Neural Information Retrieval: In search of meaningful progressBhaskar Mitra
The emergence of deep learning based methods for search poses several challenges and opportunities not just for modeling, but also for benchmarking and measuring progress in the field. Some of these challenges are new, while others have evolved from existing challenges in IR benchmarking exacerbated by the scale at which deep learning models operate. Evaluation efforts such as the TREC Deep Learning track and the MS MARCO public leaderboard are intended to encourage research and track our progress, addressing big questions in our field. The goal is not simply to identify which run is "best" but to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This entails a wider conversation in the IR community about what constitutes meaningful progress, how benchmark design can encourage or discourage certain outcomes, and about the validity of our findings. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track--and reflect on the state of the field and the road ahead.
Prediction of Answer Keywords using Char-RNNIJECEIAES
Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and BeyondBhaskar Mitra
The emergence of deep learning-based methods for information retrieval (IR) poses several challenges and opportunities for benchmarking. Some of these are new, while others have evolved from existing challenges in IR exacerbated by the scale at which deep learning models operate. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track, and reflect on the road ahead.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
In recent years the growth of digital data is increasing dramatically, knowledge discovery and data mining have attracted immense attention with coming up need for turning such data into useful information and knowledge. Keyword extraction is considered an essential task in natural language processing (NLP) that facilitates mapping of documents to a concise set of representative single and multi-word phrases. This paper investigates using of Word2Vec and Decision Tree for keywords extraction from textual documents. The Sem-Eval (2010) dataset is used as a main input for the proposed study. The words are represented by vectors with Word2Vec technique following applying pre-processing operations on the dataset. This method is based on word similarity between candidate keywords from both collecting keywords for each label and one sample from the same label. An appropriate threshold has been determined by which the percentages that exceed this threshold are exported to the Decision Tree in order to consider an appropriate classification to be taken on the text document.
Some similarity measurements were used for the classification process. The efficiency and accuracy of the algorithm was measured in the process of classification using precision, recall and F-score rates. The obtained results indicated that using of vector representation for each keyword is an effective way to identify the most similar words, so that the opportunity to recognize the correct classification of the document increases. When using word2Vec CBOW the result of F-Score was 64% with the Gini method and WordNet Lemmatizer. Meanwhile, when using Word2Vec SG the result of F-Score was 82% with Gini Index and English Porter Stemming which considered the highest ratio for all our experiments.
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
Neural Information Retrieval: In search of meaningful progressBhaskar Mitra
The emergence of deep learning based methods for search poses several challenges and opportunities not just for modeling, but also for benchmarking and measuring progress in the field. Some of these challenges are new, while others have evolved from existing challenges in IR benchmarking exacerbated by the scale at which deep learning models operate. Evaluation efforts such as the TREC Deep Learning track and the MS MARCO public leaderboard are intended to encourage research and track our progress, addressing big questions in our field. The goal is not simply to identify which run is "best" but to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This entails a wider conversation in the IR community about what constitutes meaningful progress, how benchmark design can encourage or discourage certain outcomes, and about the validity of our findings. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track--and reflect on the state of the field and the road ahead.
Prediction of Answer Keywords using Char-RNNIJECEIAES
Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and BeyondBhaskar Mitra
The emergence of deep learning-based methods for information retrieval (IR) poses several challenges and opportunities for benchmarking. Some of these are new, while others have evolved from existing challenges in IR exacerbated by the scale at which deep learning models operate. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track, and reflect on the road ahead.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
In recent years the growth of digital data is increasing dramatically, knowledge discovery and data mining have attracted immense attention with coming up need for turning such data into useful information and knowledge. Keyword extraction is considered an essential task in natural language processing (NLP) that facilitates mapping of documents to a concise set of representative single and multi-word phrases. This paper investigates using of Word2Vec and Decision Tree for keywords extraction from textual documents. The Sem-Eval (2010) dataset is used as a main input for the proposed study. The words are represented by vectors with Word2Vec technique following applying pre-processing operations on the dataset. This method is based on word similarity between candidate keywords from both collecting keywords for each label and one sample from the same label. An appropriate threshold has been determined by which the percentages that exceed this threshold are exported to the Decision Tree in order to consider an appropriate classification to be taken on the text document.
Some similarity measurements were used for the classification process. The efficiency and accuracy of the algorithm was measured in the process of classification using precision, recall and F-score rates. The obtained results indicated that using of vector representation for each keyword is an effective way to identify the most similar words, so that the opportunity to recognize the correct classification of the document increases. When using word2Vec CBOW the result of F-Score was 64% with the Gini method and WordNet Lemmatizer. Meanwhile, when using Word2Vec SG the result of F-Score was 82% with Gini Index and English Porter Stemming which considered the highest ratio for all our experiments.
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
Concurrent Inference of Topic Models and Distributed Vector RepresentationsParang Saraf
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
Semantic Similarity and Selection of Resources Published According to Linked ...Riccardo Albertoni
The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the re-engineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
Traffic analysis is a process of great importance, when it comes in securing a network. This analysis can be classified in
different levels and one of most interest is Deep Packet Inspection (DPI). DPI is a very effective way of monitoring the network,
since it performs traffic control over mostly of the OSI model’s layers (from L3 to L7). Regular Expressions (RegExp) on the
other hand is used in computer science and can make use of a group of characters, in order to create a searching pattern. This
technique can be combined with a series of mathematical algorithms for helping the individual to quickly find out the search
pattern within a text and even replace it with another value.
In this paper, we aim to prove that the use of Regular Expressions is much more productive and effective when used for
creating matching rules needed in DPI. We design, test and put into comparison Regular Expression rules and compare it
against the conventional methods. In addition to the above, we have created a case study of detecting EternalBlue and
DoublePulsar threats, in order to point out the practical and realistic value of our proposal.
On the large scale of studying dynamics with MEG: Lessons learned from the Hu...Robert Oostenveld
As part of the Human Connectome Project (HCP), which includes high-quality fMRI, anatomical MRI, DTi and genetic data from 1200 subjects, we have scanned and investigated a subset of 100 subjects (mostly comprised of pairs of twins) using MEG. The raw data acquired in the HCP has been analyzed using standard pipelines [ref1] and both raw and results at various levels of processing have been shared though the ConnectomeDB [ref2].
Throughout the process of the HCP we have not only analyzed (resting state) MEG data, but also have developed the data analysis protocols, the software and the strategies to achieve reproducible MEG connectivity results. The MEG data analysis software is based on FieldTrip, an open source toolbox [ref3], and is shared alongside the data to allow the analyses to be repeated on independent data.
In this presentation I will outline what the HCP MEG team has learned along the way and I will provide recommendations on what to do and what to avoid in making MEG studies on (resting state) connectivity more reproducible.
1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM, Marzetti L, de Pasquale F, Di Pompeo F, Stout J, Woolrich M, Luo Q, Bucholz R, Fries P, Pizzella V, Romani GL, Corbetta M, Snyder AZ; WU-Minn HCP Consortium. Adding dynamics to the Human Connectome Project with MEG. Neuroimage, 2013.
doi:10.1016/j.neuroimage.2013.05.056
2. Hodge MR, Horton W, Brown T, Herrick R, Olsen T, Hileman ME, McKay M, Archie KA, Cler E, Harms MP, Burgess GC, Glasser MF, Elam JS, Curtiss SW, Barch DM, Oostenveld R, Larson-Prior LJ, Ugurbil K, Van Essen DC, Marcus DS. ConnectomeDB-Sharing human brain connectivity data. Neuroimage, 2016. doi:10.1016/j.neuroimage.2015.04.046
3. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011. doi:10.1155/2011/156869
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
On Using Network Science in Mining Developers Collaboration in Software Engin...IJDKP
Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers’ collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.
An introduction to system-oriented evaluation in Information RetrievalMounia Lalmas-Roelleke
Slides for my lecture on IR evaluation, presented at 11th European Summer School in Information Retrieval (ESSIR 2017) at Universitat Pompeu Fabra, Barcelona.
These slides were based on
1. Evaluation lecture @ QMUL; Thomas Roelleke & Mounia Lalmas
3. Lecture 8: Evaluation @ Stanford University; Pandu Nayak & Prabhakar Raghavan
4. Retrieval Evaluation @ University of Virginia; Hongnig Wang
5. Lectures 11 and 12 on Evaluation @ Berkeley; Ray Larson
6. Evaluation of Information Retrieval Systems @ Penn State University; Lee Giles
Textbooks:
1. Information Retrieval, 2nd edition, C.J. van Rijsbergen (1979)
2. Introduction to Information Retrieval, C.D. Manning, P. Raghavan & H. Schuetze (2008)
3. Modern Information Retrieval: The Concepts and Technology behind Search, 2nd ed; R. Baeza-Yates & B. Ribeiro-Neto (2011)
Clustering Citation Distributions for Semantic Categorization and Citation Prediction
by F. Osborne, S. Peroni, E. Motta
In this paper we present i) an approach for clustering authors according to their citation distributions and ii) an ontology, the Bibliometric Data Ontology, for supporting the formal representation of such clusters. This method allows the formulation of queries which take in consideration the citation behaviour of an author and predicts with a good level of accuracy future citation behaviours. We evaluate our approach with respect to alternative solutions and discuss the predicting abilities of the identified clusters.
URL: http://oro.open.ac.uk/40784/1/lisc2014.pdf
Concurrent Inference of Topic Models and Distributed Vector RepresentationsParang Saraf
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
Semantic Similarity and Selection of Resources Published According to Linked ...Riccardo Albertoni
The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the re-engineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
Traffic analysis is a process of great importance, when it comes in securing a network. This analysis can be classified in
different levels and one of most interest is Deep Packet Inspection (DPI). DPI is a very effective way of monitoring the network,
since it performs traffic control over mostly of the OSI model’s layers (from L3 to L7). Regular Expressions (RegExp) on the
other hand is used in computer science and can make use of a group of characters, in order to create a searching pattern. This
technique can be combined with a series of mathematical algorithms for helping the individual to quickly find out the search
pattern within a text and even replace it with another value.
In this paper, we aim to prove that the use of Regular Expressions is much more productive and effective when used for
creating matching rules needed in DPI. We design, test and put into comparison Regular Expression rules and compare it
against the conventional methods. In addition to the above, we have created a case study of detecting EternalBlue and
DoublePulsar threats, in order to point out the practical and realistic value of our proposal.
On the large scale of studying dynamics with MEG: Lessons learned from the Hu...Robert Oostenveld
As part of the Human Connectome Project (HCP), which includes high-quality fMRI, anatomical MRI, DTi and genetic data from 1200 subjects, we have scanned and investigated a subset of 100 subjects (mostly comprised of pairs of twins) using MEG. The raw data acquired in the HCP has been analyzed using standard pipelines [ref1] and both raw and results at various levels of processing have been shared though the ConnectomeDB [ref2].
Throughout the process of the HCP we have not only analyzed (resting state) MEG data, but also have developed the data analysis protocols, the software and the strategies to achieve reproducible MEG connectivity results. The MEG data analysis software is based on FieldTrip, an open source toolbox [ref3], and is shared alongside the data to allow the analyses to be repeated on independent data.
In this presentation I will outline what the HCP MEG team has learned along the way and I will provide recommendations on what to do and what to avoid in making MEG studies on (resting state) connectivity more reproducible.
1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM, Marzetti L, de Pasquale F, Di Pompeo F, Stout J, Woolrich M, Luo Q, Bucholz R, Fries P, Pizzella V, Romani GL, Corbetta M, Snyder AZ; WU-Minn HCP Consortium. Adding dynamics to the Human Connectome Project with MEG. Neuroimage, 2013.
doi:10.1016/j.neuroimage.2013.05.056
2. Hodge MR, Horton W, Brown T, Herrick R, Olsen T, Hileman ME, McKay M, Archie KA, Cler E, Harms MP, Burgess GC, Glasser MF, Elam JS, Curtiss SW, Barch DM, Oostenveld R, Larson-Prior LJ, Ugurbil K, Van Essen DC, Marcus DS. ConnectomeDB-Sharing human brain connectivity data. Neuroimage, 2016. doi:10.1016/j.neuroimage.2015.04.046
3. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011. doi:10.1155/2011/156869
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
On Using Network Science in Mining Developers Collaboration in Software Engin...IJDKP
Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers’ collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.
An introduction to system-oriented evaluation in Information RetrievalMounia Lalmas-Roelleke
Slides for my lecture on IR evaluation, presented at 11th European Summer School in Information Retrieval (ESSIR 2017) at Universitat Pompeu Fabra, Barcelona.
These slides were based on
1. Evaluation lecture @ QMUL; Thomas Roelleke & Mounia Lalmas
3. Lecture 8: Evaluation @ Stanford University; Pandu Nayak & Prabhakar Raghavan
4. Retrieval Evaluation @ University of Virginia; Hongnig Wang
5. Lectures 11 and 12 on Evaluation @ Berkeley; Ray Larson
6. Evaluation of Information Retrieval Systems @ Penn State University; Lee Giles
Textbooks:
1. Information Retrieval, 2nd edition, C.J. van Rijsbergen (1979)
2. Introduction to Information Retrieval, C.D. Manning, P. Raghavan & H. Schuetze (2008)
3. Modern Information Retrieval: The Concepts and Technology behind Search, 2nd ed; R. Baeza-Yates & B. Ribeiro-Neto (2011)
Clustering Citation Distributions for Semantic Categorization and Citation Prediction
by F. Osborne, S. Peroni, E. Motta
In this paper we present i) an approach for clustering authors according to their citation distributions and ii) an ontology, the Bibliometric Data Ontology, for supporting the formal representation of such clusters. This method allows the formulation of queries which take in consideration the citation behaviour of an author and predicts with a good level of accuracy future citation behaviours. We evaluate our approach with respect to alternative solutions and discuss the predicting abilities of the identified clusters.
URL: http://oro.open.ac.uk/40784/1/lisc2014.pdf
As the volume and complexity of data from myriad Earth Observing platforms, both remote sensing and in-situ increases so does the demand for access to both data and information products from these data. The audience no longer is restricted to an investigator team with specialist science credentials. Non-specialist users from scientists from other disciplines, science-literate public, to teachers, to the general public and decision makers want access. What prevents them from this access to resources? It is the very complexity and specialist developed data formats, data set organizations and specialist terminology. What can be done in response? We must shift the burden from the user to the data provider. To achieve this our developed data infrastructures are likely to need greater degrees of internal code and data structure complexity to achieve (relatively) simpler end-user complexity. Evidence from numerous technical and consumer markets supports this scenario. We will cover the elements of modern data environments, what the new use cases are and how we can respond to them.
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.
Web Information Extraction for the DB Research Domainliat_kakun
A presentation describing my final project for an engineering degree at the Hebrew University of Jerusalem - a system for extracting information from web sites into instances of an XML schema, utilizing machine learning, structural analysis of documents and a divide & conquer strategy.
Web Information Extraction for the Database Research DomainMichael Genkin
A presentation describing my final project for an engineering degree at the Hebrew University of Jerusalem - a system for extracting information from web sites into instances of an XML schema, utilizing machine learning, structural analysis of documents and a divide & conquer strategy.
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
1. Libraries and Intelligence NSF/NIJ Symposium on Intelligence and Security Informatics. Tucson, AR. Paul Kantor June 2, 2003 Research supported in part by the National Science Foundation under Grant EIA-0087022and by the Advanced Research Development Activity under Contract 2002-H790400-000. The views expressed in this presentation are those of the author, and do not necessarily represent the views of the sponsoring agency.
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12. COMPONENTS OF AUTOMATIC MESSAGE PROCESSING (1). Compression of Text -- to meet storage and processing limitations; (2). Representation of Text -- put in form amenable to computation and statistical analysis; (3). Matching Scheme -- computing similarity between documents; (4). Learning Method -- build on judged examples to determine characteristics of document cluster (“event”) (5). Fusion Scheme -- combine methods (scores) to yield improved detection/clustering.
13. Random Projections Boolean Random Projections Robust Feature Selection Compression Representation Bag of Words Bag of Bits Matching Learning Fusion tf-idf kNN Boolean r-NN Rocchio separator Combinatorial Clustering Naïve Bayes Sparse Bayes Discriminant Analysis Support Vector Machines Non-linear Classifiers Project Components: Rutgers DIMACS MMS
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18. Mercer Kernels Mercer’s Theorem gives necessary and sufficient conditions for a continuous symmetric function K to admit this representation: “ Mercer Kernels” This kernel defines a set of functions H K , elements of which have an expansion as: This set of functions is a “reproducing kernel hilbert space” K “pos. semi-definite” Prepared by David L. Madigan
19. Support Vector Machine Two-class classifier with the form: parameters chosen to minimize: Many of the fitted ’s are usually zero; x ’s corresponding the the non-zero ’s are the “support vectors.” complexity penalty Gram matrix tuning constant Prepared by David L. Madigan
20. Regularized Linear Feature Space Model Choose a model of the form: to minimize: Solution is finite dimensional: just need to know K , not ! prediction is sign(f(x)) A kernel is a function K , such that for all x , z X where is a mapping from X to an inner product feature space F Prepared by David L. Madigan
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23. Feature space Random Subspace Score space Learning takes place in two spaces: For matching and filtering, we learn rules in the primary space of document features. For fusion processes we learn rules in a secondary space of “pseudo-features” which are assigned by entire systems, to incoming documents. Relevant Relevant
35. Performance of models Quality Prediction by Linear Combination of Textual Features (from 5 to 17 variables). Split Half for Training and Testing. Quality Factors Prediction Rate Depth 67% Author Credential 55% Accuracy 69% Source 57% Objectivity 64% Grammar 79% One Side vs Multi View 70% Verbosity 63% Readability 76%
46. Summary of Local Fusion PROBLEM CASE We ran 5 split half runs on the odd case (318) and the results persist.
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50. Our Approach to Retrieval Fusion SMART InQuery FUSION PROCESS Request DOCUMENTS SETS Result Set Delivered SET Result Set ADOPT: Fusion System Monitor Fusion Set and Receive Feedback USE: Better System Adaptive “Local” Fusion
Editor's Notes
Librarians have long been concerned to organize the materials that have been selected as worthy of inclusion in a library. Thus it has been a substantial cultural change during the past 10 years, as librarians realized that they have inherited responsibility for organizing the exploding cultural resource represented by the World Wide Web. This was made possible by techniques which had been developed 30 and 40 years, earlier on a theoretical basis, for the indexing and retrieval of arbitrary texts. Since an enormous amount of communication now takes place in electronic form, it has become possible to ask whether expansion of these techniques for organization and retrieval can facilitate the scanning of streams of communication, in order to detect (either after the fact or in advance) communications among those intent on doing harm. Since the attacks of September 11, 2001 by Al Qeada on the mainland of the United States, this agenda has been moved forward with remarkable speed. We review a number of projects underway at Rutgers University which bear on both the technical aspects and the interactive or "user oriented" aspects of this problem. Research to be described in this talk is supported in part by the National Science Foundation and by the Advanced Research Development Activity of the Intelligence Community.