Reviewerly/exHarmony huggingface dataset:
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

&lt;abstract&gt; &lt;p&gt;The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks.&lt;/p&gt; &lt;/abstract&gt;
MODERN VECTORS OF INCLUSIVE SPACE FORMATION IN HIGHER EDUCATION INSTITUTIONS

Formation and provision of high-quality inclusion in institutions of higher education is an important condition for the sustainable development of our country. After all, the activation of awareness of the essence of an individual in society, its significance, self-realization, and social benefit are so important for persons with special educational needs. It is at the stage of the formation of a person that the satisfaction of his/her educational needs is the basis that will determine the entire future life of the individual. Barrier-free access to education as well as the acquisition of a profession today are very important for the formation of an inclusive educational space of higher education. Important conditions for ensuring equal rights of students to quality higher education are defined as the primary ones; they are the following: architectural, informational and social accessibility of the educational environment, creation of a material and technical base adapted to the needs of students, availability of qualified scientific and pedagogical personnel prepared to work in an inclusive environment. The main task of inclusion is that students do not feel special, so that they participate in educational, vocational and other processes together with other students. The most important task is to provide an opportunity to reach one's success in accordance with abilities, needs and interests. Acquiring higher education by persons with special needs or disabilities should take into account and anticipate requests that will contribute to the knowledge and skills to obtain a profession and be a competitive specialist for market jobs. We have proposed the following steps for implementing the mission of inclusive education in the architecture of the educational process in higher education institutions: training teachers, creating a network of inclusive clubs and groups, creating a barrier-free environment in an educational institution, designing a tutoring network, conducting courses in intercultural communication and communication ethics, using multimedia technologies and cloud corporate technologies of the university, promoting the creation of a comfortable environment, supporting research and programs, developing an inclusive network in higher education institutions, supporting student initiatives (involvement in cultural events, initiatives in the higher education institution), ensuring a greater level of representation of various management groups and administration, expansion of the range of programs for student support, implementation of acquiring knowledge in various projects, grants, startups
PROJECT TECHNOLOGIES IN THE CONDITIONS OF THE DISTANCE FORMAT OF THE PROFESSIONAL TRAINING OF THE TEACHER

The article deals with the issues of the relevance of distance education and analyzes the problems of ensuring its effectiveness in the context of the transition to competency-oriented/personality-oriented technologies. The essence of distance education and the problems that reduce its effectiveness in the conditions of modern realities and challenges of the time are analyzed. In particular, attention is focused on the practice of formal application of innovative technologies, which include project-based technologies characterized by a discrepancy in form and content (innovative form and traditionally informative and reproductive method of organizing educational activities in its context). The authors argue that educational activity systems of schools and professional training of teachers in higher education institutions should be studied in the context of common approaches to the functioning of interdependent systems. It is proved that an adequate personality-oriented system of organizing project activities of schoolchildren/students will comply with the basic principles of competency-based education and provide the basis for its implementation in the system of competency-based education. The article offers the author's model of organizing project-based learning in the distance education system and describes algorithms for its implementation with a focus on the criteria of competency-based education and distance learning conditions. The model is equally relevant for the system of organizing educational activities of schoolchildren and professional training, as well as post-graduate education of teachers. Therefore, the problem is to systematically and gradually move towards innovative education, updating all the links and dependencies that ensure the systematic approach.
Social Media Evidence in Commercial Litigation

This chapter demonstrates that analyzing what people post on social media sites can yield powerful evidence for use in commercial litigation. This kind of analysis is a natural way of listening in on people’s conversations about products, services, brands, trademarks, and patents, all of which are often the subject of high-stakes lawsuits. An argument is made that an expert who could commission an opinion survey could now also commission a social media analysis, which will contribute to a more persuasive and often more time-appropriate body of evidence upon which to rely. Examples of the use or potential use of social media in litigation are presented, including cases that used social media evidence, such as the US government’s lawsuit against Lance Armstrong; a case involving a meat byproduct sometimes referred to as “pink slime”; a case centering on an allegedly deceptive Super Bowl beer advertisement; and many cases involving disputes having to do with intellectual property. Finally, we compile and discuss a number of issues relating to the use of social media in litigation. These issues include questions about social media’s authenticity, the best way to preserve it, and several other analytical and legal questions. The chapter concludes with a discussion of how social media analysis can migrate from the periphery of litigation evidence toward having a more central role.
Linguistic and Stylistic Requirements for Qualification Papers of Future Mathematics Teachers

The article reveals the general requirements for the qualification papers of future Mathematics teachers in the context of observing linguistic and stylistic norms of the scientific style, forming the language culture of a modern teacher. The social and communicative importance of the influence on the development of the teachers’ mathematical and scientific language has been analyzed. The peculiarities of the scientific language and the main flaws of its written presentation – the scientific style text – in the qualification papers of Mathematics teachers, who obtain the second educational level, have been revealed. Considerable attention has been paid to the formation of mathematical texts, in particular to issues of modern mathematical terminology. Typical linguistic and stylistic mistakes of authors, which they make when writing, editing and translating mathematical texts, have been analyzed. These mistakes to the specifics of scientific technical terminology, lexical and stylistic features of the style of scientific language have been related. Examples of the most frequently used lexical constructions and stable inflections in mathematical texts have been given. Recommendations on avoiding mistakes when constructing a text in a scientific style have been given. Selecting appropriate Ukrainian equivalents of commonly used lexemes of the Russian language, using established word combinations, and observing the grammatical norms of the literary language have been provided.
Optimization of fuzzy systems for risk factors in the prevention of cardiovascular diseases

Obesity and overweight are the most common cardiovascular risk factors in people who have suffered myocardial infarction. They are also considered a threat to health worldwide. People with obesity have twice the risk of suffering heart failure than people with a normal body mass index. In this paper, fuzzy logic models which are optimized are proposed to detect risk factors for heart disease according to age and body mass index.
Multiple rounds using mixed chaotic keys method for secure message cryptography

Protecting secret messages from being hacked is a vital problem. This research paper will present a simple and flexible data cryptography method. The method can encrypt-decrypt messages of any length. Also, it can be easily used to encrypt-decrypt gray and color images. The proposed method will use a private key, which contains the needed information to generate chaotic keys; these keys will be used to obtain indices keys, which will be used as a lockup table in the encryption and decryption phases. The generated key will be very sensitive to the P.K. contents. The P.K. used in the proposed method will have a complex structure, providing a huge key space that can resist attacks. The proposed method will be implemented using various messages, the obtained results will be analyzed, and several test analysis methods will be used to prove the achievements of the proposed method. The quality analysis will use the calculated values of MSE, PSNR, CC, and NSCR to prove the quality of the proposed method in both the encryption and decryption phases. A sensitivity analysis will be performed to show how the encryption and decryption phase is sensitive to the selected P.K.; any changes in the P.K. during the decryption phase will be considered a hacking attempt by producing a damaged decrypted message. A speed analysis will be provided to prove that the proposed method is efficient, and the obtained results will be compared with other methods' results to show the speedup provided by the proposed method.
Accuracy dominant frequencies of Lomb - Scargle periodogram in modelingmonthly rainfall

In the field of hydrology, many researchers have developed time series models. Usually, the modelling uses time series data, such as daily rainfall, monthly rainfall, etc. In order to produce a good model, the frequencies used in the model must represent the data being modelled. Otherwise, the inaccurate frequencies used to cause the resulting model to be inaccurate. Several methods or techniques have been used to estimate the frequencies contained in time series data. These methods are the Fast Fourier Transform and Lomb-Scargle periodogram methods. This study used the Lomb-Scargle periodogram method to predict monthly rainfall time series data. In this research, the Lomb periodogram results in rainfall frequencies. The frequencies are created by using monthly rainfall time series data. Using the frequencies, the dominant frequencies can be selected. Periodic modelling of monthly rainfall time series can be adequately simulated using the dominant frequencies. The correlation coefficient between the monthly rainfall data with the monthly rainfall model can be used to measure the accuracy of the monthly rainfall model.
Providing Intelligent Maintenance through an Autonomous Robot

The article focuses on trends in the field of intelligent maintenance in a utility company. One of the main goals of every transmission and distribution (T&D) system operator is reliable performance of their network with low interruption time. To achieve this goal, fully functional and healthy components are required. During their lifetime after installation and commissioning, components age and gradually change from new to old. Aging in this sense means continuous deterioration of their operability or resilience against normal and abnormal voltage and current stresses. This deterioration may lead to equipment failure if maintenance is not performed early enough. The article shows how automation can be used for remote management and maintenance of assets at an electrical substation.
Enhancing practical cybersecurity skills: The ECSF and the CyberSecPro European efforts

The accelerated digitalization of all business and industrial sectors (transport, government, health, finance manufacturing) will increase the number, complexity and scale of cybersecurity incidents and their impact on the economy and society. The digital transformation imposes Higher Education Institutions and training providers to enhance their role in preparing the new generation of workforce that will have the capabilities and skills to address the upcoming digital challenges. Training providers need to become the enablers of the digital transformation with the capacity to accommodate different skills needed by the market, to a variety of training specializations. Fostering collaboration with the private sector can be effective in attracting the necessary funding, state-of-the-art technological training tools needed and real-life based training material. In this paper, we describe two recent efforts coming from the European Union targeting to close the gap between the available cybersecurity training and cybersecurity marketing demands, and further analyse the human factors involved in these efforts.
Coverage Path Planning With Budget Constraints for Multiple Unmanned Ground Vehicles

This paper proposes an innovative approach to coverage path planning and obstacle avoidance for multiple Unmanned Ground Vehicles (UGVs) in a changing environment, taking into account constraints on the time, path length, number of UGVs and obstacles. Our approach leverages deformable virtual leader-follower formations to enable UGVs to adapt their formation based on both planned and real-time sensor data. A hierarchical block algorithm is employed to identify areas in the environment where UGV formations can spread out to meet time and budget constraints. Additionally, we introduce a novel control scheme that allows each UGV to generate a local steering force to dodge any static and mobile obstacles based on the closest safe angle. Results from simulations and real UGV experiments demonstrate that our approach achieves a higher coverage percentage than rule-based and reactive swarming approaches without planning. Our approach offers a promising solution for efficient coverage path planning and obstacle avoidance in complex environments with multiple UGVs.
Perceptions on Digital, Virtual Environments and the Metaverse. An Exploratory Study with Younger Users

Digital, virtual environments and the metaverse are rapidly taking shape and will generate disruptive changes in the areas of ethics, privacy, safety, and how the relationships between human beings will be developed. To uncover some of some of the implications that will impact those areas, this study investigates the perceptions of 101 younger people from the generations Y and Z. We present a first exploratory analysis of the findings, focusing on knowledge and self-perception. Results show that these young generations are seriously doubting their knowledge on the metaverse and virtual worlds – regarding both the definition and the usage. It is interesting to see only a medium confidence level, considering that the participants are young and from an academic environment, which should increase their interest in and the affinity towards virtual worlds. Males from both generations perceive themselves as significantly more knowledgeable than females. Regarding a fitting definition, almost 40% agreed on the metaverse as a “universal and immersive virtual world that is made accessible using virtual reality and augmented reality technologies”. Regarding the topic in general, several participants (almost 40%) considered themselves sceptics or “just” users (38%). Interestingly, generation Y participants were more likely than the younger generation Z participants to identify themselves as early adopters or innovators. In result, the considerable amount of “mixed feelings” regarding digital, virtual environments and the metaverse shows that in-depth studies on the perception of the metaverse as well as its ethical and integrity implications are required to create more accessible, inclusive, safe, and inclusive digital, virtual environments.
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning

Automaton based approaches have enabled robots to perform various complex tasks. However, most existing automaton based algorithms highly rely on the manually customized representation of states for the considered task, limiting its applicability in deep reinforcement learning algorithms. To address this issue, by incorporating Transformer into reinforcement learning, we develop a Double-Transformer-guided Temporal Logic framework (T2TL) that exploits the structural feature of Transformer twice, i.e., first encoding the LTL instruction via the Transformer module for efficient understanding of task instructions during the training and then encoding the context variable via the Transformer again for improved task performance. Particularly, the LTL instruction is specified by co-safe LTL. As a semantics-preserving rewriting operation, LTL progression is exploited to decompose the complex task into learnable sub-goals, which not only converts non-Markovian reward decision processes to Markovian ones, but also improves the sampling efficiency by simultaneous learning of multiple sub-tasks. An environment-agnostic LTL pre-training scheme is further incorporated to facilitate the learning of the Transformer module resulting in an improved representation of LTL. The simulation results demonstrate the effectiveness of the T2TL framework.
Designing the Metaverse: a Scoping Review to Map Current Research Effort on Ethical Implications.

The metaverse and digital, virtual environments have been part of recent history as places in which people can socialize, work and spend time playing games. However, the infancy of the development of these digital, virtual environments brings some challenges that are still not fully depicted. With this article, we seek to identify and map the currently available knowledge and scientific effort to discover what principles, guidelines, laws, policies, and practices are currently in place to allow for the design of digital, virtual environments, and the metaverse. Through a scoping review, we aimed to systematically survey the existing literature and discern gaps in knowledge within the domain of metaverse research from sociological, anthropological, cultural, and experiential perspectives. The objective of this review was twofold: (1) to examine the focus of the literature studying the metaverse from various angles and (2) to formulate a research agenda for the design and development of ethical digital, virtual environments. With this paper, we identified several works and articles detailing experiments and research on the design of digital, virtual environments and metaverses. We found an increased number of publications in the year 2022. This finding, together with the fact that only a few articles were focused on the domain of ethics, culture and society shows that there is still a vast amount of work to be done to create awareness, principles and policies that could help to design safe, secure and inclusive digital, virtual environments and metaverses.
A Dataset of Watch and Wristband for Deep Learning based Multi-view Stereo 3D Model Reconstruction

Multi-view stereo (MVS) 3D reconstruction based on deep learning has achieved great success, however, it requires a very high quality and quantity of datasets compared with other computer vision tasks. Current 3D datasets have great limitations in the reconstruction of industrial products, including low accuracy, few types of styles, and few pairwise image models. In this paper, we introduce a new dataset for MVS 3D Model Reconstruction, focusing on the watch wristband category. Better than the existing available open-source watch and wristband dataset, ours contains more than 1k multi-view high-resolution images and high-precision 3D models, covering cartoon, mechanical, vintage, etc. Most importantly, ours can be used directly for deep learning-based MVS 3D reconstruction, because besides three views of real images, we drew line sketches of the three views, and then match them to the high-precision 3D model one by one. At last, we train the MVS network based on deep learning with our dataset as input and supervision. The experiments show that we achieve significant results, and verify the effectiveness of reconstruction in the watch wristband category.