Reviewerly/exHarmony huggingface dataset:
“Myscene Tube”- A web channel to enhance English speaking skills in an ESL classroom

Education has undergone unregulated changes in terms of learning and teaching in this Industrial Revolution 4.0. Along with this, technological advancement has unlocked multiple ways for educators and learners to apply online resources in their teaching and learning process. It is vital to get more attention in the expansion of learners' skills, especially in speaking. However, learners appeared to be lacking in speaking skills and as well as lacking the confidence to utilize the target language (English). The use of traditional teaching approaches also contributes to the reason why learners feel bored and perform badly in speaking. In order to tackle these problems, using "MyScene Tube” is a promising solution to expand learners’ confidence and English-speaking skills. Therefore, this paper aims to investigate learners' confidence in utilizing the English language through the implementation of the "MyScene Tube” web channel. In this mixed-method research, survey questionnaires and YouTube Analysis were used to collect data from 118 Senior 2 learners of a secondary school in Klang, Selangor. Then, the questionnaire was analyzed using descriptive analysis. In brief, the research finding shows that "MyScene Tube” could assist learners to perform better in speaking activities. It is recommended that this study could be used by future researchers to explore the effectiveness of this innovation in enhancing learners' ability in different skills of the English Language.
Additional Vocational Education of Adults As an Element of Continuous Education

The article reflects the main stages of the formation of additional professional education. The authors analyze Russian and foreign scientific works, provide normative legal sources, and reveal the role of education as an element of continuous adult education. The article evaluates the current socioeconomic conditions for the reproduction of the professional educational potential of the society, using the example of the organization of professional education by employees of the penal system, as well as monitoring the quality of educational services provided in the Russian State Social University, according to the results of which the authors identified the need to develop new approaches to assessing the quality of additional professional programs. Based on the results of the study, the authors propose to optimize the system of additional professional education by improving the regulatory framework and requirements for the structure and content of programs.
Do It Yourself: Air Drag Force Experiment Using Paper and Scraper Sheet

Free fall motion in air medium is only influenced by gravitation acceleration. However, there are several variables that caused the observations to be different with the concept. Variables, such as air drag and terminal velocity, are often teachers not presented in detail, causing misconceptions. This study aims to develop a simple experiment on free fall motion by identifying air drag and terminal velocity. The data in this study is the video of free fall motion of paper and scraper analyzed using Tracker video analyze. From the video analyzed, information is obtained in the form of time (t), track (l, θ), and velocity (v) of the object. This study shows that the air drag force increase unto the terminal velocity. The calculation of the drag coefficient giving the number of the paper 2,16 and the scraper 2,10. According to data analyzed, the air drag force is affected by the mass (m), area (A), and the air drag force (F&lt;sub&gt;D&lt;/sub&gt;) with the linear correlation until it reaches the terminal velocity. The result of this study may use as references of free fall motion experiment with other objects and analyze.
MULTIPLE CLASSIFICATION ALGORITHMS UNIMODAL AND MULTIMODAL TARGET RECOGNITION SYSTEMS

Target recognition is of great importance in military and civil applications – object detection, security and surveillance, access and border control, etc. In the article the general structure and main components of a target recognition system are presented. The characteristics such as availability, distinctiveness, robustness, and accessibility are described, which influence the reliability of a TRS. The graph presentations and mathematical descriptions of a unimodal and multimodal TRS are given. The mathematical models for a probability of correct target recognition in these systems are presented. To increase the reliability of TRS, a new approach was proposed – to use a set of classification algorithms in the systems. This approach permits the development of new kinds of systems - Multiple Classification Algorithms Unimodal and Multimodal Systems (MAUMS and MAMMS). The graph presentations, mathematical descriptions of the MAUMS and MAMMS are described. The evaluation of the correct target recognition was made for different systems. The conditions of systems' effectiveness were established. The modality of the algorithm's recognition probabilitymaximal value determination for an established threshold level of the system's recognition probability was proposed, which will describe the requirements for the quality and, respectively, the costs of the recognition algorithms. The proposed theory permits the system's design depending on a predetermined recognition probability.
Tribrid: Stance Classification with Neural Inconsistency Detection

We study the problem of performing automatic stance classification on social media with neural architectures such as BERT.Although these architectures deliver impressive results, their level is not yet comparable to the one of humans and they might produce errors that have a significant impact on the downstream task (e.g., fact-checking).To improve the performance, we present a new neural architecture where the input also includes automatically generated negated perspectives over a given claim.The model is jointly learned to make simultaneously multiple predictions, which can be used either to improve the classification of the original perspective or to filter out doubtful predictions.In the first case, we propose a weakly supervised method for combining the predictions into a final one.In the second case, we show that using the confidence scores to remove doubtful predictions allows our method to achieve human-like performance over the retained information, which is still a sizable part of the original input.
Blockchain IoT Concepts for Smart Grids, Smart Cities and Smart Homes

DLT or Distributed Ledger Technologies and Blockchains have a noteworthy character in presenting current implementation in smart cities. Systems get complete ingress to benefits for dispersed claims and safety of DLT that incorporates agreement procedures and smart pacts by the application of blockchain technologies. The merger of data and communication technologies within the power industry has commanded to a rebellion referred to as a smart grid (SG). The evidence of a smart city is emerging very quickly, mixing smart services, grid, appliances, building, and house these subunits must be capable of interrelating, attaching, and controlling remotely, collaboratively, to accomplish an improved superiority of life, substantiality, energy-saving community, and financial growth. The present technology in the SG and smart cities stems from a security viewpoint. Data origin and data integrity are amongst the key worries in Internet of Things (IoT)-founded surroundings such as vehicular networks, smart cities, and SGs. Blockchain, energy chain, a prototype for storage and retrieving the data created by smart houses in a secure way are projected. The possible implementation of smart cities concerning technology growth within the forthcoming offers added valued conversation during this paper. Temporarily, some applied practices all across the planet, and therefore, the main barricades to its application are systematically articulated.
Abnormal Transaction Detection based on Graph Networks

This paper proposes a deep learning approach to address the two issues in abnormal transaction detection, one is to process dynamically generated transaction data, instead of having to use the static and whole graph structure; and the other is to fully explore the information in the graph structure of transactions to improve the learning ability. The proposed approach is applied on the dataset provided by Elliptic, and the experiments evaluate the proposed approach by comparing with the existing ones the precision rate, recall rate, and F1-score indicators for detecting illegal transactions, increasing by 2%, 3.9% and 3.8%, respectively.
SUSPICIOUS ACTIVITY DETECTION USING CCTV SURVEILLANCE VIDEO

Security has recently been given the highest priority with the rise in the number of antisocial activations taking place. To continuously track individuals and their interactions, CCTVs have been built in several ways. Every person is recorded on an image on average 30 times a day in a developed world with a community of 1.6 billion. The resolution of 710*570 captured at knitting will approximate 20 GB per day. Constant monitoring of human data makes it hard to judge whether the incident is an irregular one, and it is an almost uphill struggle when a population and its full support are needed. In this paper, we make a system for the detection of suspicious activity using CCTV surveillance video. There seems to be a need to demonstrate in which frame the behavior is located as well as which section of it allows the faster judgment of the suspicious activity is unusual. This is done by converting the video into frames and analyzing the persons and their activates from the processed frames. We have accepted wide support from Machine learning and Deep Learning Algorithms to make it possible. To automate that process, first, we need to build a training model using a large number of images (all possible images which describe features of suspicious activities) and a “Convolution Neural Network‟ using the Tensor Flow Python module. We can then upload any video into the application, and it will extract frames from the uploaded video and then that frame will be applied on a training model to predict its class such as suspicious or normal.
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space

In cross-lingual language models, representations for many different languages live in the same space.Here, we investigate the linguistic and non-linguistic factors affecting sentencelevel alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs.Using BERT-based LaBSE and BiLSTM-based LASER as our models, and the Bible as our corpus, we compute a taskbased measure of cross-lingual alignment in the form of bitext retrieval performance, as well as four intrinsic measures of vector space alignment and isomorphism.We then examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.The results of our analyses show that word order agreement and agreement in morphological complexity are two of the strongest linguistic predictors of cross-linguality.We also note in-family training data as a stronger predictor than languagespecific training data across the board.We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.We make the data and code for our experiments publicly available.
Photometric DIC: a unified framework for global Stereo Digital Image Correlation based on the construction of textured digital twins

An innovative approach allowing to rigorously address surface curvature and lighting effects in Digital Image Correlation (DIC) is proposed. We draw inspiration from the research work in Computer Vision (CV) regarding the physical modelling of a camera and adopt it to bring novel and significant capabilities for full-field measurements in experimental solid mechanics. It gives rise to a unified framework for global stereo DIC that we call Photometric DIC (PhDIC). It is based on the irradiance equation that relies on physical considerations and explicit assumptions, which stands for a clear breakthrough compared to the usual grey level conservation assumption. Most importantly, it allows to define a Digital Twin of the Region of Interest, which makes it possible to compare a model with different observations (real images taken from different viewpoints). This results in a consistent formalism throughout the framework, suitable for large-deformation and large-strain displacement measurements. The potential of PhDIC is illustrated on a real case. Multi-view images are first used to measure (or scan) the shape and albedo (sometimes called intrinsic texture) of an open-hole plate. The kinematic basis considered for the displacement measurement is associated to a Finite-Element mesh. Results for the shape and albedo measurement are compared for two completely different sets of pictures. Eventually, a large displacement of the structure is measured using a well-chosen single image.
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent

We consider the joint design and control of discrete-time stochastic dynamical systems over a finite time horizon. We formulate the problem as a multi-step optimization problem under uncertainty seeking to identify a system design and a control policy that jointly maximize the expected sum of rewards collected over the time horizon considered. The transition function, the reward function and the policy are all parametrized, assumed known and differentiable with respect to their parameters. We then introduce a deep reinforcement learning algorithm combining policy gradient methods with model-based optimization techniques to solve this problem. In essence, our algorithm iteratively approximates the gradient of the expected return via Monte-Carlo sampling and automatic differentiation and takes projected gradient ascent steps in the space of environment and policy parameters. This algorithm is referred to as Direct Environment and Policy Search (DEPS). We assess the performance of our algorithm in three environments concerned with the design and control of a mass-spring-damper system, a small-scale off-grid power system and a drone, respectively. In addition, our algorithm is benchmarked against a state-of-the-art deep reinforcement learning algorithm used to tackle joint design and control problems. We show that DEPS performs at least as well or better in all three environments, consistently yielding solutions with higher returns in fewer iterations. Finally, solutions produced by our algorithm are also compared with solutions produced by an algorithm that does not jointly optimize environment and policy parameters, highlighting the fact that higher returns can be achieved when joint optimization is performed.
Systematization of the Definition of «Soft Skills» in Domestic and Foreign Scientific Literature

Currently, the world educational community is actively discussing the problem of developing students’ universal competencies, flexible skills, or «soft skills». A global trend is indicated that requires young specialists to master the skills included in the definition of «soft skills», since a set of such skills and abilities allows you to effectively approach tasks and quickly adapt to changing environmental conditions. The application of these skills in everyday life is also actualized, which can help a person to be adaptive in changing conditions. Indicating the high demand and relevance of this issue, scientists note serious difficulties in using various approaches associated with the discrepancy and different use of the definitions of «soft skills». The purpose of this study was an attempt to systematize the concepts used and develop the author’s list of «soft skills», including: «creativity», «cooperation», «persuasion», «emotional intelligence», «adaptability».
YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition

Soft biometrics inference in surveillance scenarios is a topic of interest for various applications, particularly in security-related areas.However, soft biometric analysis is not extensively reported in wild conditions.In particular, previous works on gender recognition report their results in face datasets, with relatively good image quality and frontal poses.Given the uncertainty of the availability of the facial region in wild conditions, we consider that these methods are not adequate for surveillance settings.To overcome these limitations, we: 1) present frontal and wild face versions of three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a model that effectively and dynamically complements facial and body information, which makes it suitable for gender recognition in wild conditions.The frontal and wild face datasets derive from widely used Pedestrian Attribute Recognition (PAR) sets (PETA, PA-100K, and RAP), using a pose-based approach to filter the frontal samples and facial regions.This approach retrieves the facial region of images with varying image/subject conditions, where the state-of-the-art face detectors often fail.YY-Net combines facial and body information through a learnable fusion matrix and a channel-attention sub-network, focusing on the most influential body parts according to the specific image/subject features.We compare it with five PAR methods, consistently obtaining state-of-the-art results on gender recognition, and reducing the prediction errors by up to 24% in frontal samples.The announced PAR datasets versions and YY-Net serve as the basis for wild soft biometrics classification and are available in here.
Bayesian Localization in Real-Time using Probabilistic Maps and Unscented-Kalman-Filters

In this paper, based on the fusion of Lidar and Radar measurement data, high-definition probabilistic maps, and a tailored particle filter, a Real-Time Monte Carlo Localization (RT_MCL) method for autonomous cars is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects' positions and have a much higher spatial resolution. On the other hand, Radars are more accurate in measuring objects velocities and perform well in extreme weather conditions. Therefore, the merits of both sensors are combined using the UKF to provide pole-like static-objects pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) algorithm to represent each pole landmarks in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated off-line and extracted from a 3-D lidar to be used by a carefully designed Particle Filter (PF) for accurate ego-car localization. The particle filter is initialized by the combined GPS+IMU reading and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using Iterative Closest Point (ICP) algorithm. The proposed pipeline is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal and lateral localization.
The THE IMPLEMENTATION OF ENGLISH LANGUAGE SKILLS TEACHING AND LEARNING DURING THE PANDEMIC OF COVID-19

Since March 2020, based on Circular letter dated 4 March 2020, Indonesia has implemented online learning to stop the outbreak of covid-19 virus. This immediate shift requires many adjustments, especially in skill subjects such as English Language Skills. This research aimed to describe the implementation of English Language Skills teaching during covid-19 pandemic in some universities in Indonesia. This research was a descriptive qualitative research with questionnaire used to obtain the data. The respondents of this research were English lecturers from some universities in Indonesia as many as 25 lecturers. The result showed that the biggest problem in online learning was internet connection. The lecturers were required to enhance their technology literacy and to be creative in designing the learning process to motivate the students to learn. Besides, the students needed to be more active in searching learning sources independently. The active participations of both lecturers and students are definitely important for the effectiveness of online teaching and learning. It is expected that this research may be reference for all education practitioners in implementing online learning for English Language Skills subjects
Management of corporate responsibility in the business environment: Socio-psychological aspect

The article considers the methodological approach and recommends practical provisions for the management of corporate responsibility of professionals in the business environment, taking into account the socio-psychological components that ensure the trajectory of stable professional development in the food market. It is substantiated that the trajectory of professional stability with a corporate consciousness of development is not a process of continuous action, but a consistent and purposeful project with a certain result, limited in time and resources. Dividing activities into a series of projects allows specifying goals, increasing the accuracy of plans and forecasts. Social trust becomes a certain basis for understanding social relations and responsibility for the process of integration of professional development and stable business activities in the market. The psychological support of stable professional development of employees of the enterprise with corporate responsibility of the business environment is developed. Guidelines and standardized norms of the management at “professional mobility – professional stability” are defined. Signs of intensification of professional development of specialists of the enterprise on the scale of psychological well-being are interpreted. Empirical procedures for qualitative and quantitative analysis of the integral components of the professional potential of employees of the enterprise with a certain level management of corporate responsibility in the business environment are presented. A methodical approach to determining the level of influence of the social component on the corporate responsibility of professional specialists of the enterprise in the market business environment is proposed.
COVID-19 uncertainty and Bitcoin market, linking the liquidity cost to the cryptocurrency yields

The cryptocurrency market is emerging as a new asset class for the investment. As the traditional asset prices are often noted to be influenced by the liquidity risk, this study links the cryptocurrency liquidity cost to its yields. Pre-pandemic uncertainty, the Bitcoin liquidity cost was found to be priced in its returns during the same trading session. Post-pandemic crisis, the relationship was changed. The liquidity cost was reported not to be priced in the Bitcoin returns at the time of same trading session. Post-pandemic crisis, however, the liquidity cost imposed by the liquidity supplier on day t − 1 was noted to be priced in the Bitcoin return of day t . In the cryptocurrency market, this study quantifies the effects on the Bitcoin returns of its liquidity cost, and if such effects vary pre- and post-pandemic uncertainty.