Reviewerly/exHarmony huggingface dataset:
Text Generation through Hand Gesture Recognition

Development of Vision-based Hand Gesture Recognition systems has proved to be beneficial for disabled people over the past. These systems are user friendly and inexpensive. They also make room for contemporary endeavours in the Human-Computer Interaction area. Gestures can control the cursor, the music player, or even play games. Despite all the improvements made in Vision-based recognition systems, a fast, accurate, and reliable system is yet to see the light of the day. This study aims to understand the previous work in this field and develop a fast and accurate alternative to perform the same task. To render the system faster, we propose to replace the histograms from the previous approach and uses native OpenCV functions. The Use of these functions can reduce the time for calculating the histogram and loading it when required. The final system developed, succeeded in predicting 37 gestures with a 98% accuracy. The system also has a calculator mode, where some gestures are reserved for operators and others for operands. This study tells us how we can use different computer vision techniques to make the recognition process more memory efficient and lightweight. In the future, this could be used as a commercially viable option by putting it on app stores.
21ST CENTURY SKILLS AND THE NEW LITERACY: THE GENDER GAP

The rapid development of computer and information technology is changing the face of the modern world, making it more and more sensitive to innovation. Changing technologies are having an increasingly rapid impact on the development of digital skills and literacy skills. The transition to a new technological order changes the gender balance of personnel in all spheres of life. Analyzing the gender dimensions of digital literacy and skills is important both from the point of view of social justice (unequal distribution of men and women in the exact and natural sciences) and from the point of view of improving the efficiency of the use of labor resources to ensure sustainable development.
Interactive Context-Aware Furniture Recommendation using Mixed Reality

We present a Mixed Reality (MR) system, through Hololens, to provide context-aware furniture recommendation in an interactive fashion. Firstly, a ranking-based metric learning method is adopted to represent the furniture compatibility through a latent space. Then, in the recommendation process, a physical scene is captured by the cameras mounted on the MR device, and two types of scene context are analyzed: (1) category context; (2) spatial context. At last, the one with the minimal weighted ranking distance in the latent space is recommended to the user. With MR devices, a user could perceive and manipulate the recommended furniture in real-time. We conduct user study to validate the efficacy of the proposed system.
Stable Model Semantics for Guarded Existential Rules and Description Logics: Decidability and Complexity

This work investigates the decidability and complexity of database query answering under guarded existential rules with nonmonotonic negation according to the classical stable model semantics. In this setting, existential quantification is interpreted via Skolem functions, and the unique name assumption is adopted. As a first result, we show the decidability of answering first-order queries based on such rules by a translation into the satisfiability problem for guarded second-order formulas having the tree-model property. To obtain precise complexity results for unions of conjunctive queries, we transform the original problem in polynomial time into an intermediate problem that is easier to analyze: query answering for guarded disjunctive existential rules with stratified negation. We obtain precise bounds for the general setting and for various restricted settings. We also consider extensions of the original formalism with negative constraints, keys, and the possibility of negated atoms in queries. Finally, we show how the above results can be used to provide decidability and complexity results for a natural adaptation of the stable model semantics to description logics such as ELHI and the DL-Lite family.
Design of an Ultrasound Probe Holder to Minimize Motion Artifact During Sonography

Abstract Standard diagnostic ultrasound imaging procedures heavily rely on a sonographer for image acquisition. Given the ultrasound probe is manually manipulated by the sonographer, there is a potential for noise artifacts like blurry acquired images caused by involuntary hand movements. Certain surgical procedures can also cause patients to exhibit involuntary “jumping” movements while on the operating table leading to further deterioration in ultrasound image quality. In this study, we attempt to mitigate these problems by fabricating a 3D-printed ultrasound probe holder. Due to the lightweight nature of the device, it can attach to surgical retractors without influencing the functionality of the retractor. Therefore, the 3D printed probe holder not only reduces relative motion between the probe and the patient, but also reduce the need for a sonographer during complex surgeries.
Progressive Gradient Pruning for Classification, Detection and Domain Adaptation

Although deep neural networks (NNs) have achieved state-of-the-art accuracy in many visual recognition tasks, the growing computational complexity and energy consumption of networks remains an issue, especially for applications on platforms with limited resources and requiring real-time processing. Filter pruning techniques have recently shown promising results for the compression and acceleration of convolutional NNs (CNNs). However, these techniques involve numerous steps and complex optimisations because some only prune after training CNNs, while others prune from scratch during training, by integrating sparsity constraints or by modifying the loss function. In this paper, we introduce a new Progressive Gradient Pruning (PGP) technique for iterative filter pruning during training. In contrast to previous progressive pruning techniques, it relies on a novel filter selection criterion that measures the change in filter weights, uses a new hard and soft pruning strategy, and effectively adapts momentum tensors during the backward propagation pass. Experimental results obtained after training various CNNs on benchmark datasets for image classification, object detection and domain adaptation indicate that our PGP technique can achieve a better trade-off between classification accuracy and network (time and memory) complexity than PSFP and other state-of-the-art filter pruning techniques. Code is available on GitHub link: https://github.com/Anon6627/Pruning-PGP.
A Diagonally Oriented Novel Feature Extractor for Pedestrian Detection and Its Efficient Hardware Implementation

Pedestrian detection is an important issue in an intelligent transportation system and involves complex hardware architecture. This high complexity and low detection accuracy make it a prompt topic of research. This paper proposes a novel feature extractor with its efficient hardware implementation for the real-time pedestrian detection system. The proposed approach termed here as Histogram of All Significant Gradients (HASG) improves the Histogram of Significant Gradients (HSG) technique by the inclusion of diagonal gradients. The diagonal gradients give an add-on feature which improves the detection accuracy of the detection system. The overhead of extra computation is compensated here by the proposition of a hardware architecture that utilizes only integer computation. The arctangent operation which is a floating-point operation is replaced here by a simplified architecture that uses only integer computation units. The proposed hardware architecture is found to have 73.65% reduced hardware consumption in contrast to the existing one. The complete design is implemented on the Artix-7 FPGA device with a maximum achievable clock frequency of 115 MHz.
Blockchain-based Innovation in UK Construction: A User Perspective

Innovative blockchain-based technologies are promising tools to address issues of limited trust and transparency across the construction industry’s supply chains. To provide further insight on how to encourage blockchain-based technologies adoption in construction, this study examines the forces that enable and prevent blockchain adoption, in addition to the associated perceived benefits and perceived risks. Exploratory research is conducted involving eight frontier construction companies in the UK construction industry and includes consultants, architects and contractors. The findings have revealed that blockchain adoption in construction is driven by forces encouraging the change, including the pursuit of efficient technological solutions such as blockchain to overcome the declining economic environment in UK construction, a digitally savvy workforce (i.e. millennials) acting as change agents driving technological change, and the pursuit of more data availability to combat skill shortages worldwide. However, forces restraining the change also persist including the technological ambiguity of blockchain tools, lack of interoperability with enterprise systems and industry-wide misperceptions surrounding blockchain. The findings have also revealed discrepancies among the multi-disciplinary stakeholders regarding the perceived benefits and perceived risks of the technology. Lack of education, misuse of technology and financial losses are considered major risks, in addition to data security and management issues. On the other hand, major perceived benefits include increased cybersecurity, transparency and openness, and effective dispute resolution. Recommendations are developed to support blockchain-based innovations adoption in the construction industry. The main contribution of this research project is the development of a preliminary framework of potential risks and benefits of blockchain-based technologies which can assist advocates of blockchain in the development of strategies to increase the rate of adoption in the construction industry.
DISPACE 2.0 E-LEARNING SYSTEM - A FUNDAMENTAL ELEMENT OF THE DIGITAL EDUCATIONAL ENVIRONMENT (ECOSYSTEM) OF NOVOSIBIRSK STATE TECHNICAL UNIVERSITY

The article discusses the problems of content, formation and functioning conditions of the electronic informational-educational (digital) environment оf educational organizations. The main system-forming factor uniting and managing all the components of the educational process in the digital environment into a single complex is the software platform (electronic learning system, LMS), which allows for the implementation of electronic (distance, online) learning. The DiSpace 2.0 e-learning system is a popular and adapted educational system, not inferior in capabilities to the widespread learning management systems. As training objects (digital educational resources) in the Bank (depository) are designed by teachers electronic training courses, online courses, tests, interactive simulators, as well as animation, laboratory workshops (in digital format). The dynamics of growth of working digital educational resources (electronic courses and test control materials) at NSTU is shown. If you have your own electronic learning environment, there is a constant development of information and communication competencies of the NSTU academic staff, including skills to design their own educational resources. In keeping with the system-activity approach to teaching, the educational ecosystem of NSTU is supplemented by interactive simulator stands, for example, for fire and electrical safety, developed by teachers in collaboration with software developers. The types and forms of students» knowledge control are shown during the organization of independent work, both for the traditional and correspondence forms of training, involving the use of distance technologies. Considered from the point of view of the ecosystem approach, the e-learning environment is a decentralized, self-organized, emergent ecosystem, which increases its stability and quality. An algorithm has been developed for the comprehensive assessment of the quality of the e-learning environment, with the identification of rubrics and quality criteria that require additional research.
Academic Influence of China’s Sports Social Discipline Based on Bibliometrics

Background. To accurately evaluate sports social discipline’s academic influence in China, a model of academic influence evaluation of sports social discipline in China based on bibliometrics is proposed. Objective. A statistical model of the academic influence of sports social discipline is constructed, the word frequency statistics method is used to measure the literature, and the semantic characteristic quantity of the sports social discipline academic influence is extracted, using the literature method and logical analysis method to analyze sports’ social value in the Internet era from healthy sports. The combination of sports and medical treatment can comprehensively promote physical and mental health. Methods. The dual semantic feature decomposition method is used to analyze sports social discipline’s academic influence. The statistical analysis model of sports social discipline academic influence is established. The principles of fuzzy pattern recognition include the principle of maximum membership degree and proximity degree. Results. The comprehensive relative closeness of the distribution of literature statistics on sports and social disciplines’ academic influence is constructed. The linear programming analysis of literature statistics is carried out using the standardized grid computing method. Conclusions. The combination of association rule feature extraction and semantic feature extraction is used to realize the quantitative calculation of literature statistics and academic influence. The simulation results show that the statistical analysis of the academic influence of sports and social discipline by this method is accurate, and the level of confidence is high.
Fast Decentralized Linear Functions Via Successive Graph Shift Operators

Decentralized signal processing performs learning tasks on data distributed over a multi-node network which can be represented by a graph. Implementing linear transformations emerges as a key task in a number of applications of decentralized signal processing. Recently, some decentralized methods have been proposed to accomplish that task by leveraging the notion of graph shift operator, which captures the local structure of the graph. However, existing approaches have some drawbacks such as considering special instances of linear transformations, or reducing the family of transformations by assuming that a shift matrix is given such that a subset of its eigenvectors spans the subspace of interest. In contrast, this paper develops a decentralized method to compute linear transformations in a small number of iterations. To this end, a set of successive graph shift operators is designed. Hence, a new optimization problem is proposed whose goal is to compute the desired transformation as fast as possible.
Educational leadership in vulnerable contexts during the age of COVID-19

Just before the COVID-19 crisis and closure occurred, a group of Spanish researchers were teaching a pilot course for school leadership teams (LTs) in the context of a project entitled "Leadership Programme for a Whole Child Development in Disadvantaged Contexts-WCDL". The changes required by the new circumstances were so many that the researchers decided to document and describe the process of fighting against the crisis. Consequently, the research's main objective was to describe how eight leadership teams in vulnerable schools faced the COVID-19 crisis and whether their actions coincide with those described by academics for leadership in times of crisis. Leadership competences in times of crisis Research on crisis management in the educational environment has been scarce and related to some specific contexts and problems, partially because "never before have we witnessed educational disruption on such a scale" (UNESCO, 2020). However, it is becoming increasingly necessary for school leaders to deal with crises and learn from them, which justifies this investigation.
SOCIO-PEDAGOGICAL DETERMINANTS OF THE FORMATION OF THE PERSONAL-PROFESSIONAL POSITION OF THE FUTURE TEACHER AS AN EDUCATOR

In the situation of increasing attention of society and the state to the educational component of the educational process, the insufficient level of formation of the personal and professional position of teachers as educators is obvious. This position, along with competence, becomes one of the markers of the quality of professional training of future teachers. The role of educational institutions of higher education in the formation of the student's position as a future specialist is underestimated, which entails a number of deformations and negative consequences. The socio-pedagogical conditions that ensure the effectiveness of formative influences on the personal and professional development of future teachers as educators are also taken into account to an incomplete extent. The purpose of the study is to identify the socio-pedagogical determinants of the formation of the personal-professional position of future teachers as educators. Materials and methods: the paper uses the results of the analysis, generalization and systematization of domestic theories of education, professional education of future teachers. The most significant determinants are identified and ranked based on the results of a survey of students of the Faculty of Humanities of the Voronezh State Pedagogical University (83 students studying undergraduate and graduate programs). The basic methodological approaches are personal-activity and socio-pedagogical. As a result of the research, it is argued that the formation of the personal-professional position of future teachers as educators is a complex process in terms of meaning, content and activity. Its effectiveness is possible only in the presence of a wide range of socio-pedagogical conditions that provide it, the basic core of which is socio-pedagogical determinants. In accordance with the components of the personal and professional position, the most significant socio-pedagogical determinants of the formation of the personal-professional position of the future teacher are identified. Ranking the results of the selection of significant determinants by students allows us to conclude that certain components of the personal-professional position (value, target, ethical, reflexive) are underestimated by students due to insufficient attention to these aspects of professional training.
Review on Emotion Recognition Using Facial Expressions

The advent of artificial intelligence technology has reduced the gap between humans and machines as equips man to create more near-perfect humanoids. Facial expression is an important tool to communicate one’s emotions as a non-verbally overview of emotion recognition using facial expressions. A remarkable advantage of such a technique recently improved public security through tracking and recognizing, thus led to the high attention to keep up the scientific research in the field. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolution Neural Network (CNN), Active Appearance and Machine learning which all used to classify emotions based on certain parts of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. By comparison, the reviews have shown that the average accuracy of the basic emotion ranged from 51% up to 100%, whereas carrying through 7% to 13% in the compound emotions, hence indicated that the indispensable emotion is much comfortable to recognize.
A Survey of Distributed Database Systems based on Blockchain

With the prevalence of digital cryptocurrency and online transactions, the amount of data carried in cyberspace has been increasing rapidly. How to ensure the security and reliability of cyberspace data is a challenging problem. Blockchain has been widely recognized as a reliable technical solution for distributed data storage and sharing, and has attracted great attention from both industry and academia. This article first provides a brief introduction to the blockchain technology and distributed database systems, with a focus on the characteristics of decentralization and traceability. Then, it surveys the state-of-the-art work on blockchain-based distributed database systems, and compares these technologies. Afterwards, the application scenarios of blockchain-based distributed database systems are discussed. Finally, some promising research directions and suggestions focused on blockchain-based data storage are presented.
Restaurant Chatbot Using IBM Watson

Restaurant chatbot is an automated system for carrying out day to day works in a restaurant such as ordering, reservations, FAQs at reception, and many more tasks which are not that much important but take much time of receptionist while working. Chatbot is a system backed by an Artificial Intelligence technology to learn, adapt and work according to surrounding environment. In this particular system, development of a chatbot is done with the help of API provided by IBM named as WATSON to create, train, and deploy AI and ML models for various purposes to cut down the too much efforts done while developing an AI/ML model. System helps out staff at restaurant to carry out above stated tasks with less to no efforts. Then, staff can give attention to other important tasks related to their routine. Many restaurants and similar organizations offer services like webpages for reservations, helplines with the help of call centers. But these systems are not much efficient and doesn’t answer much of the questions of customer. Chat bots form a solid surface for this situation, answers much of the questions of customers, carry out some operations available online like collecting online payments, reserving rooms, answering different questions like address of an organization, facilities provided by them, vacant rooms available or not and many more. For the development of a restaurant chatbot as a last year university project on experimental basis, adding large facilities is avoided. Data sets for training chatbot model is collected from a leading website KAGGLE, which provides various data sets for models. A chatbot is a conversation agent where a computer program is made to simulate an intelligent conversation. It can use user inputs in several formats such as text, voice. For such different open-source platforms can be found. A Chatbot plays an important role in human-machine interaction. A Chatbot has three modules: the user interface, an interpreter, and a knowledge base. It is a program that tries to simulate the typed text in such a manner that human feels like it's talking to another human, not a machine. There are many chatbots that are on the internet for different purposelike education, customer service, entertainment.