Reviewerly/exHarmony huggingface dataset:
Dynamic Hand Gesture Recognition Based on Multi-skeletal Features for Sign Language Recognition System

The action recognition system based on deep learning has achieved great success in recent years. By this, the dynamic hand gesture recognition development provides the possibility for new human-computer interaction methods. However, most existing hand gesture recognition feature extraction models are difficult to balance the accuracy and speed of recognition. To improve the performance of the hand gesture recognition model, this paper analyzes the hand skeleton sequence, proposes Multi-skeletal Features (MSF), and constructs a dynamic hand gesture feature extraction model based on the MSF. The MSF includes the distance of the hand skeleton points and the angle of the skeleton connections, which can effectively reduce the angle of view and distance noise in the expression of skeleton features. The model has superior performance on the SHREC dataset and can achieve an accuracy of 96.43% in 14 gesture classifications. At the same time, we combine our method with a hand pose estimator to design a real-time sign language recognition (SLR) system.
ORGANIZATION OF ONLINE TESTING IN MATHEMATICS USING THE ONLINE TEST PAD SERVICE

The paper describes the possibility of creating control tests in mathematics in the online service Online Test Pad. The advantages of this service for creating mathematical tests are highlighted: the presence of a convenient formula editor, the ability to create groups of questions of the same type. As an example, the structure and variants of questions in the discipline «Mathematical analysis» on the topic «Double integrals» are given. It is noted that test tasks for online testing should be formulated in such a way that the student cannot use applied mathematical packages when answering them.
PROBLEMS OF REVISION OF THE CONTENT OF MATHEMATICAL COURSES

Significant reduction of the mathematical courses volume requires general revision of their content. Usually course adaptation occurs spontaneously consideration of several questions being simplified without thorough analysis of whole course. The revision of the course should be based on a clear understanding of its general purposes and the roles of its various parts. The multilevel character of educational aims should be taken into consideration. Situational exclusion of individual topics can lead to a loss of consistency in the presentation of material.
Large-Parallax Multi-camera Calibration Method for Indoor Wide-Baseline Scenes

Multi-camera calibration plays a crucial role in enabling efficient vision-based human-robot interaction and applications. This paper introduces a novel approach for multi-camera calibration, specifically tailored for indoor wide-baseline scenes, by leveraging 3D models. The task of multi-camera calibration in such scenarios is particularly challenging due to significant variations in camera perspectives. The limited and distant common view area of multiple cameras further exacerbates the difficulty in performing feature point matching. Traditional multi-camera calibration methods rely on matching 2D feature points, such as structure-from-motion, or employing large-area common view calibration plates, as seen in stereo camera calibration. In contrast, our proposed method eliminates the need for calibration boards or feature point pairs. Instead, we calibrate the external parameters of the multi-camera system by computing the optimal vanishing point through the extraction of orthogonal parallel lines within each camera’s view. This approach begins by extracting orthogonal parallel lines from the image to establish an accurate indoor 3D model. Furthermore, we incorporate the easily obtainable camera height as a prior, enhancing the estimation of the transformation matrix among the cameras. Extensive experiments were conducted in both real and simulated environments to evaluate the performance of our method. The experimental results validate the superiority of our approach over manual marker-based structure-from-motion methods, establishing its effectiveness in multi-camera calibration.
APPLYING OF PYTHON TOOLS IN A STATISTICS COURSE

The methodological aspects of teaching mathematical statistics in a technical university using laboratory work are considered. Examples of tasks that can be solved using the SciPy and NumPy libraries of the Python language are given. The advantages of this programming language over other computer mathematical systems are substantiated. The problems of generating data from a given distribution, constructing confidence intervals, testing hypotheses, correlation analysis, and some others are considered.
Object Tracking Algorithm Based on Dual Layer Attention

The attention mechanism is an important part of the Transformer trackers. But the attention mechanism has some limitations in calculating the correlation. When the attention mechanism calculates the wrong correlation, it affects the quality of the final correlation and affects the overall tracking performance. To solve the above problems, we designed a dual layer attention module to improve the accuracy of correlation by dual layer attention, so that the attention mechanism can accomplish feature augmentation and fusion more effectively, thus improving the performance of the tracking algorithm. The proposed algorithm consists of a Swin-Transformer-based backbone network, a dual layer attention-based fusion module and a prediction branch. Experiments indicate that our algorithm achieves robust results on five publicly available datasets, running at approximately 35 FPS on the GPU, allowing for real-time tracking.
ENLARGEMENT OF DIDACTIC LEARNING UNITS AND INCLUSION IN A PROFESSIONAL CONTEXT

The article considers the possibilities of pedagogical technology for the enlargement of didactic units to solve the problem of inclusion in a professional context in fundamental engineering education. Inclusion in a professional context makes it possible to systematize the content of professional education. The concept of enlargement of didactic units was developed by Academician MP Erdniev in the second half of the 20th century. The studies of the Soviet pedagogical school not only have not lost their relevance, but also provide a scientific and methodological basis for the development of modern teaching technologies.
Paradigm Shift in Health-Related Academic Research with Cloud Computing

Cloud computing is a relatively new and rapidly expanding trend in healthcare. It is quickly becoming one of the most well-known and promising innovations. New ways of producing, distributing, and utilizing services are enabled by ubiquitous, on-demand access to essentially infinite resources combined with a pay-per-use paradigm. As a result, health researchers are suggesting cloud computing as a new commercial model for exchanging biomedical data. This chapter exposes the core principles, models, services, infrastructure, history, benefits, and important effects of cloud computing in conveying health-related research. It also emphasized and maintained that the choice of cloud computing in making health-related research easy and beneficial by providing a common digital platform to enhance collaborative research, thereby improving the overall performance of health researchers as virtual equipment expenses are reduced and the convenience of cloud computing is increased. Health-related researchers will further benefit from the suggestions by shifting the paradigm of health-related research to cloud computing.
METHODOLOGICAL ASPECTS OF THE TASK DESCRIPTION FORMALIZATION

The problems of improving the mathematical training of students are considered. Attention is focused on the development of the ability to mathematical modeling. The difficulties arising during the formalization of tasks set verbally are investigated. Methodological recommendations for solving difficulties arising in the construction of mathematical models are presented. To improve the quality of mastering professional disciplines of the academic discipline, it is proposed to introduce the so-called semester works. Their main difference from traditional independent work is that their implementation is proposed to be carried out synchronously with the development of academic disciplines of specialized training. In addition, their implementation includes the development of appropriate mathematical models and conducting mathematical experiments based on them.
AS-TransUnet: Combining ASPP and Transformer for Semantic Segmentation

Semantic segmentation is a task to classify each pixel in an image. Most recent semantic segmentation methods adopt full convolutional network FCN. FCN uses a fully convolutional network with encoding and decoder architecture. Encoders are used for feature extraction, and the decoder uses encoder-encoded features as input to decode the final segmentation prediction results. However, the convolutional kernel of feature extraction is not too large, so the model can only use local information to understand the input image, limiting the initial receptive field of the model. In addition, semantic segmentation tasks also need details in addition to semantic information, such as contextual information. To solve the above problems, we innovatively introduced the space pyramid structure (ASPP) into TransUnet, a model based on Transformers and U-Net, which is called AS-TransUnet. The spatial pyramid module can obtain more receptive fields to obtain multi-scale information. In addition, we add an attention module to the decoder to help the model learn relevant features. To verify the performance and efficiency of the model, we conducted experiments on two common data sets and compared them with the latest model. Experimental results show the superiority of this model.
Messy Tales from Fieldwork for Design

Fieldwork is a central component in several research methodologies and traditions, and there are many handbooks dedicated to the craftmanship of how to do fieldwork. Nevertheless, fieldwork often tends to be described in many of these books in a relatively problem-free fashion. Within the fields of HCI and CSCW, most literature typically focuses on research outcomes and findings, providing a limited description of the fieldwork process. This is typically sanitized from the messy complexities that fieldwork unavoidably entails. This chapter lifts the veil and sheds light on the behind-the-scenes processes of constructing the research project and the field site, focusing specifically on messy encounters experienced during fieldwork. Drawing inspiration from feminist studies, reflexive research, and confessionals, I provide snapshots of messy situations from four different research projects. The intention of this chapter is to orient fieldwork gaze towards messy uncertainties and demonstrate how these can be used as a catalyst for learning, challenging perceived understandings, and sparking new insights about the research, the researcher, and the participants.
Breaking New Ground: Stories from the Wild

In this chapter, we present an account of our experiences with the LionAlert project in the Okavango Delta, Botswana, situated between wildlife conservation, HCI for Development and Participatory Design. We do so by reporting on a dialogue between the two of us, in which we compared and contrasted the different ways in which or experience manifested for two young and inexperienced researchers.
Maneuvering Star-Convex Extended Target Tracking Based on Modified ExpectedMode Augmentation Algorithm

In utilizing a variable-structure multiple-model (VSMM) algorithm for kinematic state estimation, the core step is the model set design. This study aims to refine the existing expected-mode augmentation (EMA) algorithm, a method of model set design. First, the OTSU algorithm is employed to determine an adaptive threshold, which in turn allows for a reasonable partition of the basic model set. Next, a subset of possible models is preserved, reactivating models adjacent to the one with the highest prediction probability, eliminating improbable models, and yielding an augmented expected mode. Additionally, the study leverages the translation properties of radial functions and inverse trigonometric function formulas to derive a maneuvering model for star-convex extended targets under uniformly accelerated conditions. In order to assess the effectiveness of the proposed algorithm and the validity of the established maneuvering model, simulation experiments were carried out in both fixed and random scenarios. The proposed algorithm demonstrates improved performance when compared to the interactive multiple-model algorithm and the unmodified EMA algorithm.
HUMAN ACTION RECOGNITION: IMPROVING THE ACCURACY OF DEEP CONV-LSTM ARCHITECTURE THROUGH NOISE CLEANING PRIOR TO KEY FRAMES SELECTION

Recognizing human actions has numerous practical applications that can address issues and enhance the effectiveness and living standards across various domains. For solving the human action recognition task from videos, the Deep Conv-LSTM model architecture has been used on the UCF101 dataset. To preprocess the frames, a method has been proposed that integrates two algorithms: Noise Cleaning and Dissimilarity-Based Key Frame Selec¬t¬i¬o¬n (KFS). By employing these algorithms cohesively and effectively, image quality has been enhanced and unwanted data have been eliminated. The Uniform Frame Selection, Dissimilarity-Based KFS, and the proposed algorithm are evaluated, and their performances are compared based on the accuracy and data size reduction. The results show that the Dissimilarity-Based KFS algorithm outperforms the Uniform Frame Selection algorithm in accuracy by 2%, and the proposed method shows a 3% and 5% improvement in accuracy compared to the first two algorithms, respectively. Furthermore, the proposed algorithm reduces the data size by 26%, making it computationally efficient.
Researching Dark Voices Within the Veil of the Academy

I grew up in the predominantly Islamic-oriented Northern part of Nigeria where the culture of the Islamic religion influenced everyday interaction in our communities. I attended an all-boys boarding secondary school where before dawn we learn to recite (and memorize) the Qur’an, enrol in the usual science subjects during the day, and then end the day by studying Islamic scriptures (the Holy Qur’an, the Hadith, and other scholarly texts). The orientation I learned from childhood was not to be an ‘African’, but rather a Black English-like gentlemen. My chapter concentrates on the use of language rather than the analysis of structures because being ‘black’ in the academy is, above all, about language and power, about trying to find a voice. We can, I believe, provide an alternative mode of expression, one where the reader is encouraged to become more aware of the implicit danger of reading and writing in a colonizing language, one where the reader might be charged with questioning the knowledge production process of writing in HCI. Who decides what form critique should take, what rhetorics are acceptable, and why the experiences, beliefs, traditions, emotions, and feelings of someone who has actually been through it can be ignored? Why is the ‘subjective’ so discounted?
More Complex Rules

Construction of solutions of the IVP by exploiting patterns in preimage sequences. Rules 172, 168 and 164. Pattern decomposition method. Solutions of rules 156 and 78.
Rules with Additive Invariants

Number-conserving rules and their properties. Rule 184 as a number-conserving rule. Representation of preimage sets by finite state machines (FSM). Using FSM graphs for rule 184 to obtain the solution of the IVP in a recursive form. Higher order invariants and Hattori-Takesue theorem. Elementary CA with higher-order invariants. Recursive solutions of the IVP for rules 142, 34 and 14.
Another Rant About Technology

Ethnographic studies are typically understood to make a significant, even foundational, contribution to contextual design. Nevertheless, even when participatory principles are brought to bear, there are sometimes situations where a more ‘critical’ lens is required, one which focuses on the political backdrop to design decisions. In this chapter, we discuss the notion of ‘problematisation’ as a strategy for challenging the assumptions that underpin many decisions and our own reactions to being confronted with challenges that were largely beyond our control.
Music in the aspect of the genesis of art

The article is devoted to the problem of the genesis of musical art. To date, the problem of the origin of music is one of the most undeveloped. The historical and archaeological direction can explore only what has been preserved and presented in material media. Therefore, the genesis of musical art is practically impossible to verify. Nevertheless, there is also a fundamentally different way of studying the genesis of musical art, since ontogenesis to a certain extent reproduces some essential stages of phylogenesis, since the study of children’s musical abilities, often manifested almost from the moment of birth of a child, can also provide answers to questions related to the genesis of music as such. In addition, the study of the folklore tradition of peoples who are almost at the primitive stage of development is also able to reveal the most important stages of the formation of music. But the main problem remains the definition of the ontological status of music, its rootedness in being. Mythology has already tried to give an answer to this question, although it is the philosophical analysis and ontological justifications for the existence of this art form that are crucial in the development of music. The article holds the position that the aesthetic and philosophical consideration of music not only sums up the already existing state of musical art, but also opens up new ways of further development. In addition, the relationship between music theory and practice is emphasized, which does not always precede its theoretical understanding, but is a consequence of certain theoretical constructions and conclusions.
Location of Obstacles on Transport Routes in a Centralized Multi-agent System

The commercially available autonomous mobile robots navigate the environment with the previously prepared map and the obstacles currently detected in the immediate vicinity of the robot. The purpose of this paper is to create a solution for early detection of potential obstacles to eliminate the already blocked paths in advance. The centralized multi-agent system could be implemented in a production enterprise with mixed transportation methods, i.e. mobile robots and personnel-operated carts and forklifts. The authors present a Node-RED based system to manage the robot fleet based on the input of other agents.
Benchmarking Generative AI: A Comparative Evaluation and Practical Guidelines for Responsible Integration into Academic Research

Generative artificial intelligence (AI) systems show immense potential to transform scholarship, but their capabilities and responsible use require rigorous scrutiny. This pioneering study provides an empirical benchmarking of four leading generative models. Through standardized assessments, the systems are evaluated on their ability to assist ten key aspects of academic research spanning literature reviews to hypothesis generation. Quantitative scoring of completeness, accuracy, and relevance combined with thematic analysis of the AI systems' perspectives reveal nuances between their strengths, risks, and needs for validation. Key findings show promising competencies in focused tasks like summarization but major limitations in contextual adaptation, reasoning, and bias mitigation. While narrow augmentation appears feasible presently, fully automating scholarly work remains challenging. The study yields critical insights including pragmatic adoption strategies, governance priorities, and ethical considerations to steer these technologies towards responsible integration. It also articulates future research directions such as enhancing transparency and reasoning. Overall, this work constitutes a crucial roadmap for realizing generative AI's immense potential in scholarship while proactively addressing risks and limitations. With prudent oversight and wisdom, these rapidly evolving tools could positively transform academic discovery for the betterment of society.
EFEKTIFITAS GERAK DAN LAGU “PERGI KE SEKOLAH” DALAM MENINGKATKAN KEMAMPUAN BAHASA ANAK USIA 5–6 TAHUN

The purpose of this study was to determine how effective the motion and song "Going To School" in improving the language skills of children aged 5-6 years in TK Pertiwi Banaran Kab. Nganjuk This research is a quantitative study with a pre-experimental design method showing that the motion and the song "Going to School" are effective for stimulating language skills of children aged 5-6 years in group B in TK Pertiwi Banaran Kab. Nganjuk The number of samples in this study were 21 children. Pretest results with an average value of 9.76, a maximum value of 14, and a minimum value of 5. While posttest results with an average value of 14.14, a maximum value of 20, and a minimum value of 4. Results of data analysis using Paired Test Sample T-Test with df = 20 shows a t table of 2.085 and a t-test value of 12.112. This shows t arithmetic from the t table value so that the motion and the song "Go to School" are effective in stimulating language skills of children aged 5-6 years.
MANAJEMEN PENDIDIKAN DAN PELATIHAN PENDAMPING PKH (PROGRAM KELUARGA HARAPAN) BERBASIS BLENDED LEARNING

This study aims to determine the description of training and education management PKH Based on Blended Learning in BBPPKS Bandung. The management of this training includes the planning, implementation, and evaluation stages. This research is a descriptive study with a qualitative approach. Data collection techniques in this study through interviews, document study, and observation. The validity of the data was checked by triangulation. The results showed that the training and education management PKH Based on Blended Learning includes: 1) Planning which includes analysis of training needs, preparation of training objectives, preparation of training plans, and preparation of administration; 2) The implementation of this training stage includes online learning, opening, offline learning and training closing; 3) Evaluation which consists of four forms of evaluation, namely evaluation of training participants, evaluation of lecturers, evaluation of the implementation of education and training and evaluation of post-training. In addition, training and education PKH Based on Blended Learning is also influenced by the availability of the internet network, the seriousness of the training participants, the mastery of technology owned by the participants, the PBL location, the readiness of the organizers, the completeness. infrastructure, and availability of costs.
A Futuristic Past: Online Educational Tools to Teach Medieval and Early Modern Periods in 3D and 360 Degrees

Previous articleNext article No AccessPart 2: Adapting to Online Teaching and Learning: Inquiry, Language Teaching, and the Digital HumanitiesA Futuristic Past: Online Educational Tools to Teach Medieval and Early Modern Periods in 3D and 360 DegreesFelipe MoragaFelipe MoragaUniversity of Wisconsin-Madison Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by Volume 51, Number S1Supplement 2020Early Modern Classroom The journal of the Sixteenth Century Society and Conference Article DOIhttps://doi.org/10.1086/SCJ5199024 © 2020 The Sixteenth Century Society and Conference. All rights reserved.PDF download Crossref reports no articles citing this article.
Identify the needs of people with disabilities to form a behavioral model for the automatic operation of smart homes with methods based on support vector machine

Identify the needs of people with disabilities to form a behavioral model for the automatic operation of smart homes with methods based on support vector machine