Reviewerly/exHarmony huggingface dataset:
A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition.
Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.
Landmark Identification from Low-Resolution Real-Time Image for Pose Estimation

The study of human posture estimation has produced an excellent illustration of the present state of human and computer vision. Various experts from across the world are now performing considerable studies on this topic. Human action recognition and posture are key criteria for preserving consistency inside real-time backdrops and objects. In order to comprehend human behavior, key points must be identified. One can better comprehend future functions by evaluating mechanical models of the human body. This article proposes a new method to identify and anticipate human action key points. A full description of the recent methods related to human posture estimation is also illustrated with reference to a benchmark database. When discussing the suggested strategy’s outcomes and conversations, it is clear that the proposed approach has superior performance and works considerably better than the previous approaches. It is predicted that the proposed technique would provide a new dimension to humanistic recognition observation research.
Quantifying Inconsistencies in the Hamburg Sign Language Notation System

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‘Extended Senses: Embodying Technology’

This Special Issue is a series of articles generated from the Extended Senses Symposium 2022 , held at University of Greenwich, 8–9 September 2022, including an exhibition (8–10 September in the Stephen Lawrence Gallery). The main theme of the symposium was to explore ways to extend and expand the body through new and emerging modalities and technologies. Focusing primarily on the body as a site of knowledge production within this field, it highlighted immersion, haptic engagement and evolving body led interfaces to understand and enable the bridge between the analogue and the digital realms. Participants were encouraged to present socially responsible work engaged with issues such as: the climate crisis and environmental sustainability, data ethics, personal data ownership, surveillance and privacy, women’s health and equality and other urgent issues that affect the body. Talks and artwork traversed between the physical and the virtual, including but not limited to, electronic or smart textiles, research exploring multimodal interfaces and human–technology interaction, dance, theatre, music and other performance modes, as well all work that engages with technology to translate different embodied senses and experiential sensations.
The Superplot for a story

The two-volume theatrical history of A.A. Chepurov, a well-known theatre critic, theatre historian, has a very ambitious title – “Theatrical superplot”. This name appeals not only to the well – known concept of a super task introduced into the theatre life by K. S. Stanislavsky, but also to philosophical works, especially to the books by Russian philosophers, who often addressed to one or another theatrical reality in their reflections. Literally in every paragraph of the two-volumed book by A.A. Chepurov, the author’s longing for science is felt, the desire for strictness of definitions, a scientific approach, the author’s willingness and ability to return to the conversations about the theatre in a scientific direction.Chepurov is a chronicler of the Alexandrinsky Theatre, of the acting, when he examines and talks about the “proportions” of performances, about “tiers” and complex and complexly constructed roles. And the author finds this complexity and beauty of the playing in different epochs and in different years of the life of the Alexandrinsky Theatre. A large place in the first part of the book is occupied by a super – plot called “Chekhov’s Revolution in Theatre”, most of which is connected with the famous and legendary plot – the failure of “The Seagull” on the stage of the Alexandrinsky Theatre. Among numerous Chepurov's merits, one of the most important is his long-term work to free from modern and later “layers” and distortions the history of the production, the first performance and the subsequent fate of “The Seagull” at the Alexandrinsky Theatre.
Self-build Deep Convolutional Neural Network Architecture Using Evolutionary Algorithms

The convolutional neural network (CNN) architecture has shown remarkable success in image classification and segmentation. Its popularity has increased promptly due to various factors such as exponential growth in computational resources, availability of benchmark datasets, supporting libraries, and open-source software. The efficiency of CNN architecture majorly depends on the complexity of the architecture, availability of datasets, and hyperparameter selection. But, due to the huge number of parameters in CNN architecture, its selection has completely remained ad hoc in the past works. In this article, a novel encoding technique has been proposed that can represent complex CNN architecture effectively. The article defines basic building blocks to represent CNN architecture such as the genesis block, transit block, agile block, and output block. This encoding structure is used to generate dynamic length chromosome structure and initialized using evolutionary algorithms. A comparative analysis is also presented that shows compared its effectiveness with existing encoding representations on the basis of the number of encoding parameters, training cost, and efficiency.
Simulation of Electronic Equipment Control Method Based on Improved Neural Network Algorithm

In recent years, with the continuous improvement of the degree of industrial automation in our country, the intelligent requirements for electronic equipment are also rising. For this reason, some scholars propose to apply artificial neural network algorithm to electronic equipment to control the equipment. However, the current artificial neural network algorithm is generally implemented by FPGA. This scheme has the disadvantages of high cost and lack of applicability. Therefore, the purpose of this paper is to study the simulation of electronic equipment control method based on improved neural network algorithm. This paper uses PSpice circuit simulation software to model, simulate and optimize the design of the circuit. And the nonlinear function generator circuit, adder circuit and analog multiplier circuit are modeled, simulated and optimized by PSpice simulation software, and finally the adder circuit and the multiplier circuit are used to form a neural network overall circuit that meets the requirements to achieve Algorithmic function. After training and simulating the curve obtained by the boiling water experiment with the method in this paper, the experiment shows that the curve fitted by the polynomial is basically consistent with the curve obtained by the experiment, and the maximum error obtained by calculation is 1.35%. Through the research of this paper, it fills the blank of our country's neural algorithm in the control simulation of electronic equipment, and promotes the economic development of our country.
Directing method of Igor Terentiev

The directoring activity of I. G. Terentiev can be considered as the the context of the avantgarde tradition. The reconstruction of his performances is always an appeal to the principles and techniques of futuristic arcane poetry (zaum’), which were formulated by him back in the Tiflis period of his work as a poet, artist and zaum’ theorist. Also, Terentiev’s theatrical experience is closely linked with his contemporaries, and often the performance staging was a special kind of polemic, sometimes a rebellious attack against the theatrical system. The theatre thusbecomes the most adequate form of expression of Terentiev’s revolutionary ideas. His theatrical searches are an appeal to amateur theatre, attempts to interpret the classical text using arcane language, as well as a constant search for new expressiveness. His experiments lacked evidences, with only a few photographs of his performances surviving, mostly negative criticism and eyewitness reviews that can be counted on fingers. The article examines the features of I. G. Terentiev’s directing method on the example of his most famous performances: John Reed, The Inspector General and Natalya Tarpova. The evolution of the director’s creative method is traced, which became more complicated and transformed with each performance. Three directions are singled out, the main for Terentiev’s rebellious directing method. These are propaganda performances in an amateur theatre, futuristic direction using the techniques of arcane poetry and attempts to realize on stage the process of mastering a new proletarian novel. In the article are published the photographs from the performances The Inspector General and Natalya Tarpova, that were found in the RGALI and the funds of the State Central Theatre Museum n.a. A.A. Bakhrushin. These photographs give an idea of performances’ pecularities, directing technique, scenography. The article lists a set of directorial techniques characteristic of various periods of Terentiev’s work, the method of working on the text, and concludes why Terentiev became the last director of the Russian theatrical avant-garde
Visual Cryptography: A Detailed Analysis

In today's world, securing data is becoming a very important issue for an individual. Some cryptographic techniques are already discovered and some are yet to be revealed. In this paper, we are surveying an advance method of data hiding i.e. Visual Cryptography. Visual cryptography is an encryption technique which hides the information by using images. The encrypted image can be decrypted by the human vision if the correct image key is used. In visual cryptography, we can transform a secret image into various shared images and the combination of these shared images can reveal the secret original image. This paper reviews about visual cryptography, the three techniques of visual cryptography i.e., Binary Image, Gray-Scale Image and Colored Image with its applications.
A reliable solution to detect deepfakes using Deep Learning

Recently, it has become simple to produce trustworthy face video exchanges that leave a few signs of deception thanks to in-depth free reading software tools (DF). Despite decades of effective use of visual effects in digital video deception, recent developments in in-depth learning have significantly improved the genuine nature of misleading content and the accessibility that can be achieved with it. This is referred to as AI-synthesized media or DF in short. Making DF is a simple task that uses practical tools. However, it is a significant difficulty if these DFs are discovered, because it is hard to train the algorithm for identifying DF. CNNs and RNNs have helped us come closer to DF. The Convolutional Neural Network (CNN) is used by the system to extract features at the individual level. The continuous neural network (RNN) states learn to recognize whether or not a video is being deceived and be able to spot temporary anomalies among the frames given by DF's creative tools thanks to these capabilities. An extensive collection of pseudo-videos gathered from a common data source is the anticipated outcome. We demonstrate how our method can produce a competitive outcome in this work that is simple to utilize.
Self-supervised Image Classification Using Convolutional Neural Network

Image classification has been a trendy research topic in the field of pattern recognition and computer vision, which extracts different features of images and predict the category of images. Thanks to the development of deep learning, powerful convolutional neural networks can be used in the field of image recognition. However, existing deep learning-based image recognition research mostly follows the framework of supervised learning, and model learning relies on a large number of accurate labels. Providing a large amount of label data will undoubtedly require laborious human effort and expensive costs. Therefore, image recognition based on unsupervised learning (without using any image category labels to achieve classification) has become a spotlight for research. In this paper, explorations on the image classification by self-supervised framework SimCLR on image classification successfully clusters a large number of images into an optimum amount categories. Qualitative results have show SimCLR is particularly effective in recognizing the colors of images; both qualitative and quantitative results shows SimCLR is great at identifying simple contours. However, when the colors are similar, and contour lines are complex, SimCLR does not obtain satisficing results. The accuracy on classifying Mnist dataset is 32%.
Blockchain-based internet of medical things (IoMT) for healthcare management

When it comes to the use of the Internet of Things (IoT), the healthcare sector is set to become the next frontier of the digital revolution thanks to the Internet of Medical Things (IoMT). Due to their weight, importance, and sensitivity, these files must be protected in the strictest manner. Now that blockchain is becoming more widespread, scientists are concentrating on how to use blockchain tactics within healthcare management to improve data security. Nevertheless, owing to the differing needs of these two technologies, such integration is exceptionally complex and demanding. In order to help users, take full control of their data, this study provides an overview of the current state of blockchain platforms for the IoMT by focusing on the difficulties presented by combination systems. This article will examine blockchain's use in healthcare IoT, including supply chain transparency, health data arrangement, smart contracts, and IoT security for remote monitoring. The final portions focus on challenges and potential developments in the future.
A Comprehensive review on IoT-based healthcare security through blockchain technology

Blockchain is a rapidly developing modern technology being used in various fields, including IoT-based healthcare, which is widely used nowadays because of its ability to increase the security, robustness, and reliability of distributed systems. Key features such as data immutability, decentralization, transparency, privacy, and distributed ledger are highly necessary to make blockchain an attractive technology. This paper gives a comprehensive survey of the application of IoT based blockchain technology. IoT and blockchain are required to enable real-time information processing and transaction implementation in an orderly manner. Digital data has become important for health services due to the hectic routine of daily life for patients and doctors. This study sheds light on the use of blockchain for IoT-based healthcare.
Enhancing accessibility and comfort: A smart wheelchair with advanced features

This paper presents the design and implementation of a smart wheelchair with advanced features to enhance the accessibility and comfort of wheelchair users. The proposed system incorporates voice control, biometric authentication, obstacle detection, and temperature monitoring, among other features, to provide a more intuitive and personalized user experience. The user interface is designed to be user-friendly and can be operated using a touchscreen display, a joystick, or voice command. The system is powered by a battery and includes a microcontroller board that controls the various functions of the wheelchair. The proposed system aims to improve the quality of life for wheelchair users by providing a more efficient and user-friendly mode of mobility.
Post-Scam Crypto Recovery: Final Clarity or Deceptive Simplicity?

In Jones v Persons Unknown [2022] EWHC 2543 (Comm), Nigel Cooper QC handed down the first final judgment on a claim to recover cryptoassets following a scam. While both the result and the reasoning may seem straightforward at first glance, a deeper analysis of the judgment reveals a concealed plethora of legal complications. This note critiques certain aspects of the court's reasoning and examines the interesting issues that arise relating to property and title in cryptoassets.
MagView++: Data Exfiltration via CPU Magnetic Signals Under Video Decoding

Air-gapped networks achieve security by using physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to exfiltrate from the Faraday-cage and the air gap. Despite the success of such covert channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagView++</monospace> , where sensitive information is embedded in other data such as video and can be transmitted over the internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic signals. The “separation” of information embedding and leaking, combined with the fact that the data can be exfiltrated from any computer in a distributed manner, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type, reducing the quantization parameter, and changing the timestamp of the frame, without video quality degradation. We prototype <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagView++</monospace> and achieve 8.9 bps throughput with 0.0057 BER when using a smartphone as the receiver, and 59 bps throughput with 0.0025 BER when using a dedicated devices with high sampling rate as the receiver. Experiments under various environments are conducted to show the robustness of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagView++</monospace> . Limitations and possible countermeasures are also discussed.
Path planning optimization strategy of mobile robot in two-dimensional fixed environment

In two-dimensional space, this paper aims at the problems of slow convergence and poor real-time performance of existing path planning algorithms. A free growing tree algorithm (SOGT) is proposed. The algorithm mainly includes three parts: heuristic biased sampling, exploratory expansion to generate sampling points, and path optimization. The heuristic bias sampling part uses the concept of dynamic target gravity field, which can improve the utilization rate of sampling points, control the sampling range to the direction of the target point, and improve the convergence speed of the algorithm; When choosing the parent node, the heuristic expansion approach can minimize the number of collision detection.; Path optimization employs maximum distance and corner optimization techniques to shorten the path’s length, as well as its angle and number of corners. The simulation results show that the SOGT algorithm is feasible and effective, and achieves high real-time performance and fast convergence speed.
Impact analysis of feature selection techniques on cyberstalking detection

Internet-based applications are making the habitual society and exploring new ways to perform online-based crimes. Numerous cybercriminals are engaged in the different platforms of the internet-based virtual world, carrying out cybercrimes according to predetermined and preplanned agendas. As technology advances, cyberstalking, cyberbullying, and other forms of cyber harassment are growing on social media, email, and other online platforms. Cyberstalking uses internet-based technology to harass, intimidate, and undermine individuals online with different approaches. In order to examine the impact of feature selection strategies for improving model performance, this paper proposes a machine learning-based cyberstalking detection model. The proposed model used the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to extract features, and three distinct approaches, TF-IDF + Chi-Square Test, and TF-IDF + Information Gain, were used to select the different numbers of relevant features. In the cyberstalking detection model, a Support Vector Machine (SVM) was employed for classification purposes. Based on the SVM classifier's performance, each feature selection approach's impact on the various feature sets was assessed. According to experimental findings, the TF-IDF + Chi-Square Test outperformed other applied approaches and improved detection mode performance. Additionally, experimental findings demonstrate that the TFIDF + Chi-Square Test approach also performs better in a small collection of relevant features than other approaches that have been utilized.
Transfer Learning for Speech Recognition

Speech recognition is also an important research area of transfer learning. Speech recognition has several scenarios: cross-domain ASR and cross-lingual ASR. In this chapter, we introduce how to implement these two applications using PyTorch and EspNet. Note that for easy installation of EspNet, we provide a docker environment for the users: jindongwang/espnet:all11. For easy usage of EspNet, we also provide a wrapper for it: https://github.com/jindongwang/EasyEspnet .