active stations to the SCSN real-time processing system, as will as station/channel information for stations that have parametric data at the SCEDC i.e., for users retrieving data via STP. Additionally, the SIS will supply information required to generate dataless SEED and COSMOS V0 volumes and allow stations to be added to the system with a minimum, but incomplete set of information using predefined defaults that can be easily updated as more information becomes available. Finally, the system will facilitate statewide metadata exchange for both real-time processing and provide a common approach to CISN historic station metadata. Moment Tensor Solutions: The SCEDC is currently archiving and delivering Moment Magnitudes and Moment Tensor Solutions (MTS) produced by the SCSN in real-time and post-processing solutions for events spanning back to 1999. The automatic MTS runs on all local events with magnitudes > 3.0, and all regional events > 3.5. The distributed solution automatically creates links from all USGS Simpson Maps to a text e-mail summary solution, creates a .gif image of the solution, and updates the moment tensor database tables at the SCEDC. Searchable Scanned Waveforms Site: The Caltech Seismological Lab has made available 12,223 scanned images of pre-digital analog recordings of major earthquakes recorded in Southern California between 1962 and 1992 at http://www.data.scec.org/research/scans/. The SCEDC has developed a searchable web interface that allows users to search the available files, select multiple files for download and then retrieve a zipped file containing the results. Scanned images of paper records for M>3.5 southern California earthquakes and several significant teleseisms are available for download via the SCEDC through this search tool.

Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

PubMed

Tang, Guoyu; Ni, Yuan; Wang, Keqiang; Yong, Qin

2018-01-01

The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the workload on the doctors, a better method is to automatically retrieve the semantically equivalent question from the archive. We present a Generative Adversarial Networks (GAN) based approach to automatically retrieve patient question. We apply supervised deep learning based approaches to determine the similarity between patient questions. Then a GAN framework is used to fine-tune the pre-trained deep learning models. The experiment results show that fine-tuning by GAN can improve the performance.

Patent Retrieval in Chemistry based on Semantically Tagged Named Entities

DTIC Science & Technology

2009-11-01

their corresponding synonyms. An ex- ample query for TS-15 is: (" Betaine " OR "Glycine betaine " OR "Glycocol betaine " OR "Glycylbetaine" OR ...) AND...task in an automatic way based on noun- phrase detection incorporating the OpenNLP chun- 3 Informative Term Synonyms Source Betaine Glycine betaine ...Glycocol betaine , Glycylbetaine etc. ATC Peripheral Artery Disease Peripheral Artery Disorder, Peripheral Arterial Disease etc. MeSH Diels-Alder reaction

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services.

PubMed

Shi, Longxiang; Li, Shijian; Yang, Xiaoran; Qi, Jiaheng; Pan, Gang; Zhou, Binbin

2017-01-01

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

PubMed Central

Yang, Xiaoran; Qi, Jiaheng; Pan, Gang; Zhou, Binbin

2017-01-01

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective. PMID:28299322

Long-Term Aftereffects of Response Inhibition: Memory Retrieval, Task Goals, and Cognitive Control

ERIC Educational Resources Information Center

Verbruggen, Frederick; Logan, Gordon D.

2008-01-01

Cognitive control theories attribute control to executive processes that adjust and control behavior online. Theories of automaticity attribute control to memory retrieval. In the present study, online adjustments and memory retrieval were examined, and their roles in controlling performance in the stop-signal paradigm were elucidated. There was…

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James

1994-01-01

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

A content-based retrieval of mammographic masses using the curvelet descriptor

NASA Astrophysics Data System (ADS)

Narváez, Fabian; Díaz, Gloria; Gómez, Francisco; Romero, Eduardo

2012-03-01

Computer-aided diagnosis (CAD) that uses content based image retrieval (CBIR) strategies has became an important research area. This paper presents a retrieval strategy that automatically recovers mammography masses from a virtual repository of mammographies. Unlike other approaches, we do not attempt to segment masses but instead we characterize the regions previously selected by an expert. These regions are firstly curvelet transformed and further characterized by approximating the marginal curvelet subband distribution with a generalized gaussian density (GGD). The content based retrieval strategy searches similar regions in a database using the Kullback-Leibler divergence as the similarity measure between distributions. The effectiveness of the proposed descriptor was assessed by comparing the automatically assigned label with a ground truth available in the DDSM database.1 A total of 380 masses with different shapes, sizes and margins were used for evaluation, resulting in a mean average precision rate of 89.3% and recall rate of 75.2% for the retrieval task.

Automatic Retrieval of Newly Instructed Cue-Task Associations Seen in Task-Conflict Effects in the First Trial after Cue-Task Instructions.

PubMed

Meiran, Nachshon; Pereg, Maayan

2017-01-01

Novel stimulus-response associations are retrieved automatically even without prior practice. Is this true for novel cue-task associations? The experiment involved miniblocks comprising three phases and task switching. In the INSTRUCTION phase, two new stimuli (or familiar cues) were arbitrarily assigned as cues for up-down/right-left tasks performed on placeholder locations. In the UNIVALENT phase, there was no task cue since placeholder's location afforded one task but the placeholders were the stimuli that we assigned as task cues for the following BIVALENT phase (involving target locations affording both tasks). Thus, participants held the novel cue-task associations in memory while executing the UNIVALENT phase. Results show poorer performance in the first univalent trial when the placeholder was associated with the opposite task (incompatible) than when it was compatible, an effect that was numerically larger with newly instructed cues than with familiar cues. These results indicate automatic retrieval of newly instructed cue-task associations.

Automatic processing influences free recall: converging evidence from the process dissociation procedure and remember-know judgments.

PubMed

McCabe, David P; Roediger, Henry L; Karpicke, Jeffrey D

2011-04-01

Dual-process theories of retrieval suggest that controlled and automatic processing contribute to memory performance. Free recall tests are often considered pure measures of recollection, assessing only the controlled process. We report two experiments demonstrating that automatic processes also influence free recall. Experiment 1 used inclusion and exclusion tasks to estimate recollection and automaticity in free recall, adopting a new variant of the process dissociation procedure. Dividing attention during study selectively reduced the recollection estimate but did not affect the automatic component. In Experiment 2, we replicated the results of Experiment 1, and subjects additionally reported remember-know-guess judgments during recall in the inclusion condition. In the latter task, dividing attention during study reduced remember judgments for studied items, but know responses were unaffected. Results from both methods indicated that free recall is partly driven by automatic processes. Thus, we conclude that retrieval in free recall tests is not driven solely by conscious recollection (or remembering) but also by automatic influences of the same sort believed to drive priming on implicit memory tests. Sometimes items come to mind without volition in free recall.

QCS : a system for querying, clustering, and summarizing documents.

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

Dunlavy, Daniel M.

2006-08-01

Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test setsmore » from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence ''trimming'', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.« less

QCS: a system for querying, clustering and summarizing documents.

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

Dunlavy, Daniel M.; Schlesinger, Judith D.; O'Leary, Dianne P.

2006-10-01

Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test setsmore » from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence 'trimming', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.« less

Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users

PubMed Central

Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John

2008-01-01

Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

PubMed Central

2014-01-01

Background Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. Methods G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Results Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. Conclusions G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user. PMID:25474588

G-Bean: an ontology-graph based web tool for biomedical literature retrieval.

PubMed

Wang, James Z; Zhang, Yuanyuan; Dong, Liang; Li, Lin; Srimani, Pradip K; Yu, Philip S

2014-01-01

Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.

A framework for automatic information quality ranking of diabetes websites.

PubMed

Belen Sağlam, Rahime; Taskaya Temizel, Tugba

2015-01-01

Objective: When searching for particular medical information on the internet the challenge lies in distinguishing the websites that are relevant to the topic, and contain accurate information. In this article, we propose a framework that automatically identifies and ranks diabetes websites according to their relevance and information quality based on the website content. Design: The proposed framework ranks diabetes websites according to their content quality, relevance and evidence based medicine. The framework combines information retrieval techniques with a lexical resource based on Sentiwordnet making it possible to work with biased and untrusted websites while, at the same time, ensuring the content relevance. Measurement: The evaluation measurements used were Pearson-correlation, true positives, false positives and accuracy. We tested the framework with a benchmark data set consisting of 55 websites with varying degrees of information quality problems. Results: The proposed framework gives good results that are comparable with the non-automated information quality measuring approaches in the literature. The correlation between the results of the proposed automated framework and ground-truth is 0.68 on an average with p < 0.001 which is greater than the other proposed automated methods in the literature (r score in average is 0.33).

Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping.

PubMed

Jaakkola, Anttoni; Hyyppä, Juha; Hyyppä, Hannu; Kukko, Antero

2008-09-01

Automated processing of the data provided by a laser-based mobile mapping system will be a necessity due to the huge amount of data produced. In the future, vehiclebased laser scanning, here called mobile mapping, should see considerable use for road environment modelling. Since the geometry of the scanning and point density is different from airborne laser scanning, new algorithms are needed for information extraction. In this paper, we propose automatic methods for classifying the road marking and kerbstone points and modelling the road surface as a triangulated irregular network. On the basis of experimental tests, the mean classification accuracies obtained using automatic method for lines, zebra crossings and kerbstones were 80.6%, 92.3% and 79.7%, respectively.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

Automatic Analysis of Critical Incident Reports: Requirements and Use Cases.

PubMed

Denecke, Kerstin

2016-01-01

Increasingly, critical incident reports are used as a means to increase patient safety and quality of care. The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval and analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this paper is to identify potential use cases for automatic methods that analyse critical incident reports. In more detail, we will describe how faceted search could offer an intuitive retrieval of critical incident reports and how text mining could support in analysing relations among events. To realise an automated analysis, natural language processing needs to be applied. Therefore, we analyse the language of critical incident reports and derive requirements towards automatic processing methods. We learned that there is a huge potential for an automatic analysis of incident reports, but there are still challenges to be solved.

A comparison of conscious and automatic memory processes for picture and word stimuli: a process dissociation analysis.

PubMed

McBride, Dawn M; Anne Dosher, Barbara

2002-09-01

Four experiments were conducted to evaluate explanations of picture superiority effects previously found for several tasks. In a process dissociation procedure (Jacoby, 1991) with word stem completion, picture fragment completion, and category production tasks, conscious and automatic memory processes were compared for studied pictures and words with an independent retrieval model and a generate-source model. The predictions of a transfer appropriate processing account of picture superiority were tested and validated in "process pure" latent measures of conscious and unconscious, or automatic and source, memory processes. Results from both model fits verified that pictures had a conceptual (conscious/source) processing advantage over words for all tasks. The effects of perceptual (automatic/word generation) compatibility depended on task type, with pictorial tasks favoring pictures and linguistic tasks favoring words. Results show support for an explanation of the picture superiority effect that involves an interaction of encoding and retrieval processes.

Overlearned responses hinder S-R binding.

PubMed

Moeller, Birte; Frings, Christian

2017-01-01

Two mechanisms that are important for human action control are the integration of individual action plans (see Hommel, Müsseler, Aschersleben, & Prinz, 2001) and the automatization of overlearned actions to familiar stimuli (see Logan, 1988). In the present study, we analyzed the influence of automatization on action plan integration. Integration with pronunciation responses were compared for response incompatible word and nonword stimuli. Stimulus-response binding effects were observed for nonwords. In contrast, words that automatically triggered an overlearned pronunciation response were not integrated with pronunciation of a different word. That is, automatized response retrieval hindered binding effects regarding the retrieving stimulus and a new response. The results are a first indication of the way that binding and learning processes interact, and might also be a first step to understanding the more complex interdependency of the processes responsible for stimulus-response binding in action control and stimulus-response associations in learning research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

User-Centered Indexing for Adaptive Information Access

NASA Technical Reports Server (NTRS)

Chen, James R.; Mathe, Nathalie

1996-01-01

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Leading and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.

BROWSER: An Automatic Indexing On-Line Text Retrieval System. Annual Progress Report.

ERIC Educational Resources Information Center

Williams, J. H., Jr.

The development and testing of the Browsing On-line With Selective Retrieval (BROWSER) text retrieval system allowing a natural language query statement and providing on-line browsing capabilities through an IBM 2260 display terminal is described. The prototype system contains data bases of 25,000 German language patent abstracts, 9,000 English…

Photogrammetric retrieval of volcanic ash cloud top height from SEVIRI and MODIS

NASA Astrophysics Data System (ADS)

Zakšek, Klemen; Hort, Matthias; Zaletelj, Janez; Langmann, Bärbel

2013-04-01

Even if erupting in remote areas, volcanoes can have a significant impact on the modern society due to volcanic ash dispersion in the atmosphere. The ash does not affect merely air traffic - its transport in the atmosphere and its deposition on land and in the oceans may also significantly influence the climate through modifications of atmospheric CO2. The emphasis of this contribution is the retrieval of volcanic ash plume height (ACTH). ACTH is important information especially for air traffic but also to predict ash transport and to estimate the mass flux of the ejected material. ACTH is usually estimated from ground measurements, pilot reports, or satellite remote sensing. But ground based instruments are often not available at remote volcanoes and also the pilots reports are a matter of chance. Volcanic ash cloud top height (ACTH) can be monitored on the global level using satellite remote sensing. The most often used method compares brightness temperature of the cloud with the atmospheric temperature profile. Because of uncertainties of this method (unknown emissivity of the ash cloud, tropopause, etc.) we propose photogrammetric methods based on the parallax between data retrieved from geostationary (SEVIRI) and polar orbiting satellites (MODIS). The parallax is estimated using automatic image matching in three level image pyramids. The procedure works well if the data from both satellites are retrieved nearly simultaneously. MODIS does not retrieve the data at exactly the same time as SEVIRI. To compensate for advection we use two sequential SEVIRI images (one before and one after the MODIS retrieval) and interpolate the cloud position from SEVIRI data to the time of MODIS retrieval. ACTH is then estimated by intersection of corresponding lines-of-view from MODIS and interpolated SEVIRI data. The proposed method was tested using MODIS band 1 and SEVIRI HRV band for the case of the Eyjafjallajökull eruption in April 2010. The parallax between MODIS and SEVIRI data can reach over 30 km which implies ACTH of more than 12 km. The accuracy of ACTH was estimated to 0.6 km. The limitation of this procedure is that it has difficulties with automatic image matching if the ash cloud is not opaque.

An Information Retrieval Approach for Robust Prediction of Road Surface States.

PubMed

Park, Jae-Hyung; Kim, Kwanho

2017-01-28

Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods.

An Information Retrieval Approach for Robust Prediction of Road Surface States

PubMed Central

Park, Jae-Hyung; Kim, Kwanho

2017-01-01

Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods. PMID:28134859

The effects of aging on emotion-induced modulations of source retrieval ERPs: evidence for valence biases.

PubMed

Newsome, Rachel N; Dulas, Michael R; Duarte, Audrey

2012-12-01

Many behavioral studies have shown that memory is enhanced for emotionally salient events across the lifespan. It has been suggested that this mnemonic boost may be observed for both age groups, particularly the old, in part because emotional information is retrieved with less effort than neutral information. Neuroimaging evidence suggests that inefficient retrieval processing (temporally delayed and attenuated) may contribute to age-related impairments in episodic memory for neutral events. It is not entirely clear whether emotional salience may reduce these age-related changes in neural activity associated with episodic retrieval for neutral events. Here, we investigated these ideas using event-related potentials (ERPs) to assess the neural correlates of successful source memory retrieval ("old-new effects") for neutral and emotional (negative and positive) images. Behavioral results showed that older adults demonstrated source memory impairments compared to the young but that both groups showed reduced source memory accuracy for negative compared to positive and neutral images; most likely due to an arousal-induced memory tradeoff for the negative images, which were subjectively more arousing than both positive and neutral images. ERP results showed that early onsetting old-new effects, between 100 and 300 ms, were observed for emotional but not neutral images in both age groups. Interestingly, these early effects were observed for negative items in the young and for positive items in the old. These ERP findings offer support for the idea that emotional events may be retrieved more automatically than neutral events across the lifespan. Furthermore, we suggest that very early retrieval mechanisms, possibly perceptual priming or familiarity, may underlie the negativity and positivity effects sometimes observed in the young and old, respectively, for various behavioral measures of attention and memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

PubMed Central

Sadesh, S.; Suganthe, R. C.

2015-01-01

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio. PMID:26221626

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE).

PubMed

Hanauer, David A; Mei, Qiaozhu; Law, James; Khanna, Ritu; Zheng, Kai

2015-06-01

This paper describes the University of Michigan's nine-year experience in developing and using a full-text search engine designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs). The system, called the Electronic Medical Record Search Engine (EMERSE), functions similar to Google but is equipped with special functionalities for handling challenges unique to retrieving information from medical text. Key features that distinguish EMERSE from general-purpose search engines are discussed, with an emphasis on functions crucial to (1) improving medical IR performance and (2) assuring search quality and results consistency regardless of users' medical background, stage of training, or level of technical expertise. Since its initial deployment, EMERSE has been enthusiastically embraced by clinicians, administrators, and clinical and translational researchers. To date, the system has been used in supporting more than 750 research projects yielding 80 peer-reviewed publications. In several evaluation studies, EMERSE demonstrated very high levels of sensitivity and specificity in addition to greatly improved chart review efficiency. Increased availability of electronic data in healthcare does not automatically warrant increased availability of information. The success of EMERSE at our institution illustrates that free-text EHR search engines can be a valuable tool to help practitioners and researchers retrieve information from EHRs more effectively and efficiently, enabling critical tasks such as patient case synthesis and research data abstraction. EMERSE, available free of charge for academic use, represents a state-of-the-art medical IR tool with proven effectiveness and user acceptance. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Automatic Indexing Using Term Discrimination and Term Precision Measurements

ERIC Educational Resources Information Center

Salton, G.; And Others

1976-01-01

These two indexing systems are briefly described and experimental evidence is cited showing that a combination of both theories produces better retrieval performance than either one alone. Appropriate conclusions are reached concerning viable automatic indexing procedures usable in practice. (Author)

Retrieval and classification of food images.

PubMed

Farinella, Giovanni Maria; Allegra, Dario; Moltisanti, Marco; Stanco, Filippo; Battiato, Sebastiano

2016-10-01

Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification. Copyright © 2016 Elsevier Ltd. All rights reserved.

A method for the automatic reconstruction of fetal cardiac signals from magnetocardiographic recordings

NASA Astrophysics Data System (ADS)

Mantini, D.; Alleva, G.; Comani, S.

2005-10-01

Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period.

Neural Mechanisms of Context Effects on Face Recognition: Automatic Binding and Context Shift Decrements

PubMed Central

Hayes, Scott M.; Baena, Elsa; Truong, Trong-Kha; Cabeza, Roberto

2011-01-01

Although people do not normally try to remember associations between faces and physical contexts, these associations are established automatically, as indicated by the difficulty of recognizing familiar faces in different contexts (“butcher-on-the-bus” phenomenon). The present functional MRI (fMRI) study investigated the automatic binding of faces and scenes. In the Face-Face (F-F) condition, faces were presented alone during both encoding and retrieval, whereas in the Face/Scene-Face (FS-F) condition, they were presented overlaid on scenes during encoding but alone during retrieval (context change). Although participants were instructed to focus only on the faces during both encoding and retrieval, recognition performance was worse in the FS-F than the F-F condition (“context shift decrement”—CSD), confirming automatic face-scene binding during encoding. This binding was mediated by the hippocampus as indicated by greater subsequent memory effects (remembered > forgotten) in this region for the FS-F than the F-F condition. Scene memory was mediated by the right parahippocampal cortex, which was reactivated during successful retrieval when the faces were associated with a scene during encoding (FS-F condition). Analyses using the CSD as a regressor yielded a clear hemispheric asymmetry in medial temporal lobe activity during encoding: left hippocampal and parahippocampal activity was associated with a smaller CSD, indicating more flexible memory representations immune to context changes, whereas right hippocampal/rhinal activity was associated with a larger CSD, indicating less flexible representations sensitive to context change. Taken together, the results clarify the neural mechanisms of context effects on face recognition. PMID:19925208

A Compositional Relevance Model for Adaptive Information Retrieval

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James; Lu, Henry, Jr. (Technical Monitor)

1994-01-01

There is a growing need for rapid and effective access to information in large electronic documentation systems. Access can be facilitated if information relevant in the current problem solving context can be automatically supplied to the user. This includes information relevant to particular user profiles, tasks being performed, and problems being solved. However most of this knowledge on contextual relevance is not found within the contents of documents, and current hypermedia tools do not provide any easy mechanism to let users add this knowledge to their documents. We propose a compositional relevance network to automatically acquire the context in which previous information was found relevant. The model records information on the relevance of references based on user feedback for specific queries and contexts. It also generalizes such information to derive relevant references for similar queries and contexts. This model lets users filter information by context of relevance, build personalized views of documents over time, and share their views with other users. It also applies to any type of multimedia information. Compared to other approaches, it is less costly and doesn't require any a priori statistical computation, nor an extended training period. It is currently being implemented into the Computer Integrated Documentation system which enables integration of various technical documents in a hypertext framework.

IRRA at TREC 2009: Index Term Weighting based on Divergence From Independence Model

DTIC Science & Technology

2009-11-01

weighting scheme ( Salton and Buckley, 1988), where TF stands for the term frequency and IDF stands for the inverse document frequency. In contrast to TF...IDF is a collection dependent factor, which identifies the terms that concentrates in a few documents of the collection. Salton and Buckley (1988...chapter 4, pages 35–56. Butterworths, Oxford, UK, 1981. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. In Information Processing and Management, pages 513–523, 1988. 15

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Automated generation of individually customized visualizations of diagnosis-specific medical information using novel techniques of information extraction

NASA Astrophysics Data System (ADS)

Chen, Andrew A.; Meng, Frank; Morioka, Craig A.; Churchill, Bernard M.; Kangarloo, Hooshang

2005-04-01

Managing pediatric patients with neurogenic bladder (NGB) involves regular laboratory, imaging, and physiologic testing. Using input from domain experts and current literature, we identified specific data points from these tests to develop the concept of an electronic disease vector for NGB. An information extraction engine was used to extract the desired data elements from free-text and semi-structured documents retrieved from the patient"s medical record. Finally, a Java-based presentation engine created graphical visualizations of the extracted data. After precision, recall, and timing evaluation, we conclude that these tools may enable clinically useful, automatically generated, and diagnosis-specific visualizations of patient data, potentially improving compliance and ultimately, outcomes.

Association rule mining on grid monitoring data to detect error sources

NASA Astrophysics Data System (ADS)

Maier, Gerhild; Schiffers, Michael; Kranzlmueller, Dieter; Gaidioz, Benjamin

2010-04-01

Error handling is a crucial task in an infrastructure as complex as a grid. There are several monitoring tools put in place, which report failing grid jobs including exit codes. However, the exit codes do not always denote the actual fault, which caused the job failure. Human time and knowledge is required to manually trace back errors to the real fault underlying an error. We perform association rule mining on grid job monitoring data to automatically retrieve knowledge about the grid components' behavior by taking dependencies between grid job characteristics into account. Therewith, problematic grid components are located automatically and this information - expressed by association rules - is visualized in a web interface. This work achieves a decrease in time for fault recovery and yields an improvement of a grid's reliability.

The STP (Solar-Terrestrial Physics) Semantic Web based on the RSS1.0 and the RDF

NASA Astrophysics Data System (ADS)

Kubo, T.; Murata, K. T.; Kimura, E.; Ishikura, S.; Shinohara, I.; Kasaba, Y.; Watari, S.; Matsuoka, D.

2006-12-01

In the Solar-Terrestrial Physics (STP), it is pointed out that circulation and utilization of observation data among researchers are insufficient. To archive interdisciplinary researches, we need to overcome this circulation and utilization problems. Under such a background, authors' group has developed a world-wide database that manages meta-data of satellite and ground-based observation data files. It is noted that retrieving meta-data from the observation data and registering them to database have been carried out by hand so far. Our goal is to establish the STP Semantic Web. The Semantic Web provides a common framework that allows a variety of data shared and reused across applications, enterprises, and communities. We also expect that the secondary information related with observations, such as event information and associated news, are also shared over the networks. The most fundamental issue on the establishment is who generates, manages and provides meta-data in the Semantic Web. We developed an automatic meta-data collection system for the observation data using the RSS (RDF Site Summary) 1.0. The RSS1.0 is one of the XML-based markup languages based on the RDF (Resource Description Framework), which is designed for syndicating news and contents of news-like sites. The RSS1.0 is used to describe the STP meta-data, such as data file name, file server address and observation date. To describe the meta-data of the STP beyond RSS1.0 vocabulary, we defined original vocabularies for the STP resources using the RDF Schema. The RDF describes technical terms on the STP along with the Dublin Core Metadata Element Set, which is standard for cross-domain information resource descriptions. Researchers' information on the STP by FOAF, which is known as an RDF/XML vocabulary, creates a machine-readable metadata describing people. Using the RSS1.0 as a meta-data distribution method, the workflow from retrieving meta-data to registering them into the database is automated. This technique is applied for several database systems, such as the DARTS database system and NICT Space Weather Report Service. The DARTS is a science database managed by ISAS/JAXA in Japan. We succeeded in generating and collecting the meta-data automatically for the CDF (Common data Format) data, such as Reimei satellite data, provided by the DARTS. We also create an RDF service for space weather report and real-time global MHD simulation 3D data provided by the NICT. Our Semantic Web system works as follows: The RSS1.0 documents generated on the data sites (ISAS and NICT) are automatically collected by a meta-data collection agent. The RDF documents are registered and the agent extracts meta-data to store them in the Sesame, which is an open source RDF database with support for RDF Schema inferencing and querying. The RDF database provides advanced retrieval processing that has considered property and relation. Finally, the STP Semantic Web provides automatic processing or high level search for the data which are not only for observation data but for space weather news, physical events, technical terms and researches information related to the STP.

PubMed Phrases, an open set of coherent phrases for searching biomedical literature

PubMed Central

Kim, Sun; Yeganova, Lana; Comeau, Donald C.; Wilbur, W. John; Lu, Zhiyong

2018-01-01

In biomedicine, key concepts are often expressed by multiple words (e.g., ‘zinc finger protein’). Previous work has shown treating a sequence of words as a meaningful unit, where applicable, is not only important for human understanding but also beneficial for automatic information seeking. Here we present a collection of PubMed® Phrases that are beneficial for information retrieval and human comprehension. We define these phrases as coherent chunks that are logically connected. To collect the phrase set, we apply the hypergeometric test to detect segments of consecutive terms that are likely to appear together in PubMed. These text segments are then filtered using the BM25 ranking function to ensure that they are beneficial from an information retrieval perspective. Thus, we obtain a set of 705,915 PubMed Phrases. We evaluate the quality of the set by investigating PubMed user click data and manually annotating a sample of 500 randomly selected noun phrases. We also analyze and discuss the usage of these PubMed Phrases in literature search. PMID:29893755

Automatic Processing of Current Affairs Queries

ERIC Educational Resources Information Center

Salton, G.

1973-01-01

The SMART system is used for the analysis, search and retrieval of news stories appearing in Time'' magazine. A comparison is made between the automatic text processing methods incorporated into the SMART system and a manual search using the classified index to Time.'' (14 references) (Author)

Why does lag affect the durability of memory-based automaticity: loss of memory strength or interference?

PubMed

Wilkins, Nicolas J; Rawson, Katherine A

2013-10-01

In Rickard, Lau, and Pashler's (2008) investigation of the lag effect on memory-based automaticity, response times were faster and proportion of trials retrieved was higher at the end of practice for short lag items than for long lag items. However, during testing after a delay, response times were slower and proportion of trials retrieved was lower for short lag items than for long lag items. The current study investigated the extent to which the lag effect on the durability of memory-based automaticity is due to interference or to the loss of memory strength with time. Participants repeatedly practiced alphabet subtraction items in short lag and long lag conditions. After practice, half of the participants were immediately tested and the other half were tested after a 7-day delay. Results indicate that the lag effect on the durability of memory-based automaticity is primarily due to interference. We discuss potential modification of current memory-based processing theories to account for these effects. © 2013.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

A new method for the automatic retrieval of medical cases based on the RadLex ontology.

PubMed

Spanier, A B; Cohen, D; Joskowicz, L

2017-03-01

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

An Overview of Biomolecular Event Extraction from Scientific Documents

PubMed Central

Vanegas, Jorge A.; Matos, Sérgio; González, Fabio; Oliveira, José L.

2015-01-01

This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed. PMID:26587051

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

Improved simulation of aerosol, cloud, and density measurements by shuttle lidar

NASA Technical Reports Server (NTRS)

Russell, P. B.; Morley, B. M.; Livingston, J. M.; Grams, G. W.; Patterson, E. W.

1981-01-01

Data retrievals are simulated for a Nd:YAG lidar suitable for early flight on the space shuttle. Maximum assumed vertical and horizontal resolutions are 0.1 and 100 km, respectively, in the boundary layer, increasing to 2 and 2000 km in the mesosphere. Aerosol and cloud retrievals are simulated using 1.06 and 0.53 microns wavelengths independently. Error sources include signal measurement, conventional density information, atmospheric transmission, and lidar calibration. By day, tenuous clouds and Saharan and boundary layer aerosols are retrieved at both wavelengths. By night, these constituents are retrieved, plus upper tropospheric, stratospheric, and mesospheric aerosols and noctilucent clouds. Density, temperature, and improved aerosol and cloud retrievals are simulated by combining signals at 0.35, 1.06, and 0.53 microns. Particlate contamination limits the technique to the cloud free upper troposphere and above. Error bars automatically show effect of this contamination, as well as errors in absolute density nonmalization, reference temperature or pressure, and the sources listed above. For nonvolcanic conditions, relative density profiles have rms errors of 0.54 to 2% in the upper troposphere and stratosphere. Temperature profiles have rms errors of 1.2 to 2.5 K and can define the tropopause to 0.5 km and higher wave structures to 1 or 2 km.

Enhancing navigation in biomedical databases by community voting and database-driven text classification

PubMed Central

Duchrow, Timo; Shtatland, Timur; Guettler, Daniel; Pivovarov, Misha; Kramer, Stefan; Weissleder, Ralph

2009-01-01

Background The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Results Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Conclusion Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at . PMID:19799796

JANIS: NEA JAva-based Nuclear Data Information System

NASA Astrophysics Data System (ADS)

Soppera, Nicolas; Bossant, Manuel; Cabellos, Oscar; Dupont, Emmeric; Díez, Carlos J.

2017-09-01

JANIS (JAva-based Nuclear Data Information System) software is developed by the OECD Nuclear Energy Agency (NEA) Data Bank to facilitate the visualization and manipulation of nuclear data, giving access to evaluated nuclear data libraries, such as ENDF, JEFF, JENDL, TENDL etc., and also to experimental nuclear data (EXFOR) and bibliographical references (CINDA). It is available as a standalone Java program, downloadable and distributed on DVD and also a web application available on the NEA website. One of the main new features in JANIS is the scripting capability via command line, which notably automatizes plots generation and permits automatically extracting data from the JANIS database. Recent NEA software developments rely on these JANIS features to access nuclear data, for example the Nuclear Data Sensitivity Tool (NDaST) makes use of covariance data in BOXER and COVERX formats, which are retrieved from the JANIS database. New features added in this version of the JANIS software are described along this paper with some examples.

Relevance of Google-customized search engine vs. CISMeF quality-controlled health gateway.

PubMed

Gehanno, Jean-François; Kerdelhue, Gaétan; Sakji, Saoussen; Massari, Philippe; Joubert, Michel; Darmoni, Stéfan J

2009-01-01

CISMeF (acronym for Catalog and Index of French Language Health Resources on the Internet) is a quality-controlled health gateway conceived to catalog and index the most important and quality-controlled sources of institutional health information in French. The goal of this study is to compare the relevance of results provided by this gateway from a small set of documents selected and described by human experts to those provided by a search engine from a large set of automatically indexed and ranked resources. The Google-Customized search engine (CSE) was used. The evaluation was made using the 10th first results of 15 queries and two blinded physician evaluators. There was no significant difference between the relevance of information retrieval in CISMeF and Google CSE. In conclusion, automatic indexing does not lead to lower relevance than a manual MeSH indexing and may help to cope with the increasing number of references to be indexed in a controlled health quality gateway.

Term-Weighting Approaches in Automatic Text Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard; Buckley, Christopher

1988-01-01

Summarizes the experimental evidence that indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results superior to those obtained with more elaborate text representations, and provides baseline single term indexing models with which more elaborate content analysis procedures can be…

A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications

PubMed Central

Cameron, Delroy; Bodenreider, Olivier; Yalamanchili, Hima; Danh, Tu; Vallabhaneni, Sreeram; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Rindflesch, Thomas C.

2014-01-01

Objectives This paper presents a methodology for recovering and decomposing Swanson’s Raynaud Syndrome–Fish Oil Hypothesis semi-automatically. The methodology leverages the semantics of assertions extracted from biomedical literature (called semantic predications) along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations originally discovered manually by Swanson. Demonstrating that Swanson’s manually intensive techniques can be undertaken semi-automatically, paves the way for fully automatic semantics-based hypothesis generation from scientific literature. Methods Semantic predications obtained from biomedical literature allow the construction of labeled directed graphs which contain various associations among concepts from the literature. By aggregating such associations into informative subgraphs, some of the relevant details originally articulated by Swanson has been uncovered. However, by leveraging background knowledge to bridge important knowledge gaps in the literature, a methodology for semi-automatically capturing the detailed associations originally explicated in natural language by Swanson has been developed. Results Our methodology not only recovered the 3 associations commonly recognized as Swanson’s Hypothesis, but also decomposed them into an additional 16 detailed associations, formulated as chains of semantic predications. Altogether, 14 out of the 19 associations that can be attributed to Swanson were retrieved using our approach. To the best of our knowledge, such an in-depth recovery and decomposition of Swanson’s Hypothesis has never been attempted. Conclusion In this work therefore, we presented a methodology for semi- automatically recovering and decomposing Swanson’s RS-DFO Hypothesis using semantic representations and graph algorithms. Our methodology provides new insights into potential prerequisites for semantics-driven Literature-Based Discovery (LBD). These suggest that three critical aspects of LBD include: 1) the need for more expressive representations beyond Swanson’s ABC model; 2) an ability to accurately extract semantic information from text; and 3) the semantic integration of scientific literature with structured background knowledge. PMID:23026233

Using Bayesian networks to support decision-focused information retrieval

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

Lehner, P.; Elsaesser, C.; Seligman, L.

This paper has described an approach to controlling the process of pulling data/information from distributed data bases in a way that is specific to a persons specific decision making context. Our prototype implementation of this approach uses a knowledge-based planner to generate a plan, an automatically constructed Bayesian network to evaluate the plan, specialized processing of the network to derive key information items that would substantially impact the evaluation of the plan (e.g., determine that replanning is needed), automated construction of Standing Requests for Information (SRIs) which are automated functions that monitor changes and trends in distributed data base thatmore » are relevant to the key information items. This emphasis of this paper is on how Bayesian networks are used.« less

Automatic natural acquisition of a semantic network for information retrieval systems

NASA Astrophysics Data System (ADS)

Enguehard, Chantal; Malvache, Pierre; Trigano, Philippe

1992-03-01

The amount of information is becoming greater and greater, in industries where complex processes are performed it is becoming increasingly difficult to profit from all the documents produced when fresh knowledge becomes available (reports, experiments, findings). This situation causes a considerable and expensive waste of precious time lost searching for documents or, quite simply, results in outright repeating what has been done. One solution is to transform all paper information into computerized information. We might imagine that we are in a science-fiction world and that we have the perfect computer. We tell it everything we know, we make it read all the books, and if we ask it any question, it will find the response if that response exists. But unfortunately, we are in the real world and the last four decades have taught us to minimize our expectations of computers. During the 1960s, the information retrieval systems appeared. Their purpose is to provide access to any desired documents, in response to a question about a subject, even if it is not known to exist. Here we focus on the problem of selecting items to index the documents. In 1966, Salton identified this problem as crucial when he saw that his system, Medlars, did not find a relevant text because of the wrong indexation. Faced with this problem, he imagined a guide to help authors choose the correct indexation, but he anticipated the automation of this operation with the SMART system. It was stated previously that a manual language analysis for information items by subjects experts is likely to prove impractical in the long run. After a brief survey of the existing responses to the index choice problem, we shall present the system automatic natural acquisition (ANA) which chooses items to index texts by using as little knowledge as possible- -just by learning the language. This system does not use any grammar or lexicon, so the selected indexes will be very close to the field concerned in the texts.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Category Cued Recall Evokes a Generate-Recognize Retrieval Process

ERIC Educational Resources Information Center

Hunt, R. Reed; Smith, Rebekah E.; Toth, Jeffrey P.

2016-01-01

The experiments reported here were designed to replicate and extend McCabe, Roediger, and Karpicke's (2011) finding that retrieval in category cued recall involves both controlled and automatic processes. The extension entailed identifying whether distinctive encoding affected 1 or both of these 2 processes. The first experiment successfully…

A Vector Space Model for Automatic Indexing.

ERIC Educational Resources Information Center

Salton, G.; And Others

In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other, or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; that is, retrieval performance correlates inversely…

Application of MPEG-7 descriptors for content-based indexing of sports videos

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Ohm, Jens-Rainer

2003-06-01

The amount of multimedia data available worldwide is increasing every day. There is a vital need to annotate multimedia data in order to allow universal content access and to provide content-based search-and-retrieval functionalities. Since supervised video annotation can be time consuming, an automatic solution is appreciated. We review recent approaches to content-based indexing and annotation of videos for different kind of sports, and present our application for the automatic annotation of equestrian sports videos. Thereby, we especially concentrate on MPEG-7 based feature extraction and content description. We apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information and taking specific domain knowledge into account. Having determined single shot positions as well as the visual highlights, the information is jointly stored together with additional textual information in an MPEG-7 description scheme. Using this information, we generate content summaries which can be utilized in a user front-end in order to provide content-based access to the video stream, but further content-based queries and navigation on a video-on-demand streaming server.

An Interactive Program on Digitizing Historical Seismograms

NASA Astrophysics Data System (ADS)

Xu, Y.; Xu, T.

2013-12-01

Retrieving information from historical seismograms is of great importance since they are considered the unique sources that provide quantitative information of historical earthquakes. Modern techniques of seismology require digital forms of seismograms that are essentially a sequence of time-amplitude pairs. However, the historical seismograms, after scanned into computers, are two dimensional arrays. Each element of the arrays contains the grayscale value or RGB value of the corresponding pixel. The problem of digitizing historical seismograms, referred to as converting historical seismograms to digital seismograms, can be formulated as an inverse problem that generating sequences of time-amplitude pairs from a two dimension arrays. This problem has infinite solutions. The algorithm for automatic digitization of historical seismogram presented considers several features of seismograms, including continuity, smoothness of the seismic traces as the prior information, and assumes that the amplitude is a single-valued function of time. An interactive program based on the algorithm is also presented. The program is developed using Matlab GUI and has both automatic and manual modality digitization. Users can easily switch between them, and try different combinations to get the optimal results. Several examples are given to illustrate the results of digitizing seismograms using the program, including a photographic record and a wide-angle reflection/refraction seismogram. Digitized result of the program (redrawn using Golden Software Surfer for high resolution image). (a) shows the result of automatic digitization, and (b) is the result after manual correction.

Improving data management and dissemination in web based information systems by semantic enrichment of descriptive data aspects

NASA Astrophysics Data System (ADS)

Gebhardt, Steffen; Wehrmann, Thilo; Klinger, Verena; Schettler, Ingo; Huth, Juliane; Künzer, Claudia; Dech, Stefan

2010-10-01

The German-Vietnamese water-related information system for the Mekong Delta (WISDOM) project supports business processes in Integrated Water Resources Management in Vietnam. Multiple disciplines bring together earth and ground based observation themes, such as environmental monitoring, water management, demographics, economy, information technology, and infrastructural systems. This paper introduces the components of the web-based WISDOM system including data, logic and presentation tier. It focuses on the data models upon which the database management system is built, including techniques for tagging or linking metadata with the stored information. The model also uses ordered groupings of spatial, thematic and temporal reference objects to semantically tag datasets to enable fast data retrieval, such as finding all data in a specific administrative unit belonging to a specific theme. A spatial database extension is employed by the PostgreSQL database. This object-oriented database was chosen over a relational database to tag spatial objects to tabular data, improving the retrieval of census and observational data at regional, provincial, and local areas. While the spatial database hinders processing raster data, a "work-around" was built into WISDOM to permit efficient management of both raster and vector data. The data model also incorporates styling aspects of the spatial datasets through styled layer descriptions (SLD) and web mapping service (WMS) layer specifications, allowing retrieval of rendered maps. Metadata elements of the spatial data are based on the ISO19115 standard. XML structured information of the SLD and metadata are stored in an XML database. The data models and the data management system are robust for managing the large quantity of spatial objects, sensor observations, census and document data. The operational WISDOM information system prototype contains modules for data management, automatic data integration, and web services for data retrieval, analysis, and distribution. The graphical user interfaces facilitate metadata cataloguing, data warehousing, web sensor data analysis and thematic mapping.

A Theory of Term Importance in Automatic Text Analysis.

ERIC Educational Resources Information Center

Salton, G.; And Others

Most existing automatic content analysis and indexing techniques are based on work frequency characteristics applied largely in an ad hoc manner. Contradictory requirements arise in this connection, in that terms exhibiting high occurrence frequencies in individual documents are often useful for high recall performance (to retrieve many relevant…

Automatic Term Class Construction Using Relevance--A Summary of Work in Automatic Pseudoclassification.

ERIC Educational Resources Information Center

Salton, G.

1980-01-01

Summarizes studies of pseudoclassification, a process of utilizing user relevance assessments of certain documents with respect to certain queries to build term classes designed to retrieve relevant documents. Conclusions are reached concerning the effectiveness and feasibility of constructing term classifications based on human relevance…

Automatic sorting of toxicological information into the IUCLID (International Uniform Chemical Information Database) endpoint-categories making use of the semantic search engine Go3R.

PubMed

Sauer, Ursula G; Wächter, Thomas; Hareng, Lars; Wareing, Britta; Langsch, Angelika; Zschunke, Matthias; Alvers, Michael R; Landsiedel, Robert

2014-06-01

The knowledge-based search engine Go3R, www.Go3R.org, has been developed to assist scientists from industry and regulatory authorities in collecting comprehensive toxicological information with a special focus on identifying available alternatives to animal testing. The semantic search paradigm of Go3R makes use of expert knowledge on 3Rs methods and regulatory toxicology, laid down in the ontology, a network of concepts, terms, and synonyms, to recognize the contents of documents. Search results are automatically sorted into a dynamic table of contents presented alongside the list of documents retrieved. This table of contents allows the user to quickly filter the set of documents by topics of interest. Documents containing hazard information are automatically assigned to a user interface following the endpoint-specific IUCLID5 categorization scheme required, e.g. for REACH registration dossiers. For this purpose, complex endpoint-specific search queries were compiled and integrated into the search engine (based upon a gold standard of 310 references that had been assigned manually to the different endpoint categories). Go3R sorts 87% of the references concordantly into the respective IUCLID5 categories. Currently, Go3R searches in the 22 million documents available in the PubMed and TOXNET databases. However, it can be customized to search in other databases including in-house databanks. Copyright © 2013 Elsevier Ltd. All rights reserved.

Most people do not ignore salient invalid cues in memory-based decisions.

PubMed

Platzer, Christine; Bröder, Arndt

2012-08-01

Former experimental studies have shown that decisions from memory tend to rely only on a few cues, following simple noncompensatory heuristics like "take the best." However, it has also repeatedly been demonstrated that a pictorial, as opposed to a verbal, representation of cue information fosters the inclusion of more cues in compensatory strategies, suggesting a facilitated retrieval of cue patterns. These studies did not properly control for visual salience of cues, however. In the experiment reported here, the cue salience hierarchy established in a pilot study was either congruent or incongruent with the validity order of the cues. Only the latter condition increased compensatory decision making, suggesting that the apparent representational format effect is, rather, a salience effect: Participants automatically retrieve and incorporate salient cues irrespective of their validity. Results are discussed with respect to reaction time data.

Infrared Sky Imager (IRSI) Instrument Handbook

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

Morris, Victor R.

2016-04-01

The Infrared Sky Imager (IRSI) deployed at the Atmospheric Radiation Measurement (ARM) Climate Research Facility is a Solmirus Corp. All Sky Infrared Visible Analyzer. The IRSI is an automatic, continuously operating, digital imaging and software system designed to capture hemispheric sky images and provide time series retrievals of fractional sky cover during both the day and night. The instrument provides diurnal, radiometrically calibrated sky imagery in the mid-infrared atmospheric window and imagery in the visible wavelengths for cloud retrievals during daylight hours. The software automatically identifies cloudy and clear regions at user-defined intervals and calculates fractional sky cover, providing amore » real-time display of sky conditions.« less

Dissociating neural markers of stimulus memorability and subjective recognition during episodic retrieval.

PubMed

Bainbridge, Wilma A; Rissman, Jesse

2018-06-06

While much of memory research takes an observer-centric focus looking at participant performance, recent work has pinpointed important item-centric effects on memory, or how intrinsically memorable a given stimulus is. However, little is known about the neural correlates of memorability during memory retrieval, or how such correlates relate to subjective memory behavior. Here, stimuli and blood-oxygen-level dependent data from a prior functional magnetic resonance imaging (fMRI) study were reanalyzed using a memorability-based framework. In that study, sixteen participants studied 200 novel face images and were scanned while making recognition memory judgments on those faces, interspersed with 200 unstudied faces. In the current investigation, memorability scores for those stimuli were obtained through an online crowd-sourced (N = 740) continuous recognition test that measured each image's corrected recognition rate. Representational similarity analyses were conducted across the brain to identify regions wherein neural pattern similarity tracked item-specific effects (stimulus memorability) versus observer-specific effects (individual memory performance). We find two non-overlapping sets of regions, with memorability-related information predominantly represented within ventral and medial temporal regions and memory retrieval outcome-related information within fronto-parietal regions. These memorability-based effects persist regardless of image history, implying that coding of stimulus memorability may be a continuous and automatic perceptual process.

Mining free-text medical records for companion animal enteric syndrome surveillance.

PubMed

Anholt, R M; Berezowski, J; Jamal, I; Ribble, C; Stephen, C

2014-03-01

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals. Copyright © 2014 Elsevier B.V. All rights reserved.

A Unified Framework for Periodic, On-Demand, and User-Specified Software Information

NASA Technical Reports Server (NTRS)

Kolano, Paul Z.

2004-01-01

Although grid computing can increase the number of resources available to a user; not all resources on the grid may have a software environment suitable for running a given application. To provide users with the necessary assistance for selecting resources with compatible software environments and/or for automatically establishing such environments, it is necessary to have an accurate source of information about the software installed across the grid. This paper presents a new OGSI-compliant software information service that has been implemented as part of NASA's Information Power Grid project. This service is built on top of a general framework for reconciling information from periodic, on-demand, and user-specified sources. Information is retrieved using standard XPath queries over a single unified namespace independent of the information's source. Two consumers of the provided software information, the IPG Resource Broker and the IPG Neutralization Service, are briefly described.

Bridging the semantic gap in sports

NASA Astrophysics Data System (ADS)

Li, Baoxin; Errico, James; Pan, Hao; Sezan, M. Ibrahim

2003-01-01

One of the major challenges facing current media management systems and the related applications is the so-called "semantic gap" between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs. The framework is based on a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. An MPEG-7 compliant prototype browsing system has been implemented to demonstrate semantic retrieval and summarization of sports video.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Clinical applications of an ATM/Ethernet network in departments of neuroradiology and radiotherapy.

PubMed

Cimino, C; Pizzi, R; Fusca, M; Bruzzone, M G; Casolino, D; Sicurello, F

1997-01-01

An integrated system for the multimedia management of images and clinical information has been developed at the Isituto Nazionale Neurologico C. Besta in Milan. The Institute physicians have the daily need of consulting images coming from various modalities. The high volume of archived material and the need of retrieving and displaying new and past images and clinical information has motivated the development of a Picture Archiving and Communication System (PACS) for the automatic management of images and clinical data, related not only to the Radiology Department, but also to the Radiotherapy Department for 3D virtual simulation, to remote teleconsulting, and in the following to all the wards, ambulatories and labs.

Abbreviation definition identification based on automatic precision estimates.

PubMed

Sohn, Sunghwan; Comeau, Donald C; Kim, Won; Wilbur, W John

2008-09-25

The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation. On the Medstract corpus our algorithm produced 97% precision and 85% recall which is higher than previously reported results. We also annotated 1250 randomly selected MEDLINE records as a gold standard. On this set we achieved 96.5% precision and 83.2% recall. This compares favourably with the well known Schwartz and Hearst algorithm. We developed an algorithm for abbreviation identification that uses a variety of strategies to identify the most probable definition for an abbreviation and also produces an estimated accuracy of the result. This process is purely automatic.

An automatic indexing method for medical documents.

PubMed Central

Wagner, M. M.

1991-01-01

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported. PMID:1807564

[Technologies for Complex Intelligent Clinical Data Analysis].

PubMed

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Functional MRI evidence for the decline of word retrieval and generation during normal aging.

PubMed

Baciu, M; Boudiaf, N; Cousin, E; Perrone-Bertolotti, M; Pichat, C; Fournet, N; Chainay, H; Lamalle, L; Krainik, A

2016-02-01

This fMRI study aimed to explore the effect of normal aging on word retrieval and generation. The question addressed is whether lexical production decline is determined by a direct mechanism, which concerns the language operations or is rather indirectly induced by a decline of executive functions. Indeed, the main hypothesis was that normal aging does not induce loss of lexical knowledge, but there is only a general slowdown in retrieval mechanisms involved in lexical processing, due to possible decline of the executive functions. We used three tasks (verbal fluency, object naming, and semantic categorization). Two groups of participants were tested (Young, Y and Aged, A), without cognitive and psychiatric impairment and showing similar levels of vocabulary. Neuropsychological testing revealed that older participants had lower executive function scores, longer processing speeds, and tended to have lower verbal fluency scores. Additionally, older participants showed higher scores for verbal automatisms and overlearned information. In terms of behavioral data, older participants performed as accurate as younger adults, but they were significantly slower for the semantic categorization and were less fluent for verbal fluency task. Functional MRI analyses suggested that older adults did not simply activate fewer brain regions involved in word production, but they actually showed an atypical pattern of activation. Significant correlations between the BOLD (Blood Oxygen Level Dependent) signal of aging-related (A > Y) regions and cognitive scores suggested that this atypical pattern of the activation may reveal several compensatory mechanisms (a) to overcome the slowdown in retrieval, due to the decline of executive functions and processing speed and (b) to inhibit verbal automatic processes. The BOLD signal measured in some other aging-dependent regions did not correlate with the behavioral and neuropsychological scores, and the overactivation of these uncorrelated regions would simply reveal dedifferentiation that occurs with aging. Altogether, our results suggest that normal aging is associated with a more difficult access to lexico-semantic operations and representations by a slowdown in executive functions, without any conceptual loss.

The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems.

ERIC Educational Resources Information Center

Peat, Helen J.; Willett, Peter

1991-01-01

Identifies limitations in the use of term co-occurrence data as a basis for automatic query expansion in natural language document retrieval systems. The use of similarity coefficients to calculate the degree of similarity between pairs of terms is explained, and frequency and discriminatory characteristics for nearest neighbors of query terms are…

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

PubMed

Malhotra, Ashutosh; Gündel, Michaela; Rajput, Abdul Mateen; Mevissen, Heinz-Theodor; Saiz, Albert; Pastor, Xavier; Lozano-Rubi, Raimundo; Martinez-Lapiscina, Elena H; Martinez-Lapsicina, Elena H; Zubizarreta, Irati; Mueller, Bernd; Kotelnikova, Ekaterina; Toldo, Luca; Hofmann-Apitius, Martin; Villoslada, Pablo

2015-01-01

In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS). The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology. Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports. The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.

Hyperspectrally-Resolved Surface Emissivity Derived Under Optically Thin Clouds

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Strow, L. Larrabee; Yang, Ping

2010-01-01

Surface spectral emissivity derived from current and future satellites can and will reveal critical information about the Earth s ecosystem and land surface type properties, which can be utilized as a means of long-term monitoring of global environment and climate change. Hyperspectrally-resolved surface emissivities are derived with an algorithm utilizes a combined fast radiative transfer model (RTM) with a molecular RTM and a cloud RTM accounting for both atmospheric absorption and cloud absorption/scattering. Clouds are automatically detected and cloud microphysical parameters are retrieved; and emissivity is retrieved under clear and optically thin cloud conditions. This technique separates surface emissivity from skin temperature by representing the emissivity spectrum with eigenvectors derived from a laboratory measured emissivity database; in other words, using the constraint as a means for the emissivity to vary smoothly across atmospheric absorption lines. Here we present the emissivity derived under optically thin clouds in comparison with that under clear conditions.

Discovering relevance knowledge in data: a growing cell structures approach.

PubMed

Azuaje, F; Dubitzky, W; Black, N; Adamson, K

2000-01-01

Both information retrieval and case-based reasoning systems rely on effective and efficient selection of relevant data. Typically, relevance in such systems is approximated by similarity or indexing models. However, the definition of what makes data items similar or how they should be indexed is often nontrivial and time-consuming. Based on growing cell structure artificial neural networks, this paper presents a method that automatically constructs a case retrieval model from existing data. Within the case-based reasoning (CBR) framework, the method is evaluated for two medical prognosis tasks, namely, colorectal cancer survival and coronary heart disease risk prognosis. The results of the experiments suggest that the proposed method is effective and robust. To gain a deeper insight and understanding of the underlying mechanisms of the proposed model, a detailed empirical analysis of the models structural and behavioral properties is also provided.

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

ERIC Educational Resources Information Center

Anderson, James D.; Perez-Carballo, Jose

2001-01-01

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

Towards Assisted Moderation in Online Healthcare Social Networks: Improving Trust in YouTube Searches.

PubMed

Cañon, Daniel E; Lopez, Diego M; Blobel, Bernd

2014-01-01

Moderation of content in online Health Social Networks (HSN) is critical because information is not only published and produced by experts or health professionals, but also by users of that information. The objective of this paper is to propose a semi-automatic moderation Web Service for assessing the quality (trustworthiness) of health-related videos published on the YouTube social network. The service is relevant for moderators or community managers, who get enabled to control the quality of videos published on their online HSN sites. The HealthTrust metric was selected as the metric to be implemented in the service in order to support the assessment of trustworthiness of videos in Online HSN. The service is a RESTful service which can be integrated into open source Virtual Social Network Platforms, therefore improving trust in the process of searching and publishing content extracted from YouTube. A preliminary pilot evaluation in a simple use case demonstrated that the relevance of videos retrieved using the moderation service was higher compared to the relevance of the videos retrieved using the YouTube search engine.

Clustering document fragments using background color and texture information

NASA Astrophysics Data System (ADS)

Chanda, Sukalpa; Franke, Katrin; Pal, Umapada

2012-01-01

Forensic analysis of questioned documents sometimes can be extensively data intensive. A forensic expert might need to analyze a heap of document fragments and in such cases to ensure reliability he/she should focus only on relevant evidences hidden in those document fragments. Relevant document retrieval needs finding of similar document fragments. One notion of obtaining such similar documents could be by using document fragment's physical characteristics like color, texture, etc. In this article we propose an automatic scheme to retrieve similar document fragments based on visual appearance of document paper and texture. Multispectral color characteristics using biologically inspired color differentiation techniques are implemented here. This is done by projecting document color characteristics to Lab color space. Gabor filter-based texture analysis is used to identify document texture. It is desired that document fragments from same source will have similar color and texture. For clustering similar document fragments of our test dataset we use a Self Organizing Map (SOM) of dimension 5×5, where the document color and texture information are used as features. We obtained an encouraging accuracy of 97.17% from 1063 test images.

Enhancing biomedical text summarization using semantic relation extraction.

PubMed

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

Leveraging search and content exploration by exploiting context in folksonomy systems

NASA Astrophysics Data System (ADS)

Abel, Fabian; Baldoni, Matteo; Baroglio, Cristina; Henze, Nicola; Kawase, Ricardo; Krause, Daniel; Patti, Viviana

2010-04-01

With the advent of Web 2.0 tagging became a popular feature in social media systems. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. In the last years several researchers analyzed the impact of tags on information retrieval. Most works focused on tags only and ignored context information. In this article we present context-aware approaches for learning semantics and improve personalized information retrieval in tagging systems. We investigate how explorative search, initialized by clicking on tags, can be enhanced with automatically produced context information so that search results better fit to the actual information needs of the users. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way. We showcase our approaches in the domain of images and present the TagMe! system that enables users to explore and tag Flickr pictures. In TagMe! we further demonstrate how advanced context information can easily be generated: TagMe! allows users to attach tag assignments to a specific area within an image and to categorize tag assignments. In our corresponding evaluation we show that those additional facets of tag assignments gain valuable semantics, which can be applied to improve existing search and ranking algorithms significantly.

The CMIP5 Model Documentation Questionnaire: Development of a Metadata Retrieval System for the METAFOR Common Information Model

NASA Astrophysics Data System (ADS)

Pascoe, Charlotte; Lawrence, Bryan; Moine, Marie-Pierre; Ford, Rupert; Devine, Gerry

2010-05-01

The EU METAFOR Project (http://metaforclimate.eu) has created a web-based model documentation questionnaire to collect metadata from the modelling groups that are running simulations in support of the Coupled Model Intercomparison Project - 5 (CMIP5). The CMIP5 model documentation questionnaire will retrieve information about the details of the models used, how the simulations were carried out, how the simulations conformed to the CMIP5 experiment requirements and details of the hardware used to perform the simulations. The metadata collected by the CMIP5 questionnaire will allow CMIP5 data to be compared in a scientifically meaningful way. This paper describes the life-cycle of the CMIP5 questionnaire development which starts with relatively unstructured input from domain specialists and ends with formal XML documents that comply with the METAFOR Common Information Model (CIM). Each development step is associated with a specific tool. (1) Mind maps are used to capture information requirements from domain experts and build a controlled vocabulary, (2) a python parser processes the XML files generated by the mind maps, (3) Django (python) is used to generate the dynamic structure and content of the web based questionnaire from processed xml and the METAFOR CIM, (4) Python parsers ensure that information entered into the CMIP5 questionnaire is output as CIM compliant xml, (5) CIM compliant output allows automatic information capture tools to harvest questionnaire content into databases such as the Earth System Grid (ESG) metadata catalogue. This paper will focus on how Django (python) and XML input files are used to generate the structure and content of the CMIP5 questionnaire. It will also address how the choice of development tools listed above provided a framework that enabled working scientists (who we would never ordinarily get to interact with UML and XML) to be part the iterative development process and ensure that the CMIP5 model documentation questionnaire reflects what scientists want to know about the models. Keywords: metadata, CMIP5, automatic information capture, tool development

Integrating GPCR-specific information with full text articles

PubMed Central

2011-01-01

Background With the continued growth in the volume both of experimental G protein-coupled receptor (GPCR) data and of the related peer-reviewed literature, the ability of GPCR researchers to keep up-to-date is becoming increasingly curtailed. Results We present work that integrates the biological data and annotations in the GPCR information system (GPCRDB) with next-generation methods for intelligently exploring, visualising and interacting with the scientific articles used to disseminate them. This solution automatically retrieves relevant information from GPCRDB and displays it both within and as an adjunct to an article. Conclusions This approach allows researchers to extract more knowledge more swiftly from literature. Importantly, it allows reinterpretation of data in articles published before GPCR structure data became widely available, thereby rescuing these valuable data from long-dormant sources. PMID:21910883

Images of the Self and Self-Esteem: Do Positive Self-Images Improve Self-Esteem in Social Anxiety?

PubMed Central

Hulme, Natalie; Hirsch, Colette; Stopa, Lusia

2012-01-01

Negative self-images play an important role in maintaining social anxiety disorder. We propose that these images represent the working self in a Self-Memory System that regulates retrieval of self-relevant information in particular situations. Self-esteem, one aspect of the working self, comprises explicit (conscious) and implicit (automatic) components. Implicit self-esteem reflects an automatic evaluative bias towards the self that is normally positive, but is reduced in socially anxious individuals. Forty-four high and 44 low socially anxious participants generated either a positive or a negative self-image and then completed measures of implicit and explicit self-esteem. Participants who held a negative self-image in mind reported lower implicit and explicit positive self-esteem, and higher explicit negative self-esteem than participants holding a positive image in mind, irrespective of social anxiety group. We then tested whether positive self-images protected high and low socially anxious individuals equally well against the threat to explicit self-esteem posed by social exclusion in a virtual ball toss game (Cyberball). We failed to find a predicted interaction between social anxiety and image condition. Instead, all participants holding positive self-images reported higher levels of explicit self-esteem after Cyberball than those holding negative self-images. Deliberate retrieval of positive self-images appears to facilitate access to a healthy positive implicit bias, as well as improving explicit self-esteem, whereas deliberate retrieval of negative self-images does the opposite. This is consistent with the idea that negative self-images may have a causal, as well as a maintaining, role in social anxiety disorder. PMID:22439697

Contributions from specific and general factors to unique deficits: two cases of mathematics learning difficulties

PubMed Central

Haase, Vitor G.; Júlio-Costa, Annelise; Lopes-Silva, Júlia B.; Starling-Alves, Isabella; Antunes, Andressa M.; Pinheiro-Chagas, Pedro; Wood, Guilherme

2014-01-01

Mathematics learning difficulties are a highly comorbid and heterogeneous set of disorders linked to several dissociable mechanisms and endophenotypes. Two of these endophenotypes consist of primary deficits in number sense and verbal numerical representations. However, currently acknowledged endophenotypes are underspecified regarding the role of automatic vs. controlled information processing, and their description should be complemented. Two children with specific deficits in number sense and verbal numerical representations and normal or above-normal intelligence and preserved visuospatial cognition illustrate this point. Child H.V. exhibited deficits in number sense and fact retrieval. Child G.A. presented severe deficits in orally presented problems and transcoding tasks. A partial confirmation of the two endophenotypes that relate to the number sense and verbal processing was obtained, but a much more clear differentiation between the deficits presented by H.V. and G.A. can be reached by looking at differential impairments in modes of processing. H.V. is notably competent in the use of controlled processing but has problems with more automatic processes, such as nonsymbolic magnitude processing, speeded counting and fact retrieval. In contrast, G.A. can retrieve facts and process nonsymbolic magnitudes but exhibits severe impairment in recruiting executive functions and the concentration that is necessary to accomplish transcoding tasks and word problem solving. These results indicate that typical endophenotypes might be insufficient to describe accurately the deficits that are observed in children with mathematics learning abilities. However, by incorporating domain-specificity and modes of processing into the assessment of the endophenotypes, individual deficit profiles can be much more accurately described. This process calls for further specification of the endophenotypes in mathematics learning difficulties. PMID:24592243

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

Besides Precision & Recall: Exploring Alternative Approaches to Evaluating an Automatic Indexing Tool for MEDLINE

PubMed Central

Névéol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

Objective This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. Materials and methods The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Results Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. Conclusions The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis. PMID:17238409

Besides precision & recall: exploring alternative approaches to evaluating an automatic indexing tool for MEDLINE.

PubMed

Neveol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis.

Applying Statistical Models and Parametric Distance Measures for Music Similarity Search

NASA Astrophysics Data System (ADS)

Lukashevich, Hanna; Dittmar, Christian; Bastuck, Christoph

Automatic deriving of similarity relations between music pieces is an inherent field of music information retrieval research. Due to the nearly unrestricted amount of musical data, the real-world similarity search algorithms have to be highly efficient and scalable. The possible solution is to represent each music excerpt with a statistical model (ex. Gaussian mixture model) and thus to reduce the computational costs by applying the parametric distance measures between the models. In this paper we discuss the combinations of applying different parametric modelling techniques and distance measures and weigh the benefits of each one against the others.

Content-based image exploitation for situational awareness

NASA Astrophysics Data System (ADS)

Gains, David

2008-04-01

Image exploitation is of increasing importance to the enterprise of building situational awareness from multi-source data. It involves image acquisition, identification of objects of interest in imagery, storage, search and retrieval of imagery, and the distribution of imagery over possibly bandwidth limited networks. This paper describes an image exploitation application that uses image content alone to detect objects of interest, and that automatically establishes and preserves spatial and temporal relationships between images, cameras and objects. The application features an intuitive user interface that exposes all images and information generated by the system to an operator thus facilitating the formation of situational awareness.

Reinstatement of Individual Past Events Revealed by the Similarity of Distributed Activation Patterns during Encoding and Retrieval

PubMed Central

Wing, Erik A.; Ritchey, Maureen; Cabeza, Roberto

2015-01-01

Neurobiological memory models assume memory traces are stored in neocortex, with pointers in the hippocampus, and are then reactivated during retrieval, yielding the experience of remembering. Whereas most prior neuroimaging studies on reactivation have focused on the reactivation of sets or categories of items, the current study sought to identify cortical patterns pertaining to memory for individual scenes. During encoding, participants viewed pictures of scenes paired with matching labels (e.g., “barn,” “tunnel”), and, during retrieval, they recalled the scenes in response to the labels and rated the quality of their visual memories. Using representational similarity analyses, we interrogated the similarity between activation patterns during encoding and retrieval both at the item level (individual scenes) and the set level (all scenes). The study yielded four main findings. First, in occipitotemporal cortex, memory success increased with encoding-retrieval similarity (ERS) at the item level but not at the set level, indicating the reactivation of individual scenes. Second, in ventrolateral pFC, memory increased with ERS for both item and set levels, indicating the recapitulation of memory processes that benefit encoding and retrieval of all scenes. Third, in retrosplenial/posterior cingulate cortex, ERS was sensitive to individual scene information irrespective of memory success, suggesting automatic activation of scene contexts. Finally, consistent with neurobiological models, hippocampal activity during encoding predicted the subsequent reactivation of individual items. These findings show the promise of studying memory with greater specificity by isolating individual mnemonic representations and determining their relationship to factors like the detail with which past events are remembered. PMID:25313659

Application of the high resolution return beam vidicon

NASA Technical Reports Server (NTRS)

Cantella, M. J.

1977-01-01

The Return Beam Vidicon (RBV) is a high-performance electronic image sensor and electrical storage component. It can accept continuous or discrete exposures. Information can be read out with a single scan or with many repetitive scans for either signal processing or display. Resolution capability is 10,000 TV lines/height, and at 100 lp/mm, performance matches or exceeds that of film, particularly with low-contrast imagery. Electronic zoom can be employed effectively for image magnification and data compression. The high performance and flexibility of the RBV permit wide application in systems for reconnaissance, scan conversion, information storage and retrieval, and automatic inspection and test. This paper summarizes the characteristics and performance parameters of the RBV and cites examples of feasible applications.

Theory research of seam recognition and welding torch pose control based on machine vision

NASA Astrophysics Data System (ADS)

Long, Qiang; Zhai, Peng; Liu, Miao; He, Kai; Wang, Chunyang

2017-03-01

At present, the automation requirement of the welding become higher, so a method of the welding information extraction by vision sensor is proposed in this paper, and the simulation with the MATLAB has been conducted. Besides, in order to improve the quality of robot automatic welding, an information retrieval method for welding torch pose control by visual sensor is attempted. Considering the demands of welding technology and engineering habits, the relative coordinate systems and variables are strictly defined, and established the mathematical model of the welding pose, and verified its feasibility by using the MATLAB simulation in the paper, these works lay a foundation for the development of welding off-line programming system with high precision and quality.

Rapid Diagnostics of Onboard Sequences

NASA Technical Reports Server (NTRS)

Starbird, Thomas W.; Morris, John R.; Shams, Khawaja S.; Maimone, Mark W.

2012-01-01

Keeping track of sequences onboard a spacecraft is challenging. When reviewing Event Verification Records (EVRs) of sequence executions on the Mars Exploration Rover (MER), operators often found themselves wondering which version of a named sequence the EVR corresponded to. The lack of this information drastically impacts the operators diagnostic capabilities as well as their situational awareness with respect to the commands the spacecraft has executed, since the EVRs do not provide argument values or explanatory comments. Having this information immediately available can be instrumental in diagnosing critical events and can significantly enhance the overall safety of the spacecraft. This software provides auditing capability that can eliminate that uncertainty while diagnosing critical conditions. Furthermore, the Restful interface provides a simple way for sequencing tools to automatically retrieve binary compiled sequence SCMFs (Space Command Message Files) on demand. It also enables developers to change the underlying database, while maintaining the same interface to the existing applications. The logging capabilities are also beneficial to operators when they are trying to recall how they solved a similar problem many days ago: this software enables automatic recovery of SCMF and RML (Robot Markup Language) sequence files directly from the command EVRs, eliminating the need for people to find and validate the corresponding sequences. To address the lack of auditing capability for sequences onboard a spacecraft during earlier missions, extensive logging support was added on the Mars Science Laboratory (MSL) sequencing server. This server is responsible for generating all MSL binary SCMFs from RML input sequences. The sequencing server logs every SCMF it generates into a MySQL database, as well as the high-level RML file and dictionary name inputs used to create the SCMF. The SCMF is then indexed by a hash value that is automatically included in all command EVRs by the onboard flight software. Second, both the binary SCMF result and the RML input file can be retrieved simply by specifying the hash to a Restful web interface. This interface enables command line tools as well as large sophisticated programs to download the SCMF and RMLs on-demand from the database, enabling a vast array of tools to be built on top of it. One such command line tool can retrieve and display RML files, or annotate a list of EVRs by interleaving them with the original sequence commands. This software has been integrated with the MSL sequencing pipeline where it will serve sequences useful in diagnostics, debugging, and situational awareness throughout the mission.

Generating Concise Rules for Human Motion Retrieval

NASA Astrophysics Data System (ADS)

Mukai, Tomohiko; Wakisaka, Ken-Ichi; Kuriyama, Shigeru

This paper proposes a method for retrieving human motion data with concise retrieval rules based on the spatio-temporal features of motion appearance. Our method first converts motion clip into a form of clausal language that represents geometrical relations between body parts and their temporal relationship. A retrieval rule is then learned from the set of manually classified examples using inductive logic programming (ILP). ILP automatically discovers the essential rule in the same clausal form with a user-defined hypothesis-testing procedure. All motions are indexed using this clausal language, and the desired clips are retrieved by subsequence matching using the rule. Such rule-based retrieval offers reasonable performance and the rule can be intuitively edited in the same language form. Consequently, our method enables efficient and flexible search from a large dataset with simple query language.

New Concepts in Indexing *

PubMed Central

Shank, Russell

1965-01-01

Recent trends in indexing emphasize mechanical, not intellectual, developments. Mechanized operations have produced indexes in depth (1) of information on limited areas of science or (2) utilizing limited parameters for analysis. These indexes may include only citations or both useful data and citations of source literature. Both keyword-in-context and citation indexing seem to be passing the test of the marketplace. Mechanical equipment has also been successfully used to manipulate EAM cards for production of index copy. Information centers are increasingly being used as control devices in narrowly defined subject areas. Authors meet growing pressures to participate in information control work by preparing abstracts of their own articles. Mechanized image systems persist, although large systems are scarce and the many small systems may bring only limited relief for information control and retrieval problems. Experimentation and limited development continue on theory and technique of automatic indexing and abstracting. PMID:14306025

Enhanced quality and quantity of retrieval of Critically Appraised Topics using the CAT Crawler.

PubMed

Dong, P; Mondry, A

2004-03-01

As healthcare moves towards the implementation of Evidence-Based Medicine (EBM), Critically Appraised Topics (CATs) become useful in helping physicians to make clinical decisions. A number of academic and healthcare organizations have set up web-based CAT libraries. The primary objective of the presented work is to provide a one-stop search and download site that allows access to multiple CAT libraries. A web-based application, namely the CAT Crawler, was developed to serve physicians with an adequate access to available appraised topics on the Internet. Important information is extracted automatically and regularly from CAT websites, and consolidated by checking the uniqueness and availability. The principle of meta-search is incorporated into the implementation of the search engine, which finds relevant topics following keyword input. The retrieved result directs the physician to the original resource page. A full-text article of a particular topic can be converted into a proper format for downloading to Personal Digital Assistant (PDA) devices. In summary, the application provides physicians with a common interface to retrieve relevant CATs on particular clinical topics from multiple resources, and thus speeds up the decision making process.

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Intelligent medical information filtering.

PubMed

Quintana, Y

1998-01-01

This paper describes an intelligent information filtering system to assist users to be notified of updates to new and relevant medical information. Among the major problems users face is the large volume of medical information that is generated each day, and the need to filter and retrieve relevant information. The Internet has dramatically increased the amount of electronically accessible medical information and reduced the cost and time needed to publish. The opportunity of the Internet for the medical profession and consumers is to have more information to make decisions and this could potentially lead to better medical decisions and outcomes. However, without the assistance from professional medical librarians, retrieving new and relevant information from databases and the Internet remains a challenge. Many physicians do not have access to the services of a medical librarian. Most physicians indicate on surveys that they do not prefer to retrieve the literature themselves, or visit libraries because of the lack of recent materials, poor organisation and indexing of materials, lack of appropriate and available material, and lack of time. The information filtering system described in this paper records the online web browsing behaviour of each user and creates a user profile of the index terms found on the web pages visited by the user. A relevance-ranking algorithm then matches the user profiles to the index terms of new health care web pages that are added each day. The system creates customised summaries of new information for each user. A user can then connect to the web site to read the new information. Relevance feedback buttons on each page ask the user to rate the usefulness of the page to their immediate information needs. Errors in relevance ranking are reduced in this system by having both the user profile and medical information represented in the same representation language using a controlled vocabulary. This system also updates the user profiles, automatically relieving this burden from the user, but also allowing the user to explicitly state preferences. An initial evaluation of this system was done with health consumers using a web site on consumer health. It was found that users often modified their criteria for what they considered relevant not only between browsing sessions but also during a session. A user's criteria for what is relevant is constantly changing as they interact with the information. New revised metrics of recall and precision are needed to account for the partially relevant judgements and the dynamically changing criteria of users. Future research, development, and evaluation of interactive information retrieval systems will need to take into account the users' dynamically changing criteria of relevance.

Automatic annotation of protein motif function with Gene Ontology terms.

PubMed

Lu, Xinghua; Zhai, Chengxiang; Gopalakrishnan, Vanathi; Buchanan, Bruce G

2004-09-02

Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis.

PubMed

Sarker, Abeed; Chandrashekar, Pramod; Magge, Arjun; Cai, Haitao; Klein, Ari; Gonzalez, Graciela

2017-10-30

Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F 1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them. ©Abeed Sarker, Pramod Chandrashekar, Arjun Magge, Haitao Cai, Ari Klein, Graciela Gonzalez. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.10.2017.

Health search engine with e-document analysis for reliable search results.

PubMed

Gaudinat, Arnaud; Ruch, Patrick; Joubert, Michel; Uziel, Philippe; Strauss, Anne; Thonnet, Michèle; Baud, Robert; Spahni, Stéphane; Weber, Patrick; Bonal, Juan; Boyer, Celia; Fieschi, Marius; Geissbuhler, Antoine

2006-01-01

After a review of the existing practical solution available to the citizen to retrieve eHealth document, the paper describes an original specialized search engine WRAPIN. WRAPIN uses advanced cross lingual information retrieval technologies to check information quality by synthesizing medical concepts, conclusions and references contained in the health literature, to identify accurate, relevant sources. Thanks to MeSH terminology [1] (Medical Subject Headings from the U.S. National Library of Medicine) and advanced approaches such as conclusion extraction from structured document, reformulation of the query, WRAPIN offers to the user a privileged access to navigate through multilingual documents without language or medical prerequisites. The results of an evaluation conducted on the WRAPIN prototype show that results of the WRAPIN search engine are perceived as informative 65% (59% for a general-purpose search engine), reliable and trustworthy 72% (41% for the other engine) by users. But it leaves room for improvement such as the increase of database coverage, the explanation of the original functionalities and an audience adaptability. Thanks to evaluation outcomes, WRAPIN is now in exploitation on the HON web site (http://www.healthonnet.org), free of charge. Intended to the citizen it is a good alternative to general-purpose search engines when the user looks up trustworthy health and medical information or wants to check automatically a doubtful content of a Web page.

Computable visually observed phenotype ontological framework for plants

PubMed Central

2011-01-01

Background The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. Results We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. Conclusions The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community. PMID:21702966

Earlinet single calculus chain: new products overview

NASA Astrophysics Data System (ADS)

D'Amico, Giuseppe; Mattis, Ina; Binietoglou, Ioannis; Baars, Holger; Mona, Lucia; Amato, Francesco; Kokkalis, Panos; Rodríguez-Gómez, Alejandro; Soupiona, Ourania; Kalliopi-Artemis, Voudouri

2018-04-01

The Single Calculus Chain (SCC) is an automatic and flexible tool to analyze raw lidar data using EARLINET quality assured retrieval algorithms. It has been already demonstrated the SCC can retrieve reliable aerosol backscatter and extinction coefficient profiles for different lidar systems. In this paper we provide an overview of new SCC products like particle linear depolarization ratio, cloud masking, aerosol layering allowing relevant improvements in the atmospheric aerosol characterization.

Deep Question Answering for protein annotation

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/ PMID:26384372

Deep Question Answering for protein annotation.

PubMed

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

Different involvement of medial prefrontal cortex and dorso-lateral striatum in automatic and controlled processing of a future conditioned stimulus.

PubMed

Pérez-Díaz, Francisco; Díaz, Estrella; Sánchez, Natividad; Vargas, Juan Pedro; Pearce, John M; López, Juan Carlos

2017-01-01

Recent studies support the idea that stimulus processing in latent inhibition can vary during the course of preexposure. Controlled attentional mechanisms are said to be important in the early stages of preexposure, while in later stages animals adopt automatic processing of the stimulus to be used for conditioning. Given this distinction, it is possible that both types of processing are governed by different neural systems, affecting differentially the retrieval of information about the stimulus. In the present study we tested if a lesion to the dorso-lateral striatum or to the medial prefrontal cortex has a selective effect on exposure to the future conditioned stimulus (CS). With this aim, animals received different amounts of exposure to the future CS. The results showed that a lesion to the medial prefrontal cortex enhanced latent inhibition in animals receiving limited preexposure to the CS, but had no effect in animals receiving extended preexposure to the CS. The lesion of the dorso-lateral striatum produced a decrease in latent inhibition, but only in animals with an extended exposure to the future conditioned stimulus. These results suggest that the dorsal striatum and medial prefrontal cortex play essential roles in controlled and automatic processes. Automatic attentional processes appear to be impaired by a lesion to the dorso-lateral striatum and facilitated by a lesion to the prefrontal cortex.

Different involvement of medial prefrontal cortex and dorso-lateral striatum in automatic and controlled processing of a future conditioned stimulus

PubMed Central

Pérez-Díaz, Francisco; Díaz, Estrella; Sánchez, Natividad; Vargas, Juan Pedro; Pearce, John M.

2017-01-01

Recent studies support the idea that stimulus processing in latent inhibition can vary during the course of preexposure. Controlled attentional mechanisms are said to be important in the early stages of preexposure, while in later stages animals adopt automatic processing of the stimulus to be used for conditioning. Given this distinction, it is possible that both types of processing are governed by different neural systems, affecting differentially the retrieval of information about the stimulus. In the present study we tested if a lesion to the dorso-lateral striatum or to the medial prefrontal cortex has a selective effect on exposure to the future conditioned stimulus (CS). With this aim, animals received different amounts of exposure to the future CS. The results showed that a lesion to the medial prefrontal cortex enhanced latent inhibition in animals receiving limited preexposure to the CS, but had no effect in animals receiving extended preexposure to the CS. The lesion of the dorso-lateral striatum produced a decrease in latent inhibition, but only in animals with an extended exposure to the future conditioned stimulus. These results suggest that the dorsal striatum and medial prefrontal cortex play essential roles in controlled and automatic processes. Automatic attentional processes appear to be impaired by a lesion to the dorso-lateral striatum and facilitated by a lesion to the prefrontal cortex. PMID:29240804

A New Bibliographical Feature for SIMBAD: Highlighting the Most Relevant Papers for One Astronomical Object

NASA Astrophysics Data System (ADS)

Oberto, A.; Lesteven, S.; Derriere, S.; Bonnin, C.; Buga, M.; Brouty, M.; Bruneau, C.; Brunet, C.; Eisele, A.; Genova, F.; Guéhenneux, S.; Neuville, M.; Ochsenbein, F.; Perret, E.; Son, E.; Vannier, P.; Vonflie, P.; Wenger, M.; Woelfel, F.

2015-04-01

The number of bibliographical references attached to an astronomical object in SIMBAD is has been growing continuously over the years. It is important for astronomers to retrieve the most relevant papers, those that give important information about the object of study. This is not easy since there can be many references attached to one object. For instance, in 2014, more than 15,000 objects had been attached to more than 50 references. The location of the object's citations inside the paper and its number of occurrences are important criteria to extract the most relevant papers. Since 2008, because of the DJIN application (a semi-automatic tool to search for object names in full text) this information has been collected. For each article associated with an astronomical object, we know where it is cited and how many times and with which name it appears. Since September 2013, the users of SIMBAD web site can choose to retrieve the most relevant references for an astronomical object depending on its location in the publication. A new formula to sort references by combining all locations, number of occurrences, total number of objects studied, citation count, and year is presented in this paper.

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PubMed Central

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336

Design of a graphical user interface for an intelligent multimedia information system for radiology research

NASA Astrophysics Data System (ADS)

Taira, Ricky K.; Wong, Clement; Johnson, David; Bhushan, Vikas; Rivera, Monica; Huang, Lu J.; Aberle, Denise R.; Cardenas, Alfonso F.; Chu, Wesley W.

1995-05-01

With the increase in the volume and distribution of images and text available in PACS and medical electronic health-care environments it becomes increasingly important to maintain indexes that summarize the content of these multi-media documents. Such indices are necessary to quickly locate relevant patient cases for research, patient management, and teaching. The goal of this project is to develop an intelligent document retrieval system that allows researchers to request for patient cases based on document content. Thus we wish to retrieve patient cases from electronic information archives that could include a combined specification of patient demographics, low level radiologic findings (size, shape, number), intermediate-level radiologic findings (e.g., atelectasis, infiltrates, etc.) and/or high-level pathology constraints (e.g., well-differentiated small cell carcinoma). The cases could be distributed among multiple heterogeneous databases such as PACS, RIS, and HIS. Content- based retrieval systems go beyond the capabilities of simple key-word or string-based retrieval matching systems. These systems require a knowledge base to comprehend the generality/specificity of a concept (thus knowing the subclasses or related concepts to a given concept) and knowledge of the various string representations for each concept (i.e., synonyms, lexical variants, etc.). We have previously reported on a data integration mediation layer that allows transparent access to multiple heterogeneous distributed medical databases (HIS, RIS, and PACS). The data access layer of our architecture currently has limited query processing capabilities. Given a patient hospital identification number, the access mediation layer collects all documents in RIS and HIS and returns this information to a specified workstation location. In this paper we report on our efforts to extend the query processing capabilities of the system by creation of custom query interfaces, an intelligent query processing engine, and a document-content index that can be generated automatically (i.e., no manual authoring or changes to the normal clinical protocols).

Passive acoustic source localization using sources of opportunity.

PubMed

Verlinden, Christopher M A; Sarkar, J; Hodgkiss, W S; Kuperman, W A; Sabra, K G

2015-07-01

The feasibility of using data derived replicas from ships of opportunity for implementing matched field processing is demonstrated. The Automatic Identification System (AIS) is used to provide the library coordinates for the replica library and a correlation based processing procedure is used to overcome the impediment that the replica library is constructed from sources with different spectra and will further be used to locate another source with its own unique spectral structure. The method is illustrated with simulation and then verified using acoustic data from a 2009 experiment for which AIS information was retrieved from the United States Coast Guard Navigation Center Nationwide AIS database.

Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning.

PubMed

Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun; Ma, Shuai; Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma; Ma, Shuai; Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun

2018-06-01

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

Extracting Association Patterns in Network Communications

PubMed Central

Portela, Javier; Villalba, Luis Javier García; Trujillo, Alejandra Guadalupe Silva; Orozco, Ana Lucila Sandoval; Kim, Tai-hoon

2015-01-01

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense. PMID:25679311

Extracting association patterns in network communications.

PubMed

Portela, Javier; Villalba, Luis Javier García; Trujillo, Alejandra Guadalupe Silva; Orozco, Ana Lucila Sandoval; Kim, Tai-hoon

2015-02-11

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2001-01-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2000-12-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Retrieving definitional content for ontology development.

PubMed

Smith, L; Wilbur, W J

2004-12-01

Ontology construction requires an understanding of the meaning and usage of its encoded concepts. While definitions found in dictionaries or glossaries may be adequate for many concepts, the actual usage in expert writing could be a better source of information for many others. The goal of this paper is to describe an automated procedure for finding definitional content in expert writing. The approach uses machine learning on phrasal features to learn when sentences in a book contain definitional content, as determined by their similarity to glossary definitions provided in the same book. The end result is not a concise definition of a given concept, but for each sentence, a predicted probability that it contains information relevant to a definition. The approach is evaluated automatically for terms with explicit definitions, and manually for terms with no available definition.

Automated synthesis of image processing procedures using AI planning techniques

NASA Technical Reports Server (NTRS)

Chien, Steve; Mortensen, Helen

1994-01-01

This paper describes the Multimission VICAR (Video Image Communication and Retrieval) Planner (MVP) (Chien 1994) system, which uses artificial intelligence planning techniques (Iwasaki & Friedland, 1985, Pemberthy & Weld, 1992, Stefik, 1981) to automatically construct executable complex image processing procedures (using models of the smaller constituent image processing subprograms) in response to image processing requests made to the JPL Multimission Image Processing Laboratory (MIPL). The MVP system allows the user to specify the image processing requirements in terms of the various types of correction required. Given this information, MVP derives unspecified required processing steps and determines appropriate image processing programs and parameters to achieve the specified image processing goals. This information is output as an executable image processing program which can then be executed to fill the processing request.

An Electronic Tree Inventory for Arboriculture Management

NASA Astrophysics Data System (ADS)

Tait, Roger J.; Allen, Tony J.; Sherkat, Nasser; Bellett-Travers, Marcus D.

The integration of Global Positioning System (GPS) technology into mobile devices provides them with an awareness of their physical location. This geospatial context can be employed in a wide range of applications including locating nearby places of interest as well as guiding emergency services to incidents. In this research, a GPS-enabled Personal Digital Assistant (PDA) is used to create a computerised tree inventory for the management of arboriculture. Using the General Packet Radio Service (GPRS), GPS information and arboreal image data are sent to a web-server. An office-based PC running customised Geographical Information Software (GIS) then automatically retrieves the GPS tagged image data for display and analysis purposes. The resulting application allows an expert user to view the condition of individual trees in greater detail than is possible using remotely sensed imagery.

Imaged Document Optical Correlation and Conversion System (IDOCCS)

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-03-01

Today, the paper document is fast becoming a thing of the past. With the rapid development of fast, inexpensive computing and storage devices, many government and private organizations are archiving their documents in electronic form (e.g., personnel records, medical records, patents, etc.). In addition, many organizations are converting their paper archives to electronic images, which are stored in a computer database. Because of this, there is a need to efficiently organize this data into comprehensive and accessible information resources. The Imaged Document Optical Correlation and Conversion System (IDOCCS) provides a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provides the search and retrieval capability of document images. The IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives and can even determine the types of languages contained within a document. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited, e.g., imaged documents containing an agency's seal or logo, or documents with a particular individual's signature block, can be singled out. With this dual capability, IDOCCS outperforms systems that rely on optical character recognition as a basis for indexing and storing only the textual content of documents for later retrieval.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Effect of normal aging and of Alzheimer's disease on, episodic memory.

PubMed

Le Moal, S; Reymann, J M; Thomas, V; Cattenoz, C; Lieury, A; Allain, H

1997-01-01

Performances of 12 patients with Alzheimer's disease (AD), 15 healthy elderly subjects and 20 young healthy volunteers were compared on two episodic memory tests. The first, a learning test of semantically related words, enabled an assessment of the effect of semantic relationships on word learning by controlling the encoding and retrieval processes. The second, a dual coding test, is about the assessment of automatic processes operating during drawings encoding. The results obtained demonstrated quantitative and qualitative differences between the population. Manifestations of episodic memory deficit in AD patients were shown not only by lower performance scores than in elderly controls, but also by the lack of any effect of semantic cues and the production of a large number of extra-list intrusions. Automatic processes underlying dual coding appear to be spared in AD, although more time is needed to process information than in young or elderly subjects. These findings confirm former data and emphasize the preservation of certain memory processes (dual coding) in AD which could be used in future therapeutic approaches.

Automatic software correction of residual aberrations in reconstructed HRTEM exit waves of crystalline samples

DOE PAGES

Ophus, Colin; Rasool, Haider I.; Linck, Martin; ...

2016-11-30

We develop an automatic and objective method to measure and correct residual aberrations in atomic-resolution HRTEM complex exit waves for crystalline samples aligned along a low-index zone axis. Our method uses the approximate rotational point symmetry of a column of atoms or single atom to iteratively calculate a best-fit numerical phase plate for this symmetry condition, and does not require information about the sample thickness or precise structure. We apply our method to two experimental focal series reconstructions, imaging a β-Si 3N 4 wedge with O and N doping, and a single-layer graphene grain boundary. We use peak and latticemore » fitting to evaluate the precision of the corrected exit waves. We also apply our method to the exit wave of a Si wedge retrieved by off-axis electron holography. In all cases, the software correction of the residual aberration function improves the accuracy of the measured exit waves.« less

Automatic software correction of residual aberrations in reconstructed HRTEM exit waves of crystalline samples

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

Ophus, Colin; Rasool, Haider I.; Linck, Martin

We develop an automatic and objective method to measure and correct residual aberrations in atomic-resolution HRTEM complex exit waves for crystalline samples aligned along a low-index zone axis. Our method uses the approximate rotational point symmetry of a column of atoms or single atom to iteratively calculate a best-fit numerical phase plate for this symmetry condition, and does not require information about the sample thickness or precise structure. We apply our method to two experimental focal series reconstructions, imaging a β-Si 3N 4 wedge with O and N doping, and a single-layer graphene grain boundary. We use peak and latticemore » fitting to evaluate the precision of the corrected exit waves. We also apply our method to the exit wave of a Si wedge retrieved by off-axis electron holography. In all cases, the software correction of the residual aberration function improves the accuracy of the measured exit waves.« less

Selected Topics from LVCSR Research for Asian Languages at Tokyo Tech

NASA Astrophysics Data System (ADS)

Furui, Sadaoki

This paper presents our recent work in regard to building Large Vocabulary Continuous Speech Recognition (LVCSR) systems for the Thai, Indonesian, and Chinese languages. For Thai, since there is no word boundary in the written form, we have proposed a new method for automatically creating word-like units from a text corpus, and applied topic and speaking style adaptation to the language model to recognize spoken-style utterances. For Indonesian, we have applied proper noun-specific adaptation to acoustic modeling, and rule-based English-to-Indonesian phoneme mapping to solve the problem of large variation in proper noun and English word pronunciation in a spoken-query information retrieval system. In spoken Chinese, long organization names are frequently abbreviated, and abbreviated utterances cannot be recognized if the abbreviations are not included in the dictionary. We have proposed a new method for automatically generating Chinese abbreviations, and by expanding the vocabulary using the generated abbreviations, we have significantly improved the performance of spoken query-based search.

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

PubMed Central

Wei, Wei; Ji, Zhanglong; He, Yupeng; Zhang, Kai; Ha, Yuanchi; Li, Qi; Ohno-Machado, Lucila

2018-01-01

Abstract The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search. As a result, users spend time searching datasets in known repositories, and they typically do not find new repositories. The biomedical and healthcare data discovery index ecosystem (bioCADDIE) team organized a challenge to solicit new indexing and searching strategies for retrieving biomedical datasets across repositories. We describe the work of one team that built a retrieval pipeline and examined its performance. The pipeline used online resources to supplement dataset metadata, automatically generated queries from users’ free-text questions, produced high-quality retrieval results and achieved the highest inferred Normalized Discounted Cumulative Gain among competitors. The results showed that it is a promising solution for cross-database, cross-domain and cross-repository biomedical dataset retrieval. Database URL: https://github.com/w2wei/dataset_retrieval_pipeline PMID:29688374

Extracting semantically enriched events from biomedical literature

PubMed Central

2012-01-01

Background Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. Conclusions We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. PMID:22621266

Extracting semantically enriched events from biomedical literature.

PubMed

Miwa, Makoto; Thompson, Paul; McNaught, John; Kell, Douglas B; Ananiadou, Sophia

2012-05-23

Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

Prospective memory: effects of divided attention on spontaneous retrieval.

PubMed

Harrison, Tyler L; Mullet, Hillary G; Whiffen, Katie N; Ousterhout, Hunter; Einstein, Gilles O

2014-02-01

We examined the effects of divided attention on the spontaneous retrieval of a prospective memory intention. Participants performed an ongoing lexical decision task with an embedded prospective memory demand, and also performed a divided-attention task during some segments of lexical decision trials. In all experiments, monitoring was highly discouraged, and we observed no evidence that participants engaged monitoring processes. In Experiment 1, performing a moderately demanding divided-attention task (a digit detection task) did not affect prospective memory performance. In Experiment 2, performing a more challenging divided-attention task (random number generation) impaired prospective memory. Experiment 3 showed that this impairment was eliminated when the prospective memory cue was perceptually salient. Taken together, the results indicate that spontaneous retrieval is not automatic and that challenging divided-attention tasks interfere with spontaneous retrieval and not with the execution of a retrieved intention.

The chordate proteome history database.

PubMed

Levasseur, Anthony; Paganini, Julien; Dainat, Jacques; Thompson, Julie D; Poch, Olivier; Pontarotti, Pierre; Gouret, Philippe

2012-01-01

The chordate proteome history database (http://ioda.univ-provence.fr) comprises some 20,000 evolutionary analyses of proteins from chordate species. Our main objective was to characterize and study the evolutionary histories of the chordate proteome, and in particular to detect genomic events and automatic functional searches. Firstly, phylogenetic analyses based on high quality multiple sequence alignments and a robust phylogenetic pipeline were performed for the whole protein and for each individual domain. Novel approaches were developed to identify orthologs/paralogs, and predict gene duplication/gain/loss events and the occurrence of new protein architectures (domain gains, losses and shuffling). These important genetic events were localized on the phylogenetic trees and on the genomic sequence. Secondly, the phylogenetic trees were enhanced by the creation of phylogroups, whereby groups of orthologous sequences created using OrthoMCL were corrected based on the phylogenetic trees; gene family size and gene gain/loss in a given lineage could be deduced from the phylogroups. For each ortholog group obtained from the phylogenetic or the phylogroup analysis, functional information and expression data can be retrieved. Database searches can be performed easily using biological objects: protein identifier, keyword or domain, but can also be based on events, eg, domain exchange events can be retrieved. To our knowledge, this is the first database that links group clustering, phylogeny and automatic functional searches along with the detection of important events occurring during genome evolution, such as the appearance of a new domain architecture.

Multisensor fusion in gastroenterology domain through video and echo endoscopic image combination: a challenge

NASA Astrophysics Data System (ADS)

Debon, Renaud; Le Guillou, Clara; Cauvin, Jean-Michel; Solaiman, Basel; Roux, Christian

2001-08-01

Medical domain makes intensive use of information fusion. In particular, the gastro-enterology is a discipline where physicians have the choice between several imagery modalities that offer complementary advantages. Among all existing systems, videoendoscopy (based on a CCD sensor) and echoendoscopy (based on an ultrasound sensor) are the most efficient. The use of each system corresponds to a given step in the physician diagnostic elaboration. Nowadays, several works aim to achieve automatic interpretation of videoendoscopic sequences. These systems can quantify color and superficial textures of the digestive tube. Unfortunately the relief information, which is important for the diagnostic, is very difficult to retrieve. On the other hand, some studies have proved that 3D information can be easily quantified using echoendoscopy image sequences. That is why the idea to combine these information, acquired from two very different points of view, can be considered as a real challenge for the medical image fusion topic. In this paper, after a review of actual works concerning numerical exploitation of videoendoscopy and echoendoscopy, the following question will be discussed: how can the use of complementary aspects of the different systems ease the automatic exploitation of videoendoscopy ? In a second time, we will evaluate the feasibility of the achievement of a realistic 3D reconstruction based both on information given by echoendoscopy (relief) and videoendoscopy (texture). Enumeration of potential applications of such a fusion system will then follow. Further discussions and perspectives will conclude this first study.

A versatile calibration procedure for portable coded aperture gamma cameras and RGB-D sensors

NASA Astrophysics Data System (ADS)

Paradiso, V.; Crivellaro, A.; Amgarou, K.; de Lanaute, N. Blanc; Fua, P.; Liénard, E.

2018-04-01

The present paper proposes a versatile procedure for the geometrical calibration of coded aperture gamma cameras and RGB-D depth sensors, using only one radioactive point source and a simple experimental set-up. Calibration data is then used for accurately aligning radiation images retrieved by means of the γ-camera with the respective depth images computed with the RGB-D sensor. The system resulting from such a combination is thus able to retrieve, automatically, the distance of radioactive hotspots by means of pixel-wise mapping between gamma and depth images. This procedure is of great interest for a wide number of applications, ranging from precise automatic estimation of the shape and distance of radioactive objects to Augmented Reality systems. Incidentally, the corresponding results validated the choice of a perspective design model for a coded aperture γ-camera.

Computer assisted analysis of auroral images obtained from high altitude polar satellites

NASA Technical Reports Server (NTRS)

Samadani, Ramin; Flynn, Michael

1993-01-01

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images.

PGMapper: a web-based tool linking phenotype to genes.

PubMed

Xiong, Qing; Qiu, Yuhui; Gu, Weikuan

2008-04-01

With the availability of whole genome sequence in many species, linkage analysis, positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes. However, in these methods, causative genes underlying a quantitative trait locus, or a disease, are usually located within a large genomic region or a large set of genes. Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources. PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases. PGMapper is currently available for candidate gene search of human, mouse, rat, zebrafish and 12 other species. Available online at http://www.genediscovery.org/pgmapper/index.jsp.

Health on the Net Foundation: assessing the quality of health web pages all over the world.

PubMed

Boyer, Célia; Gaudinat, Arnaud; Baujard, Vincent; Geissbühler, Antoine

2007-01-01

The Internet provides a great amount of information and has become one of the communication media which is most widely used [1]. However, the problem is no longer finding information but assessing the credibility of the publishers as well as the relevance and accuracy of the documents retrieved from the web. This problem is particularly relevant in the medical area which has a direct impact on the well-being of citizens. In this paper, we assume that the quality of web pages can be controlled, even when a huge amount of documents has to be reviewed. But this must be supported by both specific automatic tools and human expertise. In this context, we present various initiatives of the Health on the Net Foundation informing the citizens about the reliability of the medical content on the web.

Telematic integration of health data: a practicable contribution.

PubMed

Guerriero, Lorenzo; Ferdeghini, Ezio M; Viola, Silvia R; Porro, Ivan; Testi, Angela; Bedini, Remo

2011-09-01

The patients' clinical and healthcare data should virtually be available everywhere, both to provide a more efficient and effective medical approach to their pathologies, as well as to make public healthcare decision makers able to verify the efficacy and efficiency of the adopted healthcare processes. Unfortunately, customised solutions adopted by many local Health Information Systems in Italy make it difficult to share the stored data outside their own environment. In the last years, worldwide initiatives have aimed to overcome such sharing limitation. An important issue during the passage towards standardised, integrated information systems is the possible loss of previously collected data. The herein presented project realises a suitable architecture able to guarantee reliable, automatic, user-transparent storing and retrieval of information from both modern and legacy systems. The technical and management solutions provided by the project avoid data loss and overlapping, and allow data integration and organisation suitable for data-mining and data-warehousing analysis.

Parallel reduced-instruction-set-computer architecture for real-time symbolic pattern matching

NASA Astrophysics Data System (ADS)

Parson, Dale E.

1991-03-01

This report discusses ongoing work on a parallel reduced-instruction- set-computer (RISC) architecture for automatic production matching. The PRIOPS compiler takes advantage of the memoryless character of automatic processing by translating a program's collection of automatic production tests into an equivalent combinational circuit-a digital circuit without memory, whose outputs are immediate functions of its inputs. The circuit provides a highly parallel, fine-grain model of automatic matching. The compiler then maps the combinational circuit onto RISC hardware. The heart of the processor is an array of comparators capable of testing production conditions in parallel, Each comparator attaches to private memory that contains virtual circuit nodes-records of the current state of nodes and busses in the combinational circuit. All comparator memories hold identical information, allowing simultaneous update for a single changing circuit node and simultaneous retrieval of different circuit nodes by different comparators. Along with the comparator-based logic unit is a sequencer that determines the current combination of production-derived comparisons to try, based on the combined success and failure of previous combinations of comparisons. The memoryless nature of automatic matching allows the compiler to designate invariant memory addresses for virtual circuit nodes, and to generate the most effective sequences of comparison test combinations. The result is maximal utilization of parallel hardware, indicating speed increases and scalability beyond that found for course-grain, multiprocessor approaches to concurrent Rete matching. Future work will consider application of this RISC architecture to the standard (controlled) Rete algorithm, where search through memory dominates portions of matching.

A Unified Mathematical Definition of Classical Information Retrieval.

ERIC Educational Resources Information Center

Dominich, Sandor

2000-01-01

Presents a unified mathematical definition for the classical models of information retrieval and identifies a mathematical structure behind relevance feedback. Highlights include vector information retrieval; probabilistic information retrieval; and similarity information retrieval. (Contains 118 references.) (Author/LRW)

Determining the relative importance of figures in journal articles to find representative images

NASA Astrophysics Data System (ADS)

Müller, Henning; Foncubierta-Rodríguez, Antonio; Lin, Chang; Eggel, Ivan

2013-03-01

When physicians are searching for articles in the medical literature, images of the articles can help determining relevance of the article content for a specific information need. The visual image representation can be an advantage in effectiveness (quality of found articles) and also in efficiency (speed of determining relevance or irrelevance) as many articles can likely be excluded much quicker by looking at a few representative images. In domains such as medical information retrieval, allowing to determine relevance quickly and accurately is an important criterion. This becomes even more important when small interfaces are used as it is frequently the case on mobile phones and tablets to access scientific data whenever information needs arise. In scientific articles many figures are used and particularly in the biomedical literature only a subset may be relevant for determining the relevance of a specific article to an information need. In many cases clinical images can be seen as more important for visual appearance than graphs or histograms that require looking at the context for interpretation. To get a clearer idea of image relevance in articles, a user test with a physician was performed who classified images of biomedical research articles into categories of importance that can subsequently be used to evaluate algorithms that automatically select images as representative examples. The manual sorting of images of 50 journal articles of BioMedCentral with each containing more than 8 figures by importance also allows to derive several rules that determine how to choose images and how to develop algorithms for choosing the most representative images of specific texts. This article describes the user tests and can be a first important step to evaluate automatic tools to select representative images for representing articles and potentially also images in other contexts, for example when representing patient records or other medical concepts when selecting images to represent RadLex terms in tutorials or interactive interfaces for example. This can help to make the image retrieval process more efficient and effective for physicians.

2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments.

PubMed

Allmer, Jens; Kuhlgert, Sebastian; Hippler, Michael

2008-07-07

The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.

Intelligent navigation to improve obstetrical sonography.

PubMed

Yeo, Lami; Romero, Roberto

2016-04-01

'Manual navigation' by the operator is the standard method used to obtain information from two-dimensional and volumetric sonography. Two-dimensional sonography is highly operator dependent and requires extensive training and expertise to assess fetal anatomy properly. Most of the sonographic examination time is devoted to acquisition of images, while 'retrieval' and display of diagnostic planes occurs rapidly (essentially instantaneously). In contrast, volumetric sonography has a rapid acquisition phase, but the retrieval and display of relevant diagnostic planes is often time-consuming, tedious and challenging. We propose the term 'intelligent navigation' to refer to a new method of interrogation of a volume dataset whereby identification and selection of key anatomical landmarks allow the system to: 1) generate a geometrical reconstruction of the organ of interest; and 2) automatically navigate, find, extract and display specific diagnostic planes. This is accomplished using operator-independent algorithms that are both predictable and adaptive. Virtual Intelligent Sonographer Assistance (VIS-Assistance®) is a tool that allows operator-independent sonographic navigation and exploration of the surrounding structures in previously identified diagnostic planes. The advantage of intelligent (over manual) navigation in volumetric sonography is the short time required for both acquisition and retrieval and display of diagnostic planes. Intelligent navigation technology automatically realigns the volume, and reorients and standardizes the anatomical position, so that the fetus and the diagnostic planes are consistently displayed in the same manner each time, regardless of the fetal position or the initial orientation. Automatic labeling of anatomical structures, subject orientation and each of the diagnostic planes is also possible. Intelligent navigation technology can operate on conventional computers, and is not dependent on specific ultrasound platforms or on the use of software to perform manual navigation of volume datasets. Diagnostic planes and VIS-Assistance videoclips can be transmitted by telemedicine so that expert consultants can evaluate the images to provide an opinion. The end result is a user-friendly, simple, fast and consistent method of obtaining sonographic images with decreased operator dependency. Intelligent navigation is one approach to improve obstetrical sonography. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Codified Hashtags for Weather Warning on Twitter: an Italian Case Study

PubMed Central

Grasso, Valentina; Crisci, Alfonso

2016-01-01

Introduction: During emergencies increasing numbers of messages are shared through social media platforms becoming a primary source of information for lay people and emergency managers. For Twitter codified hashtagging is emerging as a practical way to coordinate messages during emergencies and quickly identify relevant information. This paper considers a case study on the use of codified hashtags concerning weather warning in Italy in three different regions. Methods: From November 3rd to December 2nd 2014, tweets identified by the 3 codified hashtags #allertameteoTOS, #allertameteoLIG and #allertameteoPIE were retrieved, collecting a total of 35,558 tweets published by 7361 unique tweets authors, with the aim to assess if codified hashtags could represent an effective way to align formal and informal sources of information during weather related emergencies. An auxiliary R-package was built to lead the analytics used in this study. Authors performed a manual coding of users, hashtags and content of messages of all Twitter data considered. Results: Content analysis showed that tweets were overwhelmingly related to situational updates, with a high percentage containing geo-location information. Communication patterns of different user types were discussed for the three contexts. In accordance with previous studies, individuals showed an active participation primarily functioning as information hub during the emergency. Discussion: In the proposed cases codified hashtags have proven to be an effective tool to convey useful information on Twitter by formal and informal sources. Where institutions supported the use of the predefined hashtag in communication activities, like in Tuscany, messages were very focused, with more than 90% of tweets being situational updates. In this perspective, use of codified hashtags may potentially improve the performance of systems for automatic information retrieval and processing during disasters. Keywords: social media, emergency management, Twitter, severe weather PMID:27500010

A statistical look at the retrieval of exoplanetary atmospheres of super Earths and giant planets

NASA Astrophysics Data System (ADS)

Rocchetto, Marco; Waldmann, Ingo Peter; Tinetti, Giovanna; Yurchenko, Sergey; Tennyson, Jonathan

2015-08-01

Over the past decades transit spectroscopy has become one of the pioneering methods to characterise exoplanetary atmospheres. With the increasing number of observations, and the advent of new ground and spaced based instruments, it is now crucial to find the most optimal and objective methodologies to interpret these data, and understand the information content they convey. This is particularly true for smaller and fainter super Earth type planets.In this conference we will present a new take on the spectral retrieval of transiting planets, with particular focus on super Earth atmospheres. TauREx (Waldmann et al. 2015a,b.) is a new line-by-line radiative transfer atmospheric retrieval framework for transmission and emission spectroscopy of exoplanetary atmospheres, optimised for hot Jupiters and super Earths. The code has been built from scratch with the ideas of scalability, flexibility and automation. This allows to run retrievals with minimum user input that can be scaled to large cluster computing. Priors on the number and types of molecules considered are automatically determined using a custom built pattern recognition algorithm able to identify the most likely absorbers/emitters in the exoplanetary spectra, minimising the human bias in selecting the major atmospheric constituents.Using these tools, we investigate the impact of signal to noise, spectral resolution and wavelength coverage on the retrievability of individual model parameters from transit spectra of super Earths, and put our models to test (Rocchetto et al. 2015). Characterisation of the atmospheres of super Earths through transit spectroscopy is paramount, as it can provide an indirect - and so far unique - way to probe the nature of these planets. For the first time we analyse in a systematic way large grids of spectra generated for different observing scenarios. We perform thousands of retrievals aimed to fully map the degeneracies and understand the statistics of current exoplanetary retrieval models, in the limiting signal-to-noise regime of super Earth observations.

Memory flexibility training for autobiographical memory as an intervention for maintaining social and mental well-being in older adults.

PubMed

Leahy, Fiona; Ridout, Nathan; Holland, Carol

2018-05-07

Autobiographical memory specificity (AMS) reduces with increasing age and is associated with depression, social problem-solving and functional limitations. However, ability to switch between general and specific, as well as between positive and negative retrieval, may be more important for the strategic use of autobiographical information in everyday life. Ability to switch between retrieval modes is likely to rely on aspects of executive function. We propose that age-related deficits in cognitive flexibility impair AMS, but the "positivity effect" protects positively valenced memories from impaired specificity. A training programme to improve the ability to flexibly retrieve different types of memories in depressed adults (MemFlex) was examined in non-depressed older adults to determine effects on AMS, valence and the executive functions underlying cognitive flexibility. Thirty-nine participants aged 70+ (MemFlex, n = 20; control, n = 19) took part. AMS and the inhibition aspect of executive function improved in both groups, suggesting these abilities are amenable to change, although not differentially affected by this type of training. Lower baseline inhibition scores correlated with increased negative, but not positive AMS, suggesting that positive AMS is an automatic process in older adults. Changes in AMS correlated with changes in social problem-solving, emphasising the usefulness of AMs in a social environment.

Exploring inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video

NASA Astrophysics Data System (ADS)

Li, Jia; Tian, Yonghong; Gao, Wen

2008-01-01

In recent years, the amount of streaming video has grown rapidly on the Web. Often, retrieving these streaming videos offers the challenge of indexing and analyzing the media in real time because the streams must be treated as effectively infinite in length, thus precluding offline processing. Generally speaking, captions are important semantic clues for video indexing and retrieval. However, existing caption detection methods often have difficulties to make real-time detection for streaming video, and few of them concern on the differentiation of captions from scene texts and scrolling texts. In general, these texts have different roles in streaming video retrieval. To overcome these difficulties, this paper proposes a novel approach which explores the inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video. In our approach, the inter-frame correlation information is used to distinguish caption texts from scene texts and scrolling texts. Moreover, wavelet-domain Generalized Gaussian Models (GGMs) are utilized to automatically remove non-text regions from each frame and only keep caption regions for further processing. Experiment results show that our approach is able to offer real-time caption detection with high recall and low false alarm rate, and also can effectively discern caption texts from the other texts even in low resolutions.

Application of deep learning in determining IR precipitation occurrence: a Convolutional Neural Network model

NASA Astrophysics Data System (ADS)

Wang, C.; Hong, Y.

2017-12-01

Infrared (IR) information from Geostationary satellites can be used to retrieve precipitation at pretty high spatiotemporal resolutions. Traditional artificial intelligence (AI) methodologies, such as artificial neural networks (ANN), have been designed to build the relationship between near-surface precipitation and manually derived IR features in products including PERSIANN and PERSIANN-CCS. This study builds an automatic precipitation detection model based on IR data using Convolutional Neural Network (CNN) which is implemented by the newly developed deep learning framework, Caffe. The model judges whether there is rain or no rain at pixel level. Compared with traditional ANN methods, CNN can extract features inside the raw data automatically and thoroughly. In this study, IR data from GOES satellites and precipitation estimates from the next generation QPE (Q2) over the central United States are used as inputs and labels, respectively. The whole datasets during the study period (June to August in 2012) are randomly partitioned to three sub datasets (train, validation and test) to establish the model at the spatial resolution of 0.08°×0.08° and the temporal resolution of 1 hour. The experiments show great improvements of CNN in rain identification compared to the widely used IR-based precipitation product, i.e., PERSIANN-CCS. The overall gain in performance is about 30% for critical success index (CSI), 32% for probability of detection (POD) and 12% for false alarm ratio (FAR). Compared to other recent IR-based precipitation retrieval methods (e.g., PERSIANN-DL developed by University of California Irvine), our model is simpler with less parameters, but achieves equally or even better results. CNN has been applied in computer vision domain successfully, and our results prove the method is suitable for IR precipitation detection. Future studies can expand the application of CNN from precipitation occurrence decision to precipitation amount retrieval.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Höynck, Michael

2004-12-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Dissociating word stem completion and cued recall as a function of divided attention at retrieval.

PubMed

Clarke, A J Benjamin; Butler, Laurie T

2008-10-01

The aim of this study was to investigate the widely held, but largely untested, view that implicit memory (repetition priming) reflects an automatic form of retrieval. Specifically, in Experiment 1 we explored whether a secondary task (syllable monitoring), performed during retrieval, would disrupt performance on explicit (cued recall) and implicit (stem completion) memory tasks equally. Surprisingly, despite substantial memory and secondary costs to cued recall when performed with a syllable-monitoring task, the same manipulation had no effect on stem completion priming or on secondary task performance. In Experiment 2 we demonstrated that even when using a particularly demanding version of the stem completion task that incurred secondary task costs, the corresponding disruption to implicit memory performance was minimal. Collectively, the results are consistent with the view that implicit memory retrieval requires little or no processing capacity and is not seemingly susceptible to the effects of dividing attention at retrieval.

Knowledge supports memory retrieval through familiarity, not recollection.

PubMed

Wang, Wei-Chun; Brashier, Nadia M; Wing, Erik A; Marsh, Elizabeth J; Cabeza, Roberto

2018-05-01

Semantic memory, or general knowledge of the world, guides learning and supports the formation and retrieval of new episodic memories. Behavioral evidence suggests that this knowledge effect is supported by recollection-a more controlled form of memory retrieval generally accompanied by contextual details-to a greater degree than familiarity-a more automatic form of memory retrieval generally absent of contextual details. In the current study, we used functional magnetic resonance imaging (fMRI) to investigate the role that regions associated with recollection and familiarity play in retrieving recent instances of known (e.g., The Summer Olympic Games are held four years apart) and unknown (e.g., A flaky deposit found in port bottles is beeswing) statements. Our results revealed a surprising pattern: Episodic retrieval of known statements recruited regions associated with familiarity, but not recollection. Instead, retrieval of unknown statements recruited regions associated with recollection. These data, in combination with quicker reaction times for the retrieval of known than unknown statements, suggest that known statements can be successfully retrieved on the basis of familiarity, whereas unknown statements were retrieved on the basis of recollection. Our results provide insight into how knowledge influences episodic retrieval and demonstrate the role of neuroimaging in providing insights into cognitive processes in the absence of explicit behavioral responses. Copyright © 2018 Elsevier Ltd. All rights reserved.

Adaptive platform for fluorescence microscopy-based high-content screening

NASA Astrophysics Data System (ADS)

Geisbauer, Matthias; Röder, Thorsten; Chen, Yang; Knoll, Alois; Uhl, Rainer

2010-04-01

Fluorescence microscopy has become a widely used tool for the study of medically relevant intra- and intercellular processes. Extracting meaningful information out of a bulk of acquired images is usually performed during a separate post-processing task. Thus capturing raw data results in an unnecessary huge number of images, whereas usually only a few images really show the particular information that is searched for. Here we propose a novel automated high-content microscope system, which enables experiments to be carried out with only a minimum of human interaction. It facilitates a huge speed-increase for cell biology research and its applications compared to the widely performed workflows. Our fluorescence microscopy system can automatically execute application-dependent data processing algorithms during the actual experiment. They are used for image contrast enhancement, cell segmentation and/or cell property evaluation. On-the-fly retrieved information is used to reduce data and concomitantly control the experiment process in real-time. Resulting in a closed loop of perception and action the system can greatly decrease the amount of stored data on one hand and increases the relative valuable data content on the other hand. We demonstrate our approach by addressing the problem of automatically finding cells with a particular combination of labeled receptors and then selectively stimulate them with antagonists or agonists. The results are then compared against the results of traditional, static systems.

Accessing long-term memory representations during visual change detection.

PubMed

Beck, Melissa R; van Lamsweerde, Amanda E

2011-04-01

In visual change detection tasks, providing a cue to the change location concurrent with the test image (post-cue) can improve performance, suggesting that, without a cue, not all encoded representations are automatically accessed. Our studies examined the possibility that post-cues can encourage the retrieval of representations stored in long-term memory (LTM). Participants detected changes in images composed of familiar objects. Performance was better when the cue directed attention to the post-change object. Supporting the role of LTM in the cue effect, the effect was similar regardless of whether the cue was presented during the inter-stimulus interval, concurrent with the onset of the test image, or after the onset of the test image. Furthermore, the post-cue effect and LTM performance were similarly influenced by encoding time. These findings demonstrate that monitoring the visual world for changes does not automatically engage LTM retrieval.

Application of Rough Sets to Information Retrieval.

ERIC Educational Resources Information Center

Miyamoto, Sadaaki

1998-01-01

Develops a method of rough retrieval, an application of the rough set theory to information retrieval. The aim is to: (1) show that rough sets are naturally applied to information retrieval in which categorized information structure is used; and (2) show that a fuzzy retrieval scheme is induced from the rough retrieval. (AEF)

Disclosure Control of Natural Language Information to Enable Secure and Enjoyable Communication over the Internet

NASA Astrophysics Data System (ADS)

Kataoka, Haruno; Utsumi, Akira; Hirose, Yuki; Yoshiura, Hiroshi

Disclosure control of natural language information (DCNL), which we are trying to realize, is described. DCNL will be used for securing human communications over the internet, such as through blogs and social network services. Before sentences in the communications are disclosed, they are checked by DCNL and any phrases that could reveal sensitive information are transformed or omitted so that they are no longer revealing. DCNL checks not only phrases that directly represent sensitive information but also those that indirectly suggest it. Combinations of phrases are also checked. DCNL automatically learns the knowledge of sensitive phrases and the suggestive relations between phrases by using co-occurrence analysis and Web retrieval. The users' burden is therefore minimized, i.e., they do not need to define many disclosure control rules. DCNL complements the traditional access control in the fields where reliability needs to be balanced with enjoyment and objects classes for the access control cannot be predefined.

CCSDS Spacecraft Monitor and Control Service Framework

NASA Technical Reports Server (NTRS)

Merri, Mario; Schmidt, Michael; Ercolani, Alessandro; Dankiewicz, Ivan; Cooper, Sam; Thompson, Roger; Symonds, Martin; Oyake, Amalaye; Vaughs, Ashton; Shames, Peter

2004-01-01

This CCSDS paper presents a reference architecture and service framework for spacecraft monitoring and control. It has been prepared by the Spacecraft Monitoring and Control working group of the CCSDS Mission Operations and Information Management Systems (MOIMS) area. In this context, Spacecraft Monitoring and Control (SM&C) refers to end-to-end services between on- board or remote applications and ground-based functions responsible for mission operations. The scope of SM&C includes: 1) Operational Concept: definition of an operational concept that covers a set of standard operations activities related to the monitoring and control of both ground and space segments. 2) Core Set of Services: definition of an extensible set of services to support the operational concept together with its information model and behaviours. This includes (non exhaustively) ground systems such as Automatic Command and Control, Data Archiving and Retrieval, Flight Dynamics, Mission Planning and Performance Evaluation. 3) Application-layer information: definition of the standard information set to be exchanged for SM&C purposes.

Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.

PubMed

Abd El Aziz, Mohamed; Selim, I M; Xiong, Shengwu

2017-06-30

This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

Method and apparatus for analyzing error conditions in a massively parallel computer system by identifying anomalous nodes within a communicator set

DOEpatents

Gooding, Thomas Michael [Rochester, MN

2011-04-19

An analytical mechanism for a massively parallel computer system automatically analyzes data retrieved from the system, and identifies nodes which exhibit anomalous behavior in comparison to their immediate neighbors. Preferably, anomalous behavior is determined by comparing call-return stack tracebacks for each node, grouping like nodes together, and identifying neighboring nodes which do not themselves belong to the group. A node, not itself in the group, having a large number of neighbors in the group, is a likely locality of error. The analyzer preferably presents this information to the user by sorting the neighbors according to number of adjoining members of the group.

Application of Remote Sensing Techniques for Appraising Changes in Wildlife Habitat

NASA Technical Reports Server (NTRS)

Nelson, H. K.; Klett, A. T.; Johnston, J. E.

1971-01-01

An attempt was made to investigate the potential of airborne, multispectral, line scanner data acquisition and computer-implemented automatic recognition techniques for providing useful information about waterfowl breeding habitat in North Dakota. The spectral characteristics of the components of a landscape containing waterfowl habitat can be detected with airborne scanners. By analyzing these spectral characteristics it is possible to identify and map the landscape components through analog and digital processing methods. At the present stage of development multispectral remote sensing techniques are not ready for operational application to surveys of migratory bird habitat and other such resources. Further developments are needed to: (1) increase accuracy; (2) decrease retrieval and processing time; and (3) reduce costs.

Safe pill-dispensing.

PubMed

Testa, Massimiliano; Pollard, John

2007-01-01

Each patient is supplied with a smart-card containing a Radio Frequency IDentification (RFID) chip storing a unique identification code. The patient places the Smart-card on a pill-dispenser unit containing an RFID reader. The RFID chip is read and the code sent to a Base-station via a wireless Bluetooth link. A database containing both patient details and treatment information is queried at the Base-station using the RFID as the search key. The patient's treatment data (i.e., drug names, quantities, time, etc.) are retrieved and sent back to the pill-dispenser unit via Bluetooth. Appropriate quantities of the required medications are automatically dispensed, unless the patient has already taken his/her daily dose. Safe, confidential communication and operation is ensured.

EARLINET Single Calculus Chain - overview on methodology and strategy

NASA Astrophysics Data System (ADS)

D'Amico, G.; Amodeo, A.; Baars, H.; Binietoglou, I.; Freudenthaler, V.; Mattis, I.; Wandinger, U.; Pappalardo, G.

2015-11-01

In this paper we describe the EARLINET Single Calculus Chain (SCC), a tool for the automatic analysis of lidar measurements. The development of this tool started in the framework of EARLINET-ASOS (European Aerosol Research Lidar Network - Advanced Sustainable Observation System); it was extended within ACTRIS (Aerosol, Clouds and Trace gases Research InfraStructure Network), and it is continuing within ACTRIS-2. The main idea was to develop a data processing chain that allows all EARLINET stations to retrieve, in a fully automatic way, the aerosol backscatter and extinction profiles starting from the raw lidar data of the lidar systems they operate. The calculus subsystem of the SCC is composed of two modules: a pre-processor module which handles the raw lidar data and corrects them for instrumental effects and an optical processing module for the retrieval of aerosol optical products from the pre-processed data. All input parameters needed to perform the lidar analysis are stored in a database to keep track of all changes which may occur for any EARLINET lidar system over the time. The two calculus modules are coordinated and synchronized by an additional module (daemon) which makes the whole analysis process fully automatic. The end user can interact with the SCC via a user-friendly web interface. All SCC modules are developed using open-source and freely available software packages. The final products retrieved by the SCC fulfill all requirements of the EARLINET quality assurance programs on both instrumental and algorithm levels. Moreover, the manpower needed to provide aerosol optical products is greatly reduced and thus the near-real-time availability of lidar data is improved. The high-quality of the SCC products is proven by the good agreement between the SCC analysis, and the corresponding independent manual retrievals. Finally, the ability of the SCC to provide high-quality aerosol optical products is demonstrated for an EARLINET intense observation period.

Unified Pairwise Spatial Relations: An Application to Graphical Symbol Retrieval

NASA Astrophysics Data System (ADS)

Santosh, K. C.; Wendling, Laurent; Lamiroy, Bart

In this paper, we present a novel unifying concept of pairwise spatial relations. We develop two way directional relations with respect to a unique point set, based on topology of the studied objects and thus avoids problems related to erroneous choices of reference objects while preserving symmetry. The method is robust to any type of image configuration since the directional relations are topologically guided. An automatic prototype graphical symbol retrieval is presented in order to establish its expressiveness.

A system overview of the Aerospace Safety Research and Data Institute data management programs

NASA Technical Reports Server (NTRS)

1972-01-01

The NASA Aerospace Safety Information System, is an interactive, generalized data base management system. The on-line retrieval aspects provide for operating from a variety of terminals (or in batch mode). NASIS retrieval enables the user to expand and display (review) the terms of index (cross reference) files, select desired index terms, combine sets of documents corresponding to selected terms and display the resulting records. It also allows the user to print (record) this information on a high speed printer if desired. NASIS also provides the ability to store the strategy of any given session the user has executed. It has a searching and publication ability through generalized linear search and report generating modules which may be performed interactively or in a batch mode. The user may specify formats for the terminal from which he is operating. The system features an interactive user's guide which explains the various commands available and how to use them as well as explanations for all system messages. This explain capability may be extended, without program changes, to include descriptions of the various files in use. Coupled with the ability of NASIS to run in an MTT (multi-terminal task) mode is its automatic accumulation of statistics on each user of the system as well as each file.

A topic clustering approach to finding similar questions from large question and answer archives.

PubMed

Zhang, Wei-Nan; Liu, Ting; Yang, Yang; Cao, Liujuan; Zhang, Yu; Ji, Rongrong

2014-01-01

With the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.

Imaged document information location and extraction using an optical correlator

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-12-01

Today, the paper document is fast becoming a thing of the past. With the rapid development of fast, inexpensive computing and storage devices, many government and private organizations are archiving their documents in electronic form (e.g., personnel records, medical records, patents, etc.). Many of these organizations are converting their paper archives to electronic images, which are then stored in a computer database. Because of this, there is a need to efficiently organize this data into comprehensive and accessible information resources and provide for rapid access to the information contained within these imaged documents. To meet this need, Litton PRC and Litton Data Systems Division are developing a system, the Imaged Document Optical Correlation and Conversion System (IDOCCS), to provide a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provide a means for the search and retrieval of information from imaged documents. IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives and has the potential to determine the types of languages contained within a document. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited, e.g., imaged documents containing an agency's seal or logo can be singled out. In this paper, we present a description of IDOCCS as well as preliminary performance results and theoretical projections.

The identification of clinically important elements within medical journal abstracts: Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR).

PubMed

Dawes, Martin; Pluye, Pierre; Shea, Laura; Grad, Roland; Greenberg, Arlene; Nie, Jian-Yun

2007-01-01

Information retrieval in primary care is becoming more difficult as the volume of medical information held in electronic databases expands. The lexical structure of this information might permit automatic indexing and improved retrieval. To determine the possibility of identifying the key elements of clinical studies, namely Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR), from abstracts of medical journals. We used a convenience sample of 20 synopses from the journal Evidence-Based Medicine (EBM) and their matching original journal article abstracts obtained from PubMed. Three independent primary care professionals identified PECODR-related extracts of text. Rules were developed to define each PECODR element and the selection process of characters, words, phrases and sentences. From the extracts of text related to PECODR elements, potential lexical patterns that might help identify those elements were proposed and assessed using NVivo software. A total of 835 PECODR-related text extracts containing 41,263 individual text characters were identified from 20 EBM journal synopses. There were 759 extracts in the corresponding PubMed abstracts containing 31,947 characters. PECODR elements were found in nearly all abstracts and synopses with the exception of duration. There was agreement on 86.6% of the extracts from the 20 EBM synopses and 85.0% on the corresponding PubMed abstracts. After consensus this rose to 98.4% and 96.9% respectively. We found potential text patterns in the Comparison, Outcome and Results elements of both EBM synopses and PubMed abstracts. Some phrases and words are used frequently and are specific for these elements in both synopses and abstracts. Results suggest a PECODR-related structure exists in medical abstracts and that there might be lexical patterns specific to these elements. More sophisticated computer-assisted lexical-semantic analysis might refine these results, and pave the way to automating PECODR indexing, and improve information retrieval in primary care.

Enhanced Information Retrieval Using AJAX

NASA Astrophysics Data System (ADS)

Kachhwaha, Rajendra; Rajvanshi, Nitin

2010-11-01

Information Retrieval deals with the representation, storage, organization of, and access to information items. The representation and organization of information items should provide the user with easy access to the information with the rapid development of Internet, large amounts of digitally stored information is readily available on the World Wide Web. This information is so huge that it becomes increasingly difficult and time consuming for the users to find the information relevant to their needs. The explosive growth of information on the Internet has greatly increased the need for information retrieval systems. However, most of the search engines are using conventional information retrieval systems. An information system needs to implement sophisticated pattern matching tools to determine contents at a faster rate. AJAX has recently emerged as the new tool such the of information retrieval process of information retrieval can become fast and information reaches the use at a faster pace as compared to conventional retrieval systems.

Automatic cataloguing and characterization of Earth science data using SE-trees

NASA Technical Reports Server (NTRS)

Rymon, Ron; Short, Nicholas M., Jr.

1994-01-01

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC.

Automated encoding of clinical documents based on natural language processing.

PubMed

Friedman, Carol; Shagina, Lyudmila; Lussier, Yves; Hripcsak, George

2004-01-01

The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts. Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91. Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.

Automatic Recognition of Object Names in Literature

NASA Astrophysics Data System (ADS)

Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

2008-08-01

SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.

PubMed

García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

2013-01-01

Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results.

PubMed

De-Arteaga, Maria; Eggel, Ivan; Kahn, Charles E; Müller, Henning

2015-10-01

Log files of information retrieval systems that record user behavior have been used to improve the outcomes of retrieval systems, understand user behavior, and predict events. In this article, a log file of the ARRS GoldMiner search engine containing 222,005 consecutive queries is analyzed. Time stamps are available for each query, as well as masked IP addresses, which enables to identify queries from the same person. This article describes the ways in which physicians (or Internet searchers interested in medical images) search and proposes potential improvements by suggesting query modifications. For example, many queries contain only few terms and therefore are not specific; others contain spelling mistakes or non-medical terms that likely lead to poor or empty results. One of the goals of this report is to predict the number of results a query will have since such a model allows search engines to automatically propose query modifications in order to avoid result lists that are empty or too large. This prediction is made based on characteristics of the query terms themselves. Prediction of empty results has an accuracy above 88%, and thus can be used to automatically modify the query to avoid empty result sets for a user. The semantic analysis and data of reformulations done by users in the past can aid the development of better search systems, particularly to improve results for novice users. Therefore, this paper gives important ideas to better understand how people search and how to use this knowledge to improve the performance of specialized medical search engines.

A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis

NASA Astrophysics Data System (ADS)

Aiello, Martina; Gianinetto, Marco

2017-10-01

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.

Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network.

PubMed

Li, Shuqing; Sun, Ying; Soergel, Dagobert

2017-12-23

We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

Digitising legacy zoological taxonomic literature: Processes, products and using the output

PubMed Central

Lyal, Christopher H. C.

2016-01-01

Abstract By digitising legacy taxonomic literature using XML mark-up the contents become accessible to other taxonomic and nomenclatural information systems. Appropriate schemas need to be interoperable with other sectorial schemas, atomise to appropriate content elements and carry appropriate metadata to, for example, enable algorithmic assessment of availability of a name under the Code. Legacy (and new) literature delivered in this fashion will become part of a global taxonomic resource from which users can extract tailored content to meet their particular needs, be they nomenclatural, taxonomic, faunistic or other. To date, most digitisation of taxonomic literature has led to a more or less simple digital copy of a paper original – the output of the many efforts has effectively been an electronic copy of a traditional library. While this has increased accessibility of publications through internet access, the means by which many scientific papers are indexed and located is much the same as with traditional libraries. OCR and born-digital papers allow use of web search engines to locate instances of taxon names and other terms, but OCR efficiency in recognising taxonomic names is still relatively poor, people’s ability to use search engines effectively is mixed, and many papers cannot be searched directly. Instead of building digital analogues of traditional publications, we should consider what properties we require of future taxonomic information access. Ideally the content of each new digital publication should be accessible in the context of all previous published data, and the user able to retrieve nomenclatural, taxonomic and other data / information in the form required without having to scan all of the original papers and extract target content manually. This opens the door to dynamic linking of new content with extant systems: automatic population and updating of taxonomic catalogues, ZooBank and faunal lists, all descriptions of a taxon and its children instantly accessible with a single search, comparison of classifications used in different publications, and so on. A means to do this is through marking up content into XML, and the more atomised the mark-up the greater the possibilities for data retrieval and integration. Mark-up requires XML that accommodates the required content elements and is interoperable with other XML schemas, and there are now several written to do this, particularly TaxPub, taxonX and taXMLit, the last of these being the most atomised. We now need to automate this process as far as possible. Manual and automatic data and information retrieval is demonstrated by projects such as INOTAXA and Plazi. As we move to creating and using taxonomic products through the power of the internet, we need to ensure the output, while satisfying in its production the requirements of the Code, is fit for purpose in the future. PMID:26877659

Commercial imagery archive product development

NASA Astrophysics Data System (ADS)

Sakkas, Alysa

1999-12-01

The Lockheed Martin (LM) team had garnered over a decade of operational experience in digital imagery management and analysis for the US Government at numerous worldwide sites. Recently, it set out to create a new commercial product to serve the needs of large-scale imagery archiving and analysis markets worldwide. LM decided to provide a turnkey commercial solution to receive, store, retrieve, process, analyze and disseminate in 'push' or 'pull' modes components and adapted and developed its own algorithms to provide added functionality not commercially available elsewhere. The resultant product, Intelligent Library System, satisfies requirements for (a) a potentially unbounded, data archive automated workflow management for increased user productivity; (c) automatic tracking and management of files stored on shelves; (d) ability to ingest, process and disseminate data involves with bandwidths ranging up to multi-gigabit per second; (e) access through a thin client- to-server network environment; (f) multiple interactive users needing retrieval of filters in seconds from both archived images or in real time, and (g) scalability that maintains information throughput performance as the size of the digital library grows.

PubFinder: a tool for improving retrieval rate of relevant PubMed abstracts.

PubMed

Goetz, Thomas; von der Lieth, Claus-Wilhelm

2005-07-01

Since it is becoming increasingly laborious to manually extract useful information embedded in the ever-growing volumes of literature, automated intelligent text analysis tools are becoming more and more essential to assist in this task. PubFinder (www.glycosciences.de/tools/PubFinder) is a publicly available web tool designed to improve the retrieval rate of scientific abstracts relevant for a specific scientific topic. Only the selection of a representative set of abstracts is required, which are central for a scientific topic. No special knowledge concerning the query-syntax is necessary. Based on the selected abstracts, a list of discriminating words is automatically calculated, which is subsequently used for scoring all defined PubMed abstracts for their probability of belonging to the defined scientific topic. This results in a hit-list of references in the descending order of their likelihood score. The algorithms and procedures implemented in PubFinder facilitate the perpetual task for every scientist of staying up-to-date with current publications dealing with a specific subject in biomedicine.

Category cued recall evokes a generate-recognize retrieval process.

PubMed

Hunt, R Reed; Smith, Rebekah E; Toth, Jeffrey P

2016-03-01

The experiments reported here were designed to replicate and extend McCabe, Roediger, and Karpicke's (2011) finding that retrieval in category cued recall involves both controlled and automatic processes. The extension entailed identifying whether distinctive encoding affected 1 or both of these 2 processes. The first experiment successfully replicated McCabe et al., but the second, which added a critical baseline condition, produced data inconsistent with a 2 independent process model of recall. The third experiment provided evidence that retrieval in category cued recall reflects a generate-recognize strategy, with the effect of distinctive processing being localized to recognition. Overall, the data suggest that category cued recall evokes a generate-recognize retrieval strategy and that the subprocesses underlying this strategy can be dissociated as a function of distinctive versus relational encoding processes. (c) 2016 APA, all rights reserved).

Category Cued Recall Evokes a Generate-Recognize Retrieval Process

PubMed Central

Hunt, R. Reed; Smith, Rebekah E.; Toth, Jeffrey P.

2015-01-01

The experiments reported here were designed to replicate and extend McCabe, Roediger, and Karpicke’s (2011) finding that retrieval in category cued recall involves both controlled and automatic processes. The extension entailed identifying whether distinctive encoding affected one or both of these two processes. The first experiment successfully replicated McCabe et al., but the second, which added a critical baseline condition, produced data inconsistent with a two independent process model of recall. The third experiment provided evidence that retrieval in category cued recall reflects a generate-recognize strategy, with the effect of distinctive processing being localized to recognition. Overall, the data suggest that category cued recall evokes a generate-recognize retrieval strategy and that the sub-processes underlying this strategy can be dissociated as a function of distinctive versus relational encoding processes. PMID:26280355

Strategic development in exact calculation: group and individual differences in four achievement subtypes.

PubMed

Wylie, Judith; Jordan, Julie-Ann; Mulhern, Gerry

2012-09-01

This longitudinal study sought to identify developmental changes in strategy use between 5 and 7 years of age when solving exact calculation problems. Four mathematics and reading achievement subtypes were examined at four time points. Five strategies were considered: finger counting, verbal counting, delayed retrieval, automatic retrieval, and derived fact retrieval. Results provided unique insights into children's strategic development in exact calculation at this early stage. Group analysis revealed relationships between mathematical and/or reading difficulties and strategy choice, shift, and adaptiveness. Use of derived fact retrieval by 7 years of age distinguished children with mathematical difficulties from other achievement subtypes. Analysis of individual differences revealed marked heterogeneity within all subtypes, suggesting (inter alia) no marked qualitative distinction between our two mathematical difficulty subtypes. Copyright © 2012 Elsevier Inc. All rights reserved.

Query-by-example surgical activity detection.

PubMed

Gao, Yixin; Vedula, S Swaroop; Lee, Gyusung I; Lee, Mija R; Khudanpur, Sanjeev; Hager, Gregory D

2016-06-01

Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for surgical activity segments of interest is limited by the need for manual pre-processing, which can be prohibitive at scale. We developed a content-based information retrieval method, query-by-example (QBE), to automatically detect activity segments within surgical data recordings of long duration that match a query. The example segment of interest (query) and the surgical data recording (target trial) are time series of kinematics. Our approach includes an unsupervised feature learning module using a stacked denoising autoencoder (SDAE), two scoring modules based on asymmetric subsequence dynamic time warping (AS-DTW) and template matching, respectively, and a detection module. A distance matrix of the query against the trial is computed using the SDAE features, followed by AS-DTW combined with template scoring, to generate a ranked list of candidate subsequences (substrings). To evaluate the quality of the ranked list against the ground-truth, thresholding conventional DTW distances and bipartite matching are applied. We computed the recall, precision, F1-score, and a Jaccard index-based score on three experimental setups. We evaluated our QBE method using a suture throw maneuver as the query, on two tool motion datasets (JIGSAWS and MISTIC-SL) captured in a training laboratory. We observed a recall of 93, 90 and 87 % and a precision of 93, 91, and 88 % with same surgeon same trial (SSST), same surgeon different trial (SSDT) and different surgeon (DS) experiment setups on JIGSAWS, and a recall of 87, 81 and 75 % and a precision of 72, 61, and 53 % with SSST, SSDT and DS experiment setups on MISTIC-SL, respectively. We developed a novel, content-based information retrieval method to automatically detect multiple instances of an activity within long surgical recordings. Our method demonstrated adequate recall across different complexity datasets and experimental conditions.

ShakeCast Manual

USGS Publications Warehouse

Lin, Kuo-Wan; Wald, David J.

2008-01-01

ShakeCast is a freely available, post-earthquake situational awareness application that automatically retrieves earthquake shaking data from ShakeMap, compares intensity measures against users? facilities, and generates potential damage assessment notifications, facility damage maps, and other Web-based products for emergency managers and responders.

Activities of information retrieval in Daicel Corporation : The roles and efforts of information retrieval team

NASA Astrophysics Data System (ADS)

Yamazaki, Towako

In order to stabilize and improve quality of information retrieval service, the information retrieval team of Daicel Corporation has given some efforts on standard operating procedures, interview sheet for information retrieval, structured format for search report, and search expressions for some technological fields of Daicel. These activities and efforts will also lead to skill sharing and skill tradition between searchers. In addition, skill improvements are needed not only for a searcher individually, but also for the information retrieval team totally when playing searcher's new roles.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Retrieving and routing quantum information in a quantum network

NASA Astrophysics Data System (ADS)

Sazim, S.; Chiranjeevi, V.; Chakrabarty, I.; Srinathan, K.

2015-12-01

In extant quantum secret sharing protocols, once the secret is shared in a quantum network ( qnet) it cannot be retrieved, even if the dealer wishes that his/her secret no longer be available in the network. For instance, if the dealer is part of the two qnets, say {{Q}}_1 and {{Q}}_2 and he/she subsequently finds that {{Q}}_2 is more reliable than {{Q}}_1, he/she may wish to transfer all her secrets from {{Q}}_1 to {{Q}}_2. Known protocols are inadequate to address such a revocation. In this work we address this problem by designing a protocol that enables the source/dealer to bring back the information shared in the network, if desired. Unlike classical revocation, the no-cloning theorem automatically ensures that the secret is no longer shared in the network. The implications of our results are multi-fold. One interesting implication of our technique is the possibility of routing qubits in asynchronous qnets. By asynchrony we mean that the requisite data/resources are intermittently available (but not necessarily simultaneously) in the qnet. For example, we show that a source S can send quantum information to a destination R even though (a) S and R share no quantum resource, (b) R's identity is unknown to S at the time of sending the message, but is subsequently decided, (c) S herself can be R at a later date and/or in a different location to bequeath her information (`backed-up' in the qnet) and (d) importantly, the path chosen for routing the secret may hit a dead end due to resource constraints, congestion, etc., (therefore the information needs to be back-tracked and sent along an alternate path). Another implication of our technique is the possibility of using insecure resources. For instance, if the quantum memory within an organization is insufficient, it may safely store (using our protocol) its private information with a neighboring organization without (a) revealing critical data to the host and (b) losing control over retrieving the data. Putting the two implications together, namely routing and secure storage, it is possible to envision applications like quantum mail (qmail) as an outsourced service.

Multi-terminology indexing for the assignment of MeSH descriptors to medical abstracts in French.

PubMed

Pereira, Suzanne; Sakji, Saoussen; Névéol, Aurélie; Kergourlay, Ivan; Kerdelhué, Gaétan; Serrot, Elisabeth; Joubert, Michel; Darmoni, Stéfan J

2009-11-14

To facilitate information retrieval in the biomedical domain, a system for the automatic assignment of Medical Subject Headings to documents curated by an online quality-controlled health gateway was implemented. The French Multi-Terminology Indexer (F-MTI) implements a multiterminology approach using nine main medical terminologies in French and the mappings between them. This paper presents recent efforts to assess the added value of (a) integrating four new terminologies (Orphanet, ATC, drug names, MeSH supplementary concepts) into F-MTI's knowledge sources and (b) performing the automatic indexing on the titles and abstracts (vs. title only) of the online health resources. F-MTI was evaluated on a CISMeF corpus comprising 18,161 manually indexed resources. The performance of F-MTI including nine health terminologies on CISMeF resources with Title only was 27.9% precision and 19.7% recall, while the performance on CISMeF resources with Title and Abstract is 14.9 % precision (-13.0%) and 25.9% recall (+6.2%). In a few weeks, CISMeF will launch the indexing of resources based on title and abstract, using nine terminologies.

Electro-Optical Inspection For Tolerance Control As An Integral Part Of A Flexible Machining Cell

NASA Astrophysics Data System (ADS)

Renaud, Blaise

1986-11-01

Institut CERAC has been involved in optical metrology and 3-dimensional surface control for the last couple of years. Among the industrial applications considered is the on-line shape evaluation of machined parts within the manufacturing cell. The specific objective is to measure the machining errors and to compare them with the tolerances set by designers. An electro-optical sensing technique has been developed which relies on a projection Moire contouring optical method. A prototype inspection system has been designed, making use of video detection and computer image processing. Moire interferograms are interpreted, and the metrological information automatically retrieved. A structured database can be generated for subsequent data analysis and for real-time closed-loop corrective actions. A real-time kernel embedded into a synchronisation network (Petri-net) for the control of concurrent processes in the Electra-Optical Inspection (E0I) station was realised and implemented in a MODULA-2 program DIN01. The prototype system for on-line automatic tolerance control taking place within a flexible machining cell is described in this paper, together with the fast-prototype synchronisation program.

Data Visualization in Information Retrieval and Data Mining (SIG VIS).

ERIC Educational Resources Information Center

Efthimiadis, Efthimis

2000-01-01

Presents abstracts that discuss using data visualization for information retrieval and data mining, including immersive information space and spatial metaphors; spatial data using multi-dimensional matrices with maps; TREC (Text Retrieval Conference) experiments; users' information needs in cartographic information retrieval; and users' relevance…

A semi-automated method for the detection of seismic anisotropy at depth via receiver function analysis

NASA Astrophysics Data System (ADS)

Licciardi, A.; Piana Agostinetti, N.

2016-06-01

Information about seismic anisotropy is embedded in the variation of the amplitude of the Ps pulses as a function of the azimuth, on both the Radial and the Transverse components of teleseismic receiver functions (RF). We develop a semi-automatic method to constrain the presence and the depth of anisotropic layers beneath a single seismic broad-band station. An algorithm is specifically designed to avoid trial and error methods and subjective crustal parametrizations in RF inversions, providing a suitable tool for large-size data set analysis. The algorithm couples together information extracted from a 1-D VS profile and from a harmonic decomposition analysis of the RF data set. This information is used to determine the number of anisotropic layers and their approximate position at depth, which, in turn, can be used to, for example, narrow the search boundaries for layer thickness and S-wave velocity in a subsequent parameter space search. Here, the output of the algorithm is used to invert an RF data set by means of the Neighbourhood Algorithm (NA). To test our methodology, we apply the algorithm to both synthetic and observed data. We make use of synthetic RF with correlated Gaussian noise to investigate the resolution power for multiple and thin (1-3 km) anisotropic layers in the crust. The algorithm successfully identifies the number and position of anisotropic layers at depth prior the NA inversion step. In the NA inversion, strength of anisotropy and orientation of the symmetry axis are correctly retrieved. Then, the method is applied to field measurement from station BUDO in the Tibetan Plateau. Two consecutive layers of anisotropy are automatically identified with our method in the first 25-30 km of the crust. The data are then inverted with the retrieved parametrization. The direction of the anisotropic axis in the uppermost layer correlates well with the orientation of the major planar structure in the area. The deeper anisotropic layer is associated with an older phase of crustal deformation. Our results are compared with previous anisotropic RF studies at the same station, showing strong similarities.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Connectionist Interaction Information Retrieval.

ERIC Educational Resources Information Center

Dominich, Sandor

2003-01-01

Discussion of connectionist views for adaptive clustering in information retrieval focuses on a connectionist clustering technique and activation spreading-based information retrieval model using the interaction information retrieval method. Presents theoretical as well as simulation results as regards computational complexity and includes…

Adoption of health information exchange by emergency physicians at three urban academic medical centers.

PubMed

Genes, N; Shapiro, J; Vaidya, S; Kuperman, G

2011-01-01

Emergency physicians are trained to make decisions quickly and with limited patient information. Health Information Exchange (HIE) has the potential to improve emergency care by bringing relevant patient data from non-affiliated organizations to the bedside. NYCLIX (New York CLinical Information eXchange) offers HIE functionality among multiple New York metropolitan area provider organizations and has pilot users in several member emergency departments (EDs). We conducted semi-structured interviews at three participating EDs with emergency physicians trained to use NYCLIX. Among "users" with > 1 login, responses to questions regarding typical usage scenarios, successful retrieval of data, and areas for improving the interface were recorded. Among "non-users" with ≤1 login, questions about NYCLIX accessibility and utility were asked. Both groups were asked to recall items from prior training regarding data sources and availability. Eighteen NYCLIX pilot users, all board certified emergency physicians, were interviewed. Of the 14 physicians with more than one login ,half estimated successful retrieval of HIE data affecting patient care. Four non-users (one login or less) cited forgotten login information as a major reason for non-use. Though both groups made errors, users were more likely to recall true NYCLIX member sites and data elements than non-users. Improvements suggested as likely to facilitate usage included a single automated login to both the ED information system (EDIS) and HIE, and automatic notification of HIE data availability in the EDIS All respondents reported satisfaction with their training. Integrating HIE into existing ED workflows remains a challenge, though a substantial fraction of users report changes in management based on HIE data. Though interviewees believed their training was adequate, significant errors in their understanding of available NYCLIX data elements and participating sites persist.

Privacy information management for video surveillance

NASA Astrophysics Data System (ADS)

Luo, Ying; Cheung, Sen-ching S.

2013-05-01

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been proposed to automatically redact images of trusted individuals in the surveillance video. To identify these individuals for protection, the most reliable approach is to use biometric signals such as iris patterns as they are immutable and highly discriminative. In this paper, we propose a privacy data management system to be used in a privacy-aware video surveillance system. The privacy status of a subject is anonymously determined based on her iris pattern. For a trusted subject, the surveillance video is redacted and the original imagery is considered to be the privacy information. Our proposed system allows a subject to access her privacy information via the same biometric signal for privacy status determination. Two secure protocols, one for privacy information encryption and the other for privacy information retrieval are proposed. Error control coding is used to cope with the variability in iris patterns and efficient implementation is achieved using surrogate data records. Experimental results on a public iris biometric database demonstrate the validity of our framework.

Image and information management system

NASA Technical Reports Server (NTRS)

Robertson, Tina L. (Inventor); Raney, Michael C. (Inventor); Dougherty, Dennis M. (Inventor); Kent, Peter C. (Inventor); Brucker, Russell X. (Inventor); Lampert, Daryl A. (Inventor)

2009-01-01

A system and methods through which pictorial views of an object's configuration, arranged in a hierarchical fashion, are navigated by a person to establish a visual context within the configuration. The visual context is automatically translated by the system into a set of search parameters driving retrieval of structured data and content (images, documents, multimedia, etc.) associated with the specific context. The system places ''hot spots'', or actionable regions, on various portions of the pictorials representing the object. When a user interacts with an actionable region, a more detailed pictorial from the hierarchy is presented representing that portion of the object, along with real-time feedback in the form of a popup pane containing information about that region, and counts-by-type reflecting the number of items that are available within the system associated with the specific context and search filters established at that point in time.

Image and information management system

NASA Technical Reports Server (NTRS)

Robertson, Tina L. (Inventor); Kent, Peter C. (Inventor); Raney, Michael C. (Inventor); Dougherty, Dennis M. (Inventor); Brucker, Russell X. (Inventor); Lampert, Daryl A. (Inventor)

2007-01-01

A system and methods through which pictorial views of an object's configuration, arranged in a hierarchical fashion, are navigated by a person to establish a visual context within the configuration. The visual context is automatically translated by the system into a set of search parameters driving retrieval of structured data and content (images, documents, multimedia, etc.) associated with the specific context. The system places hot spots, or actionable regions, on various portions of the pictorials representing the object. When a user interacts with an actionable region, a more detailed pictorial from the hierarchy is presented representing that portion of the object, along with real-time feedback in the form of a popup pane containing information about that region, and counts-by-type reflecting the number of items that are available within the system associated with the specific context and search filters established at that point in time.

NASA Indexing Benchmarks: Evaluating Text Search Engines

NASA Technical Reports Server (NTRS)

Esler, Sandra L.; Nelson, Michael L.

1997-01-01

The current proliferation of on-line information resources underscores the requirement for the ability to index collections of information and search and retrieve them in a convenient manner. This study develops criteria for analytically comparing the index and search engines and presents results for a number of freely available search engines. A product of this research is a toolkit capable of automatically indexing, searching, and extracting performance statistics from each of the focused search engines. This toolkit is highly configurable and has the ability to run these benchmark tests against other engines as well. Results demonstrate that the tested search engines can be grouped into two levels. Level one engines are efficient on small to medium sized data collections, but show weaknesses when used for collections 100MB or larger. Level two search engines are recommended for data collections up to and beyond 100MB.

JURASSIC Retrieval Processing

NASA Astrophysics Data System (ADS)

Blank, J.; Ungermann, J.; Guggenmoser, T.; Kaufmann, M.; Riese, M.

2012-04-01

The Gimballed Limb Observer for Radiance Imaging in the Atmosphere (GLORIA) is an aircraft based infrared limb-sounder. This presentation will give an overview of the retrieval techniques used for the analysis of data produced by the GLORIA instrument. For data processing, the JUelich RApid Spectral SImulation Code 2 (JURASSIC2) was developed. It consists of a set of programs to retrieve atmospheric profiles from GLORIA measurements. The GLORIA Michelson interferometer can run with a wide range of parameters. In the dynamics mode, spectra are generate with a medium spectral and a very high temporal and spatial resolution. Each sample can contain thousands of spectral lines for each contributing trace gas. In the JURASSIC retrieval code this is handled by using a radiative transport model based on the Emissivity Growth Approximation. Deciding which samples should be included in the retrieval is a non-trivial task and requires specific domain knowledge. To ease this problem we developed an automatic selection program by analysing the Shannon information content. By taking into account data for all relevant trace gases and instrument effects, optimal integrated spectral windows are computed. This includes considerations for cross-influence of trace gases, which has non-obvious consequence for the contribution of spectral samples. We developed methods to assess the influence of spectral windows on the retrieval. While we can not exhaustively search the whole range of possible spectral sample combinations, it is possible to optimize information content using a genetic algorithm. The GLORIA instrument is mounted with a viewing direction perpendicular to the flight direction. A gimbal frame makes it possible to move the instrument 45° to both direction. By flying on a circular path, it is possible to generate images of an area of interest from a wide range of angles. These can be analyzed in a 3D-tomographic fashion, which yields superior spatial resolution along line of site. Usually limb instruments have a resolution of several hundred kilometers. In studies we have shown to get a resolution of 35km in all horizontal directions. Even when only linear flight patterns can be realized, resolutions of ≈70km can be obtained. This technique can be used to observe features of the Upper Troposphere Lower Stratosphere (UTLS), where important mixing processes take place. Especially tropopause folds are difficult to image, as their main features need to be along line of flight when using common 1D approach.

Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

ERIC Educational Resources Information Center

Griffiths, Alan; And Others

1984-01-01

Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

Overview of the gene ontology task at BioCreative IV.

PubMed

Mao, Yuqing; Van Auken, Kimberly; Li, Donghui; Arighi, Cecilia N; McQuilton, Peter; Hayman, G Thomas; Tweedie, Susan; Schaeffer, Mary L; Laulederkind, Stanley J F; Wang, Shur-Jen; Gobeill, Julien; Ruch, Patrick; Luu, Anh Tuan; Kim, Jung-Jae; Chiang, Jung-Hsien; Chen, Yu-De; Yang, Chia-Jung; Liu, Hongfang; Zhu, Dongqing; Li, Yanpeng; Yu, Hong; Emadzadeh, Ehsan; Gonzalez, Graciela; Chen, Jian-Ming; Dai, Hong-Jie; Lu, Zhiyong

2014-01-01

Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

PubMed

Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

2013-01-01

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

Affective Video Retrieval: Violence Detection in Hollywood Movies by Large-Scale Segmental Feature Extraction

PubMed Central

Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

2013-01-01

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology “out of the lab” to real-world, diverse data. In this contribution, we address the problem of finding “disturbing” scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis. PMID:24391704

ERP evidence for flexible adjustment of retrieval orientation and its influence on familiarity.

PubMed

Ecker, Ullrich K H; Zimmer, Hubert D

2009-10-01

The assumption was tested that familiarity memory as indexed by a mid-frontal ERP old-new effect is modulated by retrieval orientation. A randomly cued category-based versus exemplar-specific recognition memory test, requiring flexible adjustment of retrieval orientation, was conducted. Results show that the mid-frontal ERP old-new effect is sensitive to the manipulation of study-test congruency-that is, whether the same object is repeated identically or a different category exemplar is presented at test. Importantly, the effect pattern depends on subjects' retrieval orientation. With a specific orientation, only same items elicited an early old-new effect (same > different = new), whereas in the general condition, the old-new effect was graded (same > different > new). This supports the view that both perceptual and conceptual processes can contribute to familiarity memory and demonstrates that the rather automatic process of familiarity is not only data driven but influenced by top-down retrieval orientation, which subjects are able to adjust on a flexible basis.

Attentional requirements for the selection of words from different grammatical categories.

PubMed

Ayora, Pauline; Janssen, Niels; Dell'acqua, Roberto; Alario, F-Xavier

2009-09-01

Two grammatical classes are commonly distinguished in psycholinguistic research. The open-class includes content words such as nouns, whereas the closed-class includes function words such as determiners. A standing issue is to identify whether these words are retrieved through similar or distinct selection mechanisms. We report a comparative investigation of the allocation of attentional resources during the retrieval of words from these 2 classes. Previous studies used a psychological-refractory-period paradigm to establish that open-class word retrieval is supported by central attention mechanisms. We applied the same logic to closed-class word retrieval. French native speakers named pictures with determiner noun phrases while they concurrently identified the pitch of an auditory tone. The ease of noun and determiner retrieval was manipulated independently. Results showed that both manipulations affected picture naming and tone discrimination responses in similar ways. This suggests the involvement of central attentional resources in word production, irrespective of word class. These results argue against the commonly held hypothesis that closed-class retrieval is an automatic consequence of syntactic specific processes. (c) 2009 APA, all rights reserved.

Issues in the design of a pilot concept-based query interface for the neuroinformatics information framework.

PubMed

Marenco, Luis; Li, Yuli; Martone, Maryann E; Sternberg, Paul W; Shepherd, Gordon M; Miller, Perry L

2008-09-01

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

Picture archiving and communication system--Part one: Filmless radiology and distance radiology.

PubMed

De Backer, A I; Mortelé, K J; De Keulenaer, B L

2004-01-01

Picture archiving and communication system (PACS) is a collection of technologies used to carry out digital medical imaging. PACS is used to digitally acquire medical images from the various modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and digital projection radiography. The image data and pertinent information are transmitted to other and possibly remote locations over networks, where they may be displayed on computer workstations for soft copy viewing in multiple locations, thus permitting simultaneous consultations and almost instant reporting from radiologists at a distance. Data are secured and archived on digital media such as optical disks or tape, and may be automatically retrieved as necessary. Close integration with the hospital information system (HIS)--radiology information system (RIS) is critical for system functionality. Medical image management systems are maturing, providing access outside of the radiology department to images throughout the hospital via the Ethernet, at different hospitals, or from a home workstation if teleradiology has been implemented.

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

PubMed Central

Li, Yuli; Martone, Maryann E.; Sternberg, Paul W.; Shepherd, Gordon M.; Miller, Perry L.

2009-01-01

This paper describes a pilot query interface that has been constructed to help us explore a “concept-based” approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface. PMID:18953674

Listen up, eye movements play a role in verbal memory retrieval.

PubMed

Scholz, Agnes; Mehlhorn, Katja; Krems, Josef F

2016-01-01

People fixate on blank spaces if visual stimuli previously occupied these regions of space. This so-called "looking at nothing" (LAN) phenomenon is said to be a part of information retrieval from internal memory representations, but the exact nature of the relationship between LAN and memory retrieval is unclear. While evidence exists for an influence of LAN on memory retrieval for visuospatial stimuli, evidence for verbal information is mixed. Here, we tested the relationship between LAN behavior and memory retrieval in an episodic retrieval task where verbal information was presented auditorily during encoding. When participants were allowed to gaze freely during subsequent memory retrieval, LAN occurred, and it was stronger for correct than for incorrect responses. When eye movements were manipulated during memory retrieval, retrieval performance was higher when participants fixated on the area associated with to-be-retrieved information than when fixating on another area. Our results provide evidence for a functional relationship between LAN and memory retrieval that extends to verbal information.

Topological Aspects of Information Retrieval.

ERIC Educational Resources Information Center

Egghe, Leo; Rousseau, Ronald

1998-01-01

Discusses topological aspects of theoretical information retrieval, including retrieval topology; similarity topology; pseudo-metric topology; document spaces as topological spaces; Boolean information retrieval as a subsystem of any topological system; and proofs of theorems. (LRW)

Partial Automation of Requirements Tracing

NASA Technical Reports Server (NTRS)

Hayes, Jane; Dekhtyar, Alex; Sundaram, Senthil; Vadlamudi, Sravanthi

2006-01-01

Requirements Tracing on Target (RETRO) is software for after-the-fact tracing of textual requirements to support independent verification and validation of software. RETRO applies one of three user-selectable information-retrieval techniques: (1) term frequency/inverse document frequency (TF/IDF) vector retrieval, (2) TF/IDF vector retrieval with simple thesaurus, or (3) keyword extraction. One component of RETRO is the graphical user interface (GUI) for use in initiating a requirements-tracing project (a pair of artifacts to be traced to each other, such as a requirements spec and a design spec). Once the artifacts have been specified and the IR technique chosen, another component constructs a representation of the artifact elements and stores it on disk. Next, the IR technique is used to produce a first list of candidate links (potential matches between the two artifact levels). This list, encoded in Extensible Markup Language (XML), is optionally processed by a filtering component designed to make the list somewhat smaller without sacrificing accuracy. Through the GUI, the user examines a number of links and returns decisions (yes, these are links; no, these are not links). Coded in XML, these decisions are provided to a "feedback processor" component that prepares the data for the next application of the IR technique. The feedback reduces the incidence of erroneous candidate links. Unlike related prior software, RETRO does not require the user to assign keywords, and automatically builds a document index.

Achievements in optical data storage and retrieval

NASA Technical Reports Server (NTRS)

Nelson, R. H.; Shuman, C. A.

1977-01-01

The present paper deals with the current achievements in two technology efforts, one of which is a wideband holographic recorder which uses multichannel recording of data in the form of holograms on roll film for storage and retrieval of large unit records at hundreds of megabit per second. The second effort involves a system (termed DIGIMEN) which uses binary spot recording on photographic film in the form of microfiche to provide a mass storage capability with automatic computer-controlled random access to stored records. Some potential design improvements are noted.

Information Retrieval in Biomedical Research: From Articles to Datasets

ERIC Educational Resources Information Center

Wei, Wei

2017-01-01

Information retrieval techniques have been applied to biomedical research for a variety of purposes, such as textual document retrieval and molecular data retrieval. As biomedical research evolves over time, information retrieval is also constantly facing new challenges, including the growing number of available data, the emerging new data types,…

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

Information content of thermal infrared a microwave bands for simultaneous retrieval of cirrus ice water path and particle effective diameter

NASA Astrophysics Data System (ADS)

Bell, A.; Tang, G.; Yang, P.; Wu, D.

2017-12-01

Due to their high spatial and temporal coverage, cirrus clouds have a profound role in regulating the Earth's energy budget. Variability of their radiative, geometric, and microphysical properties can pose significant uncertainties in global climate model simulations if not adequately constrained. Thus, the development of retrieval methodologies able to accurately retrieve ice cloud properties and present associated uncertainties is essential. The effectiveness of cirrus cloud retrievals relies on accurate a priori understanding of ice radiative properties, as well as the current state of the atmosphere. Current studies have implemented information content theory analyses prior to retrievals to quantify the amount of information that should be expected on parameters to be retrieved, as well as the relative contribution of information provided by certain measurement channels. Through this analysis, retrieval algorithms can be designed in a way to maximize the information in measurements, and therefore ensure enough information is present to retrieve ice cloud properties. In this study, we present such an information content analysis to quantify the amount of information to be expected in retrievals of cirrus ice water path and particle effective diameter using sub-millimeter and thermal infrared radiometry. Preliminary results show these bands to be sensitive to changes in ice water path and effective diameter, and thus lend confidence their ability to simultaneously retrieve these parameters. Further quantification of sensitivity and the information provided from these bands can then be used to design and optimal retrieval scheme. While this information content analysis is employed on a theoretical retrieval combining simulated radiance measurements, the methodology could in general be applicable to any instrument or retrieval approach.

Sentence-Level Attachment Prediction

NASA Astrophysics Data System (ADS)

Albakour, M.-Dyaa; Kruschwitz, Udo; Lucas, Simon

Attachment prediction is the task of automatically identifying email messages that should contain an attachment. This can be useful to tackle the problem of sending out emails but forgetting to include the relevant attachment (something that happens all too often). A common Information Retrieval (IR) approach in analyzing documents such as emails is to treat the entire document as a bag of words. Here we propose a finer-grained analysis to address the problem. We aim at identifying individual sentences within an email that refer to an attachment. If we detect any such sentence, we predict that the email should have an attachment. Using part of the Enron corpus for evaluation we find that our finer-grained approach outperforms previously reported document-level attachment prediction in similar evaluation settings.

Tell it like it is.

PubMed

Lee, S L

2000-05-01

Nurses, therapists and case managers were spending too much time each week on the phone waiting to read patient reports to live transcriptionists who would then type the reports for storage in VNSNY's clinical management mainframe database. A speech recognition system helped solve the problem by providing the staff 24-hour access to an automated transcription service any day of the week. Nurses and case managers no longer wait in long queues to transmit patient reports or to retrieve information from the database. Everything is done automatically within minutes. VNSNY saved both time and money by updating its transcription strategy. Now nurses can spend more time with patients and less time on the phone transcribing notes. It also means fewer staff members are needed on weekends to do manual transcribing.

15 CFR 950.9 - Computerized Environmental Data and Information Retrieval Service.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Information Retrieval Service. 950.9 Section 950.9 Commerce and Foreign Trade Regulations Relating to Commerce... Computerized Environmental Data and Information Retrieval Service. The Environmental Data Index (ENDEX... computerized, information retrieval service provides a parallel subject-author-abstract referral service. A...

15 CFR 950.9 - Computerized Environmental Data and Information Retrieval Service.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Information Retrieval Service. 950.9 Section 950.9 Commerce and Foreign Trade Regulations Relating to Commerce... Computerized Environmental Data and Information Retrieval Service. The Environmental Data Index (ENDEX... computerized, information retrieval service provides a parallel subject-author-abstract referral service. A...

15 CFR 950.9 - Computerized Environmental Data and Information Retrieval Service.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Information Retrieval Service. 950.9 Section 950.9 Commerce and Foreign Trade Regulations Relating to Commerce... Computerized Environmental Data and Information Retrieval Service. The Environmental Data Index (ENDEX... computerized, information retrieval service provides a parallel subject-author-abstract referral service. A...

15 CFR 950.9 - Computerized Environmental Data and Information Retrieval Service.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Information Retrieval Service. 950.9 Section 950.9 Commerce and Foreign Trade Regulations Relating to Commerce... Computerized Environmental Data and Information Retrieval Service. The Environmental Data Index (ENDEX... computerized, information retrieval service provides a parallel subject-author-abstract referral service. A...

15 CFR 950.9 - Computerized Environmental Data and Information Retrieval Service.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Information Retrieval Service. 950.9 Section 950.9 Commerce and Foreign Trade Regulations Relating to Commerce... Computerized Environmental Data and Information Retrieval Service. The Environmental Data Index (ENDEX... computerized, information retrieval service provides a parallel subject-author-abstract referral service. A...

Quick multitemporal approach to get cloudless improved multispectral imagery for large geographical areas

NASA Astrophysics Data System (ADS)

Colaninno, Nicola; Marambio Castillo, Alejandro; Roca Cladera, Josep

2017-10-01

The demand for remotely sensed data is growing increasingly, due to the possibility of managing information about huge geographic areas, in digital format, at different time periods, and suitable for analysis in GIS platforms. However, primary satellite information is not such immediate as desirable. Beside geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical images. In terms of land cover, such a contamination is intended as missing information and should be replaced. Generally, image reconstruction is classified according to three main approaches, i.e. in-painting-based, multispectral-based, and multitemporal-based methods. This work relies on a multitemporal-based approach to retrieve uncontaminated pixels for an image scene. We explore an automatic method for quickly getting daytime cloudless and shadow-free image at moderate spatial resolution for large geographical areas. The process expects two main steps: a multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase, based on automatic selection of uncontaminated pixels from an image stack. The result is a composite image based on middle values of the stack, over a year. The assumption is that, for specific purposes, land cover changes at a coarse scale are not significant over relatively short time periods. Because it is largely recognized that satellite imagery along tropical areas are generally strongly affected by clouds, the methodology is tested for the case study of the Dominican Republic at the year 2015; while Landsat 8 imagery are employed to test the approach.

An approach to 3D model fusion in GIS systems and its application in a future ECDIS

NASA Astrophysics Data System (ADS)

Liu, Tao; Zhao, Depeng; Pan, Mingyang

2016-04-01

Three-dimensional (3D) computer graphics technology is widely used in various areas and causes profound changes. As an information carrier, 3D models are becoming increasingly important. The use of 3D models greatly helps to improve the cartographic expression and design. 3D models are more visually efficient, quicker and easier to understand and they can express more detailed geographical information. However, it is hard to efficiently and precisely fuse 3D models in local systems. The purpose of this study is to propose an automatic and precise approach to fuse 3D models in geographic information systems (GIS). It is the basic premise for subsequent uses of 3D models in local systems, such as attribute searching, spatial analysis, and so on. The basic steps of our research are: (1) pose adjustment by principal component analysis (PCA); (2) silhouette extraction by simple mesh silhouette extraction and silhouette merger; (3) size adjustment; (4) position matching. Finally, we implement the above methods in our system Automotive Intelligent Chart (AIC) 3D Electronic Chart Display and Information Systems (ECDIS). The fusion approach we propose is a common method and each calculation step is carefully designed. This approach solves the problem of cross-platform model fusion. 3D models can be from any source. They may be stored in the local cache or retrieved from Internet, or may be manually created by different tools or automatically generated by different programs. The system can be any kind of 3D GIS system.

45 CFR 205.35 - Mechanized claims processing and information retrieval systems; definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... claims processing and information retrieval systems; definitions. Section 205.35 through 205.38 contain...: (a) A mechanized claims processing and information retrieval system, hereafter referred to as an automated application processing and information retrieval system (APIRS), or the system, means a system of...

45 CFR 205.35 - Mechanized claims processing and information retrieval systems; definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... claims processing and information retrieval systems; definitions. Section 205.35 through 205.38 contain...: (a) A mechanized claims processing and information retrieval system, hereafter referred to as an automated application processing and information retrieval system (APIRS), or the system, means a system of...

Graph-Based Interactive Bibliographic Information Retrieval Systems

ERIC Educational Resources Information Center

Zhu, Yongjun

2017-01-01

In the big data era, we have witnessed the explosion of scholarly literature. This explosion has imposed challenges to the retrieval of bibliographic information. Retrieval of intended bibliographic information has become challenging due to the overwhelming search results returned by bibliographic information retrieval systems for given input…

45 CFR 205.35 - Mechanized claims processing and information retrieval systems; definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... claims processing and information retrieval systems; definitions. Section 205.35 through 205.38 contain...: (a) A mechanized claims processing and information retrieval system, hereafter referred to as an automated application processing and information retrieval system (APIRS), or the system, means a system of...

21 CFR 1300.01 - Definitions relating to controlled substances.

Code of Federal Regulations, 2012 CFR

2012-04-01

... substances having a depressant effect on the central nervous system, including its salts, isomers, and salts... paragraph. Automated dispensing system means a mechanical system that performs operations or activities... 1305.06. Readily retrievable means that certain records are kept by automatic data processing systems...

21 CFR 1300.01 - Definitions relating to controlled substances.

Code of Federal Regulations, 2013 CFR

2013-04-01

... substances having a depressant effect on the central nervous system, including its salts, isomers, and salts... paragraph. Automated dispensing system means a mechanical system that performs operations or activities... 1305.06. Readily retrievable means that certain records are kept by automatic data processing systems...

21 CFR 1300.01 - Definitions relating to controlled substances.

Code of Federal Regulations, 2014 CFR

2014-04-01

... substances having a depressant effect on the central nervous system, including its salts, isomers, and salts... paragraph. Automated dispensing system means a mechanical system that performs operations or activities... 1305.06. Readily retrievable means that certain records are kept by automatic data processing systems...

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... (or controlling) authority for the enterprise identifier. Item means a single hardware article or a...-readable means an automatic identification technology media, such as bar codes, contact memory buttons...

Term Relevance Weights in On-Line Information Retrieval

ERIC Educational Resources Information Center

Salton, G.; Waldstein, R. K.

1978-01-01

Term relevance weighting systems in interactive information retrieval are reviewed. An experiment in which information retrieval users ranked query terms in decreasing order of presumed importance prior to actual search and retrieval is described. (Author/KP)

42 CFR 433.116 - FFP for operation of mechanized claims processing and information retrieval systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... and information retrieval systems. 433.116 Section 433.116 Public Health CENTERS FOR MEDICARE... FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.116 FFP for operation of mechanized claims processing and information retrieval systems. (a) Subject to paragraph (j) of...

7 CFR 277.18 - Establishment of an Automated Data Processing (ADP) and Information Retrieval System.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) and Information Retrieval System. 277.18 Section 277.18 Agriculture Regulations of the Department of... Data Processing (ADP) and Information Retrieval System. (a) Scope and application. This section... costs of planning, design, development or installation of ADP and information retrieval systems if the...

42 CFR 433.116 - FFP for operation of mechanized claims processing and information retrieval systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

... and information retrieval systems. 433.116 Section 433.116 Public Health CENTERS FOR MEDICARE... FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.116 FFP for operation of mechanized claims processing and information retrieval systems. (a) Subject to paragraph (j) of...

7 CFR 277.18 - Establishment of an Automated Data Processing (ADP) and Information Retrieval System.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) and Information Retrieval System. 277.18 Section 277.18 Agriculture Regulations of the Department of... Data Processing (ADP) and Information Retrieval System. (a) Scope and application. This section... costs of planning, design, development or installation of ADP and information retrieval systems if the...

42 CFR 433.116 - FFP for operation of mechanized claims processing and information retrieval systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... and information retrieval systems. 433.116 Section 433.116 Public Health CENTERS FOR MEDICARE... FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.116 FFP for operation of mechanized claims processing and information retrieval systems. (a) Subject to paragraph (j) of...

7 CFR 277.18 - Establishment of an Automated Data Processing (ADP) and Information Retrieval System.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) and Information Retrieval System. 277.18 Section 277.18 Agriculture Regulations of the Department of... Data Processing (ADP) and Information Retrieval System. (a) Scope and application. This section... costs of planning, design, development or installation of ADP and information retrieval systems if the...

42 CFR 433.116 - FFP for operation of mechanized claims processing and information retrieval systems.

Code of Federal Regulations, 2012 CFR

2012-10-01

... and information retrieval systems. 433.116 Section 433.116 Public Health CENTERS FOR MEDICARE... FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.116 FFP for operation of mechanized claims processing and information retrieval systems. (a) Subject to paragraph (j) of...

7 CFR 277.18 - Establishment of an Automated Data Processing (ADP) and Information Retrieval System.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) and Information Retrieval System. 277.18 Section 277.18 Agriculture Regulations of the Department of... Data Processing (ADP) and Information Retrieval System. (a) Scope and application. This section... costs of planning, design, development or installation of ADP and information retrieval systems if the...

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

The role of retrieval practice in memory and analogical problem-solving.

PubMed

Hostetter, Autumn B; Penix, Elizabeth A; Norman, Mackenzie Z; Batsell, W Robert; Carr, Thomas H

2018-05-01

Retrieval practice (e.g., testing) has been shown to facilitate long-term retention of information. In two experiments, we examine whether retrieval practice also facilitates use of the practised information when it is needed to solve analogous problems. When retrieval practice was not limited to the information most relevant to the problems (Experiment 1), it improved memory for the information a week later compared with copying or rereading the information, although we found no evidence that it improved participants' ability to apply the information to the problems. In contrast, when retrieval practice was limited to only the information most relevant to the problems (Experiment 2), we found that retrieval practice enhanced memory for the critical information, the ability to identify the schematic similarities between the two sources of information, and the ability to apply that information to solve an analogous problem after a hint was given to do so. These results suggest that retrieval practice, through its effect on memory, can facilitate application of information to solve novel problems but has minimal effects on spontaneous realisation that the information is relevant.

Use of information-retrieval languages in automated retrieval of experimental data from long-term storage

NASA Technical Reports Server (NTRS)

Khovanskiy, Y. D.; Kremneva, N. I.

1975-01-01

Problems and methods are discussed of automating information retrieval operations in a data bank used for long term storage and retrieval of data from scientific experiments. Existing information retrieval languages are analyzed along with those being developed. The results of studies discussing the application of the descriptive 'Kristall' language used in the 'ASIOR' automated information retrieval system are presented. The development and use of a specialized language of the classification-descriptive type, using universal decimal classification indices as the main descriptors, is described.

Mathematics and Information Retrieval.

ERIC Educational Resources Information Center

Salton, Gerald

1979-01-01

Examines the main mathematical approaches to information retrieval, including both algebraic and probabilistic models, and describes difficulties which impede formalization of information retrieval processes. A number of developments are covered where new theoretical understandings have directly led to improved retrieval techniques and operations.…

42 CFR 433.127 - Termination of FFP for failure to provide access to claims processing and information retrieval...

Code of Federal Regulations, 2014 CFR

2014-10-01

... claims processing and information retrieval systems. 433.127 Section 433.127 Public Health CENTERS FOR... PROGRAMS STATE FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.127 Termination of FFP for failure to provide access to claims processing and information retrieval...

42 CFR 433.116 - FFP for operation of mechanized claims processing and information retrieval systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... and information retrieval systems. 433.116 Section 433.116 Public Health CENTERS FOR MEDICARE... FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.116 FFP for operation of mechanized claims processing and information retrieval systems. (a) Subject to 42 CFR 433.113(c...

42 CFR 433.127 - Termination of FFP for failure to provide access to claims processing and information retrieval...

Code of Federal Regulations, 2011 CFR

2011-10-01

... claims processing and information retrieval systems. 433.127 Section 433.127 Public Health CENTERS FOR... PROGRAMS STATE FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.127 Termination of FFP for failure to provide access to claims processing and information retrieval...

42 CFR 433.127 - Termination of FFP for failure to provide access to claims processing and information retrieval...

Code of Federal Regulations, 2010 CFR

2010-10-01

... claims processing and information retrieval systems. 433.127 Section 433.127 Public Health CENTERS FOR... PROGRAMS STATE FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.127 Termination of FFP for failure to provide access to claims processing and information retrieval...

42 CFR 433.127 - Termination of FFP for failure to provide access to claims processing and information retrieval...

Code of Federal Regulations, 2013 CFR

2013-10-01

... claims processing and information retrieval systems. 433.127 Section 433.127 Public Health CENTERS FOR... PROGRAMS STATE FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.127 Termination of FFP for failure to provide access to claims processing and information retrieval...

42 CFR 433.127 - Termination of FFP for failure to provide access to claims processing and information retrieval...

Code of Federal Regulations, 2012 CFR

2012-10-01

... claims processing and information retrieval systems. 433.127 Section 433.127 Public Health CENTERS FOR... PROGRAMS STATE FISCAL ADMINISTRATION Mechanized Claims Processing and Information Retrieval Systems § 433.127 Termination of FFP for failure to provide access to claims processing and information retrieval...

On Information Retrieval (IR) Systems: Revisiting Their Development, Evaluation Methodologies, and Assumptions (SIGs LAN, ED).

ERIC Educational Resources Information Center

Stirling, Keith

2000-01-01

Describes a session on information retrieval systems that planned to discuss relevance measures with Web-based information retrieval; retrieval system performance and evaluation; probabilistic independence of index terms; vector-based models; metalanguages and digital objects; how users assess the reliability, timeliness and bias of information;…

A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data

NASA Astrophysics Data System (ADS)

Köhler, P.; Guanter, L.; Joiner, J.

2014-12-01

Global retrievals of near-infrared sun-induced chlorophyll fluorescence (SIF) have been achieved in the last years by means of a number of space-borne atmospheric spectrometers. Here, we present a new retrieval method for medium spectral resolution instruments such as the Global Ozone Monitoring Experiment (GOME-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY (SCIAMACHY). Building upon the previous work by Joiner et al. (2013), our approach solves existing issues in the retrieval such as the non-linearity of the forward model and the arbitrary selection of the number of free parameters. In particular, we use a backward elimination algorithm to optimize the number of coefficients to fit, which reduces also the retrieval noise and selects the number of state vector elements automatically. A sensitivity analysis with simulated spectra has been utilized to evaluate the performance of our retrieval approach. The method has also been applied to estimate SIF from real spectra from GOME-2 and for the first time, from SCIAMACHY. We find a good correspondence of the absolute SIF values and the spatial patterns from the two sensors, which suggests the robustness of the proposed retrieval method. In addition, we examine uncertainties and use our GOME-2 retrievals to show empirically the low sensitivity of the SIF retrieval to cloud contamination.

48 CFR 252.211-7003 - Item unique identification and valuation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... reader or interrogator, used to retrieve data encoded on machine-readable media. Concatenated unique item... identifier. Item means a single hardware article or a single unit formed by a grouping of subassemblies... manufactured under identical conditions. Machine-readable means an automatic identification technology media...

Recognition techniques for extracting information from semistructured documents

NASA Astrophysics Data System (ADS)

Della Ventura, Anna; Gagliardi, Isabella; Zonta, Bruna

2000-12-01

Archives of optical documents are more and more massively employed, the demand driven also by the new norms sanctioning the legal value of digital documents, provided they are stored on supports that are physically unalterable. On the supply side there is now a vast and technologically advanced market, where optical memories have solved the problem of the duration and permanence of data at costs comparable to those for magnetic memories. The remaining bottleneck in these systems is the indexing. The indexing of documents with a variable structure, while still not completely automated, can be machine supported to a large degree with evident advantages both in the organization of the work, and in extracting information, providing data that is much more detailed and potentially significant for the user. We present here a system for the automatic registration of correspondence to and from a public office. The system is based on a general methodology for the extraction, indexing, archiving, and retrieval of significant information from semi-structured documents. This information, in our prototype application, is distributed among the database fields of sender, addressee, subject, date, and body of the document.

Foundations for context-aware information retrieval for proactive decision support

NASA Astrophysics Data System (ADS)

Mittu, Ranjeev; Lin, Jessica; Li, Qingzhe; Gao, Yifeng; Rangwala, Huzefa; Shargo, Peter; Robinson, Joshua; Rose, Carolyn; Tunison, Paul; Turek, Matt; Thomas, Stephen; Hanselman, Phil

2016-05-01

Intelligence analysts and military decision makers are faced with an onslaught of information. From the now ubiquitous presence of intelligence, surveillance, and reconnaissance (ISR) platforms providing large volumes of sensor data, to vast amounts of open source data in the form of news reports, blog postings, or social media postings, the amount of information available to a modern decision maker is staggering. Whether tasked with leading a military campaign or providing support for a humanitarian mission, being able to make sense of all the information available is a challenge. Due to the volume and velocity of this data, automated tools are required to help support reasoned, human decisions. In this paper we describe several automated techniques that are targeted at supporting decision making. Our approaches include modeling the kinematics of moving targets as motifs; developing normalcy models and detecting anomalies in kinematic data; automatically classifying the roles of users in social media; and modeling geo-spatial regions based on the behavior that takes place in them. These techniques cover a wide-range of potential decision maker needs.

Challenges for automatically extracting molecular interactions from full-text articles.

PubMed

McIntosh, Tara; Curran, James R

2009-09-24

The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles. We manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set. We introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.

Morphometric information to reduce the semantic gap in the characterization of microscopic images of thyroid nodules.

PubMed

Macedo, Alessandra A; Pessotti, Hugo C; Almansa, Luciana F; Felipe, Joaquim C; Kimura, Edna T

2016-07-01

The analyses of several systems for medical-imaging processing typically support the extraction of image attributes, but do not comprise some information that characterizes images. For example, morphometry can be applied to find new information about the visual content of an image. The extension of information may result in knowledge. Subsequently, results of mappings can be applied to recognize exam patterns, thus improving the accuracy of image retrieval and allowing a better interpretation of exam results. Although successfully applied in breast lesion images, the morphometric approach is still poorly explored in thyroid lesions due to the high subjectivity thyroid examinations. This paper presents a theoretical-practical study, considering Computer Aided Diagnosis (CAD) and Morphometry, to reduce the semantic discontinuity between medical image features and human interpretation of image content. The proposed method aggregates the content of microscopic images characterized by morphometric information and other image attributes extracted by traditional object extraction algorithms. This method carries out segmentation, feature extraction, image labeling and classification. Morphometric analysis was included as an object extraction method in order to verify the improvement of its accuracy for automatic classification of microscopic images. To validate this proposal and verify the utility of morphometric information to characterize thyroid images, a CAD system was created to classify real thyroid image-exams into Papillary Cancer, Goiter and Non-Cancer. Results showed that morphometric information can improve the accuracy and precision of image retrieval and the interpretation of results in computer-aided diagnosis. For example, in the scenario where all the extractors are combined with the morphometric information, the CAD system had its best performance (70% of precision in Papillary cases). Results signalized a positive use of morphometric information from images to reduce semantic discontinuity between human interpretation and image characterization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Transparent Information Systems through Gateways, Front Ends, Intermediaries, and Interfaces.

ERIC Educational Resources Information Center

Williams, Martha E.

1986-01-01

Provides overview of design requirements for transparent information retrieval (implies that user sees through complexity of retrieval activities sequence). Highlights include need for transparent systems; history of transparent retrieval research; information retrieval functions (automated converters, routers, selectors, evaluators/analyzers);…

Competitive retrieval is not a prerequisite for forgetting in the retrieval practice paradigm.

PubMed

Camp, Gino; Dalm, Sander

2016-09-01

Retrieving information from memory can lead to forgetting of other, related information. The inhibition account of this retrieval-induced forgetting effect predicts that this form of forgetting occurs when competition arises between the practiced information and the related information, leading to inhibition of the related information. In the standard retrieval practice paradigm, a retrieval practice task is used in which participants retrieve the items based on a category-plus-stem cue (e.g., FRUIT-or___). In the current experiment, participants instead generated the target based on a cue in which the first 2 letters of the target were transposed (e.g., FRUIT-roange). This noncompetitive task also induced forgetting of unpracticed items from practiced categories. This finding is inconsistent with the inhibition account, which asserts that the forgetting effect depends on competitive retrieval. We argue that interference-based accounts of forgetting and the context-based account of retrieval-induced forgetting can account for this result. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Individual Differences in Working Memory Capacity Predict Retrieval-Induced Forgetting

ERIC Educational Resources Information Center

Aslan, Alp; Bauml, Karl-Heinz T.

2011-01-01

Selectively retrieving a subset of previously studied information enhances memory for the retrieved information but causes forgetting of related, nonretrieved information. Such retrieval-induced forgetting (RIF) has often been attributed to inhibitory executive-control processes that supposedly suppress the nonretrieved items' memory…

Current Research into Chemical and Textual Information Retrieval at the Department of Information Studies, University of Sheffield.

ERIC Educational Resources Information Center

Lynch, Michael F.; Willett, Peter

1987-01-01

Discusses research into chemical information and document retrieval systems at the University of Sheffield. Highlights include the use of cluster analysis methods for document retrieval and drug design, representation and searching of files of generic chemical structures, and the application of parallel computer hardware to information retrieval.…

MIPS: analysis and annotation of proteins from whole genomes

PubMed Central

Mewes, H. W.; Amid, C.; Arnold, R.; Frishman, D.; Güldener, U.; Mannhaupt, G.; Münsterkötter, M.; Pagel, P.; Strack, N.; Stümpflen, V.; Warfsmann, J.; Ruepp, A.

2004-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein–protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de). PMID:14681354

MIPS: analysis and annotation of proteins from whole genomes.

PubMed

Mewes, H W; Amid, C; Arnold, R; Frishman, D; Güldener, U; Mannhaupt, G; Münsterkötter, M; Pagel, P; Strack, N; Stümpflen, V; Warfsmann, J; Ruepp, A

2004-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein-protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).

Contextual Information Drives the Reconsolidation-Dependent Updating of Retrieved Fear Memories

PubMed Central

Jarome, Timothy J; Ferrara, Nicole C; Kwapis, Janine L; Helmstetter, Fred J

2015-01-01

Stored memories enter a temporary state of vulnerability following retrieval known as ‘reconsolidation', a process that can allow memories to be modified to incorporate new information. Although reconsolidation has become an attractive target for treatment of memories related to traumatic past experiences, we still do not know what new information triggers the updating of retrieved memories. Here, we used biochemical markers of synaptic plasticity in combination with a novel behavioral procedure to determine what was learned during memory reconsolidation under normal retrieval conditions. We eliminated new information during retrieval by manipulating animals' training experience and measured changes in proteasome activity and GluR2 expression in the amygdala, two established markers of fear memory lability and reconsolidation. We found that eliminating new contextual information during the retrieval of memories for predictable and unpredictable fear associations prevented changes in proteasome activity and glutamate receptor expression in the amygdala, indicating that this new information drives the reconsolidation of both predictable and unpredictable fear associations on retrieval. Consistent with this, eliminating new contextual information prior to retrieval prevented the memory-impairing effects of protein synthesis inhibitors following retrieval. These results indicate that under normal conditions, reconsolidation updates memories by incorporating new contextual information into the memory trace. Collectively, these results suggest that controlling contextual information present during retrieval may be a useful strategy for improving reconsolidation-based treatments of traumatic memories associated with anxiety disorders such as post-traumatic stress disorder. PMID:26062788

Toward an Episodic Context Account of Retrieval-Based Learning: Dissociating Retrieval Practice and Elaboration

ERIC Educational Resources Information Center

Lehman, Melissa; Smith, Megan A.; Karpicke, Jeffrey D.

2014-01-01

We tested the predictions of 2 explanations for retrieval-based learning; while the elaborative retrieval hypothesis assumes that the retrieval of studied information promotes the generation of semantically related information, which aids in later retrieval (Carpenter, 2009), the episodic context account proposed by Karpicke, Lehman, and Aue (in…

Using Induction to Refine Information Retrieval Strategies

NASA Technical Reports Server (NTRS)

Baudin, Catherine; Pell, Barney; Kedar, Smadar

1994-01-01

Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

Hypothesis-confirming information search strategies and computerized information-retrieval systems

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

Jacobs, S.M.

A recent trend in information-retrieval systems technology is the development of on-line information retrieval systems. One objective of these systems has been to attempt to enhance decision effectiveness by allowing users to preferentially seek information, thereby facilitating the reduction or elimination of information overload. These systems do not necessarily lead to more-effective decision making, however. Recent research in information-search strategy suggests that when users are seeking information subsequent to forming initial beliefs, they may preferentially seek information to confirm these beliefs. It seems that effective computer-based decision support requires an information retrieval system capable of: (a) retrieving a subset ofmore » all available information, in order to reduce information overload, and (b) supporting an information search strategy that considers all relevant information, rather than merely hypothesis-confirming information. An information retrieval system with an expert component (i.e., a knowledge-based DSS) should be able to provide these capabilities. Results of this study are non conclusive; there was neither strong confirmatory evidence nor strong disconfirmatory evidence regarding the effectiveness of the KBDSS.« less

A Semi-Automatic Alignment Method for Math Educational Standards Using the MP (Materialization Pattern) Model

ERIC Educational Resources Information Center

Choi, Namyoun

2010-01-01

Educational standards alignment, which matches similar or equivalent concepts of educational standards, is a necessary task for educational resource discovery and retrieval. Automated or semi-automated alignment systems for educational standards have been recently available. However, existing systems frequently result in inconsistency in…

Semi-Automatic Determination of Citation Relevancy: User Evaluation.

ERIC Educational Resources Information Center

Huffman, G. David

1990-01-01

Discussion of online bibliographic database searches focuses on a software system, SORT-AID/SABRE, that ranks retrieved citations in terms of relevance. Results of a comprehensive user evaluation of the relevance ranking procedure to determine its effectiveness are presented, and implications for future work are suggested. (10 references) (LRW)

46 CFR 520.6 - Retrieval of information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 9 2012-10-01 2012-10-01 false Retrieval of information. 520.6 Section 520.6 Shipping FEDERAL MARITIME COMMISSION REGULATIONS AFFECTING OCEAN SHIPPING IN FOREIGN COMMERCE CARRIER AUTOMATED TARIFFS § 520.6 Retrieval of information. (a) General. Tariffs systems shall present retrievers with the...

46 CFR 520.6 - Retrieval of information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 9 2010-10-01 2010-10-01 false Retrieval of information. 520.6 Section 520.6 Shipping FEDERAL MARITIME COMMISSION REGULATIONS AFFECTING OCEAN SHIPPING IN FOREIGN COMMERCE CARRIER AUTOMATED TARIFFS § 520.6 Retrieval of information. (a) General. Tariffs systems shall present retrievers with the...

46 CFR 520.6 - Retrieval of information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 9 2014-10-01 2014-10-01 false Retrieval of information. 520.6 Section 520.6 Shipping FEDERAL MARITIME COMMISSION REGULATIONS AFFECTING OCEAN SHIPPING IN FOREIGN COMMERCE CARRIER AUTOMATED TARIFFS § 520.6 Retrieval of information. (a) General. Tariffs systems shall present retrievers with the...

46 CFR 520.6 - Retrieval of information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 9 2013-10-01 2013-10-01 false Retrieval of information. 520.6 Section 520.6 Shipping FEDERAL MARITIME COMMISSION REGULATIONS AFFECTING OCEAN SHIPPING IN FOREIGN COMMERCE CARRIER AUTOMATED TARIFFS § 520.6 Retrieval of information. (a) General. Tariffs systems shall present retrievers with the...

Mathematical, Logical, and Formal Methods in Information Retrieval: An Introduction to the Special Issue.

ERIC Educational Resources Information Center

Crestani, Fabio; Dominich, Sandor; Lalmas, Mounia; van Rijsbergen, Cornelis Joost

2003-01-01

Discusses the importance of research on the use of mathematical, logical, and formal methods in information retrieval to help enhance retrieval effectiveness and clarify underlying concepts of information retrieval. Highlights include logic; probability; spaces; and future research needs. (Author/LRW)

46 CFR 520.6 - Retrieval of information.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 9 2011-10-01 2011-10-01 false Retrieval of information. 520.6 Section 520.6 Shipping FEDERAL MARITIME COMMISSION REGULATIONS AFFECTING OCEAN SHIPPING IN FOREIGN COMMERCE CARRIER AUTOMATED TARIFFS § 520.6 Retrieval of information. (a) General. Tariffs systems shall present retrievers with the...

Study and Analysis of The Robot-Operated Material Processing Systems (ROMPS)

NASA Technical Reports Server (NTRS)

Nguyen, Charles C.

1996-01-01

This is a report presenting the progress of a research grant funded by NASA for work performed during 1 Oct. 1994 - 31 Sep. 1995. The report deals with the development and investigation of potential use of software for data processing for the Robot Operated Material Processing System (ROMPS). It reports on the progress of data processing of calibration samples processed by ROMPS in space and on earth. First data were retrieved using the I/O software and manually processed using MicroSoft Excel. Then the data retrieval and processing process was automated using a program written in C which is able to read the telemetry data and produce plots of time responses of sample temperatures and other desired variables. LabView was also employed to automatically retrieve and process the telemetry data.

Directed forgetting and aging: the role of retrieval processes, processing speed, and proactive interference.

PubMed

Hogge, Michaël; Adam, Stéphane; Collette, Fabienne

2008-07-01

The directed forgetting effect obtained with the item method is supposed to depend on both selective rehearsal of to-be-remembered (TBR) items and attentional inhibition of to-be-forgotten (TBF) items. In this study, we investigated the locus of the directed forgetting deficit in older adults by exploring the influence of recollection and familiarity-based retrieval processes on age-related differences in directed forgetting. Moreover, we explored the influence of processing speed, short-term memory capacity, thought suppression tendencies, and sensitivity to proactive interference on performance. The results indicated that older adults' directed forgetting difficulties are due to decreased recollection of TBR items, associated with increased automatic retrieval of TBF items. Moreover, processing speed and proactive interference appeared to be responsible for the decreased recall of TBR items.

Information Retrieval: A Sequential Learning Process.

ERIC Educational Resources Information Center

Bookstein, Abraham

1983-01-01

Presents decision-theoretic models which intrinsically include retrieval of multiple documents whereby system responds to request by presenting documents to patron in sequence, gathering feedback, and using information to modify future retrievals. Document independence model, set retrieval model, sequential retrieval model, learning model,…

Measuring and Predicting Tag Importance for Image Retrieval.

PubMed

Li, Shangwen; Purushotham, Sanjay; Chen, Chen; Ren, Yuzhuo; Kuo, C-C Jay

2017-12-01

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

Lessons learned from a pilot implementation of the UMLS information sources map.

PubMed

Miller, P L; Frawley, S J; Wright, L; Roderer, N K; Powsner, S M

1995-01-01

To explore the software design issues involved in implementing an operational information sources map (ISM) knowledge base (KB) and system of navigational tools that can help medical users access network-based information sources relevant to a biomedical question. A pilot biomedical ISM KB and associated client-server software (ISM/Explorer) have been developed to help students, clinicians, researchers, and staff access network-based information sources, as part of the National Library of Medicine's (NLM) multi-institutional Unified Medical Language System (UMLS) project. The system allows the user to specify and constrain a search for a biomedical question of interest. The system then returns a list of sources matching the search. At this point the user may request 1) further information about a source, 2) that the list of sources be regrouped by different criteria to allow the user to get a better overall appreciation of the set of retrieved sources as a whole, or 3) automatic connection to a source. The pilot system operates in client-server mode and currently contains coded information for 121 sources. It is in routine use from approximately 40 workstations at the Yale School of Medicine. The lessons that have been learned are that: 1) it is important to make access to different versions of a source as seamless as possible, 2) achieving seamless, cross-platform access to heterogeneous sources is difficult, 3) significant differences exist between coding the subject content of an electronic information resource versus that of an article or a book, 4) customizing the ISM to multiple institutions entails significant complexities, and 5) there are many design trade-offs between specifying searches and viewing sets of retrieved sources that must be taken into consideration. An ISM KB and navigational tools have been constructed. In the process, much has been learned about the complexities of development and evaluation in this new environment, which are different from those for Gopher, wide area information servers (WAIS), World-Wide-Web (WWW), and MOSAIC resources.

Data Discretization for Novel Relationship Discovery in Information Retrieval.

ERIC Educational Resources Information Center

Benoit, G.

2002-01-01

Describes an information retrieval, visualization, and manipulation model which offers the user multiple ways to exploit the retrieval set, based on weighted query terms, via an interactive interface. Outlines the mathematical model and describes an information retrieval application built on the model to search structured and full-text files.…

Visual working memory buffers information retrieved from visual long-term memory.

PubMed

Fukuda, Keisuke; Woodman, Geoffrey F

2017-05-16

Human memory is thought to consist of long-term storage and short-term storage mechanisms, the latter known as working memory. Although it has long been assumed that information retrieved from long-term memory is represented in working memory, we lack neural evidence for this and need neural measures that allow us to watch this retrieval into working memory unfold with high temporal resolution. Here, we show that human electrophysiology can be used to track information as it is brought back into working memory during retrieval from long-term memory. Specifically, we found that the retrieval of information from long-term memory was limited to just a few simple objects' worth of information at once, and elicited a pattern of neurophysiological activity similar to that observed when people encode new information into working memory. Our findings suggest that working memory is where information is buffered when being retrieved from long-term memory and reconcile current theories of memory retrieval with classic notions about the memory mechanisms involved.