Linguistic Modifiers Research Articles

A precise solution to the shortest path optimization problem in graphs using Z-numbers

<p>Communication networks are exposed to internal or external risks that can affect all or part of the system. The most important components that form the infrastructure of these systems are routers, which act as nodes. In the field of graph theory, there are sophisticated techniques that can be used to optimize the path of a packet as it travels through various routers from its origin to its destination. A notable example of such an algorithm is Dijkstra's algorithm, which is designed to efficiently determine the shortest path. The algorithm works under the assumption that the system operates under ideal conditions. Real-time systems can perform better if risk factors and optimal conditions are taken into account. The relationship between the nodes can be expressed by various metrics such as distance, delay, and bandwidth. The aforementioned metrics facilitate the calculation of the optimal path, with the ultimate objective of achieving low-latency networks characterized by rapid response times. Round-trip time (RTT) can be employed as a metric for measuring enhancements in a range of latency types, including those associated with processing, transmission, queuing, and propagation. The use of Z-numbers was employed in this study to incorporate risk into the optimal path metric. RTT was the preferred metric and reliability was represented by fuzzy linguistic qualifiers. A comparison of several scenarios was shown using a numerical example of a communication network. It is expected that this study will have a significant impact on the evolution from models that consider only ideal conditions to real-time systems that include risks using Z-numbers.</p>

Presumed Consent for Pelvic Exams Under Anesthesia Is Medical Sexual Assault

Unconsented pelvic exams under anesthesia are assaults cloaked in defense of healthcare education. Preemptive linguistic qualifiers “presumed” or “implied” attempt to justify such violations with flippancy toward their oxymoronic implications: to suggest a priori that consent can be assumed undermines its otherwise standalone social, ethical, and medico-legal reverence. In this paper I conceptualize “medical sexual assault” and argue that presumed consent for intimate exams exemplifies its definition. By bluntly describing pelvic exams as “penetration,” this work aims to reify the intimate reality of the clinical label “pelvic exam” and to call attention to cisheteronormative and androcentric assumptions involved in its practice. Additionally, this scholarship seeks to create a foundation toward broader work in conceptualizing clinical rape culture. Given recent national survey data indicating a surprising frequency of unconsented intimate exams, detailed language as to their problematics is necessary for ongoing legal and ethical efforts. Explicit consent for intimate exams must be the standard of care for conscious and unconscious patients.

MODEL ADAPTIV NEURO-FUZZY PENTRU SISTEMUL DE CONTROL AL PROCESULUI DE MACINARE A CLINKERULUI IN MORILE CU BILE DIN FABRICILE DE CIMENT

The main purpose of this study consisted in the realization of the development model of a decision support system for the cement grinding process in ball mills, including the acquisition, processing and analysis subsystems of data regarding the progress of the technological process, based on hardware technologies and intelligent software. To create the decision-making models, the graphic environment for the development of adaptive neuro-fuzzy systems from the Matlab program package was used. The paper presents a model based on the techniques proposed and developed with the application of fuzzy logic and artificial neural networks. The input/output variables, the linguistic qualifiers and the membership functions specific to each shredding batch were identified. The inference rules were defined. A standard defuzzification method was applied to defuzzify the results obtained from the inference process. At the same time, the results of the simulations of the proposed models in the Matlab environment were also presented. The testing and verification of the data obtained with the proposed inference model was carried out by comparison with the experimental data.

Knowledge transfer for labeling unknown fuzzy sets using Grammar-Guided Genetic Algorithms

Fuzzy Logic Systems have been popular in fields such as control, knowledge representation, and modeling. They involve linguistic information to represent a human perception in the form of Fuzzy Sets. Those systems are advantageous in cases where it is desired to have some interpretability. However, there are some processes, such as optimization, where the membership function related to a fuzzy set is modified. Therefore the linguistic label associated with them is unattached to the original meaning. This proposal aims to make linguistically-interpretable fuzzy sets from previously established ones but with modified parameters, thus maintaining the context of the knowledge of the domain application and the knowledge designer. The presented approach relies in the use of linguistic modifiers, which are operations linked to linguistic terms that apply on fuzzy sets, such as very, above, more-or-less, etc. A methodology using Grammar-Guided Genetic Algorithms is proposed to find the best linguistic modifier candidates to approximate the fuzzy set with modified parameters to guarantee a coherent and readable grammar structure at diverse levels of interpretability and accuracy. The obtained results support the feasibility of the proposed methodology with convex membership functions such as Gaussian, Triangular, and Trapezoidal.

Expansion and Reduction of Soft Sets and their Applications in Decision Making

In this study, we first define expansion and reduction of the soft sets that are based on the linguistic modifiers. By using these new notions, we then construct a decision-making method called soft reduction method, which selects a set of optimum alternatives. We finally present an example which shows that the methods can be successfully applied to many problems containing uncertainties.

Predicting Chinese Phrase-Level Sentiment Intensity in Valence-Arousal Dimensions With Linguistic Dependency Features

Phrase-level sentiment intensity prediction is difficult due to the inclusion of linguistic modifiers (e.g., negators, degree adverbs, and modals) potentially resulting in an intensity shift or polarity reversal for the modified words. This study develops a graph-based Chinese parser based on the deep biaffine attention model to obtain dependency structures and relations. These obtained dependency features are then used in our proposed Weighted-sum Tree GRU network to predict phrase-level sentiment intensity in the valence-arousal dimensions. Dependency parsing results using the Sinica Treebank indicate that our graph-based model outperforms transition-based methods such as MLP and stack-LSTM with identical findings for English dependency parsing. Experimental results on the Chinese EmoBank indicate that our Weighted-sum Tree GRU network model outperforms other transformer-based neural networks such as BERT, ALBERT, XLNET and ELECTRA, reflecting the effectiveness of linguistic dependencies in phrase-level sentiment intensity predication tasks. In addition, our proposed model requires fewer parameters and less inference time for quantitative analysis, making the proposed model is relatively lightweight and efficient.

An accurate fuzzy rule-based classification systems for heart disease diagnosis

Physicians and healthcare providers need to better understand the thought processes and methods used in clinical decision-making. This allows physicians to diagnose and detect diseases early, especially heart disease that causes death. The diversity and availability of healthcare data encourage clinicians to use healthcare applications in the diagnosis process. Most of these applications use machine learning techniques to make accurate and fast decisions. On the other hand, Explainability in healthcare applications increase the level of clinician confidence and reduces the risk of making wrong decisions, thus expands the scope and efficiency of healthcare applications. In this paper, we propose a novel data-driven method based on fuzzy clustering and linguistic modifiers to design a fuzzy rule-based classification system for heart disease diagnosis. The proposed system provides an interpretable knowledge base to explain the decision-making process. Regarding the experiment, we have used Cleveland, Hungarian and Va long beach heart disease datasets to compare the proposed method with five known machine learning methods for predicting heart disease: Artificial neural network, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest. The findings show that the proposed model is superior in terms of balancing interpretability and precision.

Parametric and non-parametric methods for monitoring nonlinear fuzzy profiles

In many statistical process control (SPC) applications, the quality of a process or product is represented by a relationship or the so-called quality profile, between a quality characteristic and one or more explanatory variables. The process quality characteristics are sometimes measured with a reasonable degree of approximation. Often, especially when qualitative assessments arise, evaluation of quality characteristics is carried out ambiguously or using linguistic qualifiers. The fuzzy sets theory has proved to be a well-established approach for dealing with uncertainty due to the approximate measurement, ambiguity in subjective evaluations, or vagueness in linguistic variables. Our main purpose was to present and compare four methods of monitoring nonlinear fuzzy profiles, for which different nonlinear fuzzy regression modeling approaches are considered. The first two methods are “a data-driven fuzzy rule-based” and “an extended least square support vector machine (LS-SVM),” for which the profile is characterized without considering a predefined mathematical relationship. However, for the other two methods, a specific form of the profile was needed. The third method, namely “a modified fuzzy regression model,” was initially invented for linear models. Besides, the fourth method employs “the fuzzy least square method” based on linearizing transformation. The exponentially weighted moving average (EWMA) control statistic was used to derive the control statistic to be plotted on the univariate as well as multivariate control charts. An extensive simulation study was conducted to compare the performance of the methods, and the average run length (ARL) criterion was considered to assess the detect-ability of control charts against various out-of-control conditions. Our comparison results indicated that the multivariate EWMA (MEWMA) chart based on the LS-SVM method outperforms the rest in detecting process shifts with smaller values of ARL when the process undergoes out-of-control conditions.

Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction

Linguistic modifiers such as negators (e.g., not), intensifiers (e.g., very) and modals (e.g., would) are commonly used in expressing opinions. These modifiers play an important role in recognizing the sentiment intensity of multi-word phrases because they may lead to an intensity shift and polarity reversal for the words they modify. Appropriately modeling the effect of such modifiers on the intensity shift can greatly improve the performance of phrase-level sentiment intensity prediction. To this end, this paper proposes two neural network (NN) models organized in a pipelined fashion to determine 1) the intensity of individual words and 2) the shift weights of modifiers representing the degrees of intensity change for the words they modify. The intensity of a phrase can then be determined by combining the intensity of the constituent word and the shift weight of the modifier within the phrase. When measuring the word intensity, the first NN model introduces a hidden layer as a filter to select appropriate similar seed words in the prediction process. Automatic word intensity prediction can address the unknown intensities of words not covered in sentiment lexicons. In learning the modifier weights, the second NN model considers both the weights of individual modifiers and groups of modifiers to capture various intensity shift effects caused by them. Experiments on a SemEval-2016 dataset showed that the proposed method yielded better prediction performance for both single words and multi-word phrases.

Drawing on the iPad to input fuzzy sets with an application to linguistic data science

Large amounts of collected and stored data require specialized processing skills. However, a human-computer interaction is crude and far from natural. Users are forced to interact with databases using languages understood by machines. A simple way to learn more about phenomena represented by data can be done via representing the data as human-perceived concepts and enabling a human-like interpretation of it. The users should be able to use linguistic terms – for example LARGE, SLOW, MOST – as their representations of concepts in order to gain a better understanding of data. Yet, other issues arise: how to enter definitions of such terms, how to incorporate individual's understanding of their meanings, how to ensure their proper interpretation, and of course, how to do all this in an easy and simple way.In this paper, we present and describe an iPad-based software that enables an easy procedure of defining linguistic terms – such as LARGE, MEDIUM, SMALL – and linguistic qualifiers – ALL, MOSTLY – that are suitable for data analysis purposes. We state, that linguistic terms and qualifiers represented as fuzzy sets embody human defined concepts and allow users to better understand data. This understating is achieved via ‘mapping’ data into models built with definitions of imprecise concepts familiar to users. Further, fuzzy-based definitions of concepts and fuzzy operations on them facilitate a human-like analysis of data as an important aspect of data science. The main contribution is a tablet application – called Tablet input of Fuzzy Sets (TiFS) – that allows users to define terms in a simple way via drawing their ‘shapes’ using fingers. We provide a detailed description of the developed iPad application for that purpose and show its benefits when used in defining linguistic terms for data summarization processes.

Expansions and Reductions on Neutrosophic Classical Soft Set

In the paper, we first proposed a new notation is called expansion and reduction of the neutrosophic classical soft sets that are based on the linguistic modifiers. By using this new notions, we then developed a neutrosophic classical soft reduction method and present a reel example for the method.

A new proposal to deal with hesitant linguistic expressions on preference assessments

Information fusion and hesitant information fusion represent an important part of decision making processes. This paper focuses on hesitant expressions and the way to take them into account in the computations, using weights served by a simple but efficient process. In a previous paper we have proposed to use an operator called the symbolic weighted medianto express hesitant linguistic assessments such as “I hesitate between this and that but I tend to lean toward that alternative”. Now we go further in explaining in detail how to transform such expressions into our hesitant operators. Inspired by language science research, several hesitant linguistic expressions are discussed, including linguistic modifiers and qualifiers, then they are transformed into weight vectors before being aggregated to complete information fusion.

An Associational Examination of the CaptialCube Effect Context for the MPV over the Linguistic Partitions: Testing Sensitivity & Specificity

In this third examination of the CapitalCubeÔ Market Navigation Platform [CCMNP] we have selected the previously vetted set of embedded variables: Market Performance Variables [MPV] for their Linguistic Qualifiers [LQ] considering their directional market effects or MPV[LQ[{Neutral: Unfavorable: Favorable}]]. In the testing, we are interested in the Sensitivity and the Specificity of these vetted variables over the annual S&P500 Panel from 2005 to 2013. The inference framework employed a Median Split: High or Low for each of the 13 MPV tested and a random selection to avoid the FPE-jeopardy that is part of the Chi2 testing model. We used the Tamhane & Dunlop cut-off to identify Chi2 cells effects of interest and used these to develop the Sensitivity and the Specificity tests. Results: We were able to reject the a priori Nulls proffered for the testing protocols indicating that one may reject the supposition that the labeling of the LQ is formed by random processes in the CCMNP.

On the syntax and semantics of virtual linguistic terms for information fusion in decision making

The virtual linguistic model is a good technique for linguistic decision making and has been widely used in applications including linguistic information fusion. The main purpose of this paper is to define and specify the syntax and semantics of virtual linguistic terms (VLTs) in detail, and then to serve as the theoretical foundation of the computational models based on VLTs. The syntactical rule generates VLTs by a symbolic transformation, and then the semantic rule presents the semantics of VLTs by means of linguistic modifiers. Based on the syntax and semantics, VLTs could be a possible alternative for solving some current challenges of qualitative information fusion in decision making.

A Linguistic Examination of the CapitalCube™ Market Effect Variables

Introduction : Linguistic variables and the nuanced market information they afford are critical to the efficient and effective functioning of market trading platforms. In this research, we report on our investigation of the Linguistic Variables of the CapitalCubeÔ market navigation platform [CCMNP]. Study Précis : The focus of the study is to determine if there is directional meaning: { Neutral : Unfavorable : Favorable } implied by the unique Linguistic Qualifiers [LQ] attached to each set of Market Performance Variables [MPV] of the CCMNP. To this end, we collected these directional indications and linkages from a sample of Experts and also Informed & Trained Students. Results : We found that: (i) there was a high degree of agreement between the two rating groups respecting these directional indications for the LQ over the MPV, (ii) these directional rating results were differentiable from a random assignment, and (iii) there was general agreement relative to the directional indications respecting an a priori scoring given by two other experts. Impact : The LQ tested for the selected MPV of the CCMNP exhibited directional relevance, sensitivity, and specificity and therefore seem to be an intelligent set of linguistic descriptors enhancing the navigation acuity of the CapitalCubeÔ market navigation platform.

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users’ comfort. It is, therefore, appropriate to employ the paradigm of “ computing with words ” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human–machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users’ feedback; the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system.

Soft Computing Techniques for Querying XBRL Data

SummaryAvailability of business data is an important aspect of effective financial activities. An easy access to financial information has immense influence on actions and decisions regarding investing, trade and operations of companies and firms. The proposed standard – eXtensible Business Reporting Language (XBRL) – provides a means to create a uniform framework for representing corporate and financial information. XBRL defines an easily interpretable, machine‐readable and XML‐based data format. Its flexibility allows for representing business data using different languages, as well as following different regulation standards.One of important benefits of XBRL is application of XML‐based tools and systems that enable easy preparation, processing and validation of corporate data. It is also possible to use XML‐based storage and query systems.In this paper we propose and describe a concept of soft queries. They provide the users with a human‐friendly interface for interacting with XBRL data. These queries are equipped with linguistic terms (such as large, medium, small) and linguistic qualifiers (all, mostly). Such queries are able to provide the users with results similar to the results obtained when they analyse data themselves. Linguistic terms and qualifiers are represented as fuzzy sets. Fuzzy‐based operations and aggregation operators allow for mimicking a human‐like processing of data. The proposed approach is illustrated with queries executed on an XBRL document. Copyright © 2015 John Wiley & Sons, Ltd.

Die sprachlichen Realisierungen der "Verstärker" in den Argumentationsprozessen. Kontrastive Analyse der modificateurs surréalisants im Deutschen und im Tschechischen

This article deals with, from a contrasting point of view, the linguistic means that amplify the applicability of the lexical predicate in argumentation processes. The theoretical basis consists of the works of Anscombre and Ducrot, which have treated the specific role of linguistic modifiers in argumentation processes since the 1970s, as well as a comprehensive study by Atayan (2006). Special attention is devoted to the modificateurs surréalisants (M.S.) defined by Negroni (1995). In everyday communicative situations, these modifiers obviously behave differently from modificateurs réalisants (M.R.) and modificateurs déréalisants (M.D.) – the two groups described by Ducrot. First, the composition as a possibility to realise M.S. in German is analysed in connection with the analysis of the Czech prefix pře- in the role of M.S. and further the suffixes -ánsk(ý), -anánsk(ý), -it(ý), -ostn(ý) in the role of M.S., and their equivalents in German are discussed. Lastly, diminuation as a potential means to express M.S. in both languages is presented in more detail. In the comparative analysis of the selected linguistic realisations of this specific group of argumentative modifiers, the samples proposed by Negroni are critically discussed on the one hand, and on the other, the potential of argumentation-oriented analyses for translation science is presented.

An evaluation of retranslation methods in computing with words

To represent output fuzzy values of a computing with words (CW) system in natural language, a retranslation unit is required. In this work, retranslation methods applicable to a CW system are explored. Several methods that employ similarity measures of fuzzy sets, linguistic modifiers, or linguistic quantifiers have been applied to three real-world case studies. Performances of the applied methods have been evaluated through degree of validity, and comparison of characteristics of fuzzy sets such as fuzziness and specificity. Results show that invalid linguistic terms might be used in the retranslation process which also cause incomprehensible phrases in natural language.

Extended symbolic approximate reasoning based on linguistic modifiers

Approximate reasoning allows inferring with imperfect knowledge. It is based on a generalization of modus ponens (MP) known as generalized modus ponens (GMP). We are interested in approximate reasoning within symbolic multi-valued logic framework. In a previous work, we have proposed a new GMP based on linguistic modifiers in the multi-valued logic framework. The use of linguistic modifiers allows having a gradual reasoning; moreover, it allows checking axiomatics of approximate reasoning. In this paper, we extend our approximate reasoning to hold with complex rules, i.e., rules whose premises are conjunction or disjunction of propositions. For this purpose, we introduce a new operator that aggregates linguistic modifiers and verifies the required properties of logical connectives within the multi-valued logic framework.