Reviewerly/exHarmony huggingface dataset:
A Study on How the Pandemic Changed the Cybersecurity Landscape

This article studies the main changes inflicted by COVID-19 on the cybersecurity field.First, we analyze the main changes in how people used technology during the pandemic compared to before.The changes are classified into two categories: those that take place in the personal life and those specific to the professional environment.Then, the article studies to what extent each of the two categories impacted the cybersecurity domain.The main types of attacks that raised in popularity during the pandemic are discussed, together with causes, consequences, and mitigation strategies.Our work shows that the most important changes in terms of incidents have been related to ransomware, phishing, and remote desktop protocol (RDP) attacks.We studied how COVID-19 restrictions generated the increase in phishing and RDP attacks.Then, to prove that ransomware was also influenced by the pandemic, we had to validate our hypothesis that the increase in both RDP and phishing attacks were the main causes of the intensification of ransomware attacks.We obtained strong correlation indicators which validated our hypothesis.The measures taken by companies are further discussed, whether they are cybersecurityrelated companies or specialized in other areas.The paper also studies the evolution of cybersecurity companies' stocks before and after the start of the pandemic.A correlation matrix based on the stock price evolution was performed, which indicates the influence of the pandemic on cybersecurity.
Effective Privacy-Preserving Collection of Health Data from a User’s Wearable Device

The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual’s health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer’s personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users’ wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach.
Lexical Normalization for Code-switched Data and its Effect on POS Tagging

Lexical normalization, the translation of noncanonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media.Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media.In this paper, we propose three normalization models specifically designed to handle codeswitched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De).For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging.Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input. 1
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings

We propose a method for online news stream clustering that is a variant of the nonparametric streaming K-means algorithm.Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier.The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective.We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering.Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents.
InterCriteria Analysis: Application for ECG Data Analysis

The InterCriteria Analysis (ICrA) is based on the mathematical formalisms of index matrices and intuitionistic fuzzy sets. It has been elaborated to discern possible similarities in the behavior of criteria pairs when multiple objects are considered, allowing also the accounting of information uncertainty. The focus of this study is to validate the applicability of ICrA over a large set of ECG criteria extracted for arrhythmia analysis and to evaluate its ability to support the pre-selection of criteria that could be further involved in decision making procedures. ICrA is applied over 88 ECG criteria (resulting in 3828 criteria pairs) calculated for 8528 ECGs from PhysioNet/CinC Challenge 2017 database. Three criteria pairs show strong positive consonance, another 26—positive consonance, while another 15 are in negative consonance. ICrA also reveals lack of dependencies in 98 criteria pairs. The correspondence between our observations (high degrees of agreement/disagreement and lack of dependencies) and our expectations based on knowledge of the principles involved in the computation of the ECG criteria validates the application of ICrA for reliable evaluation of the relation between different criteria. This potential of ICrA to highlight useful relations between ECG criteria makes it suitable in the ECG pre-processing stage for criteria pre-selection. Thus, optimization of the feature space could be achieved together with minimization of the computations’ complexity.
Reweighting neural network examples for robust object detection at sea

Deep neural networks have had profound significance in addressing visual object detection and classification tasks. However, though with the caveat of needing large amounts of annotated training data. Furthermore, the possibility of neural networks overfitting to the biases and faults included in their respective datasets. In this work, methods for achieving robust neural networks, able to tolerate untrusted and possibly erroneous training data, are explored. The proposed method is shown to improve performance and help neural networks learn from untrusted data, provided a thoroughly annotated subset.
STUDENTS’ PERCEPTIONS OF USING YOUTUBE AS ENGLISH ONLINE LEARNING MEDIA DURING COVID-19 PANDEMIC

This study aims to analyze the students' perceptions in using YouTube as online English learning media during the Covid-19 pandemic. at grade XI of SMA Negeri 4 Pematangsiantar. The aspects of students’ perception cover attractiveness, effectiveness, relevancy, and motivation. These aspects are seen in students’ learning experiences in classes. The research subjects include students in class XI PMIA 6 and XI PMIA 7 at SMA Negeri 4 Pematangsiantar. The data are collected using qualitative research methods, namely the type of basic interpretive studies where the data are obtained using a questionnaire distributed to students in the form of a Google Form. The result of data analysis shows that most students gave a positive response to each statement in each aspect of the questionnaire given. Based on the research results obtained, this study can be concluded that the use of YouTube as an online English learning medium is attractive, effective, and relevant to the course content. It also motivates students in English online learning during the Covid-19 pandemic grade XI of SMA Negeri 4 Pematangsiantar.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough

Optimising the approximation of Average Precision (AP) has been widely studied for image retrieval. Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance. However, we claim that only penalizing negative instances before positive ones is enough, because the loss only comes from these negative instances. To this end, we propose a novel loss, namely Penalizing Negative instances before Positive ones (PNP), which can directly minimize the number of negative instances before each positive one. In addition, AP-based methods adopt a fixed and sub-optimal gradient assignment strategy. Therefore, we systematically investigate different gradient assignment solutions via constructing derivative functions of the loss, resulting in PNP-I with increasing derivative functions and PNP-D with decreasing ones. PNP-I focuses more on the hard positive instances by assigning larger gradients to them and tries to make all relevant instances closer. In contrast, PNP-D pays less attention to such instances and slowly corrects them. For most real-world data, one class usually contains several local clusters. PNP-I blindly gathers these clusters while PNP-D keeps them as they were. Therefore, PNP-D is more superior. Experiments on three standard retrieval datasets show consistent results with the above analysis. Extensive evaluations demonstrate that PNP-D achieves the state-of-the-art performance. Code is available at https://github.com/interestingzhuo/PNPloss
Arabic CyberBullying Detection Using Arabic Sentiment Analysis

The Sentiment Analysis is used for the text analysing, and classification of the text attitude.We are using the computing advancement in the form of Machine Learning (ML) and Support Vector Machine (SVM) algorithm to train a dataset which is collected automatically through ArabiTools and Twitter API. The dataset contents are labelled by both means, automatic and manual, in order to maintain the efficiency of the detection of CyberBullying tweets. The dataset is automatically labelled with respect to the nature of the tweet. If a tweet contains one or more CyberBullying words, it is labelled as CyberBullying, while if there isn't any word with aggressive meaning found, it is marked as the NonCyberBullying. After the data collection, there are several pre-processing techniques utilized, including the Normalization, Tokenization, Light Stemmer, ArabicStemmerKhoja, and Term Frequency-Inverse Document Frequency (TF-IDF)" term weighting schema." After the preliminary steps, (SVM), a "supervised algorithm," is used with WEKA and Python. There are three experiments that take place one with the WEKA tool using the Light Stemmer, the other is again with WEKA using ArabicStemmerKhoja, and the final experiment was performed with Python. The results are showing the WEKA is more efficient in classifying the text correctly, while Python is more effective with time to build the model. WEKA using the Light Stemmer have the efficiency of 85.49% and taken 352.51 seconds, and the WEKA using ArabicStemmerKhoja have the efficiency of 85.38% and taken 212.12 seconds, while the Python have the efficiency of 84.03% and taken 142.68 seconds
Perceived Organizational Support and Employee Performance

The employee’s in-role and extra-role performance has badly effected due to prevalence of COVID-19. The aim of the study is to examine the relationship of perceived organizational support and employee performance. The role of organizational justice is examined as a moderator. A sample of 222 bank employees was selected on convenience basis. The responses were recorded through closed ended questionnaires. The responses were analyzed through Partial Least Square Structural Equation Modeling Method. In times of COVID-19 the perceived organizational support has an antidotal effect on employee’s performance. At the same time the organizational justice has a significant role in the relationship of perceived organizational support and employee performance. The managers interested to have good performance of employees have to ensure organizational support along with justice in organizational practices. The perceived organizational support and justice induce trust among employees and compel them to work with diligence. The organizational justice has greater contributions towards performance. The study has examined the non-linear relationship between perceived organizational support and employees’ performance in bank employees in the context of COVID-19. In such times the people expect increased need for organizational support to fight out the challenges
Educational continuity during the pandemic: Challenges to pedagogical management in segregated Chilean schools

The COVID-19 outbreak has created an uncertain scenario for educational systems, leading many countries to deploy unprecedented remote learning programmes. Chilean schools have not been the exception and have thus developed and put in place several actions to uphold effective pedagogical management (PM) and continuity of learning; however, as we emphasise in this paper, the Chilean educational system is highly segregated. PM encompasses actions and decisions aimed at safeguarding quality education by focusing on four domains: technological, curricular, methodological and assessment. This descriptive and exploratory quantitative study seeks to explore the challenges facing PM, as perceived by actors working in Chile’s three different administrative and financial school governance systems in the framework of remote education during the COVID-19 pandemic.Our findings highlight the disparities among actors belonging to the three types of administrative and financial governance systems regarding the perceived challenges facing PM. Thus, publicly funded schools face greater challenges in practically all four PM domains as compared to private schools. The main differences among schools revolve around the technological and assessment domains. While the technological domain is more of a concern for public schools, the assessment domain presents more challenges for private schools. Beyond the schools´ administrative and financial governance system, an important finding was the scant importance attached to the curriculum as a challenging issue as well as an excessive focus on the methodological domain, indicative of a prevailing logic of efficiency as applied to the learning process.
A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.
Intelligent Traffic Light Controller using Fuzzy Logic and Image Processing

Today's traffic congestion in the big city is a serious problem, as it causes a lot of environmental pollution and difficulty in transportation, which leads to difficult daily life for the human beings in addition to material losses.In this work a smart traffic light controller was designed using fuzzy logic and image processing with MATLAB, to control movement in two ways, aided by a camera and auto sensors.The Fuzzy logic has two inputs and six outputs designed, the console input is the number of cars on each road and the time of the assumed red, yellow and green signal according to the vehicles congestion.The simulation result is similar to the proposed control unit, as it deals with the lights simultaneously according to the number of cars in each branch of the road, which leads to the use of all the time to operate the stoplights.Our system can be employed in solving the problem of traffic congestion in the big cities or the smart cities.
Teaching models on good teaching practices in virtual classrooms

The study of good educational practices in virtual classrooms is focused on knowing the characteristics of pedagogical experiences that have been successful for the achievement of learning. However, in the studies published to date, a coincident approach that integrates the characteristics that distinguish good practices and the traits that are relevant to assess pedagogical interventions in virtual classrooms is not identified. Faced with this situation, this paper presents a comparative analysis of teaching models on good educational practices in virtual education, to identify the characteristics of effective practice. A bibliographic review consisting of 11 proposals and 25 evaluation features is synthesized; in which it was found that continuous feedback, interactivity, knowing the student’s needs, multisensory teaching materials and promoting active learning are some of the most successful practices. Based on these results, it is concluded that the characterization of good educational practices in virtual classrooms is more focused on pedagogical strategies than on the technological supports of virtual education, therefore, the interest of teachers considered effective in their practice, is inclined towards their didactic and academic specialization.
Fighting Deepfakes Using Body Language Analysis

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.
Complete structural restoring of transferred multi-qubit quantum state

In this work, we study optimal transfer of quantum states via spin chains. We develop the protocol for structural restoring of multi-quantum coherence matrices of multi-qubit quantum states transferred from sender to receiver along a spin-1/2 chain. We also develop a protocol for constructing such 0-order coherence matrix that can be perfectly transferred in this process. The restoring protocol is based on the specially constructed unitary transformation applied to the state of the extended receiver. This transformation for a given length of the chain and Hamiltonian is universally optimal in the sense that ones constructed it can be applied to optimally restore any higher-order coherence matrices.
Second-Order SCA Security with almost no Fresh Randomness

Masking schemes are among the most popular countermeasures against Side-Channel Analysis (SCA) attacks. Realization of masked implementations on hardware faces several difficulties including dealing with glitches. Threshold Implementation (TI) is known as the first strategy with provable security in presence of glitches. In addition to the desired security order d, TI defines the minimum number of shares to also depend on the algebraic degree of the target function. This may lead to unaffordable implementation costs for higher orders.For example, at least five shares are required to protect the smallest nonlinear function against second-order attacks. By cuttingsuch a dependency, the successor schemes are able to achieve the same security level by just d + 1 shares, at the cost of high demand for fresh randomness, particularly at higher orders. In this work, we provide a methodology to realize the second-order glitch-extended probing-secure implementation of a group of quadratic functions with three shares and no fresh randomness. This allows us to construct second-order secure implementations of several cryptographic primitives with very limited number of fresh masks, including Keccak, SKINNY, Midori, PRESENT, and PRINCE.
Fundamentals of UI/UX design as a component of the pre-service specialist's curriculum

Nowadays digitalization is considered to be a key driver of socio-economic development. Digital technologies are becoming the basis for creating new products and services as well as for the transition to new business models. Ukrainian course for the development of the digital economy requires the readiness of the education system to provide training of the specialists who are able to provide such transformations, which should be reflected both in the emergence of new areas of training and in focusing the content of educational training on the formation of new digital competencies of pre-service professionals. One of the competencies which is crucial and in demand today for a wide range of specialists, is the mastering the fundamentals of UX/UI-design. The purpose of the paper is to cover the approaches to the creation of the content of pre-service specialists' training on the fundamentals of UX/UI-design. The significance of the said fundamentals learning in the process of various specialists' training is discussed. It is proposed the practically-driven approach to the curriculum building. It is distinguished invariant and variable parts in the curriculum of such a training. The content of three core modules which make invariant part of the curriculum is presented. Some relevant forms of work with students are offered within each module. The prospects of the work make the preparation and doing empirical research directed on the verification of the offered approach to the curriculum building in the process of different specialists' training.