Current search technologies can locate text or indexes , ranked by the frequency a term appears in a document , but not the term 's relevance to a set of symptoms . Current search technologies operate by diagnosis category , key words , indices , and content text . The program ranks matches by frequency , index , and content . Hence , there is a necessity to handle multi-allelic genotype data . Here , we introduce a multi-allelic version of the Positional Burrows-Wheeler Transform ( mPBWT ) based on the bi-allelic version for compression and searching . The time-complexity for constructing the data structure and searching within a panel containing t-allelic sites increases by a factor of t. However , considering the small value for t , the time increase for the multi-allelic PBWT will be negligible . This search method was capable to search the shortest path which representing the fewest number of deleted mappings , and also search the cheapest cost which representing the smallest total amount of deleted mappings ' confidence value . A∗ Search was both complete and optimal to minimize mapping repair size . Abstract Many file organizations consist of an index or a directory , which is used for search purposes , and a set of data leaves in which the data records are stored . In order to decrease the index size so that it can fit into main memory , such file organizations may use multi-bucket leaves ( instead of “ ordinary ” data leaves ) , resulting , however , in a reduction of the average disk space utilization . The present invention discloses a method for the wireless connection of a mobile terminal , the method comprising : initiating a search for an access point in the current environment ; determining whether to search for an access point within a predetermined time , the determination result is yes , the acquired search search and pause current access point ; Analyzing access pre-recorded information matches the current access point , the determination result is yes , the use of the access information to access the current access point ; current access point into the list ; continued search for the next access point , and returns to step determines whether the access point is searched within a predetermined time . The present invention also discloses a mobile terminal . It is shown , by the martingale method , that the standard deviation of the weaker ? -s search without deep cutoffs is of the same order as the expected number of leaves evaluated . A nearly optimal upper bound on the variance of the general ? Autonomous aerial vehicles play an important role in military applications such as in search , surveillance and reconnaissance . Multi-player stochastic pursuit – evasion ( PE ) differential game is a natural model for such operations involving intelligent moving targets with uncertainties . Existing methods such as UDDI Uni- versal Description Discovery and Integration 2 and MDS Monitoring and Discovery Ser- vice 1 were discounted due to the limitation of a string matching syntactic approach 3 .A semantic approach was chosen because of the need to understand the complex relationships between system capabilities . The design focus onontologyartifactsinrelationtothewholedis- covery process is the central theme — explor- ingsearch performance withpractical Financial Services FS knowledge topologies . Introduction to AltaVista search getting started with AltaVista search - about the AltaVista Search home page , simple search skills advanced search searching Usenet newsgroups providing information the AltaVista way - how AltaVista Search works for Web pages , excluding pages or sites from AltaVista search , designing your site , webmaster techniques using AltaVista Search A to Z reference the AltaVista story . Appendices : the top 1000 most common words on the World Wide Web a sample of 1000 queries frequency of words used in AltaVista Search queries . Though technically possible , it is uncommon to use beam search when training with SEARN / DAgger . The early - update and LaSO training strategies , however , explicitly account for beam search , and describe strategies for updating parameters when the gold structure becomes unreachable during search . Early update and LaSO differ primarily in that the former discards a training example after the first search error , whereas LaSO resumes searching after an error from a state that includes the gold partial structure . otherwise , where we have a margin violation at iff , and where considers the scores given by . This search procedure is illustrated in the top portion of Figure [ reference ] . In the forward pass of our training algorithm , shown as the first part of Algorithm [ reference ] , we run this version of beam search and collect all sequences and their hidden states that lead to losses . Before the popularity of visual question answering ( VQA ) , text question answering ( QA ) had already been established as a mature research problem in the area of natural language processing . Previous QA methods include searching for the key words of the question in a search engine ; parsing the question as a knowledge base ( KB ) query ; or embedding the question and using a similarity measurement to find evidence for the answer . Recently , memory networks were proposed for solving the QA problem . [ reference ][ reference ] . However , this type of approach can be memory - consuming and prohibitively slow , especially for searching tasks in large - scale datasets . Therefore , an aggregation method that can produce a compact template representation is desired . The basic idea is to let the model use its own predictions at training time to produce a sequence of actions ( e.g. , the choice of the next word ) . Then , a search algorithm is run to determine the optimal action at each time step , and a classifier ( a.k.a . policy ) is trained to predict that action . A similar idea was later proposed by dagger in an imitation learning framework . present versions of the TreeRNN model which are capable of operating over unparsed inputs . However , these methods require an expensive search process at test time . Our model presents a much faster alternative approach . User issued search queries often do not follow formal grammatical structure , and require specialized language processing . Traditional natural language processing ( NLP ) tools trained on formal text ( e.g. treebanks ) often have difficulty analyzing search queries ; the lack of regularity in the structure of queries makes it difficult to train models that can optimally process the query to extract information that can help understand the user intent behind the query . One clear direction to improve query processing is to annotate a large number of queries with the desired annotation scheme . Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation . Although studies have shown that the users of Web-based search engines typically enter short , term-based queries and rarely use search operators , little information exists concerning the effects of term and operator usage on the relevancy of search results . Seven papers and a letter to the editor have been accepted for publication in the journal . The first paper , by Bevan Koopman , Peter Bruza , Laurianne Sitbon and Michael Lawley , titled “ Towards Semantic Search and Inference in Electronic Medical Records ” , presents concept – based information retrieval for searching electronic medical records and demonstrates that the approach outperforms keyword – based search , working especially well for queries where the latter performs poorly . This paper was awarded the best paper prize at the workshop . In this paper , a distributed and cooperative strategy for information retrieaval on the Web is proposed to substitute the centralized mode adopted by the current search engines . Then a new information retrieval system model IRSM is presented , which supports the retrieval of metadata about Web documents and uses Z39.50 standard protocol to unify the heterogeneous interfaces of different systems . Based on that , a distributed and cooperative information retrieval framework , called DCIRF , is designed to help users in fast and effective information retrieval on the Web . The system also provides a highly customizable platform for researchers who want to investigate individual and group information seeking behaviors in a lab or a field setting . The demo not only shows back-end components and front-end interaction elements of the system , but also how one could easily configure Coagmento for user studies involving information seeking/retrieval with digital libraries ( including the Web ) . Utilizing the power of this widely-available , higher-level statistical package should facilitate the application of this technique for similar and related analyses of sequence-based activities . The discussion includes identification of applications in information retrieval analysis , common problems with statistical and analytical technique , samples of workabk- , code , and examples of relevant output . In this paper , we present FIRE - a multimodal interface for informa- tion retrieval deployed in the Intelligent Room at the MIT AI Lab . FIRE differs from most othe ri nterfaces forinformation retrieval in that it combines a couple of interaction modalities to improve the search process . In this chapter we discuss the integration of information retrieval information from two sources-a social network and a document reference network-for enhancing reference-based search engine rankings . In particular , current models of information retrieval are blind to the social context that surrounds information resources , thus they do not consider the trustworthiness of their authors when they present the query results to the users . Joins.- An Efficient Indexing Scheme for Moving Objects ' Trajectories on Road Networks.- Spatial Index Compression for Location-Based Services Based on a MBR Semi-approximation Scheme.- XML Query Processing.- KCAM : Concentrating on Structural Similarity for XML Fragments.- A New Structure for Accelerating XPath Location Steps.- Efficient Evaluation of Multiple Queries on Streamed XML Fragments.- Information Retrieval I.- Automated Extraction of Hit Numbers from Search Result Pages.- Keyword Extraction Using Support Vector Machine.- LSM : Language Sense Model for Information Retrieval.- Information Retrieval II.- Succinct and Informative Cluster Descriptions for Document Repositories.- LRD : Latent Relation Discovery for Vector Space Expansion and Information Retrieval.- Web Image Retrieval Refinement by Visual Contents.- Sensor Networks and Grid Computing.- An Effective Approach for Hiding Sensitive Knowledge in Data Publishing.- Tracking Network-Constrained Moving Objects with Group Updates.- Dynamic Configuring Service on Semantic Grid.- Peer-to-Peer.- Object Placement and Caching Strategies on AN.P2P.- Role-Based Peer-to-Peer Model : Aimed at the defects of the traditional keyword-based retrieval method , an ontology-basedsemantic query-oriented information retrieval model is built . Based on the establishment of domainontology knowledge base , the system combined the intelligent semantic Web technology with theinformation retrieval method , with the core that users can validly search the information of professionalareas by means of Chinese word segmentation algorithm and semantic expansion and that adomain-oriented intelligent retrieval system is established . It achieves 7.42 % which is another 1.3 % absolute improvement ( or 15 % relative improvement ) over the best previous result of . subsection : Information Retrieval with Paragraph Vectors We turn our attention to an information retrieval task which requires fixed - length representations of paragraphs . This research was supported in part by Netherlands Organization for Scientific Research through the Exploratory Political Search project ( ExPoSe , NWO CI # 314.99.108 ) , by the Digging into Data Challenge through the Digging Into Linked Parliamentary Data project ( DiLiPaD , NWO Digging into Data # 600.006.014 ) , and by the Center for Intelligent Information Retrieval . Any opinions , findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors . Larger corpora for EBM tasks have been derived using ( noisy ) automated annotation approaches . This approach has been used to build , e.g. , datasets to facilitate work on Information Retrieval ( IR ) models for biomedical texts . Similar approaches have been used to ‘ distantly supervise ’ annotation of full - text articles describing clinical trials . Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality . Therefore , the proposed approach is an improvement over state-of-the-art approaches for object retrieval . Promising new directions have been opened up for content-based visual retrieval in recent years . Object-based retrieval which allows users to manipulate video objects as part of their searching and browsing interaction , is one of these . It is the purpose of this thesis to constitute itself as a part of a larger stream of research that investigates visual objects as a possible approach to advancing the use of semantics in content-based visual retrieval . As the object retrieval problem can not be well solved by pairwise distances , many algorithms have been developed for improving visual retrieval result . In recent studies , it is demonstrated that the contextual (dis-)similarities based on diffusion process can be obtained directly by solving an optimization problem with a general regularization framework , which contains a smoothness constraint and a fitting constraint . The Improved PSO-OSD and Three-phased PSO-OSD algorithms have been tested on five benchmark problems and the results have been compared . Finally , using the improved radial basis function neural network we propose a new method for object image retrieval . The images to be retrieved are object images that can be divided into foreground and background . The experimental results demonstrate that our method is able to produce correct results in the presence of articulations , stretching , and contour deformations . The most significant scientific contributions of this paper include ( i ) the introduction of a novel partition point extraction technique for point context and contour segments as well as ( ii ) a new fused similarity measure for object matching and recognition , and ( iii ) the impressive robustness of the method in an object retrieval scenario as well as in a real application for environmental microorganism recognition . This method uses holographic associative memory principles combined with digital image processing techniques . The inclusion of digital image processing helps in eliminating the iterations which are otherwise mandatory when using neural network principles for object information retrieval . The implementation method is explained and simulation results are presented . These techniques have been applied to overall image contents without taking into account the characteristics of individual objects . While the techniques work well for the retrieval of images with similar overall contents ( including backgrounds ) , their accuracies are limited because they are unable to take advantage of individual object 's visual characteristics , and to perform object-level retrieval . This paper looks specifically at the use of colour-pair technique for fuzzy object-level image retrieval . More specifically , we will focus on the gPb-owt-ucm segmentation algorithm and the SIFT local features since they are the most contrasted techniques in their respective fields . Furthermore , using an object retrieval benchmark , this dissertation research will analyze three basic questions : ( i ) the usefulness of an interest points hierarchy based on a contour strength signal , ( ii ) the influence of the context on both interest points location and description , and ( iii ) the analysis of regions as spatial support for bundling interest points . Firstly , E2LSH(Exact Euclidean Locality Sensitive Hashing ) is used , and a group of scalable random visual vocabularies is generated;then , a new object model consisting of contextual semantic information is devised , which is drawn from the visual elements surrounding the query object;finally , the Kullback-Leibler divergence is introduced as a similarity measurement to accomplish object retrieval . Experimental results indicate that the distinguishability of objects is effectively improved and the object retrieval performance method is substantially boosted compared with the traditional methods . The advantage of long range connections makes it possible to stick with a small number of probing filters , while the small number of probing filters makes it possible to directly use fully connected layers . Object classification is widely used in computer vision as a testbed for evaluating neural network designs , and the neural network parameters learned from this task may be transferred to other high - level understanding tasks such as object retrieval and scene parsing . Thus we choose 3D object classification as the task for evaluating our FPNN . Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments . There has also been rapid progress in learning geometric representations on 3D data . 3D ShapeNets [ reference ] introduced 3D deep learning for modeling 3D shapes , and several recent works [ reference ][ reference ][ reference ] also compute deep features from 3D data for the task of object retrieval and classification . While these works are inspiring , their focus is centered on extracting features from complete 3D object models at a global level . Proximitysearchis a concept from information retrieval ( IR ) that we applied to searching graph-structured databases [ GSVGM98 ] . In a traditional IR setting , proximity search is typically implemented with a Near operator and is effective for identifying documents that contain multiple keywords in close proximity— where distance is defined based on the number of characters separating the keywords . But when structure is present , textual nearness is not always appropriate . That is simply because it turns out that , by nothing that the directories have actually done by themselves and by pure luck , the business is perfectly suited for internet . It has great content for proximity searching . This means that you can look for the closest flower shop , closest taxi , etc . Text Analysis : R & RStudio . Using text analysis you can create word clouds , do proximity searches , and show frequency of a word across data . Our system is based on our Japanese text retrieval system [ 4 ] , to which English tokenizer/stemmer has been added to process English text . Our indexing system stores term positions , thus providing proximity-based search , in which the user can specify the distance between query terms . What our system does is outlined as follows : In particular , most real-world graphs are heterogeneous , containing objects of multiple types , which present new opportunities for many problems on graphs . Consider a typical proximity search problem on graphs , which boils down to measuring the proximity between two given nodes . Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity , without accounting for different " semantic classes"-for instance , on a social network two users can be close for different reasons , such as being classmates or family members , which represent two distinct semantic classes . Typed proximity search of the form type = person NEAR company"IBM " , paper"XML " is an increasingly useful search paradigm in ER graphs . Proximity search implementations either perform a Pagerank-like computation at query time , which is slow , or precompute , store and combine per-word Pageranks , which can be very expensive in terms of preprocessing time and space . We present HubRank , a new system for fast , dynamic , spaceecient proximity searches in ER graphs . A Proximity Search Engine ( PSE ) which finds neighboring vectors of a specific input vector is used in various important applications such as neural network-based recognition systems , vector quantizers ( VQ ) and content addressable memories ( CAM ) . It has been known , however , that the PSE is area and power hungry , since many arithmetic operations are required . A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming . In our framework , large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-proposed proximity search paradigm . Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach . Proximity searching is central to several applications , ranging from multimedia indexing and querying to data compression and clustering . In this paper we present a new approach to solve the proximity searching problem . Our solution is based on indexing the database with the k-nearest neighbor graph ( knng ) , which is a directed graph connecting each element to its k closest neighbors . Our method can recover local strand details and has real-time performance . State-of-the-art hair modeling techniques rely on large hairstyle collections for nearest neighbor retrieval and then perform ad-hoc refinement . Our deep learning approach , in contrast , is highly efficient in storage and can run 1000 times faster while generating hair with 30 K strands . We introduce Generalized Dictionary Learning ( GDL ) , a simple but practical framework for learning dictionaries over the manifold of positive definite matrices . We illustrate GDL by applying it to Nearest Neighbor ( NN ) retrieval , a task of fundamental importance in disciplines such as machine learning and computer vision . GDL distinguishes itself from traditional dictionary learning approaches by explicitly taking into account the manifold structure of the data . Experimental evaluations on four publicly available high-dimensional datasets validate our claims . The proposed scheme outperforms , often significantly , other hashing algorithms on the task of near neighbor retrieval with set containment . Our proposal is simple and easy to implement in practice . Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval , but which also enable the interpretability of these operations by assigning explicit relevance weights to the single data components . Starting with the work [ 1 ] , it has been noticed , however , that this procedure has very limited validity in the important case of high data dimensionality or high feature correlations : the resulting relevance profiles are random to a large extend , leading to invalid interpretation and fluctuations of its accuracy for novel data . Its basic idea is based on the use of similarity template , which is a data structure that enumerates all the possible patterns of calculating similarity between two examples . In the method , the nearest neighbor retrieval process is optimized by generating retrieval queries from an input and similarity templates in a certain order . Its major advantages are as follows : 1 ) unlike the hierarchical organization of memory , it is guaranteed that examples with the greatest similarity value according to the given similarity measure are retrieved , 2 ) it is easy to add new examples to the example database , 3 ) without any expensive hardware such as massively parallel computers , nearly constant time nearest neighbor retrieval can be achieved , independently of the number of examples in the database . First , the separability of semantically distinct image classes was measured according to their reduced representations on the biplot . Then , a nearest-neighbor retrieval scheme was run on the emerged low-dimensional terrain to measure the suitability of the biplot for performing content-based image retrieval ( CBIR ) . The achieved organization performance when compared with the performance of a contemporary system was found superior . The knowledge source of aircraft fault ontology is analyzed according to ontology elements , and the construction method of ontology engineering for aircraft fault knowledge is proposed . On the basis of the ontology model , semantic retrieval model of aircraft fault knowledge is researched , similarity matching algorithm of semantic retrieval based on nearest neighbor retrieval strategy and its implementation process are proposed . A prototype of aircraft fault knowledge management system is developed and applied in an aircraft manufacturing enterprise . The index of character was formed and given to the numerical value of the original power . The nearest neighbors method retrieval was introduced and mathematic formula of similitude was given . By the visual basic program language , the retrieval interface was formed . First , CBR related theory and techniques are introduced . Second , a design study on the specific implementation of the CBR-based digital design for precision planter , propose a new retrieval algorithm-nearest neighbor algorithm based on k-d tree which will be more accurate and efficient , and , take the precision planter for example , experimental analysis of the design process ' realize , reasoning efficiency and accuracy , shows the feasibility and advantages of the CBR of application in the design problem . Our approach uses an encoder - decoder trained over an initial parallel corpus to build multilingual sentence representations , which are then incorporated into a new margin - based method to score , mine and filter parallel sentences . In contrast to previous approaches , which rely on nearest neighbor retrieval with a hard threshold over cosine similarity , our proposed method accounts for the scale inconsistencies of this measure , considering the margin between a given sentence pair and its closest candidates instead . Our experiments show large improvements over existing methods . The basic approach is to use a Recurrent Neural Network ( RNN ) encoder to map any input sentence to a fixed - length vector representation , which is coupled with an auxiliary RNN decoder and trained on initial parallel corpus . Having done that , sentence pairs are scored using the cosine similarity of their respective embeddings , so parallel corpora can be mined through nearest neighbor search and filtered by setting a hard threshold over this cosine score . In this paper , we argue that this retrieval method suffers from the scale of cosine similarity not being globally consistent .
Current search technologies can locate text or indexes , ranked by the frequency a term appears in a document , but not the term 's relevance to a set of symptoms . Current search technologies operate by diagnosis category , key words , indices , and content text . The program ranks matches by frequency , index , and content .
Hence , there is a necessity to handle multi-allelic genotype data . Here , we introduce a multi-allelic version of the Positional Burrows-Wheeler Transform ( mPBWT ) based on the bi-allelic version for compression and searching . The time-complexity for constructing the data structure and searching within a panel containing t-allelic sites increases by a factor of t. However , considering the small value for t , the time increase for the multi-allelic PBWT will be negligible .
This search method was capable to search the shortest path which representing the fewest number of deleted mappings , and also search the cheapest cost which representing the smallest total amount of deleted mappings ' confidence value . A∗ Search was both complete and optimal to minimize mapping repair size .
Abstract Many file organizations consist of an index or a directory , which is used for search purposes , and a set of data leaves in which the data records are stored . In order to decrease the index size so that it can fit into main memory , such file organizations may use multi-bucket leaves ( instead of “ ordinary ” data leaves ) , resulting , however , in a reduction of the average disk space utilization .
The present invention discloses a method for the wireless connection of a mobile terminal , the method comprising : initiating a search for an access point in the current environment ; determining whether to search for an access point within a predetermined time , the determination result is yes , the acquired search search and pause current access point ; Analyzing access pre-recorded information matches the current access point , the determination result is yes , the use of the access information to access the current access point ; current access point into the list ; continued search for the next access point , and returns to step determines whether the access point is searched within a predetermined time . The present invention also discloses a mobile terminal .
It is shown , by the martingale method , that the standard deviation of the weaker ? -s search without deep cutoffs is of the same order as the expected number of leaves evaluated . A nearly optimal upper bound on the variance of the general ?
Autonomous aerial vehicles play an important role in military applications such as in search , surveillance and reconnaissance . Multi-player stochastic pursuit – evasion ( PE ) differential game is a natural model for such operations involving intelligent moving targets with uncertainties .
Existing methods such as UDDI Uni- versal Description Discovery and Integration 2 and MDS Monitoring and Discovery Ser- vice 1 were discounted due to the limitation of a string matching syntactic approach 3 .A semantic approach was chosen because of the need to understand the complex relationships between system capabilities . The design focus onontologyartifactsinrelationtothewholedis- covery process is the central theme — explor- ingsearch performance withpractical Financial Services FS knowledge topologies .
Introduction to AltaVista search getting started with AltaVista search - about the AltaVista Search home page , simple search skills advanced search searching Usenet newsgroups providing information the AltaVista way - how AltaVista Search works for Web pages , excluding pages or sites from AltaVista search , designing your site , webmaster techniques using AltaVista Search A to Z reference the AltaVista story . Appendices : the top 1000 most common words on the World Wide Web a sample of 1000 queries frequency of words used in AltaVista Search queries .
Though technically possible , it is uncommon to use beam search when training with SEARN / DAgger . The early - update and LaSO training strategies , however , explicitly account for beam search , and describe strategies for updating parameters when the gold structure becomes unreachable during search . Early update and LaSO differ primarily in that the former discards a training example after the first search error , whereas LaSO resumes searching after an error from a state that includes the gold partial structure .
otherwise , where we have a margin violation at iff , and where considers the scores given by . This search procedure is illustrated in the top portion of Figure [ reference ] . In the forward pass of our training algorithm , shown as the first part of Algorithm [ reference ] , we run this version of beam search and collect all sequences and their hidden states that lead to losses .
Before the popularity of visual question answering ( VQA ) , text question answering ( QA ) had already been established as a mature research problem in the area of natural language processing . Previous QA methods include searching for the key words of the question in a search engine ; parsing the question as a knowledge base ( KB ) query ; or embedding the question and using a similarity measurement to find evidence for the answer . Recently , memory networks were proposed for solving the QA problem .
[ reference ][ reference ] . However , this type of approach can be memory - consuming and prohibitively slow , especially for searching tasks in large - scale datasets . Therefore , an aggregation method that can produce a compact template representation is desired .
The basic idea is to let the model use its own predictions at training time to produce a sequence of actions ( e.g. , the choice of the next word ) . Then , a search algorithm is run to determine the optimal action at each time step , and a classifier ( a.k.a . policy ) is trained to predict that action . A similar idea was later proposed by dagger in an imitation learning framework .
present versions of the TreeRNN model which are capable of operating over unparsed inputs . However , these methods require an expensive search process at test time . Our model presents a much faster alternative approach .
User issued search queries often do not follow formal grammatical structure , and require specialized language processing . Traditional natural language processing ( NLP ) tools trained on formal text ( e.g. treebanks ) often have difficulty analyzing search queries ; the lack of regularity in the structure of queries makes it difficult to train models that can optimally process the query to extract information that can help understand the user intent behind the query . One clear direction to improve query processing is to annotate a large number of queries with the desired annotation scheme .
Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation . Although studies have shown that the users of Web-based search engines typically enter short , term-based queries and rarely use search operators , little information exists concerning the effects of term and operator usage on the relevancy of search results .
Seven papers and a letter to the editor have been accepted for publication in the journal . The first paper , by Bevan Koopman , Peter Bruza , Laurianne Sitbon and Michael Lawley , titled “ Towards Semantic Search and Inference in Electronic Medical Records ” , presents concept – based information retrieval for searching electronic medical records and demonstrates that the approach outperforms keyword – based search , working especially well for queries where the latter performs poorly . This paper was awarded the best paper prize at the workshop .
In this paper , a distributed and cooperative strategy for information retrieaval on the Web is proposed to substitute the centralized mode adopted by the current search engines . Then a new information retrieval system model IRSM is presented , which supports the retrieval of metadata about Web documents and uses Z39.50 standard protocol to unify the heterogeneous interfaces of different systems . Based on that , a distributed and cooperative information retrieval framework , called DCIRF , is designed to help users in fast and effective information retrieval on the Web .
The system also provides a highly customizable platform for researchers who want to investigate individual and group information seeking behaviors in a lab or a field setting . The demo not only shows back-end components and front-end interaction elements of the system , but also how one could easily configure Coagmento for user studies involving information seeking/retrieval with digital libraries ( including the Web ) .
Utilizing the power of this widely-available , higher-level statistical package should facilitate the application of this technique for similar and related analyses of sequence-based activities . The discussion includes identification of applications in information retrieval analysis , common problems with statistical and analytical technique , samples of workabk- , code , and examples of relevant output .
In this paper , we present FIRE - a multimodal interface for informa- tion retrieval deployed in the Intelligent Room at the MIT AI Lab . FIRE differs from most othe ri nterfaces forinformation retrieval in that it combines a couple of interaction modalities to improve the search process .
In this chapter we discuss the integration of information retrieval information from two sources-a social network and a document reference network-for enhancing reference-based search engine rankings . In particular , current models of information retrieval are blind to the social context that surrounds information resources , thus they do not consider the trustworthiness of their authors when they present the query results to the users .
Joins.- An Efficient Indexing Scheme for Moving Objects ' Trajectories on Road Networks.- Spatial Index Compression for Location-Based Services Based on a MBR Semi-approximation Scheme.- XML Query Processing.- KCAM : Concentrating on Structural Similarity for XML Fragments.- A New Structure for Accelerating XPath Location Steps.- Efficient Evaluation of Multiple Queries on Streamed XML Fragments.- Information Retrieval I.- Automated Extraction of Hit Numbers from Search Result Pages.- Keyword Extraction Using Support Vector Machine.- LSM : Language Sense Model for Information Retrieval.- Information Retrieval II.- Succinct and Informative Cluster Descriptions for Document Repositories.- LRD : Latent Relation Discovery for Vector Space Expansion and Information Retrieval.- Web Image Retrieval Refinement by Visual Contents.- Sensor Networks and Grid Computing.- An Effective Approach for Hiding Sensitive Knowledge in Data Publishing.- Tracking Network-Constrained Moving Objects with Group Updates.- Dynamic Configuring Service on Semantic Grid.- Peer-to-Peer.- Object Placement and Caching Strategies on AN.P2P.- Role-Based Peer-to-Peer Model :
Aimed at the defects of the traditional keyword-based retrieval method , an ontology-basedsemantic query-oriented information retrieval model is built . Based on the establishment of domainontology knowledge base , the system combined the intelligent semantic Web technology with theinformation retrieval method , with the core that users can validly search the information of professionalareas by means of Chinese word segmentation algorithm and semantic expansion and that adomain-oriented intelligent retrieval system is established .
It achieves 7.42 % which is another 1.3 % absolute improvement ( or 15 % relative improvement ) over the best previous result of . subsection : Information Retrieval with Paragraph Vectors We turn our attention to an information retrieval task which requires fixed - length representations of paragraphs .
This research was supported in part by Netherlands Organization for Scientific Research through the Exploratory Political Search project ( ExPoSe , NWO CI # 314.99.108 ) , by the Digging into Data Challenge through the Digging Into Linked Parliamentary Data project ( DiLiPaD , NWO Digging into Data # 600.006.014 ) , and by the Center for Intelligent Information Retrieval . Any opinions , findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors .
Larger corpora for EBM tasks have been derived using ( noisy ) automated annotation approaches . This approach has been used to build , e.g. , datasets to facilitate work on Information Retrieval ( IR ) models for biomedical texts . Similar approaches have been used to ‘ distantly supervise ’ annotation of full - text articles describing clinical trials .
Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality . Therefore , the proposed approach is an improvement over state-of-the-art approaches for object retrieval .
Promising new directions have been opened up for content-based visual retrieval in recent years . Object-based retrieval which allows users to manipulate video objects as part of their searching and browsing interaction , is one of these . It is the purpose of this thesis to constitute itself as a part of a larger stream of research that investigates visual objects as a possible approach to advancing the use of semantics in content-based visual retrieval .
As the object retrieval problem can not be well solved by pairwise distances , many algorithms have been developed for improving visual retrieval result . In recent studies , it is demonstrated that the contextual (dis-)similarities based on diffusion process can be obtained directly by solving an optimization problem with a general regularization framework , which contains a smoothness constraint and a fitting constraint .
The Improved PSO-OSD and Three-phased PSO-OSD algorithms have been tested on five benchmark problems and the results have been compared . Finally , using the improved radial basis function neural network we propose a new method for object image retrieval . The images to be retrieved are object images that can be divided into foreground and background .
The experimental results demonstrate that our method is able to produce correct results in the presence of articulations , stretching , and contour deformations . The most significant scientific contributions of this paper include ( i ) the introduction of a novel partition point extraction technique for point context and contour segments as well as ( ii ) a new fused similarity measure for object matching and recognition , and ( iii ) the impressive robustness of the method in an object retrieval scenario as well as in a real application for environmental microorganism recognition .
This method uses holographic associative memory principles combined with digital image processing techniques . The inclusion of digital image processing helps in eliminating the iterations which are otherwise mandatory when using neural network principles for object information retrieval . The implementation method is explained and simulation results are presented .
These techniques have been applied to overall image contents without taking into account the characteristics of individual objects . While the techniques work well for the retrieval of images with similar overall contents ( including backgrounds ) , their accuracies are limited because they are unable to take advantage of individual object 's visual characteristics , and to perform object-level retrieval . This paper looks specifically at the use of colour-pair technique for fuzzy object-level image retrieval .
More specifically , we will focus on the gPb-owt-ucm segmentation algorithm and the SIFT local features since they are the most contrasted techniques in their respective fields . Furthermore , using an object retrieval benchmark , this dissertation research will analyze three basic questions : ( i ) the usefulness of an interest points hierarchy based on a contour strength signal , ( ii ) the influence of the context on both interest points location and description , and ( iii ) the analysis of regions as spatial support for bundling interest points .
Firstly , E2LSH(Exact Euclidean Locality Sensitive Hashing ) is used , and a group of scalable random visual vocabularies is generated;then , a new object model consisting of contextual semantic information is devised , which is drawn from the visual elements surrounding the query object;finally , the Kullback-Leibler divergence is introduced as a similarity measurement to accomplish object retrieval . Experimental results indicate that the distinguishability of objects is effectively improved and the object retrieval performance method is substantially boosted compared with the traditional methods .
The advantage of long range connections makes it possible to stick with a small number of probing filters , while the small number of probing filters makes it possible to directly use fully connected layers . Object classification is widely used in computer vision as a testbed for evaluating neural network designs , and the neural network parameters learned from this task may be transferred to other high - level understanding tasks such as object retrieval and scene parsing . Thus we choose 3D object classification as the task for evaluating our FPNN .
Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments .
There has also been rapid progress in learning geometric representations on 3D data . 3D ShapeNets [ reference ] introduced 3D deep learning for modeling 3D shapes , and several recent works [ reference ][ reference ][ reference ] also compute deep features from 3D data for the task of object retrieval and classification . While these works are inspiring , their focus is centered on extracting features from complete 3D object models at a global level .
Proximitysearchis a concept from information retrieval ( IR ) that we applied to searching graph-structured databases [ GSVGM98 ] . In a traditional IR setting , proximity search is typically implemented with a Near operator and is effective for identifying documents that contain multiple keywords in close proximity— where distance is defined based on the number of characters separating the keywords . But when structure is present , textual nearness is not always appropriate .
That is simply because it turns out that , by nothing that the directories have actually done by themselves and by pure luck , the business is perfectly suited for internet . It has great content for proximity searching . This means that you can look for the closest flower shop , closest taxi , etc .
Text Analysis : R & RStudio . Using text analysis you can create word clouds , do proximity searches , and show frequency of a word across data .
Our system is based on our Japanese text retrieval system [ 4 ] , to which English tokenizer/stemmer has been added to process English text . Our indexing system stores term positions , thus providing proximity-based search , in which the user can specify the distance between query terms . What our system does is outlined as follows :
In particular , most real-world graphs are heterogeneous , containing objects of multiple types , which present new opportunities for many problems on graphs . Consider a typical proximity search problem on graphs , which boils down to measuring the proximity between two given nodes . Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity , without accounting for different " semantic classes"-for instance , on a social network two users can be close for different reasons , such as being classmates or family members , which represent two distinct semantic classes .
Typed proximity search of the form type = person NEAR company"IBM " , paper"XML " is an increasingly useful search paradigm in ER graphs . Proximity search implementations either perform a Pagerank-like computation at query time , which is slow , or precompute , store and combine per-word Pageranks , which can be very expensive in terms of preprocessing time and space . We present HubRank , a new system for fast , dynamic , spaceecient proximity searches in ER graphs .
A Proximity Search Engine ( PSE ) which finds neighboring vectors of a specific input vector is used in various important applications such as neural network-based recognition systems , vector quantizers ( VQ ) and content addressable memories ( CAM ) . It has been known , however , that the PSE is area and power hungry , since many arithmetic operations are required .
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming . In our framework , large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-proposed proximity search paradigm . Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach .
Proximity searching is central to several applications , ranging from multimedia indexing and querying to data compression and clustering . In this paper we present a new approach to solve the proximity searching problem . Our solution is based on indexing the database with the k-nearest neighbor graph ( knng ) , which is a directed graph connecting each element to its k closest neighbors .
Our method can recover local strand details and has real-time performance . State-of-the-art hair modeling techniques rely on large hairstyle collections for nearest neighbor retrieval and then perform ad-hoc refinement . Our deep learning approach , in contrast , is highly efficient in storage and can run 1000 times faster while generating hair with 30 K strands .
We introduce Generalized Dictionary Learning ( GDL ) , a simple but practical framework for learning dictionaries over the manifold of positive definite matrices . We illustrate GDL by applying it to Nearest Neighbor ( NN ) retrieval , a task of fundamental importance in disciplines such as machine learning and computer vision . GDL distinguishes itself from traditional dictionary learning approaches by explicitly taking into account the manifold structure of the data .
Experimental evaluations on four publicly available high-dimensional datasets validate our claims . The proposed scheme outperforms , often significantly , other hashing algorithms on the task of near neighbor retrieval with set containment . Our proposal is simple and easy to implement in practice .
Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval , but which also enable the interpretability of these operations by assigning explicit relevance weights to the single data components . Starting with the work [ 1 ] , it has been noticed , however , that this procedure has very limited validity in the important case of high data dimensionality or high feature correlations : the resulting relevance profiles are random to a large extend , leading to invalid interpretation and fluctuations of its accuracy for novel data .
Its basic idea is based on the use of similarity template , which is a data structure that enumerates all the possible patterns of calculating similarity between two examples . In the method , the nearest neighbor retrieval process is optimized by generating retrieval queries from an input and similarity templates in a certain order . Its major advantages are as follows : 1 ) unlike the hierarchical organization of memory , it is guaranteed that examples with the greatest similarity value according to the given similarity measure are retrieved , 2 ) it is easy to add new examples to the example database , 3 ) without any expensive hardware such as massively parallel computers , nearly constant time nearest neighbor retrieval can be achieved , independently of the number of examples in the database .
First , the separability of semantically distinct image classes was measured according to their reduced representations on the biplot . Then , a nearest-neighbor retrieval scheme was run on the emerged low-dimensional terrain to measure the suitability of the biplot for performing content-based image retrieval ( CBIR ) . The achieved organization performance when compared with the performance of a contemporary system was found superior .
The knowledge source of aircraft fault ontology is analyzed according to ontology elements , and the construction method of ontology engineering for aircraft fault knowledge is proposed . On the basis of the ontology model , semantic retrieval model of aircraft fault knowledge is researched , similarity matching algorithm of semantic retrieval based on nearest neighbor retrieval strategy and its implementation process are proposed . A prototype of aircraft fault knowledge management system is developed and applied in an aircraft manufacturing enterprise .
The index of character was formed and given to the numerical value of the original power . The nearest neighbors method retrieval was introduced and mathematic formula of similitude was given . By the visual basic program language , the retrieval interface was formed .
First , CBR related theory and techniques are introduced . Second , a design study on the specific implementation of the CBR-based digital design for precision planter , propose a new retrieval algorithm-nearest neighbor algorithm based on k-d tree which will be more accurate and efficient , and , take the precision planter for example , experimental analysis of the design process ' realize , reasoning efficiency and accuracy , shows the feasibility and advantages of the CBR of application in the design problem .
Our approach uses an encoder - decoder trained over an initial parallel corpus to build multilingual sentence representations , which are then incorporated into a new margin - based method to score , mine and filter parallel sentences . In contrast to previous approaches , which rely on nearest neighbor retrieval with a hard threshold over cosine similarity , our proposed method accounts for the scale inconsistencies of this measure , considering the margin between a given sentence pair and its closest candidates instead . Our experiments show large improvements over existing methods .
The basic approach is to use a Recurrent Neural Network ( RNN ) encoder to map any input sentence to a fixed - length vector representation , which is coupled with an auxiliary RNN decoder and trained on initial parallel corpus . Having done that , sentence pairs are scored using the cosine similarity of their respective embeddings , so parallel corpora can be mined through nearest neighbor search and filtered by setting a hard threshold over this cosine score . In this paper , we argue that this retrieval method suffers from the scale of cosine similarity not being globally consistent .
Phase II-SOF Knowledge Coupler-Based Phase I XML Schema Multi-allelic positional burrows-wheeler transform Minimizing the estimated solution cost with A∗ search to support minimal mapping repair Using multi-bucket data leaves with overflow chains-performance analysis Mobile terminal and wireless connection method The variance of two game tree algorithms Stochastic multi-player pursuit–evasion differential games Grid service discovery in the financial markets sector The AltaVista Search Revolution Sequence-to-Sequence Learning as Beam-Search Optimization Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering GhostVLAD for set-based face recognition Sequence Level Training with Recurrent Neural Networks A Fast Unified Model for Parsing and Sentence Understanding Identifying Well-formed Natural Language Questions Form and function: The impact of query term and operator usage on Web search results Advances in artificial intelligence research in health. Distributed and cooperative information retrieval on the World Wide Web Coagmento 2.0: A system for capturing individual and group information seeking behavior Method of Coding Search Strings as Markov Processes Using a Higher Level Language. FIRE: An Information Retrieval Interface Fo rI ntelligent Environments Social network models for enhancing reference-based search engine rankings Advances in Web-Age Information Management, 7th International Conference,WAIM 2006, Hong Kong, China, June 17-19, 2006, Proceedings Study and Design of Intelligent Retrieval System Based on Ontology Distributed Representations of Sentences and Documents Neural Ranking Models with Weak Supervision A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature Fast large-scale object retrieval with binary quantization An object-based approach to retrieval of image and video content Triplet-Based Regularized Diffusion Process for Improving Visual Retrieval An improved radial basis function neural network for object image retrieval Shape Matching Using Point Context and Contour Segments Retrieving Full Object Information from Partial Object Information using Digital Holography Content-based retrieval of segmented images Visual object analysis using regions and interest points Object Retrieval Method Based on Randomized Visual Dictionaries and Contextual Semantic Information FPNN: Field Probing Neural Networks for 3D Data DeepFlux for Skeletons in the Wild 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Summarizing and Searching Sequential Semistructured Sources Pricing and pricing policies from the perspective of a subscription agent Research Guides: Text Analysis: R & RStudio Structuring and expanding queries in the probabilistic model Metagraph-based Learning on Heterogeneous Graphs Dynami cPersonalize dPageran ki nEntity-Relatio nGraphs A fine-grain, current mode scheme for VLSI proximity search engine Proximity search heuristics for wind farm optimal layout Using the k-Nearest Neighbor Graph for Proximity Searching in Metric Spaces HairNet: Single-View Hair Reconstruction Using Convolutional Neural Networks Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment Inferring Feature Relevances From Metric Learning Optimizing nearest neighbor retrieval by similarity template and retrieval query generation On the Perceptual Organization of Image Databases Using Cognitive Discriminative Biplots Ontology modeling and semantic retrieval for aircraft fault knowledge Injection Moldbase Design Based on CBR CBR-based digital design and research for precision planter Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
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