Reviewerly/exHarmony huggingface dataset:
Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji

Research in sociology and linguistics shows that people use language not only to express their own identity but to understand the identity of others. Recent work established a connection between expression of identity and emoji usage on social media, through use of emoji skin tone modifiers. Motivated by that finding, this work asks if, as with language, readers are sensitive to such acts of self-expression and use them to understand the identity of authors. In behavioral experiments (n=488), where text and emoji content of social media posts were carefully controlled before being presented to participants, we find in the affirmative -- emoji are a salient signal of author identity. That signal is distinct from, and complementary to, the one encoded in language. Participant groups (based on self-identified ethnicity) showed no differences in how they perceive this signal, except in the case of the default yellow emoji. While both groups associate this with a White identity, the effect was stronger in White participants. Our finding that emoji can index social variables will have experimental applications for researchers but also implications for designers: supposedly ``neutral`` defaults may be more representative of some users than others.
Moderating Role of Gender in the Impact of E-Learning Quality on Students Satisfaction and Performance During Covid-19 Pandemic

This paper aims to examine the impact of E-learning quality on students' satisfaction and performance before and during the COVID-19 pandemic and the role of gender as a moderating variable. A descriptive and inferential approach was used where a specially designed questionnaire was distributed on a simple random sample of students enrolled in Public Security Training City in Riyadh, Saudi Arabia. Data were gathered from 352 respondents. The multiple regression analysis was used to justify the proposed hypotheses. The study found that E-learning system quality and E-learning service quality positively impacted student satisfaction, which made a significant and positive impact on student performance. Moreover, gender played a moderating role in the relationship between E-learning system quality, E-learning service quality, and student satisfaction. Finally, it was found that the relationship between all variables during COVID-19 was stronger than relationships that existed before the COVID-19 pandemic.
An Unsupervised Learning Approach for Visual Data Compression with Chaotic Encryption

The increased demand of multimedia leads to shortage of network bandwidth and memory capacity. As a result, image compression is more significant for decreasing data redundancy, saving storage space and bandwidth. Along with the compression the next major challenge in this field is to safeguard the compressed data further from the spy which are commonly known as hackers. It is evident that the major increments in the fields like communication, wireless sensor network, data science, cloud computing and machine learning not only eases the operations of the related field but also increases the challenges as well. This paper proposes a worthy composition for image compression encryption based on unsupervised learning i.e. k-means clustering for compression with logistic chaotic map for encryption. The main advantage of the above combination is to address the problem of data storage and the security of the visual data as well. The algorithm reduces the size of the input image and also gives the larger key space for encryption. The validity of the algorithm is testified with the PSNR, MSE, SSIM and Correlation coefficient.
Examination of Book Reading Experience Supported by Augmented Reality Technology with EEG

Augmented Reality (AR) has the potential to enrich traditional books with virtual objects. However, little is known about how the unique possibilities of this immersive technology can affect reading habits and cognitive reading awareness. This study investigates the possibilities of AR, which is one of the immersive environments of students' reading habits, with the support of Electroencephalography (EEG). 14 university students read 10 different texts from a physical book and virtual objects placed in an immersive environment, and examined the object on the screen using affine transformations. The results show that AR-assisted reading significantly engages the participant and demonstrates higher classification performance than reading from a physical book. It has been observed that the designed AR application also meets the usability characteristics.
Dreaming of utopian cities

In this chapter, I discuss some of the questions that intrigue me as researcher and artist working with practice-based research. These questions arise from my experience in combining intuition with technology, philosophy, art, and science. I believe the most significant contribution of this chapter is my pragmatic, in vivo experience, which showed me the importance of working with prototypes and small-scale projects using a methodology developed mainly from art. In the end, I discuss a practical project involving Creative AI, ArchXtonic, which uses the methodology described in the initial sections of the chapter.
A Study on the Construction of Emotion Recognition Based on Multimodal Information Fusion in English Learning Cooperative and Competitive Mode

With the development of science and education, English learning has become increasingly important. In the past, English learning was mainly based on missionaries, and students were not very motivated to learn. The purpose of this article is to use the English cooperative model to improve the enthusiasm and initiative of students in learning, and to improve the efficiency of students in learning English. A team learning model based on the game is proposed. This article constructs a cooperative and competitive model of English learning based on multimodal information fusion. The main manifestation is that students form groups in small groups, and there is a competitive relationship between the groups. The competition among students in learning is the common interest of the entire group, so that the overall interests of each student will be more competitive. This article refers to the main body association model in the literature to adjust English grammar, vocabulary, and language perception ability: learn together in team communication to improve students' multifaceted abilities. Finally, a questionnaire was designed. The results show that after changing the English team learning mode and optimizing the English team learning support system of the students' English learning team, the English learning cooperation and competition model based on multimode information fusion proposed in this article can improve the learning effect by 55%-60%. In all English teaching, the two dimensions of professional knowledge and English ability training are not mutually orthogonal and mutually exclusive, but mutually supportive and interdependent. To form an effective teaching model of “student-centered and teacher-led,” active and rich communication and feedback in the classroom are the keys, and they also help to form a gradual teaching and learning cycle.
Development of QR Code-Based Learning Multimedia to Improve Literature of Elementary School Students

The purpose of this research is to develop learning multimedia in improving the literacy of elementary school students in DKI Jakarta based on QR codes. This study uses the ADDIE Research and Development Model. The ADDIE model is an abbreviation for the five stages of the development process, namely Analysis, Design, Develop, Implement, and Evaluate. Data collection techniques using questionnaires, interviews, and documentation. Based on the results of the development of this multimedia that this multimedia can facilitate students in understanding the material. It is evident from the assessments of appropriate experts (75% material experts, 78% media experts, and 86% linguists), the responses of students and teachers are interesting (75.4% small group trials, 76.6% field trials, and 77% teacher trial). This shows that the multimedia developed by researchers can be utilized in the learning process in schools.
Adjacent Inputs With Different Labels and Hardness in Supervised Learning

An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. In this paper, we proposed a study aimed at determining the relation between the number of adjacent inputs with different labels and the required number of examples for the task of inducing a classification model. To this aim, we quantified the adjacent inputs with different labels as a property, using a measure denoted as Neighbour Input Variation (NIV). Then, we studied the relation of NIV to random data and overfitting. Second, we demonstrated that a threshold of NIV may determine if a classification model can generalize to unseen data. Third, we presented a case study analyzing threshold neural networks and the required first hidden layer size in function of NIV. Finally, we performed experiments with 5 popular algorithms analyzing the relation between NIV and the classification error on problems with few dimensions. We conclude that functions whose similar inputs have different outputs with high probability, considerably reduce the generalization capacity of classification algorithms.
Multimodality of internet-mediated communication behaviour

Chapter 2 marks a continuation of the explorations regarding the ethical aspect of humour-based communication on the internet, but it extends the discussion by covering the issue of the semiotic heterogeneity of the media communication, addressed by the term multimodality. A survey concerning the key arguments made by multimodality researchers enabled an outline of how analysing multimodal messages can be staged, taking into account the environment of their occurrence (internet) and their humour content.
Upasthiti: A Feature Learning-Inspired Remote Attendance Management System

In the contemporary era, the smart attendance system is largely dependent on the biometric methodology, where the biometric fingerprint scanners have been using in every organization and sectors. Biometric fingerprint scanners are workable when the physical touch of the active human beings is directly involved. Although, in the current global pandemic situation, when the most organizations are being run through the work-from-home paradigm, the evaluation of physical attendance of the employees is challenging. Hence, nowadays, the remote attendance management strategies are required. In this proposed paradigm, we are going to develop an automatic and remote attendance system through the facial appearances on the system-connected smart devices of the active employees. As this modeled archetype is a phenomenon under the conventional image processing context, since, this projected approach may somehow be time and space consuming. Hence, in this proposed contribution, we have evaluated this paradigm using the concepts of deep neural networks. We have performed the convolutional neural networks (CNN) and extreme learning machine (ELM) strategies over the active and participated user information within the system. This projected evaluation metrics have achieved ~98% in terms of the real-time system information accuracy that provides a satisfactory outcome of the proposed endeavor.
Comparative Assessment of Groundwater and Surface Water Quality for Domestic Water Supply in Rural Areas Surrounding Crude Oil Exploration Facilities

The purpose of this study is to assess and compare the quality of groundwater (GW) and surface water (SW) used for domestic purposes in rural areas near crude oil exploration sites in order to determine which source is more susceptible to pollution. Surface water samples were obtained from three (3) distinct locations in the study area, from upstream and downstream of the watercourses, for physicochemical, bacteriological, and heavy metal analysis using conventional laboratory procedures. Similarly, groundwater samples were taken from three (3) separate boreholes in the region for the same analysis, as per American Standard for Testing Materials (ASTM) and American Public Health Association (APHA) procedures. The relative significance of fourteen (14) water quality indicators in defining the quality of water for human consumption was evaluated in this study. The pH of the two water sources was somewhat acidic, with values ranging from 5.19 to 6.24, according to the results of the laboratory analysis. Water quality indicators such as Fe, Pb, TDS, TPC, DO, BOD, F coli, and E coli, on the other hand, were found to be above the permissible tolerance in drinking water, according to the Nigerian Standard for Drinking Water Quality (NSDWQ) and the World Health Organisation (WHO). Pollutant concentrations in surface water were higher than in groundwater, which may be attributable to the presence of humans or animal excrements (from open grazing) as well as offshore activities.
Educational Design of Mobile Learning Environments

Future directions of mobile learning concern the increase of its implementation in classroom and its institutional support through innovative online degrees, and the use of big data, augmented learning, gamification, Learning Management Systems (LMS), corporate MOOCs, linked and embedded into mobile learning. All these trends revolve around a key node, represented by mobile learning environments design, which keeps unchallenged collaborative, experiential, inquiry-based, and problem-based approaches to student learning. In this chapter is deepened mobile learning design, that leverages student-centered learning, in which learners can use mobile resources of devices at their own pace; personalized learning, where learners can choose their preferred tasks and practices; active learning, with which learners can be immediately involved; collaborative learning, to meet each other and share content; informal learning, which occurs in real-life contexts outside formal and institutional settings; and authentic learning, which develops within contexts, using mobile devices. The general theme of mobile learning environments design includes the consideration of the following aspects: open multimedia resources, which include the learning content, and tools/materials for learning activities; learner centered activities, focusing on the learning process itself and on learners’ reflections on it, more than on its final results; and teachers as facilitators.
Consensus Algorithms for Blockchains

A consensus algorithm is a mechanism that enables humans or machines to cooperate in a distributed setting. Even if some agents fail, the system must be able to agree on a common source of truth for all agents. To put it another way, the system must be tolerant to failures.
Convolutional Neural Network Architectures Used in Computer Vision

Studies on computer vision have gained momentum especially with the development of convolutional neural networks (CNN). In addition, the creation of better data sets in terms of quality and quantity, providing access to these data sets, developments in computational power and parallel programming are other issues that affect the achievement of high levels of accuracy. In this study, the concept of computer vision, the factors that make computer vision difficult, the studies that have been done in computer vision until today are summarized. The algorithms developed for this purpose are examined and the studies done in the last five years are analyzed and presented comparatively. With this study, it is aimed to provide a general perspective for future studies.
A Testing Of Case-Base Reasoning For Covid-19 Patient Status Confirmation

Currently, the world is facing a global pandemic that attacks all countries. In Indonesia, there are three types of status for suspected patients:  asymptomatic person, Person Under Supervision, and Patient Under Supervision. The statuses are issued by a paramedic, conducting medical examinations or direct interviews with patients with several criteria. We conducted several non-medical experiments to assist medical personnel in determining the asymptomatic. We exploit the case-based reasoning (CBR) for determining the suspected patients, and the K-NN (K-Nearest Neighbor) for data grouping based on the level of similarity. The patients will be interviewed regarding their travel history, direct contact history, health status, and some other information for the past 14 days. This combination delivers the information of the similarity level from the given data and previous data. As a conclusion, the percentage level of similarity can be used by a medical officer to issue the status of patients and giving several recommendations to follow health protocols.
Radio-Based Cyber-Attacks Against Pacemakers: Assessing Their Chance of Success Under Real Conditions

Proofs of concept have shown that certain models of pacemakers are vulnerable to radio-based cyber-attacks. However, to estimate an attack’s risk of occurrence, it is not enough to prove its feasibility. It is also necessary to evaluate the attack’s chances of success under real conditions. In this study, we evaluate the probability of occurrence of radio-based cyber-attacks against pacemakers. We performed some attacks and documented the difficulties encountered along the way. We then analyzed the effect that these difficulties would have on the outcome of the attacks in real life circumstances. The results of the experiments reveal that the probability of these attacks being conducted in real life is low because of the time and space requirements that are required for their success.
MULTIFUNCTIONAL USE OF VIRTUAL REALITY AS A SUPPORT TOOL FOR DISTANCE LEARNING

J. Kaščak, M. Kočiško, J. TörökFaculty of Manufacturing Technologies with the Seat in Prešov (SLOVAKIA)
The Impact of GDPR Infringement Fines on the Market Value of Firms

Previous studies have shown (varying degrees of) evidence of a negative impact of data breach announcements on the share price of publicly listed companies. Following on from this research, further studies have been carried out in assessing the economic impact of the introduction of legislation in this area to encourage firms to invest in cyber security and protect the privacy of data subjects. Existing research has been predominantly US-centric. This paper looks at the impact of the General Data Protection Regulation (GDPR) infringement fine announcements on the market value of mostly European publicly listed companies with a view to reinforcing the importance of data privacy compliance, thereby informing cyber security investment strategies for organisations. Using event study techniques, a dataset of 25 GDPR fine announcement events was analysed, and statistically significant cumulative abnormal returns (CAR) of around-1% on average up to three days after the event were identified. In almost all cases, this negative economic impact on market value far outweighed the monetary value of the fine itself, and relatively minor fines could result in major market valuation losses for companies, even those having large market capitalisations. A further dataset of four announcements where sizeable GDPR fines were subsequently appealed was also analysed and although positive returns for successful appeals were observed (and the reverse), they could not be shown to be statistically significant-perhaps due, at least in part, to COVID-19 related market volatility at that time. This research would be of benefit to business management, practitioners of cyber security, investors and shareholders as well as researchers in cyber security or related fields (pointers to future research are given). Data protection authorities may also find this work of interest.
FORMATION OF FUTURE TEACHER’S READYNESS FOR THE INFORMATION COMPETENCE SELF-DESIGN IN THE EDUCATIONAL ENVIRONMENT OF HIGHER EDUCATION ESTABLISHMENTS

The article highlights the theoretical and practical aspects of the formation of future teachers’ readiness for the information competence self-design by means of the educational environment. The essence of the concept of “information competence” from the standpoint of different sciences and methodological approaches has been revealed. An appropriate educational environment of HEE was created to implement the technology of the formation of future teachers' readiness for the information competence self-design. This educational environment is a set of organizational, psychological and pedagogical actions and tools aimed at organizational and managerial, information and didactic, scientific and methodological and logistical support of this process, its main subjects. The modular object-oriented dynamic environment of Moodle is used for the realization of educational process tasks on the formation of future teachers' readiness for the information competence self-design. Experimental testing of the developed technology was conducted on 3 stages: ascertaining (diagnostics methods development of the formation of future teacher’s readiness for the information competence self-design, conducting initial diagnostics in control and experimental groups and designing the formation process of a specialist’s specified quality on the basis of research results and developed technology of its implementation in the educational environment of HEE), formative (the developed technology introduction, pedagogical tools, scientific and methodical support of the formation process of future teacher’s readiness for the information competence self-design, monitoring the formation level of the studied phenomenon in students of control and experimental groups), control (final diagnosis, comparative analysis of initial and final diagnosis). The study results indicate the positive impact of the developed technology on the dynamics level of the future teachers’ readiness for the information competence self-design in the educational environment of HEE.&#x0D; Keywords: information competence; teacher’s information competence; functions of information competence; future teacher; educational environment of HEE; methodological approaches; self-design; technology of readiness formation; higher education establishment (HEE).
CURRENT TRENDS AND CHALLENGES IN DISTRIBUTED CONTROL SYSTEMS – AN OVERVIEW

In this paper, innovations in the field of distributed control systems have been considered. Without any claim for completeness, a short summary on current trends in this area has been provided. A special attention is paid to application of blockchain technologies in distributed control systems, game theoretical approach for distributed control applications, and advantages of distributed control for power systems. Also, one of the main issues of modern distributed control systems – cybersecurity has been considered.