Reviewerly/exHarmony huggingface dataset:
Gesture Interaction for Gaming Control Based on an Interferometric Radar

Non-contact hand gesture recognition emerged as a human-computer interactive method to facilitate human life. Compared with vision-based solutions, millimeter-wave radars consume much less power and the adverse effects of ambient environment interference such as lighting conditions can be minimized. Moreover, it can penetrate obstacles for detection and maintains a high motion detection accuracy. In this paper, a custom-designed 5.8 GHz interferometric radar system is presented and a novel real-time gesture recognition technique with a linear phase demodulation algorithm is proposed for gaming control. The experimets result shows that this system could recover the subject’s motion with micrometer accuracy. To better demonstrate the performance of the proposed gesture sensing technique, a Super Mario Bro Demo is built and it shows that one can pass the mission easily with the defined hand gesture command.
Diverse Audio-to-Video GAN using Multiscale Image Fusion

Generative adversarial networks have attained synthesised results that are not distinguishable from real examples in domains such as image, audio, text and video. While state-of-the-art image models synthesise images with high and diverse quality in many domains, video synthesis is more challenging and suffers from poor generalisation; moreover, the generated videos are not diverse, especially if the network is trained on a limited dataset. In such cases, the model overfits the training examples and performs poorly at inference time. Dataset collection, in general, is a tedious task, and it is even more challenging for video data due to its size and accessibility. Also, creating a video in the first place requires more time and effort. In this paper, we expand a previously collected video dataset with a supporting image dataset. Then, we apply a multiscale fusion method on multiple conditioned images to facilitate diverse video sample generation. We combine the multiscale fusion model with an audio extractor; then, the encoded features are input to a video decoder to generate videos synchronised with the audio signals. We compare our multiscale fusion model with other image fusion models on the Flowers, VGGFace and Animal Faces datasets. We also compare the overall architecture with other audio-to-video models. Both experiments show the effectiveness of our model over others, based on different evaluation metrics such as FID, FVD and LPIPS.
UNIVERSALITY OF THE PRINCIPLES OF THE CLASSICAL PEDAGOGICAL TRADITION: FROM THE PAST – TO THE FUTURE

The issue of choosing an educational strategy is one of the most relevant. Not only the social and scientific and technical development of the country, but sovereignty and state security directly depend on the quality of upbringing and education of the younger generations. Before the reforms of the 1990s, the Russian education system used the centuries-old achievements of Western European and Russian humanistic pedagogical traditions. It is necessary to rethink the entire arsenal of didactic and pedagogical achievements that have historically confirmed their high efficiency in order to bring the Russian education system out of crisis.
Semantic Influences on the Suppression of Information in Working Memory

Past behavioral and neuroimaging research indicates that information can be actively removed from working memory (WM) through suppression. Neuroimaging findings (Kim et al., 2020) suggest that suppressing information from WM may strip item-specific information while retaining category-general information. The current study examined if there was behavioral evidence for this claim by probing whether the semantic content of information in WM differentially influences the processes of WM suppression compared to maintenance. Participants (N = 149) performed a modified Sternberg task, in which they were shown two pictures and instructed to mentally manipulate one of them. On some trials participants maintained the target picture while on others, they suppressed it. After a brief delay, the trial concluded with a recognition test of either one of the two previously shown pictures or a novel foil picture that varied in semantic similarity (based on independent stimulus ratings) to the target picture. Consistent with a spreading activation account, participants were slower to reject novel foils that were semantically similar to the target, regardless of whether the target had been suppressed or maintained. In contrast, participants were faster to reject novel foils that were more typical of a given semantic category, when a target from that semantic category had just been suppressed. This finding suggests that suppression preserves categorical (but not item-specific) attributes of information held in WM, consistent with prior neuroimaging findings (Kim et al., 2020). Altogether, these findings support the idea that the WM suppression process may affect some stimulus attributes more than others.
METHODOLOGICAL FOUNDATIONS OF BOOK ILLUSTRATION: HISTORICAL AND THEORETICAL ASPECT

Book illustration is a graphic image that explains or supplements the main text, placed on pages or sheets included in the pagination or foliation of the document. Book illustration is a genre of graphic art. The main function of illustrations is the interpretation of the text. The formation of the structure of a book with an illustration is one of the directions in art history and cultural studies. The book is a work of art and technology - a complex ideological artistic and technical complex, all elements of which are closely interconnected. Illustration is one of these elements.
Coarse2Fine Robust Imitation Learning

This paper proposes a robust imitation learning framework with a Coarse2Fine policy to achieve repetitive long-horizon robot tasks. We formulate the framework that employs policy robustification by disturbance injection and the Coarse2Fine policy, dividing a policy into coarse policies and fine policies to reduce the amount of training data and the subsequent demonstration burden. We verify the effectiveness of the proposed method with repetitive long-horizon tasks, and the proposed method achieves the best performance to the conventional methods.
degree to which the dimensions of moral intelligence contribute to technological well-being among a sample of new students at Mu'tah University

This study aimed at identifying the degree to which the dimensions of moral intelligence contribute to technological well-being among a sample of new students at Mu'tah University in Karak, Jordan. In order to achieve the objectives of the study, the researcher developed two scales: the scale of moral intelligence and the scale of technological well-being through the revision of the theoretical literature and the previous studies and through the electronic application of the scale in 2021. The study sample consisted of (184) students. The results revealed that the level of the dimensions of moral intelligence and technological well-being among the University students was medium. The results also revealed that the dimensions of moral intelligence contribute to predicting medical technological well-being with about (56%). In the light of the results, the study recommended the necessity of paying attention to the new students in the university as well as conducting further researches and studies as well as developing counselling programs that contribute to better adaptation and adjustment.
An Automated Accurate Sound-Based Amateur Drone Detection Method Based on Skinny Pattern

Nowadays, the popularity of amateur drones (ADr) has been increased marginally and many research areas such as machine learning, security systems and military industry have been interested in ADrs. The prime goals of this paper are to present a new sound dataset for ADr detection and present an accurate classification model for ADr detection. This research proposes a novel classification model using the proposed skinny pattern and iterative neighborhood component analysis (INCA) feature selector using ADr sounds. The skinny pattern is a new nonlinear pattern and uses the substitution box (S-Box) of the skinny cipher. Tunable Q-factor wavelet transform (TQWT) and skinny pattern are used together to extract features in multilevel. The most informative features from the generated features are selected using the INCA selector. 16 classifiers were used in five categories to illustrate the general success of the presented model and these categories are decision tree (DT), discriminant (D), support vector machine (SVM), k nearest neighborhood (KNN) and ensemble classifiers (EC). An ADr sound dataset was collected, and the presented skinny pattern and INCA based model is applied to the gathered sound dataset to implement the presented model. The best classification accuracy was calculated as 99.72% using Fine KNN. The proposed model was also compared to other ADr detection/classification models, and it outperformed. These obtained results demonstrate the success of the skinny pattern and INCA based ADr detection model.
NEURAL NETWORK PREDICTION OF EARLY ANEMIA IN EXTREMELY PREMATURE CHILDREN

A current problem is the prediction of the early anemia development in newborn children. To solve this problem, an intelligent neural network system has been proposed, which, based on the criteria for anamnesis and test results, assesses the risk of developing a disease. The authors analyzed the subject area and performed a correlation assessment of the anemia development factor influence. A conceptual model of the forecasting system is proposed, input and output data are determined and a training sample is developed. The structure of the neural network is designed, including the main components of information and their weights. For training, a hybrid method is applied, which is a combination of error back-propagation algorithm, stochastic and genetic search. The hybrid combines the advantages of fast search according to the antigradient of the objective error function and the ability to overcome the local extremum due to random change of coordinates of the search point and genetic operations.
Experience in Finding and Registering Vulnerabilities in Web Applications

The article discusses current ways to search for vulnerabilities in web applications. The most popular classifications of vulnerabilities are presented. During the study, the authors discovered a vulnerability in a web application and registered it in the international database of vulnerabilities CVE and Vulnerability Data Bank (VDU).
Human Consideration in Analysis and Algorithms for Mechanism Design

Mechanism Design, a domain characterized by economically motivated agents, is an important tool in computer science and economics. Despite being extremely pervasive and applied across diverse settings, there is a renewed interest in incorporating human factors within the design and implementation of classic mechanisms. In this thesis, we cater to the aforementioned interest wherein we align the development and analysis of mechanisms with human consideration. We focus on two problems, Information Design in Affiliate Marketing and Explainability in Mechanism Design, both of which are inspired by human interactions with algorithmic systems. The research is based on a thorough literature review, theoretical analysis, and experimental validation of the solutions proposed. The methods proposed are also proven to be economically beneficial, in terms of increased profits and reduced costs, thus abiding by another key aspect of mechanism design.
Possibility spaces for citizen-driven innovations Classification and functions of urban makerspaces in Bengaluru, India

Makerspaces are shared workshops for the public to design, innovate and prototype. These spaces are primarily located in the urban regions and provide access to 'high-tech' and traditional low-tech tools needed for innovation design. Though makerspaces are a western phenomenon, their emergence in India can be traced to the early decade of the 2010s. In this paper, we study the urban makerspaces in Bengaluru, India, to explore its distinctions from the western conceptualisation. The city of Bengaluru is regarded as the technology and innovation capital of India and is also home to several makerspaces. We employ a qualitative research design and collect data through primary and secondary means. The findings point out the complexities and multifaceted nature of makerspaces in terms of their mode of operation, location and profile of actors/members, motives of operating and functions performed. The makerspaces in Bengaluru are predominantly informal spaces of innovation nested within or financed by formal institutions, such as enterprises, universities and government funding agencies.
Toward Understanding the Impact of Input Data for Multi-Image Super-Resolution

Super-resolution reconstruction is a common term for a variety of techniques aimed at enhancing spatial resolution either from a single image or from multiple images presenting the same scene. While single-image super-resolution has been intensively explored with many advancements proposed attributed to the use of deep learning, multi-image reconstruction remains a much less explored field. The first solutions based on convolutional neural networks were proposed recently for super-resolving multiple Proba-V satellite images, but they have not been validated for enhancing natural images so far. Also, their sensitiveness to the characteristics of the input data, including their mutual similarity and image acquisition conditions, has not been explored in depth. In this paper, we address this research gap to better understand how to select and prepare the input data for reconstruction. We expect that the reported conclusions will help in elaborating more efficient super-resolution frameworks that could be deployed in practical applications.
Flexible tracked robot with high mobility on rough terrains

When a severe natural disaster such as earthquakes occurs, numerous victims may be trapped under debris in vast areas. In the CURSOR project, we develop a search and rescue robot system to support first responders to make collect and rapid decisions. The system deploys many mobile robots to search inside the debris from aerial drones. However, it is challenging to move around on debris for such small robots, which can be carried and deployed by drones. Herein, we report the development of a search robot that loads mono-wheeled flexible tracks, previously proposed, which has a high capavility to overcome obstacles.
Controlled Makeup Editing with Generative Adversarial Network

Although Generative Adversarial Networks has performed well in the field of image generation, previous studies on Generative Adversarial Networks usually explored how to interpolate in the latent space to make the image generation more smoothly, without considering whether the semantic attributes are controllable or not. It fails to completely control the semantic meaning of the generated images as required by many tasks. In this work, we propose a new framework for makeup face editing, called MakeupGAN. This network decouples semantic entanglement in the latent space of Generative Adversarial Networks, learns semantic features of makeup, and achieve a more accurate control of image semantic attributes. A large number of examples show that this method can smoothly generate accurate and controllable facial makeup images.
Machine learning based fraud detection in credit card data transactions

Improvement of correspondence advancements and web based business has made the Master card as them ost well -known method of installment for both on the web band standard buys. Along these lines, security during this framework is extremely expected to stop misrepresentation exchanges. Mis representation exchanges in Visa information exchange are expanding yearly. Toward this path, analysts additionally are attempting the novel strategies to distinguish hand stop such cheats. In any case, there is consistently a need of certain procedures that ought to unequivocally and productively identify these fakes. This article includes a plan for the purpose of con artists in the visa details, with the help of the neural network (NN), unsupervised learning in the process. The proposed method is superior to the common methods of Auto Encoder (AE), the Local Emission Factor (LOF), isolation forest (IF),and the K-means clustering. The proposed method for the localization of the expression is based on the NNR operates with an accuracy of 99.87%, compared to the existing, AE, AS, PRAISE, and ’K’ Means as the strategies that will give an accuracy of about 97c/o,98c/o,98c/o, and 99.75c/o separately.Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
Interactive Relationships in the Future of Virtual Reality

This paper focuses on revealing the virtual nature of digital space and exploring the design of virtual space in digital scene by explaining the interactive relations in the future virtual reality. Digital form comes from the reality of information transformation, this kind of combined with technology and equipment of the new media will no doubt create a virtual space. The subject (audience) at the same time can satisfy different geographic space by equipment, with the help of the network can share the virtual space. By means of this virtual space perception, by means of access to information. It is different from physical space or personal spiritual space. Virtual space is a metaphorical space that connects people in different geographical locations but at the same time with the help of the Internet. This future virtual space is called "meta-universe". At present, the design of virtual space in digital scenes cannot be completely divorced from offline physical reality. More immersive experience comes from the actual experience of virtual images in AR augmented reality. In different interactive relationships, virtual space brings different experiences to subject (audience).
Bot Monitoring Jaringan Pada BMT Mentari Lampung Timur Menggunakan Mikrotik Dan API Telegram

Network notifications are very much needed by the network technical team. In addition, network notifications function to provide information to the technical team to find out the current condition of the server or router which is under the supervision of the technical team so that if the server is down, it can be handled quickly. The research method used is DIO Network Lifecycle. DIO Network Lifecycle is a network design method or commonly referred to as a network service life cycle designed to support network development. The research phase begins with design, implementation and operation. The research output produced is in the form of BOT monitoring of the BMT Mentari Lampung Timur network using Mikrotik and the Telegram API.
Fingering Measurement by Force Sensor based on Nail Deformation

This paper describes a novel force sensing system using nail deformation. Due to the two strain gauges attached to a nail, the magnitude and direction of the force applied to the fingertips can be measured. We devise a compact and easy-to-install sensor system and also introduce a calibration method. In the proof experiment, several types of basic verification were performed, then the applicability to work using fingertips was investigated.
Bibliography

This textbook gives a complete and modern introduction to mathematical logic. The author uses contemporary notation, conventions, and perspectives throughout, and emphasizes interactions with the rest of mathematics. In addition to covering the basic concepts of mathematical logic and the fundamental material on completeness, compactness, and incompleteness, it devotes significant space to thorough introductions to the pillars of the modern subject: model theory, set theory, and computability. Requiring only a modest background of undergraduate mathematics, the text can be readily adapted for a variety of one- or two-semester courses at the upper-undergraduate or beginning-graduate level. Numerous examples reinforce the key ideas and illustrate their applications, and a wealth of classroom-tested exercises serve to consolidate readers' understanding. Comprehensive and engaging, this book offers a fresh approach to this enduringly fascinating and important subject.
Speech event and its context: What requesting strategies do school-aged children employ and what does it imply for the Indonesian language instruction?

As has been widely stated in the literature, a speech event always occurs in a specific context. As a result, a speech event will always take place at a specific time, location, and for a specific purpose. Therefore, the purpose of this study was to describe the requesting strategies used by schoolchildren, as well as determine whether or not these requesting strategies can be applied to the teaching of Indonesian language. Using data from three elementary school students, we conducted a qualitative study. The data were collected through field notes and observation and then analyzed using means-ends analysis and heuristic analysis. The findings suggest there can be no doubt that elementary school children use language in accordance with context when making requests. They also support their request for granting in specific contexts. This implies that they are able to use a wide range of contexts, including those that are both concrete and abstract, in their learning. Education professionals at all levels, but particularly those in primary and secondary schools, should make use of the research findings to improve the Indonesian language instruction. The main reason for this improvement is the use of communicative and contextual approaches, which return language to its role as a tool for communication and link teaching and learning activities to the context of real-life experiences. Following a discussion of the current findings' limitations and implications, additional research is recommended.
The “Social Pedagogy” Subject in Southwest University “Neofi t Rilski” Academic Space

The main objective of the current study is to research and justify, from the position of time, the means for establishing the academic subject “Social Pedagogy” in the Faculty of Social Pedagogy at Southwest University “N. Rilski”, as well as to reveal its promising resources for quality education of students – future social pedagogues. To achieve the objective we rely on content analysis on empirical information in databases – university's administrative archive, electronic catalog of the university library, public profiles of the lecturers. Data related to the development of the Faculty of Pedagogy, the educational documentation of the subject – qualification characteristics of bachelor's and master's programs and curricula, scientific production of teachers - publications, monographs, textbooks and teaching manuals are monitored and analyzed.