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Nowadays, group decision making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts' intentions. In this paper, we propose a novel large scale group decision making method to manage the information generated by a large number of experts, using the natural language processing approach, specifically sentiment analysis. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert and thus proceed to a classification. Once the behaviours are detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation is obtained. In addition, we propose an optimised consensus analysis process, in which it is not necessary to compare all experts with each other, but only groups of experts. • We created a classifier to detect aggressiveness and positivity. • Experts are clustered according to their aggressiveness and positivity. • The expert groups are used to create an LGDM system. • The LSGDM system determines a ranking of alternatives agreed upon by the experts. [ABSTRACT FROM AUTHOR]

Universities face increasing demands to improve their visibility, public outreach, and online presence. There is a broad consensus that scientific reputation significantly increases the attention universities receive. However, in most cases estimates of scientific reputation are based on composite or weighted indicators and absolute positions in university rankings. In this study, we adopt a more granular approach to assessment of universities' scientific performance using a multidimensional set of indicators from the Leiden Ranking and testing their individual effects on university Wikipedia page views. We distinguish between international and local attention and find a positive association between research performance and Wikipedia attention which holds for regions and linguistic areas. Additional analysis shows that productivity, scientific impact, and international collaboration have a curvilinear effect on universities' Wikipedia attention. This finding suggests that there may be other factors than scientific reputation driving the general public's interest in universities. Our study adds to a growing stream of work which views altmetrics as tools to deepen science–society interactions rather than direct measures of impact and recognition of scientific outputs. [ABSTRACT FROM AUTHOR]

Since the fuzzy techniques in supplier selection, industrial procurement and inventory management form the important parts of the application in supply chain management (SCM). Considering that, the fuzzy techniques involved in the eight papers are all based on the fuzzy theory proposed by Zadeh, and they all well integrate the original fuzzy theory into various decision-making method and further properly be applied into the most popular fields of supply chain management (SCM) and finance economics. Supplier selection, procurement containing supplier and order allocation and inventory management are important parts of SCM. [Extracted from the article]

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the most dangerous type of coronavirus and has infected over 25.3 million people around the world (including causing 848,000 deaths). In this study, we investigated the similarity between the genome walks of coronaviruses in various animals and those of human SARS-CoV-2. Based on the results, although bats show a similar pattern of coronavirus genome walks to that of SARS-CoV-2 in humans, decoding the complex structure of coronavirus genome walks using sample entropy and fractal theory showed that the complexity of the pangolin coronavirus genome walk has a 94% match with the complexity of the SARS-CoV-2 genome walk in humans. This is the first reported study that found a similarity between the hidden characteristics of pangolin coronavirus and human SARS-CoV-2 using complexity-based analysis. The results of this study have great importance for the analysis of the origin and transfer of the virus. [ABSTRACT FROM AUTHOR]

By the help of the current contribution, we are interested to present a different technique compared to the exiting ones for ranking linguistic hesitant fuzzy sets (LHFSs). In the existing techniques that are referred hereafter to as the horizontal ranking procedures of LHFSs, we sum up all the multiplications of subscript of linguistic variable by the score value of the corresponding HFS. Meanwhile, through the vertical ranking technique of LHFSs, for a fixed subscript of linguistic variable, we compare firstly the corresponding HFSs using the existing raking methods to achieve the corresponding individual preference matrix. Then, all the individual preference matrices are multiplied by the subscript of that corresponding linguistic variable to get the aggregated and collective preference matrix. Eventually, the final ranking order of LHFSs is extracted by the use of collective majority decision rule. By the way, as will be shown later, all the horizontal ranking techniques suffer drawbacks to some extent, and this is the motivation for enriching the theory of ranking LHFSs by introducing the vertical ranking technique. [ABSTRACT FROM AUTHOR]

Covid-19 represents the greatest challenge facing mankind today. In December 2019, several cases of pneumonia of unknown etiology were reported from China. This coronavirus infection subsequently identified as Covid-19 aroused worldwide concern. As a result, the scientific community has focused attention on Covid-19, as revealed by recent research reported in literature based on a holistic approach. In this regard, this study conducts a bibliometric analysis of coronavirus research in the literature with an emphasis on Covid-19 disease, using as a reference the publications in the Web of Science Core Collection from 1970 to 2020. This research analyzes 12,571 publications from 1970 to (April 18) 2020 by applying advanced bibliometric techniques in SciMAT bibliometric analysis software. The current research therefore provides a complete conceptual analysis of the main coronavirus types and strains in the literature by quantifying the main bibliometric performance indicators, identifying the main authors, organizations, countries, sources, and research areas, and evaluating the development of this field. Furthermore, a science map is constructed to understand the corresponding intellectual structure and main research lines (themes). SciMAT thereby offers a complete approach to the field and evaluates the main performance indicators related to coronavirus, with a focus on Covid-19. Finally, this research serves as a framework to strengthen existing research lines and develop new ones, establishing synergistic relationships that were not visible without the maps generated herein. [ABSTRACT FROM AUTHOR]

This paper analyses the news on Covid-19 and 2019-nCoV in the Spanish media. This is done by using news retrieved from My news database. The analysis includes the total amount of news in Spanish media by type of media; the evolution of the publication in digital media considering various periods of time, and the volume of news in the most used Spanish digital media. The results show that digital media are the most used among other types to publish on the topic, with a significant increase since the beginning of the alarm state. In this sense, some arguments on the function and utility of this type of media during the confinement are exposed. Also, the essential role of digital media covering the population’s information needs is discussed. The digital media with the largest number publications are abc.es, 20minutos. es, and elconfidencial.com. The value of this work is that it can serve as starting point for others in order to carry out content analysis on the subject considering different channels and digital media. [ABSTRACT FROM AUTHOR]

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) techniques have attracted more and more scholars attention for their potential of application in many areas of human action. Although several contributions exist regarding comparative analysis of MCDM techniques, most of them are focused on demonstrating the similarities and differences of these methodologies in obtaining group decisions. However, the existing techniques comparing MCDM methods to investigate the most suitable ranking method for the case study have a critical shortcoming that limits their application to just MCDM methods resulting in total ranking order. This work contributes to reduce this shortcoming by establishing a correspondence between a non-total ranking order and a set of total ranking orders what we will call dominance-vector hesitant fuzzy set. [ABSTRACT FROM AUTHOR]

Sensor fusion is a fundamental research topic that has received significant attention in the literature. An important body of research has focused on assessing the reliability of a sensor or more generally an “information source” by comparing the readings with the ground truth in an online or offline manner. The Weighted Majority Voting algorithm [25], a well-known online learning algorithm, is a typical example of a class of approaches that assess the reliability of a sensor by comparing its readings to the ground truth in a online manner. Unlike the latter stream of research, in this article, we tackle the problem of identifying unreliable sensors without the knowledge of the ground truth– which is a novel research direction in its own right. We advocate that comparing the readings of a sensor to the rest of the sensors gives an invaluable information about its reliability. In this article, we present a solution to the problem based on the theory of S-Model Learning Automata (LA) [17]. Interestingly, the feedback to the S-Model environment LA is defined in an intuitive manner, namely, it is proportional to the number of sensors adhering to the chosen action. Our solution does not impose any constraint on the parity of the number of sensors and thus is general and can handle any arbitrary number of sensors. Apart from applying the classical S-Model LA, we develop a novel S-Model based pursuit LA algorithm that achieves a faster convergence than the legacy solution by an order of magnitude of ten while still yielding high accuracy. The devised schemes have been subjected to comprehensive experiments including comparison to the state-of-the-art. [ABSTRACT FROM AUTHOR]

The fear of sharing information experiments a decrease as new generations start being active in Social Media platforms. Everybody behaves very homogeneously within their own community, following recurrent communication patterns. But when a tragic event shakes people's minds, they feel the impulse to break these patterns and communicate their feelings to a bigger audience. In this paper, we analyse this phenomenon, dissecting the communication changes over time, identifying new patterns and discussing our findings in the context of a real scenario, namely the Charlie Hebdo terrorist attack. [ABSTRACT FROM AUTHOR]

• A new simulation methodology, intended bounded rationality methodology, is proposed. • Evaluation cost-benefit of different aspects of decision support techniques. • Classification of errors that a decision support technique can generate is proposed. • Consistent erroneous judgments and inconsistent correct judgments are possible. • A case where consistency condition in AHP provides maximum 5% of the expected return. Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as "expert", little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered. [ABSTRACT FROM AUTHOR]

Abstract: In the consensus reaching processes developed in group decision making problems we need to measure the closeness among experts’ opinions in order to obtain a consensus degree. As it is known, to achieve a full and unanimous consensus is often not reachable in practice. An alternative approach is to use softer consensus measures, which reflect better all possible partial agreements, guiding the consensus process until high agreement is achieved among individuals. Consensus models based on soft consensus measures have been widely used because these measures represent better the human perception of the essence of consensus. This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges. [Copyright &y& Elsevier]

This note analyzes two methods for calculating missing values of an incomplete reciprocal fuzzy preference relation. The first method by Herrera-Viedma et al. appeared in the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS [vol. 37, no. 1(2007) 176-1891, while the second one by Fedrizzi and Giove appeared later in the European Journal of Operational Research [vol. 183 (2007) 303-313]. The underlying concept driving both methods is the additive consistency property. We show that both methods, although different, are very similar. Both methods derive the same estimated values for the independent-missing-comparison case, while they differ in the dependent-missing-comparison case. However, it is shown that a modification of the first method coincides with the second one. Regarding the total reconstruction of an incomplete preference relation, it is true that the second method performs worse than the first one. When Herrera-Viedma et al.'s method is unsuccessful, Fedrizzi-Giove's method is as well. However, in those cases when Fedrizzi-Giove's method cannot guarantee the successful reconstruction of an incomplete preference relation, we have that Herrera-Viedma et al.'s method can. These results lead us to claim that both methods should be seen as complementary rather than competitors in their application, and as such, we propose a reconstruction policy of incomplete fuzzy preference relations using both methods. By doing this, the only unsuccessful reconstruction case is when there is a chain of missing pairwise comparisons involving each one of the feasible alternatives at least once. [ABSTRACT FROM AUTHOR]

We present the theoretical and methodological foundations for the development of a multi-agent SDI service model for specialized digital libraries, applying semantic web technologies that permit more efficient information management, improving agent-user communication processes and facilitating accurate access to relevant resources. To do this, RSS feeds are used as "current awareness bulletins" to generate personalized bibliographic alerts. The SDI service model has an RSS feeds management module and an information push module. In the first module, resources are represented as RSS feed items and are also semi-automatically assigned subject terms by matching their associated keywords against the terms of a SKOS Core format thesaurus. In the information push module, bibliographic alerts are customized according to the preferences defined on users' profiles. [ABSTRACT FROM AUTHOR]

Dealing with incomplete information is an important problem in decision making. In this paper, we present a short discussion on this topic and a new estimation method of missing values in an incomplete fuzzy preference relation which is based on the modelling of consistency of preferences via a representable uninorm. [ABSTRACT FROM AUTHOR]

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree. [ABSTRACT FROM AUTHOR]

The World Wide Web is a popular and interactive medium used to collect, disseminate, and access an increasingly huge amount of information, which constitutes the mainstay of the so-called information and knowledge society. Because of its spectacular growth, related to both Web resources (pages, sites, and services) and number of users, the Web is nowadays the main information repository and provides some automatic systems for locating, accessing, and retrieving information. However, an open and crucial question remains: how to provide fast and effective retrieval of the information relevant to specific users' needs. This is a very hard and complex task, since it is pervaded with subjectivity, vagueness, and uncertainty. The expression soft computing refers to techniques and methodologies that work synergistically with the aim of providing flexible information processing tolerant of imprecision, vagueness, partial truth, and approximation. So, soft computing represents a good candidate to design effective systems for information access and retrieval on the Web. One of the most representative tools of soft computing is fuzzy set theory. This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets and/or integrating them with other soft computing tools. In this introductory article, we first review the problem of Web retrieval and the concept of soft computing technology. We then briefly introduce the articles in this section and conclude by highlighting some future research directions that could benefit from the use of soft computing technologies. [ABSTRACT FROM AUTHOR]

An evaluation methodology based on fuzzy computing with words aimed at measuring the information quality of Web sites containing documents is presented. This methodology is qualitative and user oriented because it generates linguistic recommendations on the information quality of the content-based Web sites based on users' perceptions. It is composed of two main components, an evaluation scheme to analyze the information quality of Web sites and a measurement method to generate the linguistic recommendations. The evaluation scheme is based on both technical criteria related to the Web site structure and criteria related to the content of information on the Web sites. It is user driven because the chosen criteria are easily understandable by the users, in such a way that Web visitors can assess them by means of linguistic evaluation judgments. The measurement method is user centered because it generates linguistic recommendations of the Web sites based on the visitors' linguistic evaluation judgments. To combine the linguistic evaluation judgments we introduce two new majority guided linguistic aggregation operators, the Majority guided Linguistic Induced Ordered Weighted Averaging (MLIOWA) and weighted MLIOWA operators, which generate the linguistic recommendations according to the majority of the evaluation judgments provided by different visitors. The use of this methodology could improve tasks such as information filtering and evaluation on the World Wide Web. [ABSTRACT FROM AUTHOR]

In this paper, we present a consensus model for multiperson decision making (MPDM) problems with different preference structures based on two consensus criteria: 1) a consensus measure which indicates the agreement between experts' opinions and 2) a measure of proximity to find out how far the individual opinions are from the group opinion. These measures are calculated by comparing the positions of the alternatives between the individual solutions and collective solution. In such a way, the consensus situation is evaluated in each moment in a more realistic way. With these measures, we design a consensus support system that is able to substitute the actions of the moderator. In this system, the consensus measure is used to guide the consensus process until the final solution is achieved while the proximity measure is used to guide the discussion phases of the consensus process. The consensus support system has a feedback mechanism to guide the discussion phases based on the proximity measure. This feedback mechanism is based on simple and easy rules to help experts change their opinions in order to obtain a degree of consensus as high as possible. The main improvement of this consensus model is that it supports consensus process automatically, without moderator, and, in such a way, the possible subjectivity that the moderator can introduce in the consensus process is avoided. [ABSTRACT FROM AUTHOR]

Seeks to model the processes of the aggregation of weighted information in a linguistic framework, while presenting three aggregation operators of weighted linguistic information. Identification of these operators; Detailed information on the axiomatics of these operators.

Research in the humanities increasingly depends on how information is structured and managed and how, on the basis of that information, new knowledge is produced. Additionally, participatory approaches, which often rely on web information systems as their supportive infrastructure, have made an impact on the most recent historiographical trends, in particular in the methodological framework of digital humanities. The aim of this paper was to produce, from an operational and implementation perspective, a review of software solutions frequently used to develop web information systems for research projects in humanities and cultural heritage, in order to provide an understanding of the various possibilities available and their positives and limitations, also based on different users' requirements. An individual and comparative analysis of sixteen different application frameworks commonly used in these fields, either generic or developed for a specific research domain, has been carried out, considering their main functionalities, strengths, and weaknesses. The achieved results facilitate critical and reasoned decision-making among several available options, guiding the makers of those systems, both researcher(s) and developers(s), and providing them also with a common ground of terms and use cases to facilitate their necessary dialogue. [ABSTRACT FROM AUTHOR]

The theory of capacities provides powerful formal methodology to account for criteria dependencies in multiple criteria decision problems. The discrete Choquet and Sugeno integrals aggregate criteria valuations accounting for criteria synergies and redundancies. We address an important problem of randomly generating capacities of special classes for simulation studies and for capacity learning through evolutionary algorithms. We discuss two efficient methods suitable for k -interactive capacities. The results are supported by the extensive numerical evidence and provide a useful tool for large scale simulations. [ABSTRACT FROM AUTHOR]

In this paper we show that the aggregation of bibliometric indices is an appropriate way to evaluate scientific production of researchers because it allows us to develop much more precise new bibliometric indices that combine the advantages of each index that is aggregated. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

The manifestation of the intensity in the judgment of one alternative versus another in the peer comparison processes is a central element in some decision support techniques, such as the Analytical Hierarchy Process (AHP). However, its contribution regarding quality (expected performance) with respect to the priority vector has not been evaluated so far. Using the Intentional Bounded Rationality Methodology (IBRM), this work analyzes the gains obtained from requiring the decision-maker to report an intensity judgment in pairs (AHP) with respect to a technique that only requires expressing a preference (Ordering). The results show that when decision-makers have low levels of expertise, it is possible that a less informative and computational cheap technique (Ordering) performs better than a more informative and computational expensive one (AHP). When decision-makers have medium and high levels of expertise, AHP technique obtains modest gains with respect to the Ordering technique. This study proposes a cost-benefit analysis of decision support techniques contrasting the gains of a technique that requires more resources (AHP) against other that require less resources (Ordering). Our results can change the managing approach of the information obtained from experts' judgments. [ABSTRACT FROM AUTHOR]

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector. • Decision making models are limited by pre-defined numerical and linguistic terms. • We propose a methodology to deal with natural language and numerical assessments. • We design a deep learning model for distilling opinions from written assessments. • We present and release a dataset, which can be used for evaluating MpMcDM models. [ABSTRACT FROM AUTHOR]

To commemorate the 50th anniversary of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, this article examines and reports on its past to current topical coverage of systems science and engineering toward exploring the evolving focus of the research community. Results of a systematic bibliometric analysis are presented with associated conclusions, implications, and summary of topical areas. In addition, respective views regarding the current state of the field and where it is headed are offered by recent leaders of the IEEE Systems, Man, and Cybernetics Society, including its continued relevance and role in the advancement of systems technology. [ABSTRACT FROM AUTHOR]

• How differences in expertise, diversity, and group decision procedures affect the decision quality. • Alternatives in the strategic decision are often not observable preventing empirical studies obtain complete results. • A new simulation methodology, Intended Bounded Rationality Methodology, is used. • Concentrated decision power can be an acceptable system. This paper proposes to analyze how the differences in expertise, diversity, and group decision procedures affect the quality of the strategic decision of steering committees. Strategic decisions are difficult to anticipate, and performances of the alternatives are often not observable in their entirety, which prevent researchers from obtaining controlled empirical studies. This paper proposes to analyze the performance of steering committees where managers can err in their decisions using the Intentional Bounded Rationality (IBR). The majority procedure improves the committee's performance concerning authority when the level of diversity and expertise increases. However, in situations of low expertise, the gains over authority narrow. This work provides guidance in terms of trade-offs between the mentality of managers, their expertise, group decision procedures, and diversity, which in the empirical works are contradictory. This study contributes to current theorizations of committee management using the IBR methodology, which is new and allows quantifying the contribution of the distinct characteristics of the committee. [ABSTRACT FROM AUTHOR]

• Novel framework that estimate trust and reputation, (global reputation, and pairwise trust) between agents in a social network. • Assessing user's Reputation by evaluating their relationships and their position in the network. • User behavior tracking to isolate malicious agents. • Double supervised personalized feedback to rate interactions between users. The world wide success of large scale social information systems with diverse purposes, such as e-commerce platforms, facilities sharing communities and social networks, make them a very promising paradigm for large scale information sharing and management. However the anonymity, distributed and open nature of these frameworks, that, on the one hand, foster the communication capabilities of their users, may contribute, on the other hand, to the propagation of low quality information, attacks and manipulations from users with malicious intentions. All of these risks could end up decreasing users' confidence in these systems and in a reduction of their utilisation. With these issues in mind, the objective of this contribution is to create DeciTrustNET, a trust and reputation based framework for social networks that takes into consideration the users relationships, the historic evolution of their reputations and their profile similarity to develop a tamper resilient network that guarantees trustworthy communications and transactions. An extensive experimental analysis of the developed framework has been carried out confirming that the proposed approach supports robust trust and reputation establishment among the users, even in social network under the presence of malicious users. [ABSTRACT FROM AUTHOR]

This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals. [ABSTRACT FROM AUTHOR]

Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works. • Group recommender systems suggest items to be consumed by groups of users. • Individual preferences of any group member must be taken into account. • Considering social influence between group members leads to better recommendations. • Influence strength can be estimated from information retrieved in social networks. • Fuzzy decision making allows the influence-based aggregation of individual preferences. [ABSTRACT FROM AUTHOR]

The early and accurate detection of brain tumors is key to improve the quality of life and the survival of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. Consequently, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this contribution, we present a new model of segmentation of brain magnetic resonance images. In order to obtain the region of interest, we propose a hybrid approach that carries out both fuzzy c-mean algorithm and multiobjective optimization taking into account both compactness and separation in the clusters with the purpose of improving the cluster center detection and speed up the convergence time. This new segmentation approach is a key component of the proposed magnetic resonance image-based classification system for brain tumors. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach using the DICOM MRI database. [ABSTRACT FROM AUTHOR]

World university rankings have become well-established tools that students, university managers, and policy makers read and use. Each ranking claims to have a unique methodology capable of measuring the 'quality' of universities. The purpose of this article is to analyze to which extent these different rankings measure the same phenomenon and what it is that they are measuring. For this, we selected a total of seven world university rankings and performed a principal component analysis. After ensuring that despite their methodological differences, they all come together to a single component; we hypothesized that bibliometric indicators could explain what is being measured. Our analyses show that ranking scores from whichever of the seven league tables under study can be explained by the number of publications and citations received by the institution. We conclude by discussing policy implications and opportunities on how a nuanced and responsible use of rankings can help decision-making at the institutional level [ABSTRACT FROM AUTHOR]

There exist a high demand to provide explainability to artificial intelligence systems, where decision making models are included. This paper focuses on crowd decision making using natural language evaluations from social media with the aim to provide explainability. We present the Explainable Crowd Decision Making based on Subgroup Discovery and Attention Mechanisms (ECDM-SDAM) methodology as an a posteriori explainable process that captures the wisdom of crowds that is naturally provided in social media opinions. It extracts the opinions from social media texts using a deep learning based sentiment analysis approach called Attention based Sentiment Analysis Method. The methodology includes a backward process that provides explanations to justify its sense-making procedure by applying mainly the attention mechanism on texts and subgroup discovery on opinions. We evaluate the methodology in the real case study of the TripR-2020Large dataset for restaurant choice. The results show that the ECDM-SDAM methodology provides easy understandable explanations that elucidates the key reasons that support the output of the decision process. • Explainability in decision making is essential to increase its use and understanding. • Attention mechanisms and subgroup discovery can generate explainable decision making. • We propose a methodology that offers explanations of its internal decision mechanism. • The proposed methodology captures the wisdom of crowds from social media. • Natural language with sentiment analysis and deep learning enriches expert evaluation. [ABSTRACT FROM AUTHOR]

For multi-attribute group decision-making (MAGDM) problems, this paper proposes a three-way consensus model based on regret theory (RT) under the framework of probabilistic linguistic term sets (PLTSs), i.e., the PL-RT-GTWD model. Specifically, the PL-RT-GTWD model mainly consists of the following two components: (1) The consensus measurement considering the relativity among decision-makers (DMs); (2) A three-way consensus feedback mechanism with the minimum adjustment. First, the relative relationship between DMs is supplemented, and the consensus degree of the DM evaluation matrix is measured by a newly developed distance measure. Second, an optimization model with the minimum adjustment for all DMs to modify and reach the final desired goal (group consensus opinions) is constructed, so that DMs can achieve a balance between reaching consensus and maintaining individual independence. Then, taking the minimum adjustment before and after the adjustment as the objective function, the increase of consensus degrees and the initial range of adjustment parameters as constraints, the goal programming on adjustment parameters is constructed. It is noteworthy that the initial range of adjustment parameters is divided via three-way decision (TWD) tools that consider the regret generated during the adjustment, so as to avoid the subjectivity of the adjustment and reduce adjustment costs. Finally, comparative and sensitivity analyses are carried out to verify the feasibility and superiority of the constructed model. • A three-way consensus model with regret theory is designed. • A new distance measure in PLTSs is defined. • A PL-RT-GTWD model is established. • A feedback adjustment mechanism is considered. • An optimization model with minimum cost is explored. [ABSTRACT FROM AUTHOR]

In real-world decision problems, decision makers usually express their opinions with different preference structures. In order to deal with the heterogeneous preference information in group decision making, this paper presents an optimization-based consensus model for group decision making with heterogeneous preference structures (utility values, preference orderings, multiplicative preference relations and additive preference relations). This proposal seeks to minimize the information loss between decision makers' heterogeneous preference information and individual preference vectors and also seeks the collective solution with a consensus. Meanwhile, in order to justify the consensus model, we discuss its internal aggregation operator between the obtained individual and group preference vectors, demonstrate that the proposed model satisfies the Pareto principle of social choice theory, and prove the uniqueness of the solution to the optimization model. Furthermore, based on the proposed optimization-based consensus model, we present an automatic mechanism to support consensus reaching in the group decision making with heterogeneous preference structures. In the consensus reaching process, the obtained individual and group preference vectors are considered as a decision aid which decision makers can use as a reference to adjust their preference opinions. Finally, detailed simulation experiments and comparison analysis are conducted to demonstrate the feasibility and effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]