Reviewerly/exHarmony huggingface dataset:
Digital Crime in the Context of a Pandemic: Main Trends

On January 30, 2020, a group of experts from the world health organization announced an outbreak of SARS-CoV-2, which caused the coronavirus disease COVID-19. On March 11, 2020, the ongoing outbreak of coronavirus infection was declared a pandemic, which led to a huge number of new vectors of digital crime attacks. Cybercriminals did not shy away from the situation and used the pandemic to commit various digital scams and cyber-attacks. Due to the fact that a large number of people were working from home and using online services during the pandemic, the capacity of cybercriminals to exploit emerging opportunities and vulnerabilities increased significantly. In most cases, citizens have outdated cybersecurity systems in their homes, or they do not have them at all. Cybercriminals are preying on the opportunity to take advantage of this situation and are focusing even more on cybercrime activities. The study contains data from global reports and press releases on digital threats during the pandemic. The work identified sustainable trends in digital crime in the period of the pandemic, among them: 1) mass cyber-attacks on recently (and often quickly) deployed remote access infrastructure or remote work infrastructure; 2) the growth of phishing attacks and malware distribution due to the growth of the digital audience; 3) adaptation of «classic» fraud schemes using social engineering methods; 4) attacks and hacking of digital communication platforms («Zoombombing»); 5) the growth of criminal phenomena in online games; 6) increased demand and distribution of pornographic materials through social networks, encrypted applications and the DarkNet. A number of preventive measures to reduce the impact of digital crime during the pandemic are proposed.
Slovo: Russian Sign Language Dataset

One of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies. In addition, the sign language in each country differs significantly, which obliges the creation of new data for each of them. This paper presents the Russian Sign Language (RSL) video dataset Slovo, produced using crowdsourcing platforms. The dataset contains 20,000 FullHD recordings, divided into 1,000 classes of isolated RSL gestures received by 194 signers. We also provide the entire dataset creation pipeline, from data collection to video annotation, with the following demo application. Several neural networks are trained and evaluated on the Slovo to demonstrate its teaching ability. Proposed data and pre-trained models are publicly available $$^{1}$$ ( https://github.com/hukenovs/slovo ) $$^{2}$$ ( https://gitlab.aicloud.sbercloud.ru/rndcv/slovo ).
EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment

The scheduling of tasks in the cloud is a major challenge for improving resource availability and decreasing the total execution time and energy consumption of operations. Due to its simplicity, efficiency, and effectiveness in identifying global optimums, electric fish optimisation (EFO) has recently garnered a lot of interest as a metaheuristic method for solving optimisation issues. In this study, we apply electric fish optimisation (EAEFA) to the problem of cloud task scheduling in an effort to cut down on power usage and turnaround time. The objective is to finish all tasks in the shortest possible time, or makespan, taking into account constraints like resource availability and task dependencies. In the EAEFA approach, a school of electric fish is used to solve a multi-objective optimization problem that represents the scheduling of tasks. Because electric fish are drawn to high-quality solutions and repelled by low-quality ones, the algorithm is able to converge to a global optimum. Experiments validate EAEFA's ability to solve the task scheduling issue in cloud computing. The suggested scheduling strategy was tested on HPC2N and other large-scale simulations of real-world workloads to measure its makespan time, energy efficiency and other performance metrics. Experimental results demonstrate that the proposed EAEFA method improves performance by more than 30% with respect to makespan time and more than 20% with respect to overall energy consumption compared to state-of-the-art methods.
Working with audio in R using av

The latest version of the rOpenSci av package includes some useful new tools for working with audio data. We have added functions for reading, cutting, converting, transforming, and plotting audio data in any popular audio / video format (mp3, mkv, aac, etc). The functionality can either be used by itself, or to prepare audio data for further analysis in R using other packages.
Binary code similarity analysis based on naming function and common vector space

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match .
Current Foreign Studies of Professional Burnout in Teachers

&lt;p style="text-align: justify;"&gt;The article is aimed at the analysis of modern foreign publications devoted to the professional burnout of school teachers. It was shown that the vector of research by psychologists is focused at studying the current level of teacher burnout, various correlates of burnout, the impact of the consequences of the COVID-19 pandemic on this process, as well as the prophylaxis and prevention of burnout. It can be argued that the globally recognized approach to burnout is the approach of K. Maslak, and the theory that is gaining popularity in terms of which teachers&amp;rsquo; burnout is explained as the theory of &amp;ldquo;work requirements and resources&amp;rdquo;. Based on empirical data, it can be stated that the prevalence of burnout among teachers is up to 30%, and the average percentage of high burnout is about 10%. One of the most significant factors of burnout is thought to be the behavior of students, as well as bureaucracy. Also, the key factors influencing burnout include self-efficacy, social support, feedback from the subjects of the educational process, emotional work, lack of opportunity to learn and make decisions independently. At the same time, the inconsistency of the available empirical data is notable, which can be explained by the difference in the applied methodology and research methods. Among the methods of practical work with the phenomenon of professional burnout, there are those that are based on the practices of awareness and the cognitive-behavioral approach.&lt;/p&gt;
Student's Choice of an Individual Educational Trajectory: Subjective Position and Selection Strategies

&lt;p&gt;The article is devoted to the study of students' ideas about the choice of an individual educational trajectory. The features of the digital transformation of education are determined, the parameters of subjectivity as a conscious attitude of the student acting as a participant in globalization processes are described. Interpretations of the concept of choice and its determining factors are considered. It is assumed that students' ideas about the choice of an individual educational trajectory are characterized by partial formation and differ depending on the direction of training. 218 first-year students were examined. It is shown that students' ideas about the individual educational trajectory are quite meaningful and adequate. At the same time, the understanding of the essence of choice and their participation in its implementation are fragmented. The differences in the understanding of the phenomena of "individual educational trajectory" (X2emp.=13,9, p&amp;le;0,05) and "digital educational environment" (X2emp.=12,5, p&amp;le;0,05) by students of different directions are shown. It is described that the phenomenon of "individual educational trajectory" is understood by students quite well (less than 15% of incorrect answers), while students of technical and pedagogical directions give more correct answers than students studying natural sciences and social sciences.&lt;/p&gt;
Considering the mutual information criterion for sensor configuration selection in human activity recognition in smart homes

Ambient sensors of a smart home offer new services to occupants related to security, comfort and energy management. This makes it possible to estimate the practices of the occupants and determine energy consumption, by estimating the number of occupants per zone, their activities and routines, with a requirement for digital sobriety (i.e fewer sensors possible, but enough to ensure a service).The selection of a pertinent sensor configuration is an important aspect in human activity recognition (HAR). This problem includes the selection of the number and type of the sensors, as well as the identification of the most informative ones.In this paper, we propose a novel approach to determine the most informative sensor types (among motion detection sensors and door contact sensors) based on the criterion of mutual information. The selected configuration of sensors is applied to recognize daily activities in real-time in a real-world dataset: Aruba, from the CASAS project. The simulation results show good performance of classification and a save of processing time.
Phase encoded quantum key distribution up to 380 km in standard telecom grade fiber enabled by baseline error optimization

Abstract Phase encoding in quantum key distribution (QKD) enables long-distance information-theoretic secure communication in optical fibers. We present a novel theoretical model characterizing errors from various sources in practical phase encoding-based QKD systems, namely the laser linewidth, detector dark counts, and channel dispersion. This model provides optimized optical pulse parameters and less distortion in pulses, which eliminates system imperfections and leads to a reduced quantum bit error rate (QBER) for practical QKD scenario. This analysis is applicable to various fiber-based phase and time encoding protocols. In particular, we implement this to a differential phase shift (DPS) QKD scheme operating at a 2.5 GHz clock, which produces a secure key rate of 193 bits/s at a fiber length of 265 km and an unprecedented QBER &lt; 1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> up to 225 km length with standard telecom components. We show that by adjusting the quantum efficiency and dark count rates of detectors, proposed system can establish secure keys up to 380 km distance using standard telecom grade fiber with a QBER of 1.48%. Moreover, the system is compatible with existing optical fiber networks and capable of establishing a secure key exchange between two cities 432 km apart using ultra-low-loss (ULL) specialty fiber.
Multi-Component Feature Extraction for Few-Sample Automatic Modulation Classification

With the rapid development of deep learning (DL), Automatic Modulation Classification (AMC) has also taken a huge leap forward. The DL-based AMC methods are able to achieve high accuracy through training massive labeled samples. However, these DL-based AMC methods would deteriorate dramatically with insufficient samples. Modulation recognition under few sample condition gradually become an urgent problem. To address this problem, we propose a novel learning framework for few-sample AMC, which is termed Multi-Component Extraction Network (MCENet) and can effectively extract potentially and easily distinguishable features. Experimental results on the public available dataset RadioML2016.10a show that the proposed MCENet outperforms other contrastive few-sample AMC methods and achieves better results.
Forecasting of Poverty using the Ensemble Learning Classification Methods

Poverty is a social-cultural problem that can be categorized into monetary approach, capability approach, social exclusion, and participatory poverty assessment. However, the existing measurement methods are complex, costly, and time-consuming. This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. Ensemble Learning has improved the classification of poverty and obtained better predictive performance. The results of the algorithms showed the poverty trend, which helped to determine the poverty classification. Hence, this method will help the government to act and produce a specific plan to reduce the poverty rate. It is a strategic move to reduce global poverty, parallel to Goal 1 of Sustainable Development Goal (SDG): No Poverty
PrivacyProber: Assessment and Detection of Soft–Biometric Privacy–Enhancing Techniques

Soft–biometric privacy–enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft–biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible. Because such techniques are increasingly used in real–world applications, it is imperative to understand to what extent the privacy enhancement can be inverted and how much attribute information can be recovered from privacy–enhanced images. While these aspects are critical, they have not been investigated in the literature so far. In this paper, we, therefore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">study the robustness</i> of several state–of–the–art soft–biometric privacy–enhancing techniques to attribute recovery attempts. We propose PrivacyProber, a high–level framework for restoring soft–biometric information from privacy–enhanced facial images, and apply it for attribute recovery in comprehensive experiments on three public face datasets, i.e., LFW, MUCT and Adience. Our experiments show that the proposed framework is able to restore a considerable amount of suppressed information, regardless of the privacy–enhancing technique used (e.g., adversarial perturbations, conditional synthesis, etc.), but also that there are significant differences between the considered privacy models. These results point to the need for novel mechanisms that can improve the robustness of existing privacy–enhancing techniques and secure them against potential adversaries trying to restore suppressed information. Additionally, we demonstrate that PrivacyProber can also be used to detect privacy–enhancement in facial images (under black–box assumptions) with high accuracy. Specifically, we show that a detection procedure can be developed around the proposed framework that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning free</i> and, therefore, generalizes well across different data characteristics and privacy–enhancing techniques.
An intelligent approach for anomaly detection in credit card data using bat optimization algorithm

As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.
Retracted: PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

[This retracts the article DOI: 10.1155/2021/6633755.].
Decision‐Making and Planning Methods for Autonomous Vehicles Based on Multistate Estimations and Game Theory

A core issue inherent to decision‐making and path‐planning tasks is managing the uncertainties in the motion of dynamic obstacles. Therefore, this article proposes a new decision‐making and path‐planning framework, based on game theory, that considers the multistate future actions of surrounding vehicles. First, multistate future actions of neighboring vehicles, whose driving styles vary, are estimated and fed into a decision‐making module for risk assessment. Then, based on the Stackelberg game theory, the ego vehicle and the rear object vehicle are modeled as two players in the game, and their optimal decisions are obtained. In addition, the path‐planning model incorporates a potential‐field model that utilizes several potential functions to explain the varied styles and physical limitations of the surrounding vehicles. Finally, the trajectory of the ego vehicle is obtained through model predictive control that is based on the outputs of the decision‐making and constructed potential‐field models. The results of simulation experiments that used designed scenarios demonstrate that the proposed method effectively manages various social interactions and generates safe and appropriate trajectories for autonomous vehicles. In addition, the simulation results demonstrate that considering multistate trajectories caused the decision‐making and path‐planning modules to be appropriate for unpredictable environmental conditions.
Malware victimisation and organisational survival: A multi-method exploration of emerging market

The internet has gained widespread acceptance globally since its inception. However, the escalating threats associated with this acceptance are alarming, as cyber fraudsters continually imitate and execute grievous attacks on corporate entities. While much is known about the various dimensions of malware attacks and defense (Sharmeen et al., 2019), little attention has been given to how malware affects the socio-economic survival of organizations in Nigeria, particularly in Cross River State. This article aims to bridge this knowledge gap by presenting empirical evidence on how malware victimization impacts organizational survival in the study area. Through the use of questionnaires and in-depth interviews, a sample of 1,074 research participants, including bank staff, industrial workers, and telecommunication staff, was selected from Cross River State using a multi-stage sampling technique. The findings reveal a significant increase in malware victimization among corporate organizations in Cross River State, resulting in severe consequences for their socio-economic development. To mitigate these risks, the study recommends that organizations strengthen their network security, implement comprehensive cybersecurity awareness training programs for employees, adopt advanced detection and response technologies, and employ mobile security solutions or business internet traffic security measures to ensure their safety.
Diversified Prior Knowledge Enhanced General Language Model for Biomedical Information Retrieval

General language models have shown success in various information retrieval (IR) tasks, but their effectiveness is limited in the biomedical domain due to the specialized and complex nature of biomedical data. However, training domain-specific models is challenging and costly due to the limited availability of annotated data. To address these issues, we propose the Diversified Prior Knowledge Enhanced General Language Model (DPK-GLM) framework, which integrates domain knowledge with general language models for improved performance in biomedical IR. Our two-stage retrieval framework comprises a Knowledge-based Query Expansion method for enriching biomedical knowledge, an Aspect-based Filter for identifying highly-relevant documents, and a Diversity-based Score Reweighting method for re-ranking retrieved documents. Experimental results on public biomedical IR datasets show significant improvement, demonstrating the effectiveness of the proposed methods.
Banach Space Valued Multivariate Multi Layer Neural Network Approximation Based on Parametrized Error Activation Function

Here we describe multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types, these correspond to hidden multi-layer neural networks. The approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a parametrized error function.