Hesitant Fuzzy Linguistic Maclaurin Symmetric Mean Operators and their Applications to Multi‐Criteria Decision‐Making Problem

Due to the limitation of knowledge and the vagueness of human being thinking, decision makers prefer to use hesitant fuzzy linguistic sets (HFLSs) to estimate alternatives. Some methods of HFLSs have been researched based on the more familiar means such as the arithmetic mean and the geometric mean; however, Maclaurin symmetric mean (MSM) that can be used to reflect the interrelationships among input arguments have not been applied to solve hesitant fuzzy linguistic multi‐criteria decision‐making problems. In this paper, two hesitant fuzzy linguistic harmonic averaging operators are proposed: the hesitant fuzzy linguistic MSM (HFLMSM) operator and the hesitant fuzzy linguistic weighted MSM (HFLWMSM) operator. Furthermore, an approach based on the HFLWMSM operator is proposed. Finally, to verify the validity and feasibility of the proposed approach, an illustrative example and corresponding comparison analysis are presented in the end.     

Degradative proteomics and disease mechanisms

Protein degradation is a fundamental biological process, which is essential for the maintenance and regulation of normal cellular function. In humans and animals, proteins can be degraded by a number of mechanisms: the ubiquitin-proteasome system, autophagy and intracellular proteases. The advances in contemporary protein analysis means that proteomics is increasingly being used to explore these key pathways and as a means of monitoring protein degradation. The dysfunction of protein degradative pathways has been associated with the development of a number of important diseases including cancer, muscle wasting disorders and neurodegenerative diseases. This review will focus on the role of proteomics to study cellular degradative processes and how these strategies are being applied to understand the molecular basis of diseases arising from disturbances in protein degradation.     

Deep structure or emergence theory: contrasting theoretical foundations for information systems development

The linguistic conceptual distinction between deep and surface structures offers an interesting metaphor for developing new theories of information systems. However, the deep structure notion is both richer and more strongly contested in the linguistic field than can be communicated in published articles about new theories of information systems. This article explores the deep structure notion more fully, how faithful information system (IS)-related articles adhere to the original concept and the implication of alternative linguistic theories for the IS field.     

With different words: The arguments that can empower an e-minority

Crossing two different research fields, the socio-psychological and the linguistic one, the present work aims at exploring what means to be part of a minority group built through a social media. In particular it will deepen the argumentations used by its followers before and after a very critical phase, and mostly if their discourses entail positive effects in terms of empowerment. The main hypothesis of this study is that the empowerment process of a minority, seen as a form of re-appropriation of individual or collective efficacy, can be constructed by means of the quality of discussion and then through its arguments. The case is represented by “Roars”, an active Facebook group of Italian researchers born after a very criticized reform of University. The linguistic analysis of Roars's group aims to understand their discursive and argumentative processes, and to state if they can develop some forms of empowerment. The results show how Roars change their rhetorical moves passing from presumptive and biased to “normative” argumentations, thus improving the quality of discussions and also their level of empowerment.     

Towards A Game Theoretic Understanding of Ad-Hoc Routing

In this paper we present a new application of game theory, in which game theoretic techniques are used to provide a rigorous underpinning to the analysis of ad-hoc routing protocols. The explosion of interest in ad-hoc networks over the last few years has resulted in a very large number of routing protocols being proposed. Despite this, the science of analysing routing protocols is still relatively immature, and the question that remains is how to decide “how good” a given protocol is. We propose a game theoretic approach as a potentially effective means of answering this question. The conceptual mapping of routing into a game is, we believe, natural and simple. Furthermore, game theory provides an extensive repertoire of tools to analyse key properties. The paper describes how routing techniques can be modelled as games and presents some analytical results.     

On the difficulty of automatically detecting irony: beyond a simple case of negation

It is well known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In the absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be.     

A corpus-based approach to the multimodal analysis of specialized knowledge

Modern communication environments have changed the cognitive patterns of individuals, who are now used to the interaction of information encoded in different semiotic modalities, especially visual and linguistic. Despite this, the main premise of Corpus Linguistics is still ruling: our perception of and experience with the world is conveyed in texts, which nowadays need to be studied from a multimodal perspective. Therefore, multimodal corpora are becoming extremely useful to extract specialized knowledge and explore the insights of specialized language and its relation to non-language-specific representations of knowledge. It is our assertion that the analysis of the image-text interface can help us understand the way visual and linguistic information converge in subject-field texts. In this article, we use Frame-based terminology to sketch a novel proposal to study images in a corpus rich in pictorial representations for their inclusion in a terminological resource on the environment. Our corpus-based approach provides the methodological underpinnings to create meaning within terminographic entries, thus facilitating specialized knowledge transfer and acquisition through images.     

K-means method for rough classification of R&D employees' performance evaluation

This paper proposes an approach that can roughly cluster a data set with fuzzy linguistic entries as a prior data arrangement for performance evaluation of R&D employees. The extension principles of fuzzy linguistic numbers are used to modify the K‐means method for handling the linguistic data set. We define the absolute difference of fuzzy linguistic variables as their fuzzy distance. Based on this definition, the K‐means approach can be modified slightly for clustering purposes. The performance of employees engaged in designing and R&D‐oriented jobs is possibly related to some qualitative attributes and the evaluation of such attributes for each employee has a tendency toward semantic scales. In the proposed approach, the supervisor can evaluate the performance of each employee directly with a semantic scale. The modified K‐means approach can roughly cluster their performance into different classes in advance of applying some other sophisticated processes.     

Improving automated crisis detection via an improved understanding of crisis language: Linguistic categories in social media crises

By applying the Linguistic Category Model (LCM) in crisis communication, this study explores the potential of verb tracking on social media to examine how linguistic categories can elucidate the intentional and/or unintentional communication of crisis attribution frames. Through a content analysis, linguistic categories used in both media posts reporting three clusters of crisis and public comments on Facebook were examined. Results indicated that linguistic abstraction in both media post and public comments describing the crisis varied based on crisis cluster, suggesting that the level of linguistic abstraction reflected perceived attribution of responsibility through stability, locus and controllability. Language used to describe preventable crisis tend to be more abstract than those used to describe accidental and victim crisis. Findings of this study empirically tested the integration of LCM in crisis communication and implied potential application of LCM in building automated environmental scanning and crisis prediction systems.     

Language and design in text-based virtual worlds

Design can be characterized using a linguistic model which compares the use and power of language in real-life with its use and power in text-based virtual worlds. In this paper, the theory of speech acts is used as a background and a point of development to analyse and model design in the virtual space. Metaphors and other linguistic constructs become fundamental in the realisation of an innovative theory that helps users to design in virtual worlds. Examples of how language can be used for design purposes and a parallel with linguistic theories support the author’s thesis that words can ‘do things’ in the electronic space.     

A fuzzy ID3 induction for linguistic data sets

In real life, humans communicate by means of words. Computing with words enables flexibility via fuzzy logic to reach more informative results for the classification and decision‐making. Fuzzy logic handles the imprecise information. In our paper, we propose a novel fuzzy ID3 algorithm for the classification on linguistic data set, where data can be given as linguistic variables. Linguistic variables are defined by using triangular fuzzy numbers given as LR (left‐right) fuzzy numbers. And weighted averaging based on levels (WABL) method is used as the defuzzification method for each data. Then, fuzzy c‐means algorithm is performed to handle the membership degrees for each variable given in each data set used in an experimental study. At last, the fuzzy ID3 algorithm is applied. The rules are generated, and the reasoning is done by different T‐operators. Our study is encouraged by (using) statistical analysis. In conclusion, it is seen that our algorithm proposed for linguistic data is as good as the proposed approach for numeric data. Also, it is shown that the proposed linguistic approach by using different T‐operators on linguistic data gives better results than numerical approach on some data sets.     

Analyzing ambiguity of context-free grammars

It has been known since 1962 that the ambiguity problem for context-free grammars is undecidable. Ambiguity in context-free grammars is a recurring problem in language design and parser generation, as well as in applications where grammars are used as models of real-world physical structures.We observe that there is a simple linguistic characterization of the grammar ambiguity problem, and we show how to exploit this by presenting an ambiguity analysis framework based on conservative language approximations. As a concrete example, we propose a technique based on local regular approximations and grammar unfoldings. We evaluate the analysis using grammars that occur in RNA analysis in bioinformatics, and we demonstrate that it is sufficiently precise and efficient to be practically useful.     

A general class of simple majority decision rules based on linguistic opinions

In this paper we have introduced a class of decision rules related to simple majority, by considering individual intensities of preference. These intensities will be shown by means of linguistic labels. In order to compare the amount of opinion obtained by each alternative, we have considered the total ordered monoid generated by the sums of the original labels, according to an addition and an ordering. In this general framework different sets of linguistic labels can be employed and these sets can be represented by means of diverse mathematical objects. Moreover, on these mathematical representations of linguistic labels several orderings can be considered. Thus, flexibility is an important feature of this new class of group decision making procedures. Some examples of putting in practice the simple majority decision rules based on linguistic labels are provided, and the main properties of these voting systems are analyzed. It is worth emphasizing that these properties are satisfied for any total ordered monoid, regardless of the mathematical representation of linguistic labels or the ordering used to compare collective opinions.     

Novel hesitant fuzzy linguistic entropy and cross-entropy measures in multiple criteria decision making

In this contribution, we mainly investigate how new entropy and cross entropy measures of hesitant fuzzy linguistic term sets (HFLTSs) can be designed by using the counterparts proposed for linguistic term sets (LTSs). In this circumstance, we intend to point out some drawbacks of the existing entropies, and then extend the theory of entropy and cross entropy measures of HFLTSs by constructing a number of new entropies. Furthermore, we compare the results of the approach being proposed based on the new entropy and cross entropy measures with that of the weight-determining method and the hesitant fuzzy linguistic alternative queuing method (HFL-AQM).     

基于层次分析法的犹豫模糊语言包络分析模型及其在边缘节点网络安全评估中的应用

针对传统的网络安全评估方法仅用单一语言术语作为偏好信息难以评估复杂网络环境的安全性的问题,利用犹豫模糊语言术语集作为评估网络安全的偏好信息,提出了一种基于层次分析法(analytic hierarchy process,AHP)的犹豫模糊语言包络分析模型,用以评估边缘节点的网络安全性。该模型通过求解目标规划模型获得最优的网络安全准则权重信息,进一步构建网络安全准则权重信息对应的AHP约束锥作为犹豫模糊语言包络分析模型的约束条件,获得移动用户安全评估的排序结果。实例证明该模型能够合理地融合网络安全准则偏好信息,并有效地处理边缘节点的网络安全评估问题。     

Towards personality-based user adaptation: psychologically informed stylistic language generation

Conversation is an essential component of social behavior, one of the primary means by which humans express intentions, beliefs, emotions, attitudes and personality. Thus the development of systems to support natural conversational interaction has been a long term research goal. In natural conversation, humans adapt to one another across many levels of utterance production via processes variously described as linguistic style matching, entrainment, alignment, audience design, and accommodation. A number of recent studies strongly suggest that dialogue systems that adapted to the user in a similar way would be more effective. However, a major research challenge in this area is the ability to dynamically generate user-adaptive utterance variations. As part of a personality-based user adaptation framework, this article describes personage, a highly parameterizable generator which provides a large number of parameters to support adaptation to a user’s linguistic style. We show how we can systematically apply results from psycholinguistic studies that document the linguistic reflexes of personality, in order to develop models to control personage’s parameters, and produce utterances matching particular personality profiles. When we evaluate these outputs with human judges, the results indicate that humans perceive the personality of system utterances in the way that the system intended.     

Semiotics of virtual reality as a communication process

ABSTRACT

The experience of immersive virtual reality (VR) can be considered as a communication process between human beings, mediated by computer systems, which uses visualisation and other sensory stimulation. In this paper, we analyse how VR characteristics can be explored using semiotic theory and, with methods of generative semiotics, we explore aspects of narrative and interaction in VR. We propose a semiotic analysis of VR communication focusing on syntax, semantics and pragmatics and considering also some principles of generative semiotics. The syntactic level is analysed as determined by the characteristics of the visual communication adopted. The semantic of VR is related to the functional model chosen to realise the virtual system. The pragmatic of VR is based on the human–computer interaction that changes the user's role. We explore how these aspects can be characterised in the context of VR communication design and what principles can be adopted for a VR application, and we present an analysis and a classification of the iconic signs that are being used in VR. Moreover, we present a framework that can be used to classify and describe different kinds of virtual reality systems and to better understand communication in VR, and we use it to classify eight popular systems for e-learning and collaboration.