Reviewerly/exHarmony huggingface dataset:
Gravitational Deep Convoluted Stacked Kernel Extreme Learning Based Classification for Face Recognition

In recent times, researchers have designed several deep learning (DL) algorithms and specifically face recognition (FR) made an extensive crossover. Deep Face Recognition systems took advantage of the hierarchical framework of the DL algorithms to learn discriminative face characterization. However, when handling severe occlusions in a face, the execution of present-day methods reduces appreciably. Several prevailing works regard that, when face recognition is taken into consideration, affinity materializes to be a pivotal recognition feature. However, the rate of affinity changes when the face image for recognition is found to be illuminated, and occluded, with changes in the age of the subject. Motivated by these issues, in this work a novel method called Gravitational Deep Convoluted Stacked Kernel Extreme Learning-based (GDC-SKEL) classification for face recognition is proposed for human face recognition problems in frontal views with varying age, illumination, and occlusion. First, with the face images provided as input, Gravitational Center Loss-based Face Alignment model is proposed to minimize the intra-class difference, which can overcome the influence of occlusion in face images. Second, Deep Convoluted Tikhonov Regularization-based Facial Region Feature extraction is applied to the occlusion-removed face images. Here, by employing the Convoluted Tikhonov Regularization function, salient features are said to be extracted with an age-invariant representation. Finally, Stacked Kernel Extreme Learning-based Classification is designed. The extracted features are given to the Stacked Kernel Extreme Learning-based Classification and to identify testing samples Stacked Kernel is utilized. The performance of GDC-SKEL is evaluated on Cross-Age Celebrity Dataset. Experimental results are compared with other state-of-the-art classifiers in terms of face recognition accuracy, face recognition time, PSNR, and False Positive Rate which shows the effectiveness of the proposed GDC-SKEL classifier.
A Hierarchical Framework for Video-Based Human Activity Recognition Using Body Part Interactions

Human Activity Recognition (HAR) is an important field with diverse applications. However, video-based HAR is challenging because of various factors, such as noise, multiple people, and obscured body parts. Moreover, it is difficult to identify similar activities within and across classes. This study presents a novel approach that utilizes body region relationships as features and a two-level hierarchical model for classification to address these challenges. The proposed system uses a Hidden Markov Model (HMM) at the first level to model human activity, and similar activities are then grouped and classified using a Support Vector Machine (SVM) at the second level. The performance of the proposed system was evaluated on four datasets, with superior results observed for the KTH and Basic Kitchen Activity (BKA) datasets. Promising results were obtained for the HMDB-51 and UCF101 datasets. Improvements of 25%, 25%, 4%, 22%, 24%, and 30% in accuracy, recall, specificity, Precision, F1-score, and MCC, respectively, are achieved for the KTH dataset. On the BKA dataset, the second level of the system shows improvements of 8.6%, 8.6%, 0.85%, 8.2%, 8.4%, and 9.5% for the same metrics compared to the first level. These findings demonstrate the potential of the proposed two-level hierarchical system for human activity recognition applications.
Development of Interactive E-Module Based on Papuan Local Wisdom on Basic Mathematics Course

Lecturers have an important role in facilitating technology-based learning activities. A common problem is the lecturer's habit of using monotonous teaching media that makes students bored in Mathematics class. Therefore, researchers took steps to develop teaching materials in the form of interactive e-module based on local wisdom of Papua or Merauke on basic mathematics course. E-modules are developed by utilizing the Heyzine flipbooks application which is integrated with the wordwall.net application. The research method used is R &amp; D, the 4D development model which has stages such as Define, Design, Develop, and Disseminate. Data collection techniques are observation and questionnaires. The questionnaire used consists of three types, which are material expert, media expert, and student (user) questionnaires. Based on the assessment of media expert and material expert validators, the average feasibility of 3.48 and 3.36 was obtained, as well as the results of trials conducted on students on average 3.34, it can be concluded that the e-module developed is valid and feasible to use.
ARTIFICIAL INTELLIGENCE IN EDUCATION: TOOL, OBJECT, “SUBJECT” OF LEARNING

The active development of innovative technologies led to the introduction of artificial intelligence in all spheres of human activity, particularly, in education. As an educational technology, artificial intelligence integrates social, cultural, pedagogical, and legal aspects of application, thereby opening up new opportunities for learning and generating knowledge. At the same time, education faces new problems and challenges, one of which is determining the role of artificial intelligence in the generation of knowledge. The article analyzes the main representations of artificial intelligence in education: as a tool of education, an object of learning, and a “simulative subject”. In the complex system of “artificial intelligence &amp; education &amp; knowledge”, representations of artificial intelligence are considered evolving and becoming more complicated by overcoming contradictions embedded in systems. To understand the impact of artificial intelligence on education, its features as a tool of education and as an object of learning are revealed, and productivity and problems that arise are clarified. Special attention is paid to the status of AI as a “simulative subject” in education, whose role in the transformation of “knowledge” into “understanding” is determined by the human teacher. The interdisciplinary theoretical base is represented by studies of thinking, knowledge, and education as complex systems. A phenomenological approach was used to justify the content of learning as a transformation of knowledge into meaning and understanding of the teacher’s role in digital education.
Construction of the Educational Process Based on the Integration of Subject and Meta-Subject Learning Activities

The article examines the pedagogical conditions for designing the educational process that is most appropriate for fulfilling the requirements of the Federal State Educational Standard for Primary General Education (FSES PGE): achieving a certain level of subject and meta-subject learning outcomes for a primary schoolchild. The features of educational actions of narrow subject areas, related to the specific subject content of the training course, as well as universal ones, which, being formed on subject material, reflect generalized supra-subject methods of solving an educational task, are characterized. Mechanisms for integrating the process of forming subject and meta-subject requirements of the Federal State Educational Standard of Primary General Education are considered.
The role of teachers in the development of student’s leadership skills

Understanding the trend of supply and demand in the labor market is important for different categories of stakeholders. For higher education institutions understanding of these trends is crucial in reforming educational programs and academic disciplines to better serve society, the economy, and businesses. For individuals, knowledge of trends can be a key factor in educational and career decisions. Trends in changes in key skills were analyzed and it was concluded that critical thinking, creativity, complex problem solving, leadership and social impact remain important in the process of global change. The influence of teachers on the development of leadership skills of students in the process of academic counseling was identified and the approaches of researchers to the quality teaching in higher education were analyzed, the skills necessary for teachers to develop leadership skills were determined, the leading role and functions of academic counseling for the development of leadership skills of students were defined. It was found that the level of development of the teacher's leadership skills directly affects the development of students' leadership skills. Students' interactions with their academic advisors have been found to play an important role in their overall learning experience. It is noted that tools for developing students' leadership skills can be modeling, group work, debates, etc., participation in which contributes to the formation of such leadership skills as vision; teamwork; communication; problem solving; teaching; motivation; management and decision-making.
Reinforcement Contract Design for Vehicular-Edge Computing Scheduling and Energy Trading Via Deep Q-Network With Hybrid Action Space

The advancements in information and communication technology have led to the emergence of innovative edge computing models that incorporate the computing power of vehicles into the energy sector. Electric vehicles (EVs), functioning as edge computing nodes, offer flexible computing offloading services for charging stations (CS). However, coordinating EV computing and charging should consider the interdependence with CS's specific computing requirements due to information asymmetry. Additionally, it is crucial to consider EV's charging demands and their social distance to computing tasks. In this context, it is natural to view EVs and CSs as self-interested prosumers who prioritize their individual utilities. To address the integration of strategic EV-CS interactions and uncertainties into the joint computing scheduling and energy trading, this paper proposes a parameterized deep Q-network-based reinforcement contract design framework, which employs a hybrid action space to design contracts that facilitate CSs in pairing computing tasks and charging resources with EVs. The objective is to incentivize EV participation and maximize long-term social welfare by incorporating incentive compatibility, individual rationality constraints, and capacity constraints into the contract design. Experimental results demonstrate that the proposed framework surpasses parameterized deep deterministic policy gradient-based and greedy-based contract designs, and achieves near-optimal solutions by solving deterministic optimizations.
Graphene-Based Photonic-Electronic Multiply-Accumulate Neurons

We assess the potential of graphene optical modulators for high-speed neuromorphic computing. We numerically validate the performance in terms of analog resolution of a photonic-electronic neuron based on high-speed graphene electro-absorption modulators. The neuromorphic processor delivers up to 280 GMAC/s with a resolution spanning from 2.7 to 5.4 equivalent bits for different operating frequencies.
Best Practices in Tele-Forensic Methods of Gathering Data

Data collection changed in the wake of COVID-19. This article begins with advantages and disadvantages of remotely gathering data via telehealth. The article then provides a discussion of ethics in the form of best practices, sufficient practices, and insufficient practices. A thorough discussion of the support for gathering data through interviewing parents, interviewing children, testing, and collecting collateral data follows.
Applications of artificial intelligence in dementia

The recent evolution of artificial intelligence (AI) can be considered life-changing. In particular, there is great interest in emerging hot topics in AI such as image classification and natural language processing. Our world has been revolutionized by convolutional neural networks and transformer for image classification and natural language processing, respectively. Moreover, these techniques can be used in the field of dementia. We introduce some applications of AI systems for treating and diagnosing dementia, including image-classification AI for recognizing facial features associated with dementia, image-classification AI for classifying leukoaraiosis in MRI images, object-detection AI for detecting microbleeding in MRI images, object-detection AI for support care, natural language-processing AI for detecting dementia within conversations, and natural language-processing AI for chatbots. Such AI technologies can significantly transform the future of dementia diagnosis and treatment. Geriatr Gerontol Int 2024; 24: 25-30.
The Universality of the Aesthetic Attitude to the World as the Creative Principle of N.I. Kiyashchenko

The article is devoted to the discussion of the philosophical and methodological potential of one of the fruitful ideas of N.I. Kiyashchenko – the principle of the universality of a person’s aesthetic attitude to the world. In contrast to the rather widespread Hegelian point of view, according to which the aesthetic attitude to the world is limited mainly to the sphere of art, he, relying on the ac­tivity approach developed in Russian philosophy, insisted on the presence and essential role of the aesthetic attitude to the world in all spheres of human life and society in general. Through out Kiyashchenko’s creative life, this interpreta­tion of the role of the aesthetic acted as a connecting thematic line that united the diversity of scientific interests and conceptual incarnations of both himself and his fellow specialists in various fields and directions of research of the modern culture. The team headed by Kiyashchenko discussed the aesthetic aspects of un­derstanding both humanitarian and natural reality (including such fields of sci­ence as genetics, synergetics, neuroscience, etc.). The result of this collective work was a series of works associated, according to the plan of N.I. Kiya­shchenko, with the transformation of modern aesthetic ideas in their concep­tual expression. And in essence, these works represent the ideological heritage of Nikolai Ivanovich Kiyashchenko.
Disentangling Structure and Appearance in ViT Feature Space

We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are “painted” with the visual appearance of their semantically related objects in a target appearance image. To integrate semantic information into our framework, our key idea is to leverage a pre-trained and fixed Vision Transformer (ViT) model. Specifically, we derive novel disentangled representations of structure and appearance extracted from deep ViT features. We then establish an objective function that splices the desired structure and appearance representations, interweaving them together in the space of ViT features. Based on our objective function, we propose two frameworks of semantic appearance transfer – “Splice”, which works by training a generator on a single and arbitrary pair of structure-appearance images, and “SpliceNet”, a feed-forward real-time appearance transfer model trained on a dataset of images from a specific domain . Our frameworks do not involve adversarial training, nor do they require any additional input information such as semantic segmentation or correspondences. We demonstrate high-resolution results on a variety of in-the-wild image pairs, under significant variations in the number of objects, pose, and appearance. Code and supplementary material are available in our project page: splice-vit.github.io.
LagunAR: A City-Scale Mobile Outdoor Augmented Reality Application for Heritage Dissemination

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest.
Comparison of machine learning algorithms used to catalog Google Appstore

Background: Google Play Store is a popular Android app store where users’ reviews and ratings provide valuable insights. As part of application development, clients and app designers have a significant impact on the market. Accurately predicting market trends is critical to the success of applications, and this is where information mining comes in. By evaluating various factors such as application name, pricing, reviews, and category, we can predict which types of apps are most likely to be successful. Methods: To do this, we can use machine learning techniques that help us analyze data from diverse metrics and identify relationships. In this work, five different types of existing machine learning computation techniques like decision tree algorithm (DTA), support vector machine (SVM), random forest (RF), stochastic gradient boosting (SGB) and gated recurrent unit (GRU) were used to show a comprehensive comparison between the models to describe and forecast the structure of Android Market applications. Through these methods, we can extract a wealth of information to make wise decisions and present data more effectively. Results: Our findings have shown that the GRU-based technique performance accuracy of our predictions is high, with an average of 89.4465%, which can be best visualized as an unusual forest picture better than the four baseline algorithms. Among all the five techniques, GRU classifies 5,616 apps and provides the most precise results for both users and developers, whereas the other classifiers only classified 3,744 apps. Conclusions: According to the result analysis, we can conclude that all five machine learning algorithms were capable of analysing the android market, GRU outperforms in terms of accuracy.
PENGEMBANGAN E-MODUL BERBASIS DISCOVERY LEARNING PADA MATERI PROTISTA UNTUK SISWA SMA KELAS X

This research and development (research and development) aims to determine the quality of the development of discovery learning-based e-modules on protist material. The development method used in this study is the ADDIE model (analysis, design, development, implementation, and evaluation). The quality of the e-module developed was seen from the assessment of the team of experts (material experts, media experts, and pedagogic experts), learning practitioners and students. The assessment instruments used were validation sheets, practitioner sheets, and student response sheets. The results of the expert team's assessment got a score of 4.47 with a very valid category. The results of the assessment from learning practitioners obtained a score of 84.71% in the very practical category. Furthermore, the results of student responses to the use of discovery learning -based e-modules get a score of 3.64 in the good category. Based on the results of this assessment, it can be concluded that the discovery learning-based e-module developed by researchers is a quality product and is suitable for use in the learning process.
Constructing a Hybrid Writing Teaching Model for Cultivating Critical Thinking Skills in English Education Majors

The English writing course is a compulsory subject for English teacher education majors. In response to three pain points: students' weak language foundation, lack of writing knowledge, limited self-learning ability, low interest in learning, low participation in learning activities, and the disconnect between classroom teaching and industry demands leading to inadequate application skills, the English writing course teaching team has implemented an "online teaching + offline teaching, classroom teaching + internship training, professional teachers + school internship mentors" (three-dimensional) approach. The teaching model involves the active participation of both the school and the internship institution, with a focus on future job positions. The teaching objectives have been optimized, teaching content has been restructured, teaching resources have been diversified and expanded, forming a diverse learning community, and incorporating the "guidance-integration-discussion-consolidation-reflection" teaching framework. Additionally, a comprehensive assessment system has been established, involving the four main stakeholders - teachers, internship mentor', students, and the platform - utilizing multiple forms of formative evaluation. Based on these methods, students' critical thinking abilities are effectively enhanced.
Efficacy of the National Security Innovations Network's Hacking / Designing for Defense Programs

The National Security Innovation Network requires further reform to achieve its hope for long-term effects on defense innovation.
An English course practice evaluation system based on multi-source mobile information and IoT technology

With the increased use of online English courses, the quality of the course directly determines its efficacy. Recently, various industries have continuously employed Internet of Things (IoT) technology, which has considerable scene adaptability. To better supervise the specific content of English courses, we discuss how to apply multi-source mobile Internet of Things information technology to the practical evaluation system of English courses to boost the performance of English learning evaluation. Therefore, by analyzing the problems of existing English course evaluation and the characteristics of multi-source mobile Internet of Things information technology, this article designs an English course practical evaluation system based on multi-source data collection, processing, and analysis. The system can collect real-time student voices, behavior, and other data through mobile devices. Then, analyze the data using cloud computing and data mining technology and provide real-time learning progress and feedback. We can demonstrate that the accuracy of the evaluation system can reach 80.23%, which can effectively improve the efficiency of English learning evaluation, provide a new method for English teaching evaluation, and further improve and optimize the English education teaching content to meet the needs of the actual teaching environment.