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During the last two decades, a systematic re-examination of the whole information science field has taken place around the FIS--Foundations of Information Science--initiative. With the occasion of its Fourth Conference in Beijing 2010, a group of selected contributors and leading practitioners of those fields have been invited to contribute to this Special Issue. What is the status of information science today? What is the relationship between information and the laws of nature? Is information merely "physical"? What is the difference between information and computation? Has the genomic revolution changed the contemporary views on information and life? And what about the nature of social information? Cogent answers to these questions and to quite many others are attempted in the contributions that follow. [ABSTRACT FROM AUTHOR]

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches. [ABSTRACT FROM AUTHOR]

The Information Retrieval user experience has remained largely unchanged since its inception for computers and mobile devices alike. However, recent developments in Virtual Reality hardware (pioneered by Oculus Rift in 2013) could introduce a new environment for Information Retrieval. This paper reports the results of a Scoping Literature Review (PRISMA-ScR) by rigorously examining the entire body of relevant literature with reproducible methods. The following research questions are answered: "What prototypes and concepts of Virtual Reality Information Retrieval systems with current generation hardware exist?", "How are user interaction and especially user input realised in these systems?", "What Retrieval features are used in these systems?", "How are search results displayed in these systems?" and "Can these VR IR systems compare to traditional (non-VR) IR systems?". After querying Google Scholar, Scopus and Web of Science, 1042 documents were reviewed in depth. Key features and attributes of the systems are summarised and discussed. Sketches of the user interfaces are included as well. The 30 documents that were relevant to the research questions include 16 distinct systems or theories. They discuss and utilise several user input technologies, ranging from controllers, voice input or hand tracking. Although conventional retrieval features are less common, systems enable retrieval of literature, 3D objects, images, books and texts and arrange them in a virtual space (e.g. as grids, arcs or maps). Finally, many of these systems were compared to conventional counterparts through user evaluation (n = 10). Most found user task times to be shorter or equal (n = 5, n = 3). In the seven papers that measured user performance (rate of correct solutions), three reported better performance (one equal). Notably, users always were more satisfied with the Virtual Reality systems compared to conventional ones. Possible limitations of these evaluations are demographic selection and the quality of baseline systems (control). [ABSTRACT FROM AUTHOR]

This paper examines the decision points over the course of ten years of development of an institutional repository. Specifically, the focus is on the impact and influence from the open-source community, the needs of the local institution, the role that team dynamics plays, and the chosen platform. Frequently, the discussion revolves around the technology stack and its limitations and capabilities. Inherently, any technology will have several features and limitations, and these are important in determining a solution that will work for your institution. However, the people running the system and developing the software, and their enthusiasm to continue work within the existing software environment in order to provide features for your campus and the larger open-source community, will play a bigger role than the technical platform. These lenses are analyzed through three points in time: the initial roll out of our institutional repository, our long-term running and maintenance, and eventual new development and why we made the decisions we made at each of those points in time. [ABSTRACT FROM AUTHOR]

In the biomedical field due to tremendous medical research, every year the medical papers generated is very huge. In order to process these tremendous amounts of textual data, the information retrieval techniques are combined with machine learning methods like natural language processing (NLP) for finding the useful insights in data for NLP tasks. In this work, we have addressed the challenges involved in existing NLP techniques like Word2Vec, Doc2Vec, and Biosent2Vec and followed by the traditional classification of articles with Concept2Vec and random forest classification. Concept vector techniques are emerging methods in NLP and in information retrieval systems. Pretrained concept encoders are available in Machine learning, whereas no such exists in biomedical domain. In this paper a proposal on Concept2Vec trained from biomedical text is highlighted. Our work accurately emphasizes on the quality of semantics defined between the medical terms, which can help the medical practitioners in taking errorless decisions. In this work 20 thousand biomedical documents are considered for Concept2Vec embeddings and the proposed approach have shown best concept similarity in terms of relevant measures compared with existing approaches such as Word2Vec, global vectors (GloVe), fastText, and BioSentVec, bidirectional encoder representations from transformers (BERT), biomedical-BERT. [ABSTRACT FROM AUTHOR]

The process of data analytics on large-scale government administrative data — that belong to various domains like education, transport, energy, and health — can be enhanced by retrieving pertinent documents from diverse data sources. Without a supporting framework of metadata, big data analytics can be daunting. Even though statistical algorithms can perform extensive analyses on a variety of data with little help from metadata, applying these techniques to heterogeneous data may not always result in reliable findings. Recently, semantics-aware (or semantic search) search techniques received much attention as they utilize implicit knowledge to enhance the search. Similarly, traditional search engines rely on the inherent linkages within the underlying data model to improve their search quality. In the case of general-purpose information retrieval systems, to gather information from the internet (open access data) or to access open government administrative data, a domain agnostic ontology shall be employed to supply background knowledge. This paper draws on research undertaken by the authors at IIIT Bangalore Center for Open Data Research (CODR) in developing a semantics-aware data lake framework to host and analyze government administrative data. In this study, we present an ontology-based document retrieval solution where an ontology serves as an intermediary to close the gap between what the user seeks and what the search retrieves. Although our study settings are based on the Government of Karnataka (GoK, India), we believe the findings have wider resonance. Our experimental results based on agricultural data from the GoK look promising. [ABSTRACT FROM AUTHOR]

Information retrieval aims to retrieve the documents that answer users' queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR for a better understanding system effectiveness. This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapted the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set, nor the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query. [ABSTRACT FROM AUTHOR]

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon's, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a "subjective information", i.e., trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. [ABSTRACT FROM AUTHOR]

Query, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization. [ABSTRACT FROM AUTHOR]

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes' identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood. [ABSTRACT FROM AUTHOR]

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used. [ABSTRACT FROM AUTHOR]

Let us consider the following scenario: A human is working with a corpus of text documents. In this corpus, the human needs to know documents with similar content and highlight relevant locations in the retrieved documents. An information system displaying the contents of the corpus and providing an information retrieval agent will help the human. To perform information retrieval on the corpus, the agent used internally in the information system may need additional data associated with the documents. In order to support this, the so-called Subjective Content Descriptions (SCDs) provide additional location-specific data for text documents. SCDs are subjective in the sense that the agent associates data with sentences to reflect the beliefs of users. In our scenario, the agent needs SCDs referencing sentences of similar content across various documents in the corpus and most text documents are not associated with SCDs. Therefore, this paper presents UESM, the Unsupervised Estimator for SCD Matrices, an approach to associate any corpus with SCDs. In an evaluation, we show that the performance of UESM in estimating topics of similar content in the corpus is on par with Latent Dirichlet Allocation, while UESM provides the SCDs referencing sentences of similar content. [ABSTRACT FROM AUTHOR]

The analysis of the information retrieval system focuses on the notion of appropriate and irrelevant documents. The performance predictor, including accuracy and reminder, is used to establish how well the device satisfies consumer requirements. The effectiveness of the indexing and retrieval is calculated by contrasting a typical collection of queries and documents with the efficacy, functionality, and systemic approach. Important evaluations are used to measure functionality, performance (precession and retraction), compilation, and interface assessments. Document and query indexing, query assessment, and system assessment are key issues in information retrieval. This paper uses the human–computer interaction based on the intelligent information retrieval method (HCI-IRM). The proposed method concentrates on customer satisfaction, the main success assessment metric. It identifies the collection of related records at a given time within the collection. An information retrieval system's main objective is to obtain the information. It is either the actual information or the documents containing the information substitutes which completely or partly correspond to the customer's query evaluation. The extraction and recruitment of knowledge-based data from a database are usually related to the retrieval of information. The retrieval and precise technology are used to assess the efficiency of the data recovery system framework. As a result, HCI-IRM enhances the response time, and the relevance of the outcomes is key to customer satisfaction. Comparison of Yahoo and Google search engines focused on accuracy and reminder technology. [ABSTRACT FROM AUTHOR]

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques. [ABSTRACT FROM AUTHOR]

Due to the tremendous use of computers, the need for dealing with digital information continues to grow. This has led to the need for constructing a structure for saving information in a way that eases data retrieval and processing. An information system is a table form that contains all the data needed for a user to reach a decision. Information systems are the most common form used for knowledge and data representation, and attribute reduction plays an important role in data processing. This paper is devoted to proposing a strategy for attributes reduction based on the similarity relation and pretopological concepts. Moreover, new types of pretopological spaces are to be constructed from the information system. Furthermore, a comparison between pretopologies and their pre-interiors constructed from the information system, as well as the different types of pretopological spaces, is investigated. Finally, the concept of the cover pretopology is applied to the information system. [ABSTRACT FROM AUTHOR]

Data structures and especially the relationship among the data entities have changed in the last couple of years. The network-like graph representations of data-model are becoming more and more common nowadays, since they are more suitable to depict these, than the well-established relational data-model. The graphs can describe large and complex networks — like social networks — but also capable of storing rich information about complex data. This was mostly of relational data-model trait before. This also can be achieved with the use of the knowledge representation tool called "hypergraphs". To utilize the possibilities of this model, we need a practical way to store and process hypergraphs. In this paper, we propose a way by which we can store hypergraphs model in the SAP HANA in-memory database system which has a "Graph Core" engine besides the relational data model. Graph Core has many graph algorithms by default however it is not capable to store or to work with hypergraphs neither are any of these algorithms specifically tailored for hypergraphs either. Hence in this paper, besides the case study of the two information systems, we also propose pseudo-code level algorithms to accommodate hypergraph semantics to process our IS model. [ABSTRACT FROM AUTHOR]

Ontology design and querying the data set to obtain accurate results has been a topic of research. The aim of the paper is to represent ontological knowledge in the field of Medical Information Systems to store the knowledge about Swine flu disease and thereby querying the data and formalize the knowledge base development. The developed knowledge from RDF is converted to rules and then querying is done through different methods and performance analysis has been done on the retrieved query. The results predicted determine the suitability of the method where querying gives the accurate result for swine flu disease ontology. [ABSTRACT FROM AUTHOR]

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other’s original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of $(\epsilon, \delta)$ -data-privacy, defined as the probability of $\epsilon $ -accurate estimate (the difference of an estimation and the original data is within $\epsilon $ ) is no larger than $\delta $ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor’s original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm. [ABSTRACT FROM AUTHOR]

We consider the minimax estimation problem of a discrete distribution with support size $k$ under privacy constraints. A privatization scheme is applied to each raw sample independently, and we need to estimate the distribution of the raw samples from the privatized samples. A positive number $\epsilon $ measures the privacy level of a privatization scheme. For a given $\epsilon $ , we consider the problem of constructing optimal privatization schemes with $\epsilon $ -privacy level, i.e., schemes that minimize the expected estimation loss for the worst-case distribution. Two schemes known in the literature provide order optimal performance in the high privacy regime where $\epsilon $ is very close to 0, and in the low privacy regime where $e^{\epsilon }\approx k$ , respectively. In this paper, we propose a new family of schemes which substantially improve the performance of the existing schemes in the medium privacy regime when $1\ll e^{\epsilon } \ll k$. More concretely, we prove that when $3.8 < \epsilon <\ln (k/9) $ , our schemes reduce the expected estimation loss by 50% under $\ell _{2}^{2}$ metric and by 30% under $\ell _{1}$ metric over the existing schemes. We also prove a lower bound for the region $e^{\epsilon } \ll k$ , which implies that our schemes are order optimal in this regime. [ABSTRACT FROM AUTHOR]

This paper utilizes social cognitive learning theory to guide and explain the development of prototype educational experiences for information systems concepts. The paper applies the major framework of social cognitive learning theory, its recognition of the reciprocity and interaction among cognitive, behavioral, environmental, and physiological/affective influences. In this learning process, it is argued that people learn from a variety of experiences and from the observation of the actions of others. This paper uses social cognitive learning theory to explain the structure for an MIS educational experience, and applies a group support system (GSS) in a course module with specific learning objectives. Finally, the paper describes an MIS classroom experience (a module covering systems analysis [SA] methodology) which has been used to apply the GSS to prototype MIS problems (working scenarios with questions and organizational data). [ABSTRACT FROM AUTHOR]

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user's model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed. • Modern information access systems should balance fairness and effectiveness. • A single eigensystem achieves simultaneous maximization. • Fairness, effectiveness, and access measures are crucial in trade-offs. [ABSTRACT FROM AUTHOR]

Background: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. Methods: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. Results: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. Conclusion: The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies. [ABSTRACT FROM AUTHOR]

The term ''Big Data'' refers to massive volumes of data that grow at an increasing rate and encompass complex data types such as audio and video. While the applications of Big Data and analytic techniques for business purposes have received considerable attention, it is less clear how external sources of Big Data relate to the transaction processing-oriented world of accounting information systems. This paper uses the Resource-Event- Agent Enterprise Ontology (REA) (McCarthy 1982; International Standards Organization [ISO] 2007) to model the implications of external Big Data sources on business transactions. The five-phase REA-based specification of a business transaction as defined in ISO (2007) is used to formally define associations between specific Big Data elements and business transactions. Using Big Data technologies such as Apache Hadoop and MapReduce, a number of information extraction patterns are specified for extracting business transaction-related information from Big Data. We also present a number of analytics patterns to demonstrate how decision making in accounting can benefit from integrating specific external Big Data sources and conventional transactional data. The model and techniques presented in this paper can be used by organizations to formalize the associations between external Big Data elements in their environment and their accounting information artifacts, to build architectures that extract information from external Big Data sources for use in an accounting context, and to leverage the power of analytics for more effective decision making. [ABSTRACT FROM AUTHOR]

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, privacy and anonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal. [ABSTRACT FROM AUTHOR]

Rules in artificial intelligence are able to provide inference facilities for making effective decisions in information retrieval. In such a scenario, a keyword-based information retrieval systems use the keywords as indexes generated by Web crawlers without considering semantics. Moreover, many Web search engines perform re-ranking based on relevance feedback. However, the relevant documents in e-learning applications must be stored in local repositories and must be updated dynamically based on the level of the users. Therefore, it is necessary to have an information retrieval system which performs relevant information extraction and stores such relevant information dynamically in local e-learning repositories. In order to address these issues, a new information retrieval and local storage system is proposed in this paper by applying rules for making effective decisions in the storage and retrieval algorithms which are newly proposed in this paper. For this purpose, two new algorithms called intelligent rule-based relevant information retrieval algorithm with semantics and a secured information storage using semantic knowledge representation are proposed in this paper for effectively retrieving the e-learning contents from the Web on computer science subject and to store them in local repositories with semantic indexing. The major advantages of the proposed information retrieval system include increase in accuracy, reduction in retrieval time and effective storage in local repositories for further use. [ABSTRACT FROM AUTHOR]

Purpose: Consumers often encounter issues of perceived ambiguity and performance risk when attempting to evaluate experience goods being offered online. Sellers try to alleviate this knowledge gap often seen in a medium of low naturalness by engaging in effective compensatory adaptation. This research theoretically looks into three primary aspects of compensatory adaption and their potential in securing communication of high-quality information between the online seller and consumer. Design/methodology/approach: Utilizing survey data and structural equation modeling, this study tests the effectiveness of different aspects of compensatory adaption to alleviate the knowledge gap in a medium of low naturalness. Findings: Drawing on media naturalness theory and the tripartite model of attitude, this paper identifies three theoretical components that significantly affect the effectiveness of compensatory adaption. They are information retrieval capability from the cognitive/logical aspect, information richness from the affective/audiovisual aspect and interactivity from the behavioral aspect. The effectiveness of compensatory adaptation proves to have a positive impact on perceived information quality. Originality/value: To the best of our knowledge, this is the first paper in the information systems literature to examine the compensatory adaptation tools for effective transfer of information. This study contributes to the academics by providing three handles to improve effectiveness of compensatory adaptation toward information quality. We focus on three compensatory adaptation tools in cognitive/logical, affective/audiovisual and behavioral aspects, and this compensation perspective leads to three practical factors that affect effective transfer of information between online sellers and consumers. The result of this study complements the nomological network of the enablers and impediments of e-commerce. [ABSTRACT FROM AUTHOR]

Nowadays, current information systems are so large and maintain huge amount of data. At every time, they process millions of documents and millions of queries. In order to choose the most important responses from this amount of data, it is well to apply what is so called early termination algorithms. These ones attempt to extract the Top-K documents according to a specified increasing monotone function. The principal idea behind is to reach and score the most significant less number of documents. So, they avoid fully processing the whole documents. WAND algorithm is at the state of the art in this area. Despite it is efficient, it is missing effectiveness and precision. In this paper, we propose two contributions, the principal proposal is a new early termination algorithm based on WAND approach, we call it MWAND (Modified WAND). This one is faster and more precise than the first. It has the ability to avoid unnecessary WAND steps. In this work, we integrate a tree structure as an index into WAND and we add new levels in query processing. In the second contribution, we define new fine metrics to ameliorate the evaluation of the retrieved information. The experimental results on real datasets show that MWAND is more efficient than the WAND approach. [ABSTRACT FROM AUTHOR]

The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL SAM (UBCF LDA SIMILAR ADD MEAN) which integrates topic model with user-based collaborative filtering model. UL SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL SAM on improving recommendation effectiveness that UL SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model. [ABSTRACT FROM AUTHOR]

The volume of scientific documents and articles has increased dramatically in the last decade. It makes too difficult to find the relevant documents based on the user’s query. Information retrieval systems help researchers to find relevant scientific articles. One of the capabilities that help researchers to find the relevant papers is the feature of finding related scientific articles to an article. In other words, this feature allows the researcher to view other related articles by selecting one article. The purpose of this research is to present a method based on the analysis of bibliographic references to identify related scientific articles in digital libraries. The statistical population of this research is the articles published in the last 5 years in Persian and English publications indexed in the ISC in the field of computer science. The proposed method is able to find the articles that are most similar to the given article by analyzing the references of the articles and ranking them based on their similarity. To do that, after extracting the title and obtaining the similarity between the existing references among the articles, those articles that have the most similarity are identified and sorted based on their similarity. The proposed method has been compared with other methods, and the obtained results on both Persian and English data are promising. [ABSTRACT FROM AUTHOR]

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP). [ABSTRACT FROM AUTHOR]

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Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of Information Retrieval (IR) evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter $p$ and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales. [ABSTRACT FROM AUTHOR]

The task of researching and developing information retrieval systems is becoming important in the big data age. Current search methods try to mention to fast searching based on keyword matching or similar semantic between query and documents but have not got a really effective engine for semantic search . In this paper, we propose a method for information retrieval based on probability inference with the DLMC model to search by semantic equivalents and a topic word with score for fast searching. Results of the experimental with 952 Vietnamese documents show that our method is really effective for Vietnamese document retrieval system. [ABSTRACT FROM AUTHOR]

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users' feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate. [ABSTRACT FROM AUTHOR]

This paper investigates error correction in two‐dimensional (2‐D) intersymbol interference (ISI) channels using polar codes. 2‐D channels offer high data storage capacity compared to traditional one‐dimensional (1‐D) channels but suffer from greater ISI effects. Error correction codes can be used to mitigate the effects of ISI in 2‐D data storage systems and improve their performance and reliability. Polar codes achieve the symmetric capacity for the class of binary‐input discrete memoryless channels (B‐DMCs). A post‐processing technique is proposed to improve the performance of polar codes in the 2‐D ISI channel. The simplified successive cancellation (SSC) polar decoder is useful for data storage systems owing to its good waterfall region performance, low error floors and good throughput rates and is therefore adopted for the study. Simulation results indicate that the proposed technique has an error correction performance comparable to some existing decoders and has better performance than other decoding techniques. [ABSTRACT FROM AUTHOR]

The article focuses on the audio and video analysis for multimedia interactive services. It describes a system that automates home video editing. It automatically extracts a set of highlight segments from a set of raw home videos and aligns them with user-supplied incidental music based on the content of the video and incidental music. Finally, it introduces a method for interactive image retrieval using query feedback. It learns the user query as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session.

Tamil literature has many valuable thoughts that can help the human community to lead a successful and happy life. Tamil literary works are abundantly available and searchable on the Internet. However, the existing search systems follow a keyword-based match strategy that fails to satisfy user needs. This necessitates the demand for a focused information retrieval system that semantically analyzes the Tamil literary text, which will eventually improve the search system performance. This paper proposes a novel information retrieval framework that uses discourse processing techniques in aiding semantic analysis and representation of Tamil literary texts. The proposed framework was tested using two ancient literary works, the Thirukkural and Naladiyar, which were written in 300 Before the Common Era (BCE). The Thirukkural comprises 1,330 couplets, each 7 words long, while the Naladiyar consists of 400 quatrains, each 15 words long. The proposed system, tested with all the 1,330 Thirukkural couplets and 400 Naladiyar quatrains, achieved a mean average precision (MAP) score of 89 percent. The performance of the proposed framework was compared with Google Tamil search and a keyword-based search, which was a substandard version of the proposed framework. Google Tamil search achieved a MAP score of 56 percent while keyword-based method achieved a MAP score of 62 percent. It showed that the discourse processing techniques could improve the search performance of an information retrieval system. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers’ safety if used in smartphones and other information retrieval systems in vehicles. [ABSTRACT FROM AUTHOR]

Clinical Decision Support (CDS) is widely seen as an information retrieval (IR) application in the medical domain. The goal of CDS is to help physicians find useful information from a collection of medical articles with respect to the given patient records, in order to take the best care of their patients. Most of the existing CDS methods do not sufficiently consider the semantic relation between texts, hence the potential in improving the performance in biomedical articles retrieval. This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set. Evaluation results show that our method outperforms the strong baselines and is able to improve over the best runs in the TREC CDS tasks. [ABSTRACT FROM AUTHOR]

The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks. [ABSTRACT FROM AUTHOR]

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work. [ABSTRACT FROM AUTHOR]

The Semantic Web shares many goals with Decision Support Systems (DSS), e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields (Semantic Web and DSS), based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios. [ABSTRACT FROM AUTHOR]

Background. During the last fifty years, improved information retrieval techniques have become necessary because of the huge amount of information people have available, which continues to increase rapidly due to the use of new technologies and the Internet. Stemming is one of the processes that can improve information retrieval in terms of accuracy and performance. Aim. This paper provides a detailed assessment of the current status of the stemming process framed in an information retrieval application field by tracing its historical evolution. Method. Papers presenting the first approaches for stemming were reviewed to extract their main features, benefits and drawbacks. Additionally, papers dealing with stemmers for non-English languages or with some more recent proposals were also consulted and compiled. Finally, experimental papers defining the most well-known methods and metrics aimed at evaluating and classifying stemmers were also taken into account to expose their contributions and results. Results. Even if not all researchers agree on the benefits and drawbacks of using stemming in an information retrieval process in general terms, many of them agree on its benefits in specific contexts, such as when the language is highly inflective, when documents are short or when there is limited space for storing data. Some researchers also state that the nature of the documents can influence the performance and the accuracy of the stemmer. Conclusions. Despite many researchers having investigated this field over many years, there are still some open questions, such as how to evaluate a stemmer independently of the information retrieval process, or how much a stemmer improves an information retrieval application in terms of speed. As a summary, some guidelines are also provided to help readers to determine which is the best stemmer for their needs and the tasks they have to carry out. [ABSTRACT FROM AUTHOR]