Reviewerly/exHarmony huggingface dataset:
Construction of Sports Training Management Information System Using AI Action Recognition

With the development of science and technology, more and more fields have begun to use AI to provide convenient services for humans. Artificial intelligence (AI) refers to a new technology that uses human thinking to respond accordingly through computers and robots to assist human beings. Action recognition is an important research project that needs to be broken through in many industries, such as security system, martial arts instruction, and dance training. This paper aims to study a method for action recognition using AI technology and to build a sports training management information system. In this paper, a recognition model and related algorithms using a convolutional neural network (CNN) are proposed, and an intelligent sports training management information system is constructed. The system and the model are tested, the action recognition effect of 60 athletes in a university is tested, and the comparison with the traditional recognition algorithm is carried out. The results show that the CNN recognition accuracy test results used in this paper are generally more than 90%, while the traditional recognition accuracy rate is only about 75%, and the highest is not more than 86%; the training management information system of this paper takes about 15.7 s, and the maximum time is not more than 10 s, while the traditional recognition system takes about 15.7 s, which is about twice the time of the system in this paper. Therefore, it shows that the CNN recognition method in this paper has a significantly better effect on the recognition of athletes’ movements, and the sports training management information system constructed in this paper is less time-consuming and faster and has certain feasibility.
Designing with Feeling

Technological supports for collective, embodied ideation that were only dimly envisioned by 20th century futurists are now solid fact. For instance, advances in location tracking, mixed reality, and agent-based modeling have converged, enabling groups to collectively construct and animate shared, dynamic representations in real time. Pre-existing genres of activity may support activity design here, including participatory simulations; interactive theater; embodied play; or games. However, if we want emerging tools for collective ideation to be expressive for all groups, we need to attend carefully to how participants themselves conceptualize their potential. This paper describes a project that engaged high-school students in designing embodied participatory activities for groups of younger students, about a topic they selected as important: sustainability in social-ecological systems. Students’ design work was diverse and generative. We focus here on one emergent theme: design decisions that aimed to evoke particular feelings in participants, to foster learning and reflection on actions.
Note: Evaluating Trust in the Context of Conversational Information Systems for new users of the Internet

Most online information sources are text-based and in Western Languages like English. However, many new and first time users of the Internet are in contexts with low English proficiency and are unable to access vital information online. Several researchers have focused on building conversational information systems over voice for this demographic, and also highlighted the importance of building trust towards the information source. In this work we develop four versions of a voice based chat-bot on the Google Assistant platform in which we vary the gender, friendliness and personalisation of the bot. We find that the users rank the female version of the bot with more personalisations over the others; however when rating the bots individually, the ratings depend on the ability of the bot to understand the users’ spoken query and respond accurately.
Application of Conditional Random Field Model Based on Machine Learning in Online and Offline Integrated Educational Resource Recommendation

It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an educational resource recommendation system based on a graph attention network and conditional random field fusion model. It builds all comments for each student and educational resource into a comment graph. Through the graph's topological structure to capture the network and the dependency between words in the commentary text, the adjacency information of each node is aggregated by the graph attention network based on connection relation. After the graph attention network layer, the conditional random field inference layer is added. The label sequence with the highest probability is output by the dependent random field inference layer, which is taken as the final recommendation result of the model. Experimental results show that the proposed algorithm has better performance in accuracy and diversity than the traditional recommendation algorithm.
Multi-word terms selection for information retrieval

Purpose A number of approaches and algorithms have been proposed over the years as a basis for automatic indexing. Many of these approaches suffer from precision inefficiency at low recall. The choice of indexing units has a great impact on search system effectiveness. The authors dive beyond simple terms indexing to propose a framework for multi-word terms (MWT) filtering and indexing. Design/methodology/approach In this paper, the authors rely on ranking MWT to filter them, keeping the most effective ones for the indexing process. The proposed model is based on filtering MWT according to their ability to capture the document topic and distinguish between different documents from the same collection. The authors rely on the hypothesis that the best MWT are those that achieve the greatest association degree. The experiments are carried out with English and French languages data sets. Findings The results indicate that this approach achieved precision enhancements at low recall, and it performed better than more advanced models based on terms dependencies. Originality/value Using and testing different association measures to select MWT that best describe the documents to enhance the precision in the first retrieved documents.
Designing A Virtual Talking Companion to Support the Social-Emotional Learning of Children with ASD

Conversational agents' ability to communicate in natural language through voice and text interfaces poses an opportunity in helping children with autism spectrum disorder (ASD) develop their social communication skills. In this paper, we describe the design of Amy, a conversational agent that explains social situations to help guide a child in understanding when to use socially appropriate behavior. A breathing exercise feature for emotion regulation is activated when a negative emotion is detected from the child's input. Interviews with parents and a child psychologist informed the design of Amy as well as the 12 social stories themes that Amy shares with children. Interview inputs and previous works suggested four design considerations for social-emotional learning companions to facilitate better interaction by including relevant social story themes, formulating open-domain conversation flow, incorporating appropriate and guided emotion regulation exercise, and using lively visual user interface.
Reporting Back in HCI Work with Children

It is widely understood within the field of HCI that participation in research and design activities, especially by children, requires careful planning and execution. Whilst the HCI community, and related groups like IDC, have gone some way to ensure aspects such as the selection and participation of children in research and design work is considered carefully, there has not yet been careful consideration, or reporting, of how children get to find out what has happened to the research that they have contributed to. In this paper we describe how we worked with children in overseas contexts and actively reported back to them the outcomes from the work they contributed to. We describe how our desire to report back influenced the planning the analysis of the data to ensure the results could be packaged in a child friendly way. We offer a simple to use checklist for practitioners and researchers to follow and a challenge for the IDC community to incorporate planned or actual feedback into the reporting of studies.
Evolutionary Algorithm for Solving Supervised Classification Problems: An Experimental Study

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Over the years, EAs have been successfully applied to many classification problems. In this paper, we propose to demonstrate the performance of an improved evolutionary algorithm for synthesizing classifiers in supervised data scenarios. This approach generates an arithmetic expression DAG (Directed Acyclic Graph) for each training class in order to adjust each test class to one of them. We compare our approach with well-known machine learning methods, such as SVM and KNN. The performance of the improved algorithm for evolving classifiers is competitive with respect to the solution quality.
Induction Mechanism of Auditory-Assisted Vision for Target Search Localization in Mixed Reality (MR) Environments

In MR (mixed reality) environments, visual searches are often used for search and localization missions. There are some problems with search and localization technologies, such as a limited field of view and information overload. They are unable to satisfy the need for the rapid and precise location of specific flying objects in a group of air and space targets under modern air and space situational requirements. They lead to inefficient interactions throughout the mission process. A human being’s decision and judgment will be affected by inefficient interactions. Based on this problem, we carried out a multimodal optimization study on the use of an auditory-assisted visual search for localization in an MR environment. In the spatial–spherical coordinate system, the target flight object position is uniquely determined by the height h, distance r, and azimuth θ. Therefore, there is an urgent need to study the cross-modal connections between the auditory elements and these three coordinates based on a visual search. In this paper, an experiment was designed to study the correlation between auditory intuitive perception and vision and the cognitive induction mechanism. The experiment included the three cross-modal mappings of pitch–height, volume–distance, and vocal tract alternation–spatial direction. The research conclusions are as follows: (1) Visual cognition is induced by high, medium, and low pitches to be biased towards the high, medium, and low spatial regions of the visual space. (2) Visual cognition is induced by loud, medium, and low volumes to be biased towards the near, middle, and far spatial regions of the visual space. (3) Based on the HRTF application, the vocal track alternation scheme is expected to significantly improve the efficiency of visual interactions. Visual cognition is induced by left short sounds, right short sounds, left short and long sounds, and right short and long sounds to be biased towards the left, right, left-rear, and right-rear directions of visual space. (4) The cognitive load of search and localization technologies is significantly reduced by incorporating auditory factors. In addition, the efficiency and effect of the accurate search and positioning of space-flying objects have been greatly improved. The above findings can be applied to the research on various types of target search and localization technologies in an MR environment and can provide a theoretical basis for the subsequent study of spatial information perception and cognitive induction mechanisms in an MR environment with visual–auditory coupling.
Video Conferencing Solutions for Students – Future Teachers’ Professional Socialization

Introduction. Despite the numerous publications on the implementation of distance technologies, there are practically no studies on the systematic analysis of various forms of video conferencing that support educational technologies and serve as a practical guide for teachers in teacher training. The purpose of the study is to analyze the experience of implementing video conferencing in various forms of interaction, to identify and experimentally test the effectiveness of their implementation in the process of professional socialization of student teachers. Materials and Methods. The study was conducted on the basis of the method of reflexive-system analysis of the implementation of video conferencing in the professional training of student teachers. Based on the questionnaire and the method of expert assessments, testing of the level of social and psychological adaptability, professional and behavioral skills, social and emotional comfort of students was implemented. The experiment involved 209 students of the Glazov State Pedagogical Institute and Kazan Federal University. Results. As a result of the study, it was revealed that the implementation of the group form of video conferencing significantly increases the involvement of almost all students in active communication activities in the classroom. Communication activity in the implementation of social and educational interaction between small and large groups made it possible to most effectively form professional socialization among students. Statistical analysis showed that it was classes in a remote format using video conferencing in subgroups of 4‒5 students that had the greatest positive effect. Discussion and Conclusion. The conclusions made by the authors contribute to the development of new forms of effective communication through social and educational interaction in the implementation of video conferencing. The materials of the article can be useful in the implementation of teacher education to increase the level of studentsʼ involvement in professional training in the context of distance education.
Analysis of climate change mitigations by nuclear energy using nonlinear fuzzy set theory

Following the climate-related disasters considered by several efforts, the nuclear capacity needs to double by 2050 compared to 2015. So, it is reasonable to investigate global warming incorporated with the fuzzy set theory for nuclear energy consumption in the aspect of fuzziness and nonlinearity of temperature variations. The complex modeling is proposed for the enhanced assessment of climate change where simulations indicate the degree of influence with the Boolean values between 0.0 and 1.0 in the designed variables. In the case of OIL, there are many 1.0 values between 20th and 60th months in the simulations where there are 10 times more for a 1.0 value in influence. Hence, the temperature variable can give the effective time using this study for 100 months. In the analysis, the 1.0 value in NUCLEAR means the highest influence of the modeling as the temperature increases resulting in global warming. In detail, the first influence happens near the 8th month and then there are four times more influences than effects in the early part of the temperature mitigation. Eventually, in the GLOBAL WARMING, the highest peak is around the 20th month, and then it is stabilized.
Endorsement Analysis of Migrant-related Deliberations on YouTube: Prior to and During 2022 Ukrainian crisis

Extant literature has noted that migrant-related deliberations on social media platforms are primarily associated with negative sentiments. However, the literature has rarely probed – whether these negative sentiments get endorsed by other users? If yes, does it depend on who the migrants are – especially if they are cultural others? The 2022 Ukrainian refugee crisis allows probing these intricate issues. We have analyzed 110,803 (prior to this 2022 crisis) and 21,453 (during this crisis) migrant-related comments on the YouTube platform. Specifically, we investigate the relationship between user endorsement and sentiments of these comments. Both datasets indicate that users endorse comments with positive sentiments and reveal a negative propensity to endorse hate speeches, i.e., comments that use swear words. However, the analysis of the recent dataset reveals a negative propensity to endorse comments with negative sentiments, but the earlier dataset indicates a positive propensity. Thus, the endorsement pattern of comments with negative sentiments may depend on who the migrants are!
Visualization of aggregated information to support class-level software evolution

Software is inherently prone to constant change, especially in the source code, making it difficult for developers to keep track of changes performed over time and to fully understand their implications. To this end, we present an Eclipse plug-in for visualizing heterogeneous information, collected from multiple sources, at different levels of granularity. This visualization provides a single graphical representation of a system’s change histories over multiple versions, allowing developers to identify the previous and present dependencies in the system, while adding new or removing and modifying the current functionalities of a software. Summarizing and associating the relevant changes in a single graph, further supports developers, not familiar with the overall system, to conduct a self-study and explore the systems design and changes of its functionality over time. Our tool, DejaVu, initially infers and further visualizes change scenarios that have been applied to a given class in source code, across multiple versions of a software. DejaVu additionally augments the change information with prior commits from GitHub repositories, as well as associated issues from Jira issue tracking system. As part of the evaluation, we conducted a controlled experiment, recruiting participants with research or industrial programming experiences. The participants were asked to investigate and assess a set of change stories with and without the use of the DejaVu. As such, we empirically evaluated the impact of DejaVu in alleviating developers’ understanding of code class-level changes across multiple versions. Our results showed an average of 52% reduction in completion time and a 51% increase in correctness of several change-comprehension tasks once, users adopted DejaVu in comparison to the manual completion of the same tasks. A student’s t-test verified the significant improvement in time and correctness of the tasks with p-values of 0.01 and 0.002. Visualizing aggregated information from multiple sources provides developers with a more comprehensive intuition of the change and its rationale, facilitating software maintenance tasks.
Chinese Online Language Dissemination from the Perspective of Mobile Information System

The emergence of new media technology will inevitably bring new changes to people's daily communication. The development and change of society supports the development and change of language. Language networks are created and developed along with Internet applications. It is a new variant of the traditional language adapted to the new media environment of the Internet Age. With the increasing popularity and importance of network media, network language has received extensive attention and research on local languages. This paper analyzes and investigates the dissemination of Chinese Internet language from the perspective of mobile information system. On the basis of researching and proposing the mobile information system model, the structure of the system is deeply analyzed, and the key characteristics and technologies of various fields are discussed. Wireless mobile terminal wireless Internet access technology, portable intelligent terminal mobile application development technology, and local database technology are very suitable for Chinese network analysis and research to reduce wireless connections, and the effective range is from 50 feet to 1000 feet. The article uses the communication theories and methods of linguistics, cultural studies, network neologism generation, and communication characteristics analysis. It uses students as examples to explore their online neologism use and dyslexia and their problem-solving strategies for obstacles. The results show that 50% of the students can accept the new words on the Internet very well, and 30% of the students do not understand it.
A PLS-SEM Approach for Analysing Job Satisfaction and Human Resource Practices in Indian Banking Sector

In the economic growth of every country like India, the Indian financial sector plays a crucial role. However, employees in this industry are mainly not left to their job pleasure. This article seeks to identify the current work satisfaction criteria for bank employees in order to ensure the stable economic growth of India, and to advocate that regulatory authorities advance their degree of satisfaction. A total of 220 workers were randomly selected as examples for this study from the bank of India. The investigation was completed via a structured questionnaire. SPSS software has been utilised for the analysis of demographic information. Multivariate methods such as Structural Equation Modeling (SEM) were also utilised to analyse data to discover characteristics which are critical for the work satisfaction of bank employees using SmartPLS software. This survey shows that performance assessment, salaries, and bonuses significantly affect Bangladeshi banking workers’ job satisfaction. This article argues that both the management and regulatory bodies should guarantee performance evaluation and that the regulatory bodies for this industry should profit from salaries.
An adaptive differential evolution with extended historical memory and iterative local search

Non-decomposition methods are designed to tackle high-dimensional problems as a whole. These methods often utilize different effective approaches or are integrated with some optimization components to enhance the performance. The non-decomposition method SHADE-ILS, SHADE with iterative local search, adopts a historical memory that stores successful information of control parameters to make better guidance for parameters’ adaptation. In order to achieve more about this successful information and improve the adaptation, a modified version, an adaptive differential evolution with extended historical memory (mSHADE), is proposed and the Solis and Wets method is applied in the new algorithm. Besides, a permanent record is designed to increase the diversity to prevent the algorithm from stagnant. Finally, specially designed to tackle high-dimensional problems, we combine mSHADE with iterative local search. The proposed algorithm is tested on CEC’2010, SOCO’2011 and CEC’2013 large-scale global optimization benchmarks, and compared with seven state-of-the-art algorithms. The results show that our proposed algorithm is competitive for high-dimensional problems.
High-Performance Enhancement of SWIR Images

Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture.