Search

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

The article presents information on the use of a type of matrix to analyze citations of scientific papers. Scientific papers have a series of citations belonging to the previous literature. It is possible to investigate the internal connections among papers in a group, whose papers are frequently cited by themselves, by examining their citations. The main purpose of this study is to investigate the measurements by which the internal connections of a group of scientific papers are measured. The techniques of using the type of matrix is further developed to carry out the analysis of a certain type of group of scientific papers.

In a conventional library the librarian eliminates individual differences in concepts in queries and maintains the reliability of information. In a digital library, however, the goal is to not involve people, and so queries which eliminate precision problems in query results are in demand as a replacement for the librarian. In current retrieval systems, precision is improved by using a hierarchical method such as synonym notation processing. In methods based on word systems, processing to handle the inadequate expression of the intention of a query or the semantic interpretation of a query are insufficient, and information retrieval from a more hierarchical perspective is needed. In this paper the authors propose a retrieval method using a hierarchical perspective based on a system of concepts, and not a retrieval method based on conventional word systems. By adopting the EDR electronic dictionary as a thesaurus and using concept information, queries and words which appear in papers are not processed merely as character strings, but are used in queries with consideration for the meaning of the words. The authors' method can estimate imprecise information in the intentions of queries and the meaning hidden in words, allowing for appropriate query results to be provided to users without the cooperation of a librarian. © 2001 Scripta Technica, Syst Comp Jpn, 32(8): 40–52, 2001 [ABSTRACT FROM AUTHOR]

Paper filtering systems that support the effective collection of related journal papers are becoming important as the technological progress has been rapid. However, previous systems achieved insufficient filtering effectiveness because of a lack of available information in the abstract parts of journal papers that are available to the public. In this paper, we propose a paper filtering method using multiple information such as structure, co-authors, subject category, terminology, and terms in patent document. In an evaluation of 15 subjects using 4875 journal papers, it became clear that for all subjects the effectiveness of the proposed method exceeded that of the conventional method when all of the information above was used, thus increasing suitability for practical use. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(5): 21-32, 2012; Published online in Wiley Online Library (). DOI 10.1002/ecj.10423 [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

This paper deals with the specific features of historical papers relevant for information retrieval and bibliometrics. The analysis is based mainly on the citation indexes accessible under the Web of Science (WoS) but also on field-specific databases: the Chemical Abstracts Service (CAS) literature database and the INSPEC database. First, the journal coverage of the WoS (in particular of the WoS Century of Science archive), the limitations of specific search fields as well as several database errors are discussed. Then, the problem of misspelled citations and their 'mutations' is demonstrated by a few typical examples. Complex author names, complicated journal names, and other sources of errors that result from prior citation practice are further issues. Finally, some basic phenomena limiting the meaning of citation counts of historical papers are presented and explained. [ABSTRACT FROM AUTHOR]

Due to the progress of Internet technology and the increase of distributed information on networks, the present approach to knowledge management has been based more and more on the performance of various experienced users. In addition to the increase in electronic documents, the use of paper documents has not been reduced due to their convenience. This paper describes a method of tracking paper document locations and contents using radio frequency identification (RFID) technology. This research also focuses on the expression of a task process and the seamless structuring of related electronic and paper documents as a result of task knowledge formalization using information organizing. A system is proposed here that implements information organization for both Web documents and paper documents using the task model description and RFID technology. Examples of a prototype system are also presented. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(8): 52-63, 2012; Published online in Wiley Online Library (). DOI 10.1002/ecj.10370 [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is impossible to accurately transport the aerial ground wire repair robot to the repair point and efficiently repair the ground wire broken strands and scattered strands, this paper designs an aerial ground wire repair robot based on Unmanned Aerial Vehicle (UAV). Firstly, the mechanical structure of the overhead ground wire repair robot is selected and designed, and the information transmission and the cooperative operation of each module are realized. A landing mark based on ArUco code is designed, which uses C++ for visual navigation, and then uses the double closed‐loop Proportional Integral Derivative (PID) control method to control the position and attitude of the UAV, so as to realize the tracking of the robot position by the UAV. At the same time, the mechanical structure and the control method of the UAV hanging device are designed to cooperate with the overhead ground wire repair robot to ensure the accurate completion of the repair operation. Finally, a UAV‐based aerial ground wire repair robot prototype was developed and successfully applied to the repair operation of the broken strand of the aerial ground wire of the 66 to 67 tower of the 500 kV Jiangzi line in Ya'an. It is verified that the UAV‐based aerial ground wire repair robot studied in this paper has a good engineering application value. [ABSTRACT FROM AUTHOR]

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings ( MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing. [ABSTRACT FROM AUTHOR]

The article presents information on the inclusion of alerting service on health topics for medical journalists. A good journalist has to be able to access the latest information, analyse and filter it, assessing its quality and authoritativeness, while dealing with ever-increasing sources of information. The alerting service was designed using predefined criteria. Medical journals were selected on the basis of their impact factor. The selection of papers was discretional or based on their editorial comments. Various publisher's press alerts where available. Articles were selected when they had received an editorial comment or had been mentioned in the original press alerts from publishers. The service was expected to be objective, authoritative, and to provide items with real news value.

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

This paper reports on an investigation into the scholarly impact of the TRECVid (Text Retrieval and Evaluation Conference, Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis à vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams 'scored' highly against the evaluation criteria and the relationship between 'top scoring' teams at TRECVid and the 'top scoring' papers in terms of citations is analyzed. A strong relationship was found between 'success' at TRECVid and 'success' at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed. [ABSTRACT FROM AUTHOR]

Controllable synthesis of high‐performance materials with low‐cost is critical for the development of functional devices. Herein, a series of sequence‐controlled low‐dispersity oligomers with acid–base chromotropic capability via simple copolymerization between N‐substituted maleimides (NMI) and methyldiallylamine (MDAA) are synthesized. The structure and molecular weight of the oligomers are characterized. Owing to the presence of strong electrophilic carbonyl oxygen and imine nitrogen, a rapid‐responsive color switching system is achieved. This change is attributed to the acid/base‐triggered isomerization between enolate state and the enol or keto tautomer via intramolecular proton transfer. More importantly, the color of P(MDAA/NMI) is regulated by the variation of the substituent group of the N‐substituted maleimide. A new type of rewritable paper based on the designed N‐substituted maleimide oligomers is fabricated by a simple spin‐coating process, on which images with high resolution can be acid‐printed and base‐erased for over ten cycles. The writing and erasure times can be as short as 10 s and the legible time can be more than 90 days under ambient conditions. The as‐formed rewritable paper with excellent rewriting performances is low‐cost and easy for large‐scale production and may find more advanced potential applications in memory devices, rewritable labels, an sensors. [ABSTRACT FROM AUTHOR]

One of the most significant inaccuracies of bibliometric databases is that of omitted citations, namely, missing electronic links between a paper of interest and some citing papers, which are (or should be) covered by the database. This paper proposes a novel approach for estimating a database's omitted-citation rate, based on the combined use of 2 or more bibliometric databases. A statistical model is also presented for (a) estimating the 'true' number of citations received by individual papers or sets of papers, and (b) defining an appropriate confidence interval. The proposed approach could represent a first step towards the definition of a standard for evaluating the accuracy level of databases. [ABSTRACT FROM AUTHOR]

In this article, we propose a graph-based interactive bibliographic information retrieval system-GIBIR. GIBIR provides an effective way to retrieve bibliographic information. The system represents bibliographic information as networks and provides a form-based query interface. Users can develop their queries interactively by referencing the system-generated graph queries. Complex queries such as 'papers on information retrieval, which were cited by John's papers that had been presented in SIGIR' can be effectively answered by the system. We evaluate the proposed system by developing another relational database-based bibliographic information retrieval system with the same interface and functions. Experiment results show that the proposed system executes the same queries much faster than the relational database-based system, and on average, our system reduced the execution time by 72% (for 3-node query), 89% (for 4-node query), and 99% (for 5-node query). [ABSTRACT FROM AUTHOR]

This paper examines in close relation two fields of growing importance: model‐based systems engineering (MBSE) and natural language processing (NLP). System models provide a structured description of engineering data, whose inherent semantics often remains hard to explore. Natural language understanding, (i.e., the machine analysis of texts produced by humans) an important field of NLP, focuses on semantic text comprehension but cannot directly account for structured information sources. In this paper, we investigate natural language understanding of MBSE artifacts as they appear in industrial scenarios. In this context, the wide and heterogeneous knowledge space and user base calls for novel techniques to facilitate information retrieval from complex system models. We thus propose to leverage domain‐specific text generators to transform models into a descriptive text corpus on which we apply state‐of‐the‐art semantic search and analysis techniques. We illustrate the approach on relevant MBSE examples by performing a qualitative evaluation of intuitive text search and comprehension in textual descriptions obtained from system models. [ABSTRACT FROM AUTHOR]

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited. Aims/Objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research. Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts. Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted. Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community. [ABSTRACT FROM AUTHOR]

Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features -- protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F-measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated -- information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications. [ABSTRACT FROM AUTHOR]

This article focuses on critique of contemporary work in information retrieval. Bar-Hillel's use of the term "literature search" likewise has objections. According to the established meaning, a chemist "searches the literature" to find out (for example) if a given compound was over before produced. He wants at least a "yes" or "no" answer. Thus an information retrieval system can provide great assistance to a chemist in his job of searching the literature by helping him to find documents, but it cannot do his "literature searching."

Previous analyses identified research on environmental tobacco smoke to be subject to strong fluctuations as measured by both quantitative and qualitative indicators. The evolution of search algorithms (based on the Web of Science and Web of Knowledge database platforms) was used to show the impact of errors of omission and commission in the outcomes of scientometric research. Optimization of the search algorithm led to the complete reassessment of previously published findings on the performance of environmental tobacco smoke research. Instead of strong continuous growth, the field of environmental tobacco smoke research was shown to experience stagnation or slow growth since mid-1990s when evaluated quantitatively. Qualitative analysis revealed steady but slow increase in the citation rate and decrease in uncitedness. Country analysis revealed the North- European countries as leaders in environmental tobacco smoke research (when the normalized results were evaluated both quantitatively and qualitatively), whereas the United States ranked first only when assessing the total number of papers produced. Scientometric research artifacts, including both errors of omission and commission, were shown to be capable of completely obscuring the real output of the chosen research field. [ABSTRACT FROM AUTHOR]

Examines the practicality of the Electronic Data Gathering Analysis and Retrieval (EDGAR) pilot program of the Securities and Exchange Commission in the U.S. Development of the EDGAR pilot program; Preparation for electronic filing using the pilot program; Problems and system issues encountered on the use of the EDGAR pilot program.

Search and rescue operations are all time‐sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real‐life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators. [ABSTRACT FROM AUTHOR]

Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real‐time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation process of proposed blockchain is divided into five subroutines: hash pointer computation, real‐time data pudding, signature value iteration, interruption, block header assembly. The real‐time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and enhances the speed of new block generation and the bandwidth of data storage on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real‐time electrical data transmission application, raising the data transmission reliability with no harm to real‐time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid. [ABSTRACT FROM AUTHOR]

Background: Information specialists conducting searches for systematic reviews need to consider key questions around which and how many sources to search. This is particularly important for public health topics where evidence may be found in diverse sources. Objectives: The objective of this review is to give an overview of recent studies on information retrieval guidance and methods that could be applied to public health evidence and used to guide future searches. Methods: A literature search was performed in core databases and supplemented by browsing health information journals and citation searching. Results were sifted and reviewed. Results: Seventy‐two papers were found and grouped into themes covering sources and search techniques. Public health topics were poorly covered in this literature. Discussion: Many researchers follow the recommendations to search multiple databases. The review topic influences decisions about sources. Additional sources covering grey literature eliminate bias but are time‐consuming and difficult to search systematically. Public health searching is complex, often requiring searches in multidisciplinary sources and using additional methods. Conclusions: Search planning is advisable to enable decisions about which and how many sources to search. This could improve with more work on modelling search scenarios, particularly in public health topics, to examine where publications were found and guide future research. [ABSTRACT FROM AUTHOR]

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field‐specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time‐consuming, laborious, costly, and error‐prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open‐source Python package and an accompanying web‐app, Paperfetcher, to automate the retrieval of article metadata for handsearching. With Paperfetcher's assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. Paperfetcher has an easy‐to‐use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, Paperfetcher is the first tool to automate handsearching with high usability and a multi‐disciplinary focus. [ABSTRACT FROM AUTHOR]

In this paper, we address a problem arising in information retrieval (IR) called composite retrieval problem (CRP) of diverse and complementary bundles. The CRP aims to group items into bundles and then select a subset of such bundles, so that we can maximise the similarity of the items within a bundle and, simultaneously, can maximise the complementarity of the selected bundles. To this end, the CRP approach considers the existing relations among items’ attributes, leading to the selection of bundles that satisfy users’ expectations without the needing for any refining query and, thus, improving the searching experience, with respect to traditional IR approaches. In this study, we propose three efficient yet straightforward algorithms, namely Local Search, Iterative Local Search and Variable Neighbourhood Search. Further, two different neighbourhood moves are evaluated at each algorithm. Although the first neighbourhood move is focused on the exploitation of the nearby search space, the second one is focused on the exploration of larger portions of the search space. All these algorithms are applied to two real-world publicly available instances and compared to the state-of-the-art algorithms in CRP. Obtained results suggest that combining both neighbourhood moves leads to better results in terms of both, the complementarity of the bundles and similarity of the items. Further, results show that, after statistical analysis, the proposed algorithms are significantly better, for the vast majority of the experiments performed in this study, when compared to the state-of-the-art algorithms in CRP. [ABSTRACT FROM AUTHOR]

The number of authors of papers in four scientific journals is positively correlated with the number of significant words in the titles of the same papers. A fifth journal showed no correlation. Implications for information retrieval are explored and tentative explanations are offered. [ABSTRACT FROM AUTHOR]

Computational diffractive imaging eschews the use of traditional lenses and curved mirrors and relies instead upon ultra-miniature diffractive optical elements paired with application-specific computational algorithms to achieve a desired end-to-end imaging function. The diffraction grating does not yield a traditional, human-interpretable image, but instead acts to preserve spatial information sufficient for estimation algorithms such as Tikhonov regularization or fast Fourier deconvolution to compute an image. Special purpose gratings can extract application-specific visual information for tasks such as motion estimation, point tracking, bar-code reading, and more. [ABSTRACT FROM AUTHOR]

Optimal design of urban cloud transportation planning based on information retrieval and social IoT is proposed in this manuscript. Transportation is a necessary part of the human society's production, life and economic development. With the development of the times and advancement of science and technology, people are strongly dependent on transportation and at the same time, they have put forward more demands on transportation. In this paper, based on the omnidirectional and complex road conditions of vehicle nodes in the urban road network environment, and the road environment has blocking and superimposed interference effects on wireless signals, combined with the intelligent driving model, this paper analyzes and studies the propagation of network worms and builds a stable and reliable system. The experimental results have proven the performance, and the future research directions have been pointed out. [ABSTRACT FROM AUTHOR]

Quality assurance (QA) is an important process in ensuring that systematic reviews and other evidence syntheses are supported by a high‐quality search. This paper describes how the National Institute for Health and Care Excellence (NICE) in the UK established a development pathway to ensure its information specialists had the skills, knowledge and confidence to undertake search QA. The key component of the pathway is that it blends technical knowledge with interpersonal skills. The pathway develops technical skills in the early steps before using peer support activities to build confidence while undertaking a range of searches. QA is effective when the search lead communicates the contextual information that has influenced search development. QA is treated as a collaboration to get the right search for the review. The key requirements for search QA, alongside technical knowledge, are communication, collaboration and negotiation skills. [ABSTRACT FROM AUTHOR]

Discussions relating to the identification of courses and the development of curricula for the information- science field cannot yield meaningful results unless the conceptual framework of the field is first delimited and its basic segments defined. Starting with the current ASIS definition of information science, an analysis is made of the rationale of current thinking relevant to the conceptual boundaries and intellectual content of the field. Information science is analyzed to be comprised of the segments of: (1) Conceptualization, (2) Storage/Transmission, and (3) Utilization. Two of the segments are shown to be based on subjective notions, which can be investigated, for the most part, through the employment of statistical and normative survey methodology. One of the segments is shown to be susceptible of the application of more rigorous research methodology capable of yielding empirical proof. Since all three segments form integral parts of the field, they need to be fully represented in the information science curriculum. The technologist as well as the humanist can make significant contributions to the growth and development of the information-science field. [ABSTRACT FROM AUTHOR]

Summary: Due to the outstanding performance of the deep network architecture–based hash for data storage and retrieval in recent years, it has been widely applied in massive image retrieval. Most previous approaches have not paid attention to the significant effect on the hash model of quantization error during the learning process. Furthermore, the saturated loss function may result in similar hash codes being generated by large‐difference images. The underuse of classification information in the training process also brings about poor performance by hash codes in retrieval assignments. In this paper, we propose a novel quantitative regularization term with an exponential convergence rate to minimize the impact of quantization error on the model and accelerate the convergence speed of the network. In the training process, to resolve the dilemma caused by saturation loss functions, a new sigmoid function with a slope parameter that can be changed automatically according to the number of iterations is proposed. For the sufficient application of classification information, triplet labels and image labels are used in parallel under the same framework by integrating image labels into the output layer. The experiment results indicate that our algorithm is superior to several advanced hash methods on two standard datasets. [ABSTRACT FROM AUTHOR]

The article introduces various topics covered in the issue including benefits of using sequence representations in medical field, the effectiveness of the retrieval-given queries, and major improvements in template extraction post temporality classifications.

As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval. [ABSTRACT FROM AUTHOR]

Music–visual retrieval is of broad interest in the field of Music Information Retrieval (MIR). Most research relies on emotional tags or is based on content but does not consider stylistic and cultural differences between music and videos. As a result, only one‐sided dimensions are considered for automatic music retrieval for videos, while the stylistic correlation between audio‐visual is ignored. At the same time, the needs of different cultural regions cannot be well met. Therefore, the first labelled extensive Music Video (MV) dataset, Next‐MV, is constructed in this paper consisting of 6000 pieces of 30‐s MV fragments, including five music style labels and four cultural labels. The proposed Next‐Net framework is built to study the correlation between music style and visual style. The optimal audiovisual feature set and model structure are obtained in the experiments. The accuracy reached 71.1%, higher than the baseline model (66.9%). Furthermore, in the cross‐cultural experiment, it is found that the accuracy of the general fusion model (71.1%) is between the model trained by within‐dataset (76%) and the model trained by cross‐dataset (60%), indicating that culture has a significant influence on the correlation between music and visual. The experiments of pair classification on cultures are further carried out. It is found that Rock and Dance are more culturally influenced than R&B and Hip‐hop. Among all the cultures discussed, Chinese and Japanese music and videos show great differences among most of the styles, while Korean music videos styles are more similar to western styles than other eastern cultures. [ABSTRACT FROM AUTHOR]

Recently, resistance random access memories (RRAMs) have been studied extensively, because the demand for information storage is increasing. However, it remains challenging to obtain a flexible device because the active materials involved need to be nontoxic, nonpolluting, distortion‐tolerable, and biodegradable as well adhesive to diverse flexible substrates. In this paper, tannic acid (TA) and an iron ion (FeIII) coordination complex were employed as the active layer in a sandwich‐like (Al/active layer/substrate) device to achieve memory performance. A nontoxic, biocompatible TA‐FeIII coordination complex was synthesized by a one‐step self‐assembly solution method. The retention time of the TA‐FeIII memory performance was up to 15 000 s, the yield up to 53 %. Furthermore, the TA‐FeIII coordination complex can form a high‐quality film and shows stable ternary memory behavior on various flexible substrates, such as polyethylene terephthalate (PET), polyimide (PI), printer paper, and leaf. The device can be degraded by immersing it in vinegar solution. Our work will broaden the application of organic coordination complexes in flexible memory devices with diverse substrates. Flexible memory: Tannic acid (TA) and an iron (FeIII) coordination complex were employed as an active layer in a sandwich‐like (Al/active layer/substrate) device to achieve memory performance. A nontoxic, biocompatible TA‐FeIII coordination complex was synthesized by a one‐step self‐assembly solution method. This complex can form a film on various flexible substrates (PET, PI, paper, and leaf) and all display ternary memory performance (see graphic). [ABSTRACT FROM AUTHOR]

Research patterns could enhance understanding of the Information Systems (IS) field. Citation analysis is the methodology commonly used to determine such research patterns. In this study, the citation methodology is applied to one of the top-ranked Information Systems conferences—International Conference on Information Systems (ICIS). Information is extracted from papers in the proceedings of ICIS 2000 to 2002. A total of 145 base articles and 4,226 citations are used. Research patterns are obtained using total citations, citations per journal or conference, and overlapping citations. We then provide the citation ranking of journals and conferences. We also examine the difference between the citation ranking in this study and the ranking of IS journals and IS conferences in other studies. Based on the comparison, we confirm that IS research is a multidisciplinary research area. We also identify the most cited papers and authors in the IS research area, and the organizations most active in producing papers in the top-rated IS conference. We discuss the findings and implications of the study. [ABSTRACT FROM AUTHOR]