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The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [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]

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of "right" and "wrong" examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community. [ABSTRACT FROM AUTHOR]

This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia. [ABSTRACT FROM AUTHOR]

Finding new ways to help researchers and administrators understand academic fields is an important task for information scientists. Given the importance of interdisciplinary research, it is essential to be aware of disciplinary differences in aspects of scholarship, such as the significance of recent changes in a field. This paper identifies potential changes in 25 subject categories through a term comparison of words in article titles, keywords and abstracts in 1 year compared to the previous 4 years. The scholarly influence of new research issues is indirectly assessed with a citation analysis of articles matching each trending term. While topic‐related words dominate the top terms, style, national focus, and language changes are also evident. Thus, as reflected in Scopus, fields evolve along multiple dimensions. Moreover, while articles exploiting new issues are usually more cited in some fields, such as Organic Chemistry, they are usually less cited in others, including History. The possible causes of new issues being less cited include externally driven temporary factors, such as disease outbreaks, and internally driven temporary decisions, such as a deliberate emphasis on a single topic (e.g., through a journal special issue). [ABSTRACT FROM AUTHOR]

LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document‐topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to document‐topic pairs instead of single‐valued relevance labels. We propose five new probabilistic loss functions to deal with the higher expressive power provided by relevance judgments distributions and show how they can be applied both to neural and gradient boosting machine (GBM) architectures. Moreover, we show how training a LETOR model on a sampled version of the relevance judgments from certain probability distributions can improve its performance when relying either on traditional or probabilistic loss functions. Finally, we validate our hypothesis on real‐world crowdsourced relevance judgments distributions. Overall, we observe that relying on relevance judgments distributions to train different LETOR models can boost their performance and even outperform strong baselines such as LambdaMART on several test collections. [ABSTRACT FROM AUTHOR]

This paper considers an extension of the hidden Markov model, called the hierarchical hidden Markov model, and proposes an EM algorithm and an approximate algorithm. The EM algorithm proposed in this paper differs from the existing training algorithm, which is called the Baum–Welch algorithm, and guarantees that the likelihood is always increased by updating the parameters. The approximate algorithm has the advantage that it can be applied to problems in which observation of the training sentence and training of the parameters proceed in parallel. These algorithms and their derivations are simplified, compared to the existing training algorithm, by using stochastic context-free grammar. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(5): 59–69, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10172 [ABSTRACT FROM AUTHOR]

The authors describe a technique for selectively sharpening only edges that were blurred due to various causes without making interference factors such as random noise more conspicuous. The selective sharpening technique described in this paper is implemented as an iterative updating nonlinear algorithm obtained by discretizing a type of nonlinear reaction-diffusion time-evolution equations. The update terms of this algorithm consist of a quadratic nonlinear smoothing term, reaction term, and overshooting term. By performing quantitative evaluation experiments, the authors showed that the proposed technique has significantly better selective sharpness enhancement capabilities than existing sharpness enhancement techniques such as the peaking method. In addition, the authors introduced a processing parameter adaptive control method in the proposed technique and applied it to the suppression of breathing distortions in video sequences. Breathing distortions, which are video sequence distortions that often appear in old films, are time-varying blurring distortions that occur due to temporal variations of the focus. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(5): 33–47, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10086 [ABSTRACT FROM AUTHOR]

Ziv-Lempel (ZL) codes and Bayes codes are typical universal codes. An improved algorithm of the ZL code is widely used in compression software. On the other hand, practical use of Bayes codes is difficult due to the large amount of computation needed. However, a realizable algorithm in terms of computation effort has been constructed for the FSMX model group 9. In this paper, an information source generating a sequence by word units is assumed as a model that can represent the probabilistic structure of actual data such as text data. The asymptotic compression performance of both codes is analyzed and evaluated for the information source class (information source for the block unit) with a constant (fixed) word length. As a result, it is found that Bayes code cannot directly be universal as a coding algorithm for symbol units. On the other hand, the ZL78 code can be directly universal. Also, a configuration method for the Bayes coding method is given for an information source with a block unit. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(1): 58–69, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10017 [ABSTRACT FROM AUTHOR]

This paper defines a hash coding model for non-unique search keys and derives the expected number of accesses needed to retrieve all desired records from a computer storage device. The assumption that the records are stored on the basis of a non-unique key is often realized in information retrieval environments. The model assumes that the hashing algorithm and, should a collision occur, the skipping algorithm, both distribute the records randomly in memory. The results of this analysis are compared with those from a simulation in which the randomness criterion is not strictly met. [ABSTRACT FROM AUTHOR]

In information theory, the channel capacity is defined as the maximum of the transmission rate at which information can be conveyed reliably over a noisy channel. This paper presents a new algorithm for computing the capacity of discrete memoryless channels. The problem of determining the capacity is a concave programming. Thus it can be computed by solving a system of nonlinear equations termed the Kuhn-Tucker equations. Newton's method often is used to solve nonlinear equations. However, it does not have global convergence and may not converge when the starting point is not close to the solution. A globally convergent algorithm using simplicial subdivision has been proposed for the capacity evaluation, but it is very slow and cannot be applied to large-scale problems. In this paper, the Kuhn-Tucker equations are transformed into a system of equations with separable mappings by introducing auxiliary variables. This system of equations can be solved by a homotopy method using rectangular subdivision, which Is much more efficient than the simplicial-type algorithm. Moreover, the proposed algorithm has quadratic convergence, so it converges rapidly similarly to Newton's method, The global convergence of the proposed algorithm is also proved herein. By numerical examples, it is shown that the proposed algorithm is more efficient than the welt-known Arimoto algorithm.

With the rapid development of information techniques, the dimensionality of data in many application domains, such as text categorization and bioinformatics, is getting higher and higher. The high-dimensionality data may bring many adverse situations, such as overfitting, poor performance, and low efficiency, to traditional learning algorithms in pattern classification. Feature selection aims at reducing the dimensionality of data and providing discriminative features for pattern learning algorithms. Due to its effectiveness, feature selection is now gaining increasing attentions from a variety of disciplines and currently many efforts have been attempted in this field. In this paper, we propose a new supervised feature selection method to pick important features by using information criteria. Unlike other selection methods, the main characteristic of our method is that it not only takes both maximal relevance to the class labels and minimal redundancy to the selected features into account, but also works like feature clustering in an agglomerative way. To measure the relevance and redundancy of feature exactly, two different information criteria, i.e., mutual information and coefficient of relevance, have been adopted in our method. The performance evaluations on 12 benchmark data sets show that the proposed method can achieve better performance than other popular feature selection methods in most cases. [ABSTRACT FROM AUTHOR]

Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration. [ABSTRACT FROM AUTHOR]

People taking part in argumentative debates through collective annotations face a highly cognitive task when trying to estimate the group's global opinion. In order to reduce this effort, we propose in this paper to model such debates prior to evaluating their “social validation.” Computing the degree of global confirmation (or refutation) enables the identification of consensual (or controversial) debates. Readers as well as prominent information systems may thus benefit from this information. The accuracy of the social validation measure was tested through an online study conducted with 121 participants. We compared their human perception of consensus in argumentative debates with the results of the three proposed social validation algorithms. Their efficiency in synthesizing opinions was demonstrated by the fact that they achieved an accuracy of up to 84%. [ABSTRACT FROM AUTHOR]

3D image processing algorithms for real-time calibration of the baseline length in a single-camera stereo system are presented in this paper, and the performance of less than 0.03 mm depth measurement in precision and about 0.45 s processing time in the inspection are also presented based on the applications to the experimental equipment. It is technically noted that the baseline of this single-camera stereo system was precisely calibrated by the image processing procedure. The scope of application of this simply configured stereo system could be expanded to the wider fields of industrial 3D measurements. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 50–59, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10155 [ABSTRACT FROM AUTHOR]

Many algorithms have been implemented for the problem of text classification. Most of the work in this area was carried out for English text. Very little research has been carried out on Arabic text. The nature of Arabic text is different than that of English text, and preprocessing of Arabic text is more challenging. This paper presents an implementation of three automatic text-classification techniques for Arabic text. A corpus of 1445 Arabic text documents belonging to nine categories has been automatically classified using the kNN, Rocchio, and naïve Bayes algorithms. The research results reveal that Naïve Bayes was the best performer, followed by kNN and Rocchio. [ABSTRACT FROM AUTHOR]

Quite often algorithms designed for no-collision-detection radio networks use a hidden form of collision detection: it is assumed that a station can simultaneously send and listen. If it cannot hear its own message, apparently the message has been scrambled by another station sending at the same time. Industrial standard IEEE 802.11 says that a station can either send or listen to a radio channel at a given time, but not both. In order to relate the industrial standard and theoretical algorithms we consider a weak radio network model with no collision detection in which a station cannot simultaneously send and receive signals. Otherwise we talk about a strong model. In this paper we consider a measure called energy cost (or ‘power consumption’) which is equal to the maximum over all stations of the number of steps in which the station is sending or listening. We show that computational power of weak and strong single-hop radio networks differ substantially in the deterministic case: deterministic leader election requires $\Omega(\log n)$ energy cost in the weak model and can be solved by a practical algorithm with $O(\sqrt{\log n})$ energy cost in the strong model. By contrast, we present a very efficient randomized simulation of strong radio networks by weak ones, with preprocessing that requires $O(n)$ steps and has energy cost $O(\log \log n)$. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An algorithmic approach for generating a loosely structured set of documents based upon references and citations is described and initially tested. The technique is based upon a small subset (or single relevant document) of known relevant documents which form the basis of the network of structured references and citations. Suggestions for improvement and refinement point out potentially ripe research avenues for investigating the identification of relevant documents pertaining to emerging interdisciplinary subject areas. [ABSTRACT FROM AUTHOR]

The block orthogonal projection algorithm exhibits an excellent convergence property in a noise-free environment. However, a compromise must be sought between the speed and the precision of the convergence when there exists a noise. This paper considers the block orthogonal projection algorithm using the conjugate-gradient method, which is one of the realizations of the block orthogonal projection algorithm. The convergent value is evaluated, and it is shown that one of the major factors that determine the convergent value is the number of iterations in the algorithm. Based on the result of the error analysis, the optimal number of iterations for the block orthogonal projection algorithm is derived so that the effect of the noise can be reduced without sacrificing the high-speed property. For this purpose, the block adaptive algorithm is proposed, based on the block orthogonal projection algorithm using the conjugate-gradient method. computational complexity can be reduced drastically compared to the block orthogonal projection algorithm. [ABSTRACT FROM AUTHOR]

Frequently it is useful to abbreviate or otherwise transform keys used for the retrieval of information. These transformations include the compression of long keys into a fixed field length by operations on characters or groups of characters, hash or random transformations in order to obtain a direct address, or phonetic coding to order to group together keys that are in some way similar. The various transformations have differing effects on file retrieval schemes. Given a transformation algorithm and data to be transformed, it is possible to characterize certain qualities of the algorithm that re late to retrieval problems. This paper is concerned with some measures of effectiveness of such transformation algorithms. [ABSTRACT FROM AUTHOR]

For measuring multilevel impact, we introduce the distributive h-indices, which balance two important components (breadth and strength) of multilevel impact at various citing levels. After exploring the theoretical properties of these indices, we studied two cases: 57 library and information science ( LIS) journals and social science research in 38 European countries/territories. Results reveal that there are approximate power-law relations between distributive h-indices and some underlying citation indicators, such as total citations, total citing entities, and the h-index. Distributive h-indices provide comprehensive measures for multilevel impact, and lead to a potential tool for citation analysis, particularly at aggregative levels. [ABSTRACT FROM AUTHOR]

Query-by-humming systems offer content-based searching for melodies and require no special musical training or knowledge. Many such systems have been built, but there has not been much useful evaluation and comparison in the literature due to the lack of shared databases and queries. The MUSART project testbed allows various search algorithms to be compared using a shared framework that automatically runs experiments and summarizes results. Using this testbed, the authors compared algorithms based on string alignment, melodic contour matching, a hidden Markov model, n-grams, and CubyHum. Retrieval performance is very sensitive to distance functions and the representation of pitch and rhythm, which raises questions about some previously published conclusions. Some algorithms are particularly sensitive to the quality of queries. Our queries, which are taken from human subjects in a realistic setting, are quite difficult, especially for n-gram models. Finally, simulations on query-by-humming performance as a function of database size indicate that retrieval performance falls only slowly as the database size increases. [ABSTRACT FROM AUTHOR]

Recently there has been much research done in the field of information retrieval concerning the various kinds of "fusion" and their applications. One method of fusion, called collection fusion, is the fusing of results from multiple, autonomous collections of data, and is often used in the context of metasearch and distributed Information Retrieval. This report examines some long-held beliefs about common, effective data fusion techniques. Prior work demonstrates that significant improvement is often seen when using standard data fusion algorithms on an arbitrary collection of result sets from different information retrieval systems . This belief is supported by the supposition that different document retrieval strategies will rank documents differently, returning different sets of relevant and non-relevant documents. If this is true, it follows that voting algorithms that boost score and rank of a relevant document that is agreed upon across component systems should indeed improve retrieval , and merging algorithms can also be used to increase the recall of relevant documents in the merged set.

In this paper, the authors propose a method of assigning probabilistic class labels to unlabeled time-series data that can be collected inexpensively to use together with labeled data as training data in order to improve the performance of hidden Markov models (HMM) as classifiers. Conventional methods of using unlabeled data according to probabilistic class labels were only for static data. They could not deal with time-series data, which are used with HMMs. Therefore, the authors extended the conventional methods as follows. First, they formed extended tied-mixture hidden Markov models (ETM-HMMs) by mixing multiple HMMs that exist independently for each class. Then, they introduced the extended Baum-Welch (EBW) algorithm as the learning algorithm from time-series data in which labeled and unlabeled data coexist in the ETM-HMMs. The authors then assumed a situation in which there existed insufficient labeled training data and used the proposed method to perform classification experiments for Japanese sign language data and voice data. When unlabeled data were added to the labeled training data according to the proposed method, the classification error rate for Japanese sign language signs improved from 38.7% to 30.4%. Also, by making the model more detailed in addition to adding unlabeled data, the classification error rate for phonemes improved from 69.1% to 52.9%. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(13): 1–12, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10537 [ABSTRACT FROM AUTHOR]

It is said that a set L1 in a class C1 is an approximation of a set L2 in a class C2 if L1 is a subset of L2. An approximation L1 is said to be optimal if there is no approximation L′1 such that L1⊂L′1 and L′1-L1 is infinite. When the class C1 = P and C2 = NP, it is known that there is no optimal approximation under a quite general condition unless P = NP. In this paper we discuss the case where C1 = the class of NP-complete sets and C2 = coNP. A similar result as above that shows the difficulty of the optimal approximation is obtained. Approximating coNP sets by NP-complete sets plays an important role in the efficient generation of test instances for combinatorial algorithms. © 1999 Scripta Technica, Syst Comp Jpn, 30(7): 47–54, 1999 [ABSTRACT FROM AUTHOR]

In line with Sunstein's opening views on republican freedom, Horkheimer and Adorno forewarned against the sirens' call to endorse the culture industry's false projection of freedom as consumer sovereignty and/or liberty as noninterference. Moreover, they do not even deliver on the consumptive individualism initially promised because industries steer choices toward a systemic mass culture of homogeneity anyway (Horkheimer & Adorno, 1947; Sunstein, 2017, p. 175). Because the wider problem is with the American culture industry rendered global, I opt to focus primarily on American policy critiques that expand Horkheimer and Adorno's original critiques of radio, telegraph, television, and telephone to extend to the mass culture industry currently produced by social media. As a democratic theorist that can identify with Sunstein through our shared affinities for both deliberative democracy and a strong heritage of democratic republicanism read back into the founding of our nation (Sunstein, 2017, pp. 44-48), I would nonetheless decidedly take a more critical position of these monoliths than Sunstein's confidence in the market equilibrium to come. [Extracted from the article]

The template matching method is utilized in the document information processing and industrial vision. It is a method to extract from the given image patterns similar to the specified pattern. With the increase of the size of the image and the kinds of template patterns, however, the high-speed realization of the method becomes a problem. In this paper, a new high-speed pattern matching method is described for the binary image, based on the sequential similarity detection algorithm (ssDA). which is known as a means of high-speed realization. In the first part, the expected computation time by SSDA is calculated using the occurrence probability of the subpattern in the template. Then a method is proposed in which the order of pixel comparison is determined based on the occurrence probability of the blockwise subpatterns in the given image. A detailed discussion is made for the case where no matching error is permitted in the method. The method is applied to the case of two-dimensional IC chip pattern, indicating the effectiveness of the method in the high-speed realization of the pattern matching. [ABSTRACT FROM AUTHOR]

Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection. [ABSTRACT FROM AUTHOR]

This essay compares three models for conceptualizing the political and ethical challenges of contemporary genetics, genomics, and postgenomics. The three analytical approaches are referred to as the state‐politics model, the biopolitical model, and the infopolitical model. Each of these models is valuable for different purposes. In terms of their influence in contemporary discussions, the first is by far the dominant approach, the second is gaining in importance, and the third is almost entirely neglected. The widespread neglect of the infopolitical dimensions of genetic sciences that are the focus of the third model is puzzling in light of the fact that genetics, genomics, and postgenomics are all preeminent information sciences. The infopolitical model thus aims to bring into clearer view the specific political and ethical problems engendered by this informational nature of the genetic sciences. This model offers a way of understanding how ethically salient and politically fraught conceptual assumptions can be embedded in informational architectures such as algorithms and the formats (or data structures) upon which they rely. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) in the past has focused on extracting mentions in a local region, within a sentence or short paragraph. When dealing with user‐generated text, the diverse and informal writing style makes traditional approaches much less effective. On the other hand, in many types of text on social media such as user comments, tweets, or question–answer posts, the contextual connections between documents do exist. Examples include posts in a thread discussing the same topic, tweets that share a hashtag about the same entity. Our idea in this work is utilizing the related contexts across documents to perform mention recognition in a collective manner. Intuitively, within a mention coreference graph, the labels of mentions are expected to propagate from more confidence cases to less confidence ones. To this end, we propose a novel semisupervised inference algorithm named parameterized label propagation. In our model, the propagation weights between mentions are learned by an attention‐like mechanism, given their local contexts and the initial labels as input. We study the performance of our approach in the Yahoo! News data set, where comments and articles within a thread share similar context. The results show that our model significantly outperforms all other noncollective NER baselines. [ABSTRACT FROM AUTHOR]

Main path analysis is a famous network‐based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de‐preprinted" main paths for the original network, this article proposes an alternative solution with two‐fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles. [ABSTRACT FROM AUTHOR]

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine‐grained image ranking. We propose to combine attribute‐based representation and online passive‐aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine‐grained image ranking, we introduce a supervised constrained clustering method to gather class‐balanced training instances for local PA‐based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state‐of‐the‐art methods in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

The relationship between information and complexity is analyzed using a detailed literature analysis. Complexity is a multifaceted concept, with no single agreed definition. There are numerous approaches to defining and measuring complexity and organization, all involving the idea of information. Conceptions of complexity, order, organization, and 'interesting order' are inextricably intertwined with those of information. Shannon's formalism captures information's unpredictable creative contributions to organized complexity; a full understanding of information's relation to structure and order is still lacking. Conceptual investigations of this topic should enrich the theoretical basis of the information science discipline, and create fruitful links with other disciplines that study the concepts of information and complexity. [ABSTRACT FROM AUTHOR]

General sentiment analysis for the social web has become increasingly useful for shedding light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general-purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces 2 new methods, mood setting and lexicon extension, to improve the accuracy of topic-specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic-specific lexicon extension involves adding topic-specific words to the default general sentiment lexicon. Experiments with 8 data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest. [ABSTRACT FROM AUTHOR]

An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality-structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal-assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank-type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two-way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines. [ABSTRACT FROM AUTHOR]

Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification is restricted to documents belonging to clusters that potentially contain a high percentage of relevant documents. Empirical results show that the proposed framework improves the performance of several existing diversification methods. The framework also gives rise to a simple yet effective cluster-based approach to result diversification that selects documents from different clusters to be included in a ranked list in a round robin fashion. We describe a set of experiments aimed at thoroughly analyzing the behavior of the two main components of the proposed diversification framework, ranking and selecting clusters for diversification. Both components have a crucial impact on the overall performance of our framework, but ranking clusters plays a more important role than selecting clusters. We also examine properties that clusters should have in order for our diversification framework to be effective. Most relevant documents should be contained in a small number of high-quality clusters, while there should be no dominantly large clusters. Also, documents from these high-quality clusters should have a diverse content. These properties are strongly correlated with the overall performance of the proposed diversification framework. [ABSTRACT FROM AUTHOR]

Reading is a common everyday activity for most of us. In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted. This approach differs from traditional search methods where the users formulate search queries and review lists of possibly relevant results. This explicit search activity can be disruptive to reading. Our approach is to minimize the user interaction involved in finding related information by removing explicit query formulation and providing a single relevant result. To evaluate the feasibility of this approach, we first examined the effectiveness of three contextual algorithms for retrieval. To evaluate the effectiveness for readers, we then developed a functional prototype that uses the text of the abstract being read as context and retrieves a single relevant Wikipedia article in response to a passage the user has highlighted. We conducted a small user study where participants were allowed to use the prototype while reading abstracts. The results from this initial study indicate that users found the prototype easy to use and that using the prototype significantly improved their stated understanding and confidence in that understanding of the academic abstracts they read. [ABSTRACT FROM AUTHOR]

The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel and Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally. [ABSTRACT FROM AUTHOR]

The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of increasingly straight lines which together form a cloud of points, being the investigated relation. The theoretical results are tested against the author co-citation relations among 24 informetricians for whom two matrices can be constructed, based on co-citations: the asymmetric occurrence matrix and the symmetric co-citation matrix. Both examples completely confirm the theoretical results. The results enable us to specify an algorithm that provides a threshold value for the cosine above which none of the corresponding Pearson correlations would be negative. Using this threshold value can be expected to optimize the visualization of the vector space. [ABSTRACT FROM AUTHOR]

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases. [ABSTRACT FROM AUTHOR]

Best match search algorithms provide an efficient means of identifying the sets' of nearest neighbors for each of the documents in a collection. These sets contain much of the important similarity data contained in a full interdocument similarity matrix and may be used for the generation of hierarchic document classifications, such as those arising from the use of the single linkage clustering method. Cluster based retrieval experiments based upon such classifications are shown to give results that are comparable in effectiveness with those obtained using the full similarity matrix. [ABSTRACT FROM AUTHOR]

A clustering algorithm which is tree-like in structure, and is based on user queries, is presented. It is compared to Bonner's Method, Rocchio's Method, Dattola's Method and the Single Link Method in three different aspects, namely system effectiveness, system efficiency and the time required for clustering. Experimental results using the Cranfield 424 collection indicate that the proposed method is superior to the other methods. [ABSTRACT FROM AUTHOR]