Reviewerly/exHarmony huggingface dataset:
Examining information and communication technology use in public primary schools in South Africa from the capability approach

In 2015 the Western Cape Government (WCG) in South Africa introduced the e-Learning Game Changer initiative to offer teachers in public schools information and communications technology (ICT) resources to improve their pedagogy. Despite the efforts by the WCG to improve ICT use for teaching in public schools, successful ICT integration hardly takes place in several historically disadvantaged (albeit affluent) schools, which constitute a minority of schools in the Western Cape. The primary aim of this study was to investigate those conversion factors allowing or impeding teachers in two public primary schools in the Western Cape from attaining the potential capabilities required for successful ICT integration. Amartya Sen’s capability approach was used as the study’s theoretical framework. The study employed a qualitative research method involving 10 educators observed during their lessons and interviewed. The findings confirmed that only a few conversion factors, such as the attainment of ICT skills through a community of practice and university training by the teachers, provided them with capabilities to use ICT to deliver curriculum content and perform basic ICT skills effectively. Several conversion factors, including teachers’ age, provincial and school policies, infrastructure and resources, prevented these teachers from realising their ICT potential. This study contends that the White Paper on e-Education (2004) policy might benefit from the results, which could help to build or rethink programmes that encourage continual teacher training.Transdisciplinarity Contribution: This research shows that policymakers need to take into consideration the conversion factors that may impact teachers’ capabilities to deliver curriculum utilising ICTs. This study’s findings provide new light on how these conversion factors limit or expand teachers’ ICT capabilities.
The Relationship Between Physical Exercise and Prosocial Behavior of Junior Middle School Students in Post-Epidemic Period: The Chain Mediating Effect of Emotional Intelligence and Sports Learning Motivation and Gender Differences

This study examined the relationship between physical exercise (PE) and prosocial behavior (PSB) and constructed a chain mediation model through the mediating effect of physical education learning motivation (PELM) and emotional intelligence (EI).Through the stratified random sampling, 1053 students (average age = 14 years, SD = 0.96 years) that complied with the requirements were surveyed from Henan Province in China. PE, EI, PELM, and PSB were assessed using standard scales. For data analysis, Pearson's correlation analysis, structural equation model test, and bias-corrected percentile Bootstrap method were carried out in turn.(1) There was a significant positive correlation between PE and PSB (r = 0.137), and the direct path between PE and PSB was significant (β = 0.09, t = 4.73, p < 0.01); (2) PE can positively predict EI (β= 0.08, t = 5.27, p < 0.01) and PELM (β= 0.04, t = 2.07, p < 0.05), EI can significantly and positively PSB (β= 0.67, t = 22.12, p < 0.01), PELM can significantly and positively predict PSB (β= 0.05, t =2.20, p<0.05); (3) EI and physical learning motivation play a significant intermediary role between PE and PSB. The mediating effect consists of three indirect effects: PE → EI → PSB (the mediating effect value is 0.055), PE → PELM → PSB (the mediating effect value is 0.002), PE → EI → PELM → PSB (the mediating effect value is 0.002); (4) The chain mediation effect is significant in girls, but not in boys.(1) PE can positively predict PSB. (2) EI and PELM had significant mediating effects between PE and PSB, including single mediating effects of EI and PELM and chain mediating effects of EI-PELM; (3) There are gender differences in the chain mediation effect.
High-speed band-limited illumination profilometry (BLIP)

Band-limited illumination profilometry (BLIP) is a reconfigurable phase-shifting fringe projection profilometry modality with a field-of-view of 1.7 m × 1.1 m and at a 3D imaging speed of up to 1200 frames per second.
Analog–Digital Combined High-Secure Optical Communication System Based on Chaotic Circuit Driving

We propose and demonstrate a new analog–digital combined high-secure optical communication system based on chaotic circuit driving, which achieves encryption in the analog and digital domains. A 3D chaotic system is used for analog domain phase encryption (ADPE) and digital domain time–frequency encryption (DDTFE) simultaneously. The ADPE is carried out by the privately chaotic signal driving the phase modulator (PM), which realizes chaotic phase encryption. The chaotic circuit comprehends highly complex nonlinear dynamics. Its size is 10 cm × 5 cm, which has the characteristics of small size and low cost. The DDTFE is performed by the frequency–time encryption of signals in the digital domain. The experimental results show that the optical physical layer encryption scheme based on analog and digital combination can successfully mask the original data. The driving signal of PM is that generated by the chaotic circuit and needs to be privately synchronized, so that the legal receiver may accurately decrypt the encrypted data and the eavesdropper is unable to intercept a valuable message. If the chaotic driving circuit produces a delay of 3 s, the bit error rate (BER) reaches more than 0.3 at the receiver. The results of experiment verify that the scheme can transmit 13.3 Gb/s 16 quadrature amplitude modulation orthogonal frequency division multiplexing (16QAM-OFDM) signal over 25 km standard single mode fiber (SSMF). This scheme achieves low-cost, high-security communication, making it a suitable foundation for high-speed, secure optical communication at the physical layer.
Research on the Informatization Management System of Hazardous Waste in Power Grid Based on the Virtual Warehouse

As the environmental protection policies become increasingly strict, hazardous waste disposal is a noteworthy issue for power grid companies to achieve clean production. Based on the radio frequency identification devices (RFID) and internet of things (IoT) technology, a virtual warehouse theory, and informatization management system was introduced in this article, which consists of waste identification and tracking, full-chain of IoT, trade matching, production forecast, and visual display. This study offers a new thought for grid companies to develop other management methods to promote environmental protection and management works.
A crypto-steganography scheme for IoT applications based on bit interchange and crypto-system

Image steganography and cryptography have been used widely due to the dramatic evolution of the internet of things (IoT) and the simplicity of capturing and transferring digital images. Pressing challenges in the context of a steganography system include security, imperceptibility, and capacity issues. In the existing schemes, fixing one issue has been indicated to affect the other and vice versa. Based on the above challenges, a new scheme has been proposed for the Crypt-steganography scheme. The proposed scheme consists of three main contributions. The first contribution is hybrid additive cryptography (HAC), which is related to encrypting secret messages before the embedding process to ensure security. The HAC depends on ElGamal elliptic curve cryptosystem (ECC) with cubic Bézier curve to achieve text confidentiality. The second contribution is a bit interchange method (BIGM), which is related to the embedding process and solves the image's imperceptibility. The third contribution is a new image partitioning method (IPM). The IPM contribution increases the randomization of selecting the embedding pixels. The IPM proposes a random pixel selection based on three iterations of the Hénon Map function used with IPM. Different parameters are used to evaluate the proposed scheme. Based on the findings, the proposed scheme gives evidence to overcome existing challenges.
A Comparative Study of Short Text Classification with Spiking Neural Networks

Short text classification is an important task widely used in many applications.However, few works investigated applying Spiking Neural Networks (SNNs) for text classification.To the best of our knowledge, there were no attempts to apply SNNs as classifiers of short texts.In this paper, we offer a comparative study of short text classification using SNNs.To this end, we selected and evaluated three popular implementations of SNNs: evolving Spiking Neural Networks (eSNN), the NeuCube implementation of SNNs, as well as the SNNTorch implementation that is available as the Python language package.In order to test the selected classifiers, we selected and preprocessed three publicly available datasets: 20-newsgroup dataset as well as imbalanced and balanced PubMed datasets of medical publications.The preprocessed 20-newsgroup dataset consists of first 100 words of each text, while for the classification of PubMed datasets we use only a title of each publication.As a text representation of documents, we applied the TF-IDF encoding.In this work, we also offered a new encoding method for eSNN networks, that can effectively encode values of input features having non-uniform distributions.The designed method works especially effectively with the TF-IDF encoding.The results of our study suggest that SNN networks may provide the classification quality is some cases matching or outperforming other types of classifiers.
Analysis Methods for the Planning and Dissemination Mode of Radio and Television Assisted by Artificial Intelligence Technology

In the context of the development of artificial intelligence (AI) technology, the planning and dissemination of radio and television must not only learn to quickly and deeply process a large amount of content and extract structured information such as classification, themes, tags, and styles from information sources, but also learn to use audience behaviors to explore and discover potential content, jump out of the editor’s subjective vision, and realize personalized content presentation. On the basis of summarizing and analyzing previous research results, this study expounded the research status and significance of the planning and dissemination mode of radio and television, elaborated on the development background, current status, and future challenges of AI technology, introduced the methods and principles of computer vision, natural language processing, semantic analysis, and video generation, discussed the content production, interview, and editing of radio and television, explored the review, archiving, and system solutions of radio and television, proposed the planning strategies for radio and television assisted by AI technology, analyzed the smart distribution, reception, and feedback of radio and television dissemination, discussed the interactive application and virtual reality of radio and television dissemination, analyzed the dissemination mode of radio and television assisted by AI technology, and finally carried out a case application and its analysis. The study results show that AI technology can be used for smart lyrics, smart writing, smart broadcasting, smart translation, smart recommendation, smart stripping, and smart retrieval, and so on. The AI-assisted radio and television planning gives audiences the right to choose news, provides them with more targeted news content by means of data capture and resource integration, and caters to audiences’ all-round and in-depth information needs through a question-and-answer method, innovating dissemination logic and expression approaches, and enhances the audience’s sense of experience and leadership in obtaining information. The AI-assisted radio and television dissemination mode has the advantages of improving the effectiveness of information communication, promoting the optimization and integration of news resources, and enhancing audience stickiness.
Human-Computer Interaction Environment Monitoring and Collaborative Translation Mode Exploration Using Artificial Intelligence Technology

Artificial intelligence now plays a significant role in both daily life and scientific research because of the rapid advancement of this technology in recent years. Making full use of the phrases in the translation phrase table for translation is challenging since the phrase matching is too accurate when the translation machine decodes. Fully automatic machine translation struggles to meet the expectations of its users since there are more or less translation faults brought on by data bottlenecks. Therefore, we require collaborative assisted translation technology for human-computer interaction. This work strengthens the research on collaborative translation techniques and ways for monitoring the human-computer interaction environment in order to further improve translation quality. This essay investigates and discusses human-computer translation techniques as well as related ideas in collaborative translation and human-computer interaction. The translation similarity model is incorporated into the translation system model together with an overall qualitative knowledge and logical reasoning capability of human-computer interaction to offer fresh strategies and methods for collaborative translation between humans and computers. According to the experimental findings, the accuracy rate of the collaborative translation system for human-computer interaction based on artificial intelligence technology can achieve 98.2% and 95.6%. The quality of the translation is enhanced after human-computer interaction, and the editing gap between the incorrect and auxiliary translations is narrowed, demonstrating the efficiency of the system and demonstrating its viability. In order to enhance the accuracy of system translation and the effectiveness of system operation, it is important to investigate the collaborative translation mode of human-computer interaction based on artificial intelligence technology.
PERANCANGAN DAN IMPLEMENTASI APLIKASI ANDROID UNTUK LAYANAN INFORMASI PARIWISATA

Android-based applications have been widely used to do daily activities such as online shopping, playing games, video streaming, and watching sports matches. One of the most commonly used applications is applications that assist people for flight or train ticket reservations, hotel bookings, and other recreation purposes. Some tourism companies in Indonesia also develop Android-based applications to provide information and services for their business. Nevertheless, with many tourism destinations in Indonesia, not all tourist attractions have services through Android-based applications published on the Google Play Store. This study aims to design and implement Android-based applications for tourism information services in two tourist attractions as the case study, namely Tay Kak Sie Temple in Semarang City and Beach tourism in Bangka Island, to help tourists obtaining information about both tourist attractions. Both applications are implemented using Compro Mobile App Builder. The testing results using the black box method demonstrate that the implementation of the two applications can run according to the specified specifications.
When Skeleton Meets Appearance: Adaptive Appearance Information Enhancement for Skeleton Based Action Recognition

Skeleton-based action recognition methods which utilize graph convolution networks (GCNs) have achieved remark-able success in recent years. However, action recognizer can be easily confused by the ambiguity caused by different actions with similar skeleton sequences when only skeleton data is trained. Introducing appearance information can effectively eliminate the ambiguity. Based on this, we introduce a two-stream network for action recognition. One trained on RGB images extracts appearance information. The other trained on skeleton data models motion information and adaptively captures appearance information of action areas at action-related intervals via a specially tailored attention mechanism. Our architecture is trained and evaluated on two large-scale datasets: NTU RGB+D and NTU RGB+D 120, and a small scale human-object interaction dataset Northwestern-UCLA. Experiment results verify the effectiveness of our method and the performance of our method exceeds the state-of-the-art with a significant margin.
Design of Broad Learning-Based Self-Healing Predictive Control for Sludge Bulking in Wastewater Treatment Process

Self-healing control plays a crucial role in taking remedial action to minimize the adverse impacts of sludge bulking in wastewater treatment process (WWTP). However, since sludge bulking is a strong nonlinear and complex process with multiple fault conditions, the conventional self-healing control is difficult to obtain reliable performance. Thus, the purpose of this article is to design a broad learning-based self-healing predictive controller (BL-SHPC) for sludge bulking in WWTP. The main innovations of the proposed controller are threefold. First, a dynamic fuzzy broad learning system with an adaptive expansion strategy is used to identify the fault conditions of sludge bulking. Then, the fault features of sludge bulking can be comprehensively extracted with desirable performance. Second, a prioritized multiobjective optimization algorithm-based predictive control, which considers the objective correlation and preference of fault conditions, is presented to obtain the optimal solutions to achieve self-healing. Then, the proposed controller can feasibly and precisely readjust manipulated variables to eliminate the sludge bulking. Third, the stability of the developed controller is proved by the Lyapunov stability theorem. Then, the stability analysis can ensure the successful application of BL-SHPC. Finally, the proposed BL-SHPC is tested on the Benchmark Simulation Model No.2 to validate its merits. The simulation results indicate that the proposed controller can obtain superior self-healing ability for sludge bulking in WWTP.
A Two-stage User Intent Detection Model on Complicated Utterances with Multi-task Learning

As one of the most natural manner of human–machine interaction, the dialogue systems have attracted much attention in recent years, such as chatbots and intelligent customer service bots, etc. Intents of concise user utterances can be easily detected with classic text classification models or text matching models, while complicated utterances are harder to understand directly. In this paper, for improving the user intent detection from complicated utterances in an intelligent customer service bot JIMI (JD Instant Messaging intelligence), which is designed for creating an innovative online shopping experience in E-commerce, we propose a two-stage model which combines sentence Compression and intent Classification together with Multi-task learning, called MCC. Besides, a dialogue-oriented language model is trained to further improve the performance of MCC. Experimental results show that our model can achieve good performance on both a public dataset and the JIMI dataset.
Empirical Identification of Perceptual Design Criteria for Ablution Concept

This paper was initiated based on the conceptual framework of Ablution Function Mean Analysis relating to a phenomenon that impacts designer perception in developing new product development for religious-based design. Empirical observation of user-designer exploring the convergent process of ablution design development was set up. The ablution performed, the perceptions verbalized, and the different design principles targeted were analyzed from a qualitative and a quantitative point of view. This study's data were first measured to obtain an appreciation of Muslims performing ablution behaviour. The study focused on the product gestalt, recognising the design act of divergent and convergent transpires in a real context, and identifying the spontaneous control reflection that influences the resulting product perception. Keywords: Design gestalt; ablution; design thinking. eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3783
A Lower Bound on the Number of Generators of a Defect Group

Abstract Assuming that the statement of the Alperin–McKay–Navarro conjecture holds for a p -block B with defect group D , we show that the number of generators of D is bounded from below by the number of height-zero characters in B fixed by a specific element of the absolute Galois group of the rational numbers.
HPC@Cloud: A Provider-Agnostic Software Framework for Enabling HPC in Public Cloud Platforms

The cloud computing paradigm democratized compute infrastructure access to millions of resource-strained organizations, applying economics of scale to massively reduce infrastructure costs. In the High Performance Computing (HPC) context, the benefits of using public cloud resources make it an attractive alternative to expensive on-premises clusters, however there are several challenges and limitations. In this paper, we present HPC@Cloud: a provideragnostic software framework that comprises a set of key software tools to assist in the migration, test and execution of HPC applications in public clouds. HPC@Cloud allows the HPC community to benefit from readily available public cloud resources with minimum efforts and features an empirical approach for estimating cloud infrastructure costs for HPC workloads. We also provide an experimental analysis of HPC@Cloud on two public clouds: Amazon AWS and Vultr Cloud.
A Framework for Multiclass Contour Visualization

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.