Reviewerly/exHarmony huggingface dataset:
Designing Interpretable Fuzzy Systems

Fuzzy sets and fuzzy logic are powerful tools widely used to represent human knowledge and mimic human reasoning capabilities, being the main constituents of fuzzy systems. Among the different approaches to fuzzy systems, fuzzy rule-based systems represent the one offering a better framework for interpretability considerations. Their applications range from classification to modeling and control. Independently on its purpose, the behavior of a fuzzy rule-based system can be evaluated in terms of its reliability and comprehensibility, two concepts usually represented by accuracy and interpretability in the context of fuzzy systems. Indeed, achieving the highest possible levels of accuracy and interpretability is one of the central aspects of designing fuzzy systems. The present chapter will go through the design process considering its different steps and analyzing the multiple options allowing us to improve interpretability or to achieve a better interpretability-accuracy balance, in the search for interpretable fuzzy systems which represent an interesting approach in the framework of explainable Artificial Intelligence. We will consider questions related to the knowledge extraction and refinement process; some examples are complexity reduction and semantic improvement. We will also analyze other questions linked to the design of the processing structure, such as the effects of applying different aggregation and implication operators.
New Benchmarks for Learning on Non-Homophilous Graphs

Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datasets for graph machine learning tasks have generally been highly homophilous, rewarding methods that leverage homophily as an inductive bias. Recent work has pointed out this particular focus, as new non-homophilous datasets have been introduced and graph representation learning models better suited for low-homophily settings have been developed. However, these datasets are small and poorly suited to truly testing the effectiveness of new methods in non-homophilous settings. We present a series of improved graph datasets with node label relationships that do not satisfy the homophily principle. Along with this, we introduce a new measure of the presence or absence of homophily that is better suited than existing measures in different regimes. We benchmark a range of simple methods and graph neural networks across our proposed datasets, drawing new insights for further research. Data and codes can be found at https://github.com/CUAI/Non-Homophily-Benchmarks.
Enhancement of Mammogram Images Using CLAHE and Bilateral Filter Approaches

In this work, we have proposed a method for enhancement of breast images. Here, the image is enhanced primarily using spatial domain technique contrast limited adaptive histogram equalization (CLAHE), which is then given as input to frequency domain enhancement technique bilateral filtering. The output obtained from the proposed method is compared with various spatial and frequency domain techniques such as log transform, histogram equalization (HE), CLAHE, Gaussian filter, Laplacian filter, and median filters using quantitative measures like peak signal-to-noise ratio (PSNR), entropy, Michelson contrast (MC), structure similarity index measure (SSIM), and absolute mean brightness error (AMBE) as performance metrics on MIAS dataset. The proposed method gives good result for entropy, PSNR, and Michelson contrast. The proposed method is experimented on publically available MIAS database.
Quantifying dynamical coherence with coherence measures

Quantum coherence, like entanglement, is a fundamental resource in quantum information. In recent years, remarkable progress has been made in formulating resource theory of coherence from a broader perspective. The notions of block-coherence and POVM-based coherence have been established. Certain challenges, however, remain to be addressed. It is difficult to define incoherent operations directly, without requiring incoherent states, which proves a major obstacle in establishing the resource theory of dynamical coherence. In this paper, we overcome this limitation by introducing an alternate definition of incoherent operations induced via coherence measures and quantify dynamical coherence based on this definition. Finally, we apply our proposed definition to quantify POVM-based dynamical coherence.
The comparative analysis of web applications frameworks in the Node.js ecosystem

The subject of the research was the comparative analysis of three frameworks for building web applications, i.e. Express, Hapi and Koa, operating in the Node.js ecosystem. An experiment was prepared consisting of a number of scenarios, during which the server response times to incoming requests from the client were measured. As part of the work, server test applications handling HTTP requests (GET, POST, PUT, DELETE) performing typical data operations were implemented. The applications contained the same functionalities and were built using the three tested frameworks. In individual scenarios, 1000 requests of a given type were sent from independent clients, the times of successive responses were measured and their averages were calculated. On the basis of the obtained results, Hapi and Koa frameworks for GET, POST, PUT, DELETE requests, operating on one object or a string "Hello World!" have achieved the best, although very similar, response times. In the case of the GET request, the Koa framework proved to be the best for higher loads, achieving response times approximately 20% better than the Express framework. For high loads, the Hapi framework achieved the worst results, reaching over 2 times longer response times than the Koa framework.
Oppositions and Antinomies of Modern Educational Reality

The article reveals some unresolved issues concerning the processes of planning, organizing, conducting and evaluating university education in modern conditions. The purpose of the study is to clarify what oppositions are currently being maintained, and what needs to be done to address problematic situations. Among the oppositions identified by the author, there are oppositions between the standardization and unification of education, which are opposed by the autonomy of universities and the individualization of the learning process; between thread-group organization of educational process, characteristic of the traditional system of higher education, and building of individual educational trajectory; between pedagogue-centered and student-centered paradigms of higher education; between the traditional system of education (teaching) and electronic learning (e-learning), between the old system of assessing students based on the exams in specific disciplines, and the need to determine the level of formation of competences; between academic and scientific models ofpostgraduate school. The following research methods were implemented: a theoretical method (analysis of scientific literature, legislative and normative documents, teaching materials, internal local acts relating to the organization of the educational activities of the University), interpretation (comprehensive and systematic study of the object of study, comparing different objects of study among themselves, summarizing their theoretical and practical experience, etc.). In the study, based on the identified oppositions in the organization of the educational process at the University - standardization and unification of education, the conclusions were made about the need for thorough and comprehensive analysis of the existing management system of higher education and suggestions for its improvement and bringing into line not only with the requirements of legislative and normative documents regulating activity of the Russian universities, but also taking into account the interests of all the stakeholders interested in the learning outcomes and the preparation of competitive specialists.
SVM Machine Learning Classifier to Automate the Extraction of SRS Elements

The process of extraction of software entities such as system, use case, and actor from an English natural language description of a user's software requirements is a linguistic and semantic process of a natural language processing application.Entity extraction is known to be a complicated and challenging problem by researchers in the fields of linguistics or computation, due to the ambiguities in natural languages.This paper presents a named entity recognition method called SyAcUcNER (System Actor Use-Case Named Entity Recognizer), for extracting the system, actor, and use case entities from unstructured English descriptions of user requirements for the software.SyAcUcNER uses one of the Machine Learning (ML) approaches, that is, the Support Vector Machine (SVM) as an effective classifier.Also, SyAcUcNER uses a semantic role labeling process to tag the words in the text of user software requirements.SyAcUcNER is the first work that defines the structure of a requirements engineering specialized NER, the first work that uses a specialized NER model as an approach for extracting actor and use case entities from English language requirements description, and the first time an SVM has been used to specify the semantic meanings of words in a certain domain of discourse; that is the Software Requirements Specification (SRS).The performance of SyAcUcNER, which utilizes WEKA's SVM, is evaluated using a binomial technique, and the results gained from running SyAcUcNER on text corpora from assorted sources give weighted averages of 76.2% for precision, 76% for recall, and 72.1% for the F-measure.
SELF-EFFICACY, AFFECTIVE WELL-BEING, AND INTENT-TO-LEAVE BY SCIENCE AND MATHEMATICS TEACHERS: A STRUCTURAL EQUATION MODEL

The current research aims to explore the impact of Science and Mathematics teachers’ self-efficacy on their intentions to leave through the mediating effects of their affective wellbeing (stress, burnout, and depression). Data were collected from 329 teachers of Science and Mathematics who were selected randomly with a clustered sampling method from 232 secondary schools in South and East Anatolia, Turkey. The structural equation model that yielded the best fit indicated that as teachers’ self-efficacy levels increase, their stress, burnout, depression, and intent-to-leave levels decrease. Teachers with high self-efficacy are less likely to develop intention-to-leave because of their positive affective well-being indicators. Results suggest that maths and science teachers who have optimistic beliefs in their capabilities can more easily cope with the stressors at work and have better affective well-being, and consequently, a lower level of intention-to-leave. The results provide educational leaders with insights as to how better to retain qualified Science and Mathematics teachers. Keywords: intent-to-leave, self-efficacy, science and mathematics teachers, wellbeing
Factors Impacting Digital Payment Adoption: An Empirical Evidence from Smart City of Dubai

The emergence of new digital payment technologies has introduced both opportunities and challenges across all industries.This research aims to examine the significant factors that influence the adoption of new e-payment technologies, specifically in smart cities, as in Dubai.A comprehensive theoretical framework based on several previous studies included the following factors; Perceived Usefulness, Perceived Trust, Perceived Personal Innovativeness, Perceived Ease of Use, Perceived Risk, and Generation Cohort.The results of this research confirm that all proposed factors significantly affect the adoption of epayment in Dubai as a case of a smart city; however, the perceived usefulness is not as significant as the other factors.Moreover, comparative analysis across the three generations showed almost similar patterns of adopting e-payment systems.
ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

Abstract Ultrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>81.06</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.
A BERT based Chinese Named Entity Recognition method on ASEAN News

Abstract As the first step of building a knowledge graph to record the ASEAN counties’ information, we aim to conduct Named-entity Recognition (NER) on the Chinese news about ASEAN counties. We employ a Bi-directional gated recurrent unit to replace the LSTM architecture to improve both models’ effectiveness and capability in understanding polysemous words. The state-of-the-art word embedding model, BERT, has also been included to generate qualified word vectors for the NER task. Besides, we also propose a similarity-based dataset partition method to help model learning the polysemy within the Chinese news. Experiments have been done to demonstrate that the combination of such improvements can benefit the models’ performance in identifying different types of named entities.
Evaluation System of College English Teaching Based on Big Data

Abstract Teaching quality is the fundamental guarantee for colleges and universities(CAU) to achieve their educational goals, and is the basis for training talents. In recent years, an important research field of big data is the research of Data mining(DM). The use of DM algorithms can find out the key factors affecting teaching quality, provide a strong basis for teaching arrangements and teaching management in universities, and then improve the teaching level of universities. The purpose of this article is to study the evaluation system of college English teaching(ET) based on big data. Based on the idea of DM, this paper designs and implements a college ET quality evaluation system(ETQES), and applies the system to the college ET quality monitoring system, and obtains a large amount of evaluation data. This paper extracts relevant data from the school’s ETQES, teacher management system, comprehensive educational administration and other system databases, and builds a data warehouse through data preprocessing. This paper implements the DM module of the ETQES to mine the data in the data warehouse. Through the analysis of these rules, the factors affecting the evaluation results of ET quality are obtained. This paper establishes a database suitable for mining association rules. Through the analysis of association rules, find out which key factors can affect the quality of ET, so as to provide a strong basis for ET decision-making and management. According to the experimental results, it can be concluded that the 4555-year-old teacher gets a higher student evaluation score, indicating that teachers of this age group are more popular with students.
Online respondent-driven detection for enhanced contact tracing of close-contact infectious diseases: benefits and barriers for public health practice

Online respondent-driven detection (RDD) is a novel method of case finding that can enhance contact tracing (CT). However, the advantages and challenges of RDD for CT have not yet been investigated from the perspective of public health professionals (PHPs). Therefore, it remains unclear if, and under what circumstances, PHPs are willing to apply RDD for CT.Between March and April 2019, we conducted semi-structured interviews with Dutch PHPs responsible for CT in practice. Questions were derived from the 'diffusion of innovations' theory. Between May and June 2019, we distributed an online questionnaire among 260 Dutch PHPs to quantify the main qualitative findings. Using different hypothetical scenarios, we assessed anticipated advantages and challenges of RDD, and PHPs' intention to apply RDD for CT.Twelve interviews were held, and 70 PHPs completed the online questionnaire. A majority of questionnaire respondents (71%) had a positive intention towards using RDD for CT. Anticipated advantages of RDD were 'accommodating easy and autonomous participation in CT of index cases and contact persons', and 'reaching contact persons more efficiently'. Anticipated challenges were 'limited opportunities for PHPs to support, motivate, and coordinate the execution of CT', 'not being able to adequately convey measures to index cases and contact persons', and 'anticipated unrest among index cases and contact persons'. Circumstances under which PHPs anticipated RDD applicable for CT included index cases and contact persons being reluctant to share information directly with PHPs, digitally skilled and literate persons being involved, and large scale CT. Circumstances under which PHPs anticipated RDD less applicable for CT included severe consequences of missing information or contact persons for individual or public health, involvement of complex or impactful measures for index cases and contact persons, and a disease being perceived as severe or sensitive by index cases and their contact persons.PHPs generally perceived RDD as a potentially beneficial method for public health practice, that may help overcome challenges present in traditional CT, and could be used during outbreaks of infectious diseases that spread via close contact. The circumstances under which CT is performed, appear to strongly influence PHPs' intention to use RDD for CT.
Robust authentication for automotive control networks through covert channels

Automotive control networks offer little resistance against security threats that come with the long-range connectivity in modern cars. Remote attacks that undermine the safety of vehicles have been shown to be practical. A range of security mechanisms have been proposed to harden resource-constrained embedded microcontrollers against malicious interference, including cryptographic protocols that establish the authenticity of in-vehicle message exchange. However, authenticated communication comes with repercussions on deployability and vehicle safety in terms of reliability, real-time compliance, backwards compatibility, and bandwidth and resource use. In this article we investigate benign, defencive uses of covert channels to implement and support vehicular message authentication mechanisms as a transparent, resource-conserving approach to automotive network security. We provide the first comprehensive evaluation of covert channels in Controller Area Networks (CAN) with respect to the attainable bandwidth and reliability of covert communication. Our analysis identifies timing-based covert channels as candidates to design a complementary nonce synchronisation channel that can enhance robustness against message loss in existing authentication schemes. We practically implement and evaluate this design on top of an open-source authenticated CAN communication library, showing that covert timing channels can improve communication robustness in benign circumstances, while not reducing the security guarantees of the underlying authentication primitives when under attack.
Systematic analysis of Movie Recommendation System through Sentiment Analysis

Hybrid Movie Recommendation System using sentiment analysis with opinion mining, is a system that helps the user by giving recommendations about the movies, based on the user preference and ordinal ratings of the movies. It removes the need for the user to go through a big list of movies before deciding what should be watched. The arrangement exploits the consumer attitude and their ideas around the attributes to give recommendations and produce output. The script centred assessments from the consumer are very optimum in enlightening the accurateness and competence of a movie recommendation arrangement. Three methods must be castoff to abstract attributes as of the movies characteristics to be utilized as attributes for collaborative filtering. The essential physical interfaces are dissimilar for all the methods. The dataset that was cast-off for the assessment of attributes with CF included of nominal scores of numerous movies. An intricate technique of collaborative filtering is efficient for the drive of recommendation throughout assessment and to associate performance of the arrangement with the addition of attributes from user assessments and deprived of the attributes from user reviews. This technique performs overall improved methodology, which is merely built on nominal ratings and movie attributes like movie genres.
Optimizing the Construction of College English Mobile Teaching Oriented by Industry Demand

With the development of the Internet and mobile terminal, mobile teaching is a new trend of reform in college English teaching.In mobile teaching, industry demand should also be taken into account, which is in accordance with the running target of Baoding University.In order to cultivate more applied talents, the paper is oriented by industry demand and explores the optimizing construction of college English mobile teaching in Baoding University, Hebei Province, China.The study employs quantitative and qualitative research design.First, an interview is designed to investigate industry demand of travel agencies.Second, a questionnaire involving 168 freshmen majoring in tourism aims to investigate the status quo of college English mobile teaching.The data collected from the interview and the questionnaire reveal the existing problems.Based on those problems, the researchers have in-depth discussions on the causes and related improvements.Then teaching reform of college English mobile teaching oriented by industry demand is carried out.Third, the other interview intends to check the effect of teaching reform.It is hoped that the study can realize the optimization of teaching environment, teaching methods, teacher-student relations, students' autonomous learning and cultivation of applied talents.
Comment on “A Social Network Based Process to Minimize In-Group Biasedness During Requirement Engineering”

The author comment on the paper “A Social Network Based Process to Minimize In-Group Biasedness During Requirement Engineering” by Mughal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , and point out that the technique can be subsequently improved by focusing on three major areas: (a) differential treatment of crowd of users; (b) ranking of requirements (i.e., determining the importance of the requirements for different stakeholders) based on analysis of social interactions among stakeholders on Facebook; and (c) extension as well as improvement in result validations by improving effectiveness calculation and including scalability as one of the efficiency metrics. Scalability will influence both effectiveness as well as efficiency of the proposed technique.