Reviewerly/exHarmony huggingface dataset:
The Influencers: Facebook's Libra, Public Blockchains, And the Ethical Considerations of Centralization

The theoretical promise of blockchain technology is truly extraordinary: a peer-to-peer distributed immutable ledger that could revolutionize economies, societies, and even our daily lives. But what if blockchain technology is not as decentralized as people think? What are the ramifications if, in reality, a blockchain’s core decisions are actually influenced by small groups of people or corporations?This short article seeks to answer that question, by demonstrating that decentralized public blockchains are only as immutable as the decentralization of their governance. Moreover, the announcement of Libra, Facebook’s new permissioned blockchain, shows a growing trend of centralized control around decentralized technologies. Libra is intended to run on highly distributed technology, but will be governed by, and therefore could be arguably controlled by, a highly centralized group of billion dollar corporations.Accordingly, this article exposes the ways in which blockchain centralization is leaving important decisions to small groups of people or corporations. These blockchain “agents of influence” have more power than many blockchain proponents acknowledge. Whenever human decision-making processes are in effect, the possibility of bias, conflicts of interest, and other ethical concerns will arise. Ironically, it is exactly this type of flawed human process that the blockchain was designed to solve.This article therefore argues that as states design laws to regulate blockchain technology, they should consider adding ethical obligations to combat the problems inherent whenever small groups of people make influential decisions. By adopting ethical guidelines at this early stage, while the technology is still evolving, states and blockchain enthusiasts may abate public fears of blockchain technology and prevent larger ethical crises from developing down the road.
Leveraging Multilingual Transformers for Hate Speech Detection.

Detecting and classifying instances of hate in social media text has been a problem of interest in Natural Language Processing in the recent years. Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment on social media involves careful evaluation of the language style, semantic content and additional pointers such as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitter post is hateful and offensive or not. We further discriminate the detected toxic content into one of the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN) and (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages. On the provided testing corpora, we achieve Macro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi respectively while performing hate speech detection and of 60.70, 53.28 and 49.74 during fine-grained classification. In our experiments, we show the efficacy of Perspective API features for hate speech classification and the effects of exploiting a multilingual training scheme. A feature selection study is provided to illustrate impacts of specific features upon the architecture's classification head.
Reversible Region-Based Embedding in Images for Secured Telemedicine Approach

In the current era, to provide quality healthcare service remotely, telemedicine is one of the well-known techniques worldwide. The prescribed diagnosis disease details the doctor for faster diagnosis over the private and public channels. Medical information needs to be secured over an unsecured internet network as it contains personal information inside, like CT-scan, X-Ray, MRI, etc. It is a challenge of security in medical data like privacy, confidentiality, and integrity of patient records. After deep investigation, the existing techniques like encryption and digital watermarking are not always resourceful in real-time. This research work analyzes the problem and delivers the solution of security for recovering medical images. Here is the basic resolution for safe image communication by preserving those selective regions of images which carry sensitive medical diagnosis information called Region of Interest (ROI). The ROI of the medical image is irregularly presumed which preserves essential information. This research work gives a broad overview of data hiding in ROI medical images by embedding in its selective blocks. The performance parameters are verified, which proves the preservation of sensitive data in medical images.
Healthy Sri Lankan Meal Planner with Evolutionary Computing Approach

Due to the busy lifestyle and the negligence of health, human beings are becoming the victims of non-communicable diseases (NCD). Further, the hereditary and lifestyle factors are major causes of NCD. My Smart Diet is a menu planning Android application with the genetic algorithmic (GA) approach. It suggests healthy meals with Sri Lankan foods and beverage. Moreover, My Smart Diet app uses calorie requirements and nutrition needs per day of a person according to age, height, weight, and daily activities. These parameters are taken from the user. Foods and actual daily calorie and nutrients that suggested by nutritionist and other sources have been stored in MySQL database. In this application, evolutionary computing has been applied on two levels. In the first level, the chromosomes have been designed using a dietary nutrient with 21 genes, where the nutrients and quantities were used as the genes. In the second level, the chromosomes have been designed using dietary foods with a variable length. The numbers of genes rely on user inputs. There, the Sri Lankan food items and quantities have represented the genes. Further, in the first level, the fitness function has been designed using user inputs and sample personal data based on nutritional data and chromosomes, while in the second level, the fitness function has been designed using foods that insert by the user, nutrition recommendation that given by the first genetic algorithm and chromosomes. Moreover, in these two levels, the individual with the lowest fitness value has been selected as the output. In addition to that, application testing was conducted by colleagues for its functionality and user satisfaction. Finally, the app has been evaluated using the preference given by its users compared with the preference received by the same group of users who used the Fitness Meal Planner app (an existing western food meal planner). After that, the preferences have been evaluated by applying the sign test. Overall, the Smart Diet app ranks higher than Fitness Meal Planner. Ultimately, Sri Lankan users can live a healthy life using My Smart Diet app.
Current Problems of Combating Drug Trafficking Using the Internet

The purpose of the article &ndash; based on the analysis of scientific and theoretical views of scientists, the results of research by international organizations in the field of prevention and counteraction to drug trafficking on the Internet, give a scientific understanding of the distribution and distribution of narcotic drugs, psychoactive, substances and precursors using the Internet as a tool to achieve criminal goals and on the basis of the obtained data to outline possible ways to minimize their spread in the Internet space. The methodological basis of the scientific article is a holistic and coherent system of methods, which allowed to properly analyze the subject of research, in particular, used scientific methods of analysis, synthesis, induction and deduction. The theoretical basis of this publication were the works of domestic and foreign scientists on aspects of combating drug crime on the Internet, the creative understanding of which allowed to draw sound conclusions. The scientific novelty of the publication lies in the systematic analysis of the most common ways of distributing drugs, psychoactive substances, drugs and their analogues via the Internet. Based on the analysis, it was found that the successful prevention of the spread of drugs via the Internet, their detection, today, is quite rare. Problems of counteracting illicit drug trafficking via the Internet have been identified. Areas of activity to detect criminal encroachments related to their sale via the Internet are outlined. With the improvement of technical devices for the use of Internet opportunities, the ways of committing such crimes are also improved. Therefore, in order to counter them, it is advisable to involve specialists who have the relevant special knowledge and professional skills in the field of computer technology with the ability to detect signs of drug crimes on the Internet, and identify new ways and opportunities to use modern tools to commit them. Conclusions. Real counteraction to the spread of psychoactive substances, drugs and their analogues on the Internet is possible only if the perception of such a phenomenon as systemic, covering a range of organizational, social, economic and other measures. To counter the proliferation of psychoactive substances, drugs and their analogues, the question of ways and mechanisms of their implementation on the Internet requires constant monitoring to predict and prevent such criminal offenses
Checkmating the Weaknesses Associated with Information and Communication Technologies in Education for Improved Effectiveness and Efficiency

Information and Communication Technologies (ICTs) have been identified as having two sides like a coin when used instructionally.While some scholars insist on the need to always prepare and deliver instructions using ICTs, others express worries over the pedagogical weaknesses thereof.This paper therefore x-rays the instructional strengths of ICTs to include; improving educational quality, enhancing access to education, easing collaboration, motivating learning, provision of remote learning resources, enhancing technological literacy, amongst others.It further enumerates the weaknesses of ICTs in teaching and learning to include high cost of acquiring and maintaining infrastructure and equipment, creation of inequality among learners, causing distraction among learners, encouraging cheating in examination and during assignments, exposing learners to technological hazards, inter alia.The paper proffers recommendations on how to effectively engage ICTs in teaching -learning to maximize the potentials, amongst which are: adequate funding for ICT sector in every educational institution; identification of needs, goals and objectives; proper budgeting and planning; engaging stakeholders for awareness and getting their inputs; ensuring safe and conducive environmental setting for the equipment; carrying out periodic employment, training and re-training of man-power; putting in place robust plagiarism software to checkmate theft of intellectual property; and providing detailed orientation for learners who are the end-users of the ICTs in the classrooms.
Memories of a teacher, colleague and friend Vadim S. Anishchenko (1943–2020)

On November 30, 2020, at the age of 78, Vadim S. Anishchenko suddenly died. He was Head of the Department of Radiophysics and Nonlinear Dynamics, Saratov State University (SSU), Doctor of Physics and Mathematics, Professor, Honored Scientist of the Russian Federation, Laureate of the International Science Prize of the Alexander von Humboldt Foundation, Honorary Worker of Education of the Russian Federation, Honorary Professor of SSU, world-class specialist in the field of nonlinear dynamics, founder of one of the leading scientific schools in radiophysics and nonlinear dynamics. The memories of people who knew Vadim S. Anishchenko closely at different periods of his life are published, as a tribute to the memory of an outstanding personality.
Bootstrapped Representation Learning on Graphs

Current state-of-the-art self-supervised learning methods for graph neural networks (GNNs) are based on contrastive learning. As such, they heavily depend on the construction of augmentations and negative examples. For example, on the standard PPI benchmark, increasing the number of negative pairs improves performance, thereby requiring computation and memory cost quadratic in the number of nodes to achieve peak performance. Inspired by BYOL, a recently introduced method for self-supervised learning that does not require negative pairs, we present Bootstrapped Graph Latents, BGRL, a self-supervised graph representation method that gets rid of this potentially quadratic bottleneck. BGRL outperforms or matches the previous unsupervised state-of-the-art results on several established benchmark datasets. Moreover, it enables the effective usage of graph attentional (GAT) encoders, allowing us to further improve the state of the art. In particular on the PPI dataset, using GAT as an encoder we achieve state-of-the-art 70.49% Micro-F1, using the linear evaluation protocol. On all other datasets under consideration, our model is competitive with the equivalent supervised GNN results, often exceeding them.
VOnDA: A Framework for Ontology-Based Dialogue Management

We present VOnDA, a framework to implement the dialogue management functionality in dialogue systems. Although domain-independent, VOnDA is tailored towards dialogue systems with a focus on social communication, which implies the need of a long-term memory and high user adaptivity. For these systems, which are used in health environments or elderly care, margin of error is very low and control over the dialogue process is of topmost importance. The same holds for commercial applications, where customer trust is at risk. VOnDA ’s specification and memory layer relies upon an extended version of RDF/OWL, which provides a universal and uniform representation, and facilitates interoperability with external data sources, e.g., from physical sensors.
An Approach for Cloud Linux Server Security and Management

Cloud computing domain is a well developed technology that everyone uses now-a-days. Most of the users use cloud for computing servers, as virtual hard disk and for analytical purposes. Cloud servers make the users on same data simultaneously and from multiple places. This feature increases productivity in all sectors. IT sector need to manage many servers because most IT organizations have their website where they host the cloud instance for the employees to work. This paper proposes an approach to centrally manage the cloud server instances and also improves the security of cloud server instances. The security maintenance of a cloud server instance is necessary issue to be considered since there is a possibility of leaving vulnerability in the cloud server instance that leads to loss of critical information of an organization. Hence this paper proposes an approach for the maintenance of cloud server instances.
Anonymous Proof-of-Asset Transactions Using Designated Blind Signatures

We propose a scheme to preserve the anonymity of users in proof-of-asset transactions. We assume bitcoin-like cryptocurrency systems in which a user must prove the strength of its assets (i.e., solvency), prior conducting further transactions. The traditional way of addressing such a problem is the use of blind signatures, i.e., a kind of digital signature whose properties satisfy the anonymity of the signer. Our work focuses on the use of a designated verifier signature scheme that limits to only a single authorized party (within a group of signature requesters) to verify the correctness of the transaction.
About Graph Unions and Intersections

Summary In this article the union and intersection of a set of graphs are formalized in the Mizar system [5], based on the formalization of graphs in [7].
Fuzzy Mediation Analysis for KOSPI-Related Variables

Stock prices and bond interest rates are inversely related.The current account plays the most important role in the rise of composite stock index.When the trade surplus increases, stock prices rise sharply.Conversely, when the exchange rate and government bond interest rates increase, the composite stock index decreases.The stock index is influenced by several economic variables, politics, the economy, and investment sentiment.In real situations, such financial variables are mostly fuzzy variables.For example, the Korea Composite Stock Price Index (KOSPI) is recorded based on the closing price.However, the daily KOSPI has many values, such as the open price, low price, and high price; therefore, if only the closing price is used for analysis, only partial information is.To analyze the relationship between KOSPIrelated variables, the KOSPI, stock deposits, government bond interest rates, and foreign security investment data are applied.We investigated the mediated relation of variables, known as mediation analysis.In particular, we implemented fuzzy mediation analysis owing to the ambiguity of given data.In the data analysis, we proposed several fuzzy mediation models using fuzzy transformed data.
Counter-Pandemic Vector of Remote Learning for University Students: Risks and Benefits of Educational Process Large-Scale Digitalization

The relevance of this study is due to the challenges of the global pandemic, which has led to the large-scale development of education digitalization. In this regard, this article is devoted to revealing the features of remote learning in the context of a global pandemic, identifying the risks and benefits of digital learning. The article reveals the discursive content of the counter-pandemic vector of distance learning for University students; defines the classification of risks and advantages of large-scale digitalization of students’ remote learning. Based on the results of the study, the authors justified the consolidated model of students distance learning, the counter-pandemic vector of which is a large-scale digitalization of the University educational process. The effectiveness of the model is proved by the results of its use in the process of large-scale digitalization of students’ remote learning in the context of a global pandemic. The materials of the article have practical application and can be useful in the development and implementation of digital educational complexes. They are recommended to teachers and students of the University, methodologists, curators, Tutors.
THE DEVELOPMENT OF TASKS FOR SELF-CONTROL AND ANALYSIS OF THEIR QUALITY USING THE MOODLE SYSTEM

When studying disciplines, not all students can objectively assess the quality of their knowledge and the level of their preparation for intermediate certification (test or exam). As a backup tool, the authors developed and introduced into the educational process one of the simple and effective ways to test oneself – tests for self-control in all sections of the course, which are integrated into the physics training course under the control of LMS MOODLE. In the example of the Mechanics section, the paper shows the technique for calculating the main characteristics and parameters of test cases. The authors analyzed the results of the test cases using the data of the built-in statistical processing module. Course approval showed that the developed test tasks are efficient in terms of improving the quality of students’ knowledge. Using the indicators defined in the LMS MOODLE Statistics module, the authors evaluated the quality of both the test cases as a whole and individual tasks. It allowed correcting the task cases, identifying and excluding unsuitable questions from the database, and identifying the sections that caused the most difficulties. The analysis showed that the created base of test tasks for self-control is suitable for further use, taking into account a slight adjustment. The calculation of the achievement rate allows predicting the assessment and specifying the topics that cause difficulties. Repeated testing makes it possible to achieve the desired result. Self-control allows creating self-educational competencies and improving the quality of education.
Methodology Matters: How We Study Socio-Technical Aspects in Software Engineering

Modern software engineering involves both human and technical aspects, the importance of which is widely accepted by practitioners and researchers alike. At a community level, software engineering researchers may be expected to choose a balance of research strategies that capture both social and technical characteristics of software development. In this paper, we consider if the research strategies we use do in fact provide this balance. We first developed a research strategy framework to help distinguish research strategies that directly study human and social aspects, from strategies that rely on data such as trace, archival or simulated data, and those that may focus more on technical or system aspects. We utilized this framework to categorize the research strategies used by 253 technical track papers from the International Conference on Software Engineering (ICSE). Using a design science lens, we further identified the types of research contributions provided in these papers---either descriptive knowledge, or the design and evaluation of technical solutions. We mapped the contribution types to the research strategies identified. We found that, at the community level, the papers we analyzed strongly favour data strategies over strategies that directly study human and social aspects, and most research contributions consist of the design or evaluation of technical solutions. We conclude by proposing that our community should diversify our use of research strategies so that we may have a deeper understanding of human and social aspects of software development practice, while balancing the design and evaluation of innovations on the technical side.
A Bag-of-Words Based Feature Extraction Scheme for American Sign Language Number Recognition from Hand Gesture Images

Human Computer Interaction (HCI) focuses on the interaction between humans and machines. An extensive list of applications exists for hand gesture recognition techniques, major candidates for HCI. The list covers various fields, one of which is sign language recognition. In this field, however, high accuracy and robustness are both needed; both present a major challenge. In addition, feature extraction from hand gesture images is a tough task because of the many parameters associated with them. This paper proposes an approach based on a bag-of-words (BoW) model for automatic recognition of American Sign Language (ASL) numbers. In this method, the first step is to obtain the set of representative vocabularies by applying a K-means clustering algorithm to a few randomly chosen images. Next, the vocabularies are used as bin centers for BoW histogram construction. The proposed histograms are shown to provide distinguishable features for classification of ASL numbers. For the purpose of classification, the K-nearest neighbors (kNN) classifier is employed utilizing the BoW histogram bin frequencies as features. For validation, very large experiments are done on two large ASL number-recognition datasets; the proposed method shows superior performance in classifying the numbers, achieving an F1 score of 99.92% in the Kaggle ASL numbers dataset.