Reviewerly/exHarmony huggingface dataset:
Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures difficult in any vision-based method.Diversity in finger shape, size, distribution and motion dynamics is also a big constraint.This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures.Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region.SIFT flow vectors obtained from every pixel can lead to overfilling, data redundancy and dimension disaster.A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem.Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework.Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database.The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise.A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.
Applying Agile Learning to Teaching English for Specific Purposes

The present paper aims to analyse the Agile learning implementation and describe the eduScrum practices in teaching English for Specific Purposes (ESP) at Information Technology and Cybernetics Faculties of Taras Shevchenko National University of Kyiv (Ukraine). Based on a mixed-method research design, our research studied Agile learning both qualitatively, and quantitatively through the use of observation, interviews, pre- and post-tests, and the questionnaire. The experimental focus group research proved the efficiency of applying Agile methods in a project-learning environment to teach Information Technology (IT) students. It showed that Agile learning through eduScrum practices encourages communication, knowledge sharing, critical thinking, creativity, enhances student engagement and the development of a mindset aimed at constant improvement. Planning, processing, presentation making, as well as analysis, and assessment practices were outlined in the ESP eduScrum teaching. They facilitated students to acquire and retain the practical experience and become confident lifelong learners in a modern VUCA-world. The criteria-based assessment resulted in drawing students’ attention to the problematic aspects of learning and evaluating their knowledge and skills more reasonably. The proposed eduScrum practices could be applied alternatively into the traditional classroom academic environment due to their productivity.
A Low-Load Distributed Stream Processing System for Continuous Conjunctive Normal Form Queries

One of the research issues for stream data is fast notification of continuous query results. Recent researches assume that query processing servers receive data from remote data sources continuously. Therefore, when a large number of processing servers are distributed in networks, large amounts of communication traffic for those are produced. This is the main cause of delaying query result notifications. To tackle this fundamental problem, we focus on the conjunction of operations and conditions in continuous queries for distributed processing systems. When queries consist of some series of operations and conditions, processing servers can stop query executions if the remaining operations or condition checking are dispensable. Thus, communications less arise and communication traffic is further reduced. Our proposed system represents continuous queries by conjunctive normal forms (CNF queries). The proposed system constructs trigger trees for evaluating the sum terms of CNF queries and determines the timing for their evaluations, in order to reduce communication traffic. We confirmed that one of the proposed methods can reduce the average amount of communication traffic by 23 percent compared with a sensor data serial aggregation method in an evaluation situation.
Call-by-name Gradual Type Theory.

We present type theory, a logic and type theory for call-by-name typing. We define the central constructions of typing (the dynamic type, type casts and type error) in a novel way, by universal properties relative to new judgments for type and term dynamism. These dynamism judgements build on prior work in blame calculi and on the gradual guarantee theorem of typing. Combined with the ordinary extensionality (eta) principles that type theory provides, we show that most of the standard operational behavior of casts is uniquely determined by the guarantee. This provides a semantic justification for the definitions of casts, and shows that non-standard definitions of casts must violate these principles. Our type theory is the internal language of a certain class of preorder categories called equipments. We give a general construction of an equipment interpreting type theory from a 2-category representing non-gradual types and programs, which is a semantic analogue of the interpretation of typing using contracts, and use it to build some concrete domain-theoretic models of typing.
Playing a Part: Speaker Verification at the Movies

The goal of this work is to investigate the performance of popular speaker recognition models on speech segments from movies, where often actors intentionally disguise their voice to play a character. We make the following three contributions: (i) We collect a novel, challenging speaker recognition dataset called VoxMovies, with speech for 856 identities from almost 4000 movie clips. VoxMovies contains utterances with varying emotion, accents and background noise, and therefore comprises an entirely different domain to the interview-style, emotionally calm utterances in current speaker recognition datasets such as VoxCeleb; (ii) We provide a number of domain adaptation evaluation sets, and benchmark the performance of state-of-the-art speaker recognition models on these evaluation pairs. We demonstrate that both speaker verification and identification performance drops steeply on this new data, showing the challenge in transferring models across domains; and finally (iii) We show that simple domain adaptation paradigms improve performance, but there is still large room for improvement.
A Historical Consideration of Higher Fisheries Education in Korea

Human beings need food although the future society comes. The fisheries industry, which is the basis of food supply, is very important. The development of fisheries industry leads to the development of fisheries human resources and fisheries education. Thus, the historical consideration of Korea
Global Sentiment Analysis Of COVID-19 Tweets Over Time

The Coronavirus pandemic has affected the normal course of life. People around the world have taken to social media to express their opinions and general emotions regarding this phenomenon that has taken over the world by storm. The social networking site, Twitter showed an unprecedented increase in tweets related to the novel Coronavirus in a very short span of time. This paper presents the global sentiment analysis of tweets related to Coronavirus and how the sentiment of people in different countries has changed over time. Furthermore, to determine the impact of Coronavirus on daily aspects of life, tweets related to Work From Home (WFH) and Online Learning were scraped and the change in sentiment over time was observed. In addition, various Machine Learning models such as Long Short Term Memory (LSTM) and Artificial Neural Networks (ANN) were implemented for sentiment classification and their accuracies were determined. Exploratory data analysis was also performed for a dataset providing information about the number of confirmed cases on a per-day basis in a few of the worst-hit countries to provide a comparison between the change in sentiment with the change in cases since the start of this pandemic till June 2020.
USING BLOOM’S REVISED TAXONOMY TO DEVELOP DIGITAL LITERACIES IN THE ESP CLASSROOM

The article explores some issues to be considered when integrating new technologies into English for Specific Purposes (ESP) learning. The main focus is on the challenges that ESP practitioners face in the context of a multilingual digital Europe. In confronting such problems as changing roles for students and teachers, the spread of English as a medium of instruction and increased emphasis on the subject content in the language classroom, it is necessary for ESP teachers to embrace innovation and develop strategies to improve students’ learning. To benefit from the use of technology, ESP teachers need to concentrate on those aspects which digital knowledge and skills share with traditional literacies. When essential elements of digital literacies are singled out, it becomes possible to employ their full potential for creating a technology enhanced learning environment. This research suggests doing it by drawing upon the Taxonomy of Educational Objectives. The framework, in this case, is used to check that goals falling within different categories of the cognitive domain of learning are aligned with technology-supported instruction delivery methods. While designing instructional techniques, we heavily rely on the idea of scaffolding in the form of visual aids for enabling learners to build on prior knowledge and internalize new concepts. As practical examples show, development of digital literacies proves not only to be compatible with formation of professional intercultural communicative competence but also providing means for activating the most complex cognitive processes of conceptual understanding, critical thinking, decision making, creation and metacognition. The findings may be useful in preparing instructional activities with the help of technological tools to support ESP teaching and learning.
Research of approaches and methods of applying artificial intelligence and machine learning in socio-economic processes

One of the problems hindering the development of the socio-economic sphere in the innovative direction is the lack of structuring of approaches and methods used in machine learning as part of the introduction of artificial intelligence (AI) in socio-economic processes. The same problem hinders the growth of the pace of innovative development and, as a result, the improvement of the scientific and technical level of the country. The article classifies and systematizes aspects of machine learning, focuses on the need to accelerate the construction and implementation of algorithms as the basis of AI for increasing the efficiency of managing socio-economic processes. To achieve this goal, the following results are presented: analysis of the concepts of machine learning and AI, study of analytical materials regarding approaches and methods to the introduction of artificial intelligence and prospects for its application in socio-economic processes. There were systematized approaches to machine learning introduction to artificial intelligence depending on the historical period, the implementation of AI, and another, and methods according to the method of machine learning, predictive model data for creating AI algorithms (e.g., probabilistic), and the idea or the nature of the research that uses this technology (assessment and collection of statistical indicators, analysis). The study of the material related to machine learning and AI construction allowed us to draw the following conclusions. The theoretical foundation in the form of mathematical and statistical methods as the basis for building algorithms for creating AI in the framework of machine learning is a necessary part of the process of teaching computers human qualities. However, information about machine learning methods and approaches is mostly scattered, and it is necessary to form a unified methodological base in order to simplify the stage of searching for the right method of creating AI to solve any social, economic or other problem. The presence of such a database will create opportunities to replace one machine learning method for creating AI with another in different fields of activity and socio-economic processes.
Generative Adversarial Networks for Visible to Infrared Video Conversion

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images.
Report on the Differential Testing of Static Analyzers

Program faults, best known as bugs, are practically unavoidable in today's ever growing software systems. One increasingly popular way of eliminating them, besides tests, dynamic analysis, and fuzzing, is using static analysis based bug-finding tools. Such tools are capable of finding surprisingly sophisticated bugs automatically by inspecting the source code. Their analysis is usually both unsound and incomplete, but still very useful in practice, as they can find non-trivial problems in a reasonable time (e.g. within hours, for an industrial project) without human intervention.&#x0D; Because the problems that static analyzers try to solve are hard, usually intractable, they use various approximations that need to be fine-tuned in order to grant a good user experience (i.e. as many interesting bugs with as few distracting false alarms as possible). For each newly introduced heuristic, this normally happens by performing differential testing of the analyzer on a lot of widely used open source software projects that are known to use related language constructs extensively. In practice, this process is ad hoc, error-prone, poorly reproducible and its results are hard to share.&#x0D; We present a set of tools that aim to support the work of static analyzer developers by making differential testing easier. Our framework includes tools for automatic test suite selection, automated differential experiments, coverage information of increased granularity, statistics collection, metric calculations, and visualizations, all resulting in a convenient, shareable HTML report.
Predicting and Explaining Privacy Risk Exposure in Mobility Data

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people's whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.
Application of Education Informatization Promoting Educational Equity in Remote Areas of China

Nowadays, an increasing number of people are concerned about the problem of educational inequality. Especially in remote areas in China, due to the backward development of the economy, the population is outflowing, which means that the teachers are not strong enough. Meanwhile, the science and technology are not advanced enough and the infrastructure construction is not perfect. With the continuous development of information technology, education informatization has brought more and more attention to education equity. Poverty reduction through education is a hot issue that the Ministry of Education of the People's Republic of China is concerned about. The education informatization we mentioned in this paper points to fundamental education informatization. This article has introduced the fundamental educational performance of China and the contribution of one of the Chinese projects, which is named cross-regional simultaneous teaching application pilot project, to improve teaching quality in terms of educational technology making use of the two-way broadband satellite and the platform called panoramic learning platform. This means that teachers and students, especially for remote areas’ teachers and students, can benefit from this project to gain more educational resources. By harnessing this, China can help more people to receive a better education so that it can bridge the gap between rich and poor.
Research on Pedestrian Detection Algorithm Based on Deep Learning

Abstract Pedestrian detection is a kind of computer vision technology which is used to judge whether there is a pedestrian in a given image or video and estimate its position accurately. Pedestrian detection technology is widely used in intelligent security monitoring, driverless and robot fields. Pedestrian detection technology has nearly 30 years of development. Although it has greatly improved in accuracy and speed, there are problems such as lighting, pedestrian individual differences, occlusion and multi-scale pedestrian targets in the actual application scenarios, which lead to more missed and false detection in pedestrian detection. The algorithm is not robust enough, which makes the actual deployment difficult.
Towards a Comprehensive Assessment of Speech Intelligibility for Pathological Speech

Speech intelligibility is an essential though complex construct in speech pathology.It is affected by multiple contextual variables and it is often measured in different ways.In this paper, we evaluate various measures of speech intelligibility based on orthographic transcriptions, with respect to their reliability and validity.For this study, different speech tasks were analyzed together with their respective perceptual ratings assigned by five experienced speech-language pathologists: a Visual Analogue Scale (VAS) and two types of orthographic transcriptions, one in terms of existing words and the other in terms of perceived segments, including nonsense words.Six subword measures concerning graphemes and phonemes were derived automatically from these transcriptions.All measures exhibit high degrees of reliability.Correlations between the six subword measures and three independent measures, VAS, word accuracy, and severity level, reveal that the measures extracted automatically from the orthographic transcriptions are valid predictors of speech intelligibility.The results also indicate differences between the speech tasks, suggesting that a comprehensive assessment of speech intelligibility requires materials from different speech tasks in combination with measures at different granularity levels: utterance, word, and subword.We discuss these results in relation to those of previous research and suggest possible avenues for future research.
Still Image Face Recognition in Hungary

The Still Image Facial Recognition System has been in operation for four years in Hungary. The present study details the experiences gained to date, as well as the possibilities, results and plans related to the subject. It briefly presents the history of still image face recognition, provides international outlook, describes the legal framework and the operation of the system, and finally outlines future development opportunities.
Addressing the eye fixation problem in gaze tracking for human computer interface using the vestibulo-ocular reflex

A custom head-mounted system to track smooth eye movements for control of a mouse cursor is implemented and evaluated. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the cursor, and the cursor moves towards the target. Two users participated in three tests to quantify the differences between incremental tracking of smooth eye movement resulting from the Vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was four times more accurate than tracking saccadic eye movements, with an average position resolution of 0.80 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.21 cm. Using the incremental tracking of smooth eye movements, the user was able to place the cursor within a target as small as a 9 x 9 pixel square 90 % of the time. However, when using the step change tracking of saccadic eye movements, the user was unable to position the cursor within the 9 x 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.45 s, whereas for the step change tracking of saccadic eye movements, it was 2.61 s.