Reviewerly/exHarmony huggingface dataset:
Auto-Paizo Games: Towards Understanding the Design of Games That Aim to Unify a Player’s Physical Body and the Virtual World

Most digital bodily games focus on the body as they use movement as input. However, they also draw the player's focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach - the "Body as a Play Material" - where a player uses their body as both input and output to unify the physical body and the virtual world. To showcase this approach, we designed three games where a player uses one of their hands (input) to play against the other hand (output) by loaning control over its movements to an Electrical Muscle Stimulation (EMS) system. We conducted a thematic analysis on the data obtained from a field study with 12 participants to articulate four player experience themes. We discuss our results about how participants appreciated the engagement with the variety of bodily movements for play and the ambiguity of using their body as a play material. Ultimately, our work aims to unify the physical body and the virtual world.
A User-Centered System Usability Evaluation of a Virtual Reality Application Developed for Solar Farm Training

The hazardous potential of the Photovoltaic (PV) industry demands the most rigorous and professional training. However, such training requires many resources and time and has potential health and safety risks. Incorporating emerging technologies in such training can be helpful. This paper proposes SolarPro, a virtual reality application for solar farm training that can aid solar farm trainees in getting initial training in the virtual environment. The application's usability is tested through multiple testing sessions with 30 participants. The testing participants experienced the virtual scenarios, completed the tasks and filled out the system usability questionnaires. The system usability scale analysis resulted in a grade A score of 81.75. The analysis based on the demographic characteristics also showed positive results. We can conclude that the SolarPro virtual reality application has high system usability. However, it is suggested to have more than just 30 respondents for the usability test to conclude more promising results.
Letters of D.V. Ulyaninsky to P.P. Shibanov as a Source on the History of Bibliophilia and Bibliography

On the basis of epistolary sources from 1895—1913 from the Department of Manuscripts of the Russian State Library and the Russian State Archive of Literature and Art, the history of the relationships between D.V. Ulyaninsky and P.P. Shibanov is reconstructed. The letters contain information about the sources and principles of the formation of D.V. Ulyaninsky’s library, his bibliographic studies, prices for antique books, personal relations, as well as about the lifestyle and communication style of the bibliophile, who avoided ritual visits on the occasion of holidays and took care of replenishing his book collection constantly, even on vacation and during illness. The sources demonstrate exactly which defects D.V. Ulyaninsky considered unacceptable for the books of his collection. The article shows the source aspects of his scientific work: determining the authenticity of book monuments, establishing their authorship and the history of the text. The unapproved coat of arms of the Ulyaninskys, which the bibliophile used on his seal in 1902, is first described. It is concluded that the letters of D.V. Ulyaninsky to P.P. Shibanov have a significant cognitive potential for studying the history of bibliophilia, bibliography, trade in antique books and intellectual life of Russian at the turn of the 19th–20th centuries.
Development of a virtual interactive system for Dahua Lou loom based on knowledge ontology-driven technology

Abstract The Dahua Lou loom, pivotal to Nanjing Yunjin weaving, constitutes an integral part of global intangible cultural heritage. Its intricate weaving technique remains unmatched by modern machinery, marking it as a vital cultural artefact warranting protection. However, current virtual interactive systems grapple with adequately demonstrating its craftsmanship due to limitations like system iteration, multi-system integration, and data interoperability. To address these challenges, this study proposes a method that synergizes knowledge ontology, virtual reality technology, and data-driven design.Knowledge ontology enables enhanced management, reuse, and wide-ranging dissemination of domain knowledge, improving system interoperability. This methodology is utilized in constructing the loom model, animation demonstration, and in integrating it into the virtual interactive system. This multifaceted application of knowledge ontology significantly bolsters the system’s efficiency and optimizes its development, maintenance, and integration processes.This research provides crucial advancements in domain knowledge modeling, 3D visualization, and virtual reality interactive systems, playing a significant role in preserving cultural heritage. Despite certain limitations, it offers an immersive, intuitive, and enriched design experience. The study concludes with a critique of the developed system, discussions on the encountered challenges, and future research directions include improving and expanding the knowledge ontology based on the opinions and practical experiences of domain experts, enhancing system compatibility, and conducting broader evaluations.
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the over-all model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thor-ough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distil-lation learning objective to aggregate the two types of con-text and the linguistic prior. The knowledge distillation en-ables the resultant one-branch temporal aggregation mod-ule to perceive local-global temporal and semantic context. This shallow temporal perception module structure facili-tates spatial perception module learning. Extensive exper-iments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.
An Area Modulation Binary Defocusing-Based Digital Fringe Projection 3-D Shape Measurement Method for Print Circuit Board Inspection

Three-dimensional (3D) measurement plays a crucial role in the inspection of printed circuit board (PCB). Binary defocusing based digital fringe projection (DFP) 3D shape measurement method can achieve high-accuracy measurement and improve the speed of traditional DFP method, which can be widely used in virtual reality, industrial manufacturing, etc. In this paper, we propose an area modulation binary defocusing based DFP 3D shape measurement method for PCB inspection. To address the limitations of the one-dimensional modulation method, a 16 × 4 pixel area pattern fringes is adopted. Then, the optimal binary defocusing grating is obtained by an optimal blurred factor determination method. The accurate geometric measurement results are obtained from the high-quality 3D point clouds, A series of experiment were conducted to evaluate the effectiveness of the proposed method. Experiment results show that the proposed method has higher phase accuracy and point cloud accuracy than that of the other methods. The proposed method achieves a measurement accuracy of less than 0.1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> , surpassing other compared methods. Additionally, it provides a new approach for PCB inspection.
Foundations of Mathematical Education in the Digital Age

Abstract This article formulates the foundations of modern mathematical education that meet the new goals and objectives of mathematical education dictated by changes in mathematics itself over the last century, by changes in its role in the modern digital world, and by changes in the world. The basic system of concepts is described, starting from which the whole modern mathematics of the finite, as well as primary mathematical education, is built. For all levels of mathematical education and for all learners, the ability to solve problems whose solutions are not known (to the student) and to do it independently with the help and support of a motivating teacher is a fundamental skill. A significant role is played by a computer mathematical experiment and the transfer of tasks with solutions having been found by the student to the computer. This article also describes the actual state of affairs in the Russian mass education system and proposes the necessary actions to implement the formulated principles of modern mathematical education in Russia. Most of the articles presented in this issue demonstrate and detail the implementation of the formulated principles.
Privacy Preserving for Electronic Medical Record Sharing in Healthchain With Group Signature

Healthchain is a new solution for electronic medical record (EMR) management in the Internet of Medical Things, which avoids data island and information tampering problems by applying blockchain technology. However, some attacks are executed on the cryptographic algorithm to steal user privacy. This article focuses on the privacy security of system users and EMR data to explore a more efficient and secure signature algorithm. An EMR group verification model has been presented to establish a transaction verification mechanism for Healthchain. The EMR data are verified by the creating group to form a transaction in Healthchain, and the transaction data are verified by the system-maintaining nodes group to achieve network consensus. Then, a lattice-based group signature scheme (GSS) is proposed to strengthen the quantum security of the EMR group verification model. This scheme can provide secure verification for the EMR validation and support group members with free joining and revoking. Moreover, correctness analyses explain that this scheme is correct; security proofs prove that it satisfies anonymity, traceability, and nonframeability; the performance analyses show that the proposed model and the GSS scheme are at least 35% time consumption saving, with low key generation time costs and verification complexity than similar work.
The influence of knowledge-oriented leadership on business model innovation and open innovation: mediating role of absorptive capacity

Purpose This paper aims to explore the importance of knowledge-oriented leadership in the context of business model innovation (BMI) and open innovation. This study also seeks to explore the existence of a mediating effect of absorptive capacity. Design/methodology/approach Data were collected using a survey, from the information technology industry in India. The relationships proposed in the conceptual framework were tested with structural equation modeling (SEM) using Smart partial least squares (PLS) 3. Findings The findings highlight a positive influence of knowledge-oriented leadership on BMI and open innovation. Results also revealed that open innovation can facilitate BMI. Additionally, findings highlighted the mediating role of absorptive capacity in these relationships. Practical implications This study suggests that businesses should employ and value leaders who can create, transform, store and use knowledge resources. Companies that employ such leaders encourage the creation and exchange of new knowledge, which consequently would lead to the successful exploration of innovative approaches. A culture of open innovation would help managers break down barriers, overcome inertia and continuously test new business models to generate revenues, increase value, enhance organizational effectiveness and ultimately gain competitive advantage. Originality/value This study is one of the first to empirically contribute to the role of knowledge-oriented leadership and BMI from the perspective of open innovation.
Psychological aspects of online learning implementation at Ukrainian universities

The relevance of the study lies in the fact that, unlike most European countries that returned to full-time education after the end of the coronavirus pandemic, Ukraine, due to active military operations on its territory, continued an online educational process for a long time and again faces the choice of a rational way of learning. That is why there is a need to evaluate all the weaknesses and strengths of the remote educational format. The purpose of the study is to analyse the advantages and disadvantages of implementing online education in Ukrainian universities during martial law. To achieve this purpose, a survey of 56 teachers of psychological disciplines with professional experience from 4 to 28 years was conducted; the answers received were analysed. The results showed that the advantages of online learning for students and teachers are common: mobility, high quality of digital content, partial automation of knowledge control, a variety of psychological and pedagogical tools, and stimulation of the development of digital and technological competence. The main problems of online learning for students are communication disorders: a lack of personality-oriented communication between peers, and a deepening of the social distance between students and teachers. In the field of psychological education, this disadvantage is closely related to the difficulty of mastering practical professional skills, which consist in organising trusting, close interaction between people. For teachers, this form of work offers wide opportunities for professional self-fulfilment, but at the same time is accompanied by overload, rapid emotional burnout, deterioration of health and quality of life. The paper substantiates the need to restore full-time education, considering the positive experience of online learning. The results of the study can become the basis for the development of practical measures aimed at preventing professional maladaptation of university teachers; combating the difficulties of socialisation and professionalisation of young people who received higher education during quarantine and martial law.
Cross-Modal Representation Learning

Abstract Cross-modal representation learning is an essential part of representation learning, which aims to learn semantic representations for different modalities including text, audio, image and video, etc., and their connections. In this chapter, we introduce the development of cross-modal representation learning from shallow to deep, and from respective to unified in terms of model architectures and learning mechanisms for different modalities and tasks. After that, we review how cross-modal capabilities can contribute to complex real-world applications.
Lexical Stability of Psychiatric Clinical Notes from Electronic Health Records over a Decade

Abstract Objective: Natural Language Processing methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice—as well as the systems and databases in which clinical notes are recorded and stored—change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested. Methods: The lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics. Results: We find lexical stability of the clinical notes over time, with minor deviations during the COVID-19 pandemic. Out of 2988 datapoints, 17 possible changepoints (corresponding to 0.6%) were detected. The majority of these were related to the discontinuation of a specific note type. Conclusion: We find lexical and syntactic stability of clinical notes from psychiatric services over time, which bodes well for the use of Natural Language Processing for predictive modelling in clinical psychiatry.
IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR EFFECTIVE TRAFFIC DISTRIBUTION IN COMPUTER NETWORKS

The solution to the problem of efficient distribution of traffic in com-puter networks using Artificial Intelligence algorithms is presented. The prob-lem of effective distribution of traffic in computer networks is considered one of the key problems in the field of network interaction organization. It is very pivotal in the field of traffic management to increase the efficiency of using the existing channels and the quality of services provided to end users. Cur-rently, this problem has become more relevant, as the scope of its application has expanded, which now includes both policy enforcement systems and the field of information security. The paper considers different Artificial Intelli-gence algorithms that can be used for the efficient distribution of network traffic: Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN), as well as their advantages and disadvantages are presented. After features used to classify network traffic were analyzed, the best set of network traffic features was selected. Step-by-step testing of an in-telligent system for effective network traffic distribution was performed ac-cording to the testing scheme. A comparative analysis of the recorded results of the mentioned Artificial Intelligence algorithms and a reasonable choice is made. Based on the chosen Random Forest (RF) algorithm, an intelligent sys-tem for the effective distribution of network traffic was designed.
Four Directed Search Algorithm: A New Optimization Method and Its Hyper Strategy Investigation

This paper promotes a new swarm-based metaheuristic called four directed search algorithm or FDSA.FDSA is designed as a directed search-based metaheuristic without deploying any neighbourhood search.It contains four directed searches that are carried out sequentially.Each search has its reference.These four references are the best member; the resultant of three shuffled members within the swarm; a shuffled member within the swarm; and the resultant of the best member, a shuffled member within the swarm, and the corresponding member.FDSA implements a strict acceptance procedure so that new solution is accepted only if it provides improvement.The investigation is carried out to evaluate the performance of FDSA in solving the 23 functions.FDSA is also confronted with five new metaheuristics: northern goshawk optimization (NGO), average subtraction-based optimization (ASBO), coati optimization algorithm (COA), mixed leader-based optimization (MLBO), and attack leave optimization (ALO).This work also investigates the contribution or dominance of each search in the context of finding the optimal solution.The result shows that FDSA is superior to all these confronters by consecutively outperforming NGO, ASBO, COA, MLBO, and ALO in the 17,13,11,18,and 11 functions.Its superiority is mainly in the high-dimension functions.Through investigation, there is no dominant search among the four directed searches in FDSA.Meanwhile, multiple search strategy is proven to improve performance significantly.On the other hand, the contribution of neighbourhood search is not significant.
Gated Recurrent Unit for Landfill Area Estimation Based on Municipal Solid Waste Prediction

Solid waste management is an imperative aspect of a hygienic environment.Lack of attention towards solid waste management leads to environmental pollution which causes catastrophic health issues and reduces the quality of life.Therefore, it's required to develop environmentally safe and adequate protocols to manage solid waste for the fortification of human health and the environment.The study presents a master plan to facilitate the local municipal authorities in developing a better waste management framework.The main aim of the research is to predict municipal solid waste (MSW) collection by using the GRU model in Multan city, Pakistan.The GRU model has utilized a dataset collected through a weighbridge at Habiba Sial landfill site (HSLS) which is the most reliable source for the data.The dataset consists of monthly data for the past five years with a total of 180 records; 60 records for months 60 records for vehicle trip numbers and 60 for collected MSW.The number of months and monthly vehicle trip numbers have been used as the input data and monthly collected MSW has been used as output data.The window of time for collected data is June 2017 through June 2022.The dataset has been divided into 3 subparts where 70% of the data has been utilized for model training, 15% of the data has been used for model testing and 15% of the data has been utilized for validation purposes.The GRU model's performance has been evaluated by using the performance metrics; mean square error (MSE), and Regression values for training, testing, and validation data.According to the results, 2-5-2-1 topology with 5 hidden neurons gives the best results with the least value of MSE being 0.0388 and the maximum value of regression being 0.94.The study also proposed a master plan for waste management, considering options like landfilling, recycling, incineration, and composting.As the landfilling method is the most common practice to dispose of waste, therefore the study estimated the required landfill area based on the predicted solid waste generation rate and its collection rate by the year 2031 for Multan city, Pakistan.The master plan can be utilized alternatively, especially for the cities with the same type of demographics which may help the municipal authorities for the development of a better solid waste management framework.
Deep Attention Based Dense Net with Visual Switch Added BiLSTM for Caption Generation from Remote Sensing Images

Remote sensing image captioning is the challenging task due to low global information, single feature extraction and lack of detailed image captions.To address these issues, this research proposed a deep attention based DenseNet with visual switch added bidirectional long short-term memory (DADN-BiLSTM) for captioning.In this research, initially the images and captions are collected from captioning dataset to smooth away small structures.After that, a double attention mechanism is applied to DenseNet for capturing weak features and to improve the problem corresponds between image feature and captioning information.At the same time, a clustering-based segmentation is more useful and easier to segment the image as smaller parts to make the access easily.Moreover, a decoder is used to improve the use of captioning context information.Then the proposed system is implemented in PYTHON and the performance is evaluated against existing methods in terms of some relevant evaluation metrics such as, recall-oriented understudy for gisting evaluation, accuracy and bilingual evaluation understudy.Finally, the experimental results achieve higher scores in all evaluation indicators such as 0.