Reviewerly/exHarmony huggingface dataset:
Multimodal Interaction Evaluation and Optimization Design of Smart Landscape Devices for Elderly People in Seaside Parks

With the rapid development of science and technology, an era of intelligence has started in people’s lives, and intelligent robots, intelligent home systems, intelligent parking systems and other products of intelligent technologies have been quietly applied to all aspects of modern life. Experiential and interactive media landscape installations based on intelligent technologies have also been introduced into seaside parks in this context. Intelligent technologies are utilized to innovate traditional landscape installations, break the traditional limitations of thinking, and change the way of interaction between landscape installations and the public in seaside parks. Thus, the public can create an interesting connection with the landscape installations to enhance the participation and experience of the public. Emotion recognition, as an integral part of AI, has received more and more attention from experts and scholars at home and abroad. The landscape installations in domestic seaside parks are becoming more and more intelligent, and are customized to meet the needs of special target groups, such as the elderly. In this paper, the intelligent setting of landscape installations is investigated using multimodal interaction. First, it explains in detail the multimodal interaction, landscape installations, and intelligence. It innovatively combines experiential and interactive media landscape installations based on intelligent technologies with seaside city parks and analyzes and summarizes the cases in China and abroad. Furthermore, it explores how interactive media landscape installations work in different seaside parks, and examines how they are designed and technically implemented. At the same time, a plan is formulated to optimize the landscape installations for different groups of people in the seaside parks. Finally, the key points for optimizing the design of intelligent landscape installations for the elderly are proposed, and experiments are conducted to analyze how the design of multimodal interaction enhances the consumption perceptions of the elderly consumer groups. The experiment demonstrates the superiority of multimodal interaction for intelligent landscape installations.
Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. After inspecting the current work on clustering Arabic text, it is observed that most researchers focus on applying K-Means clustering while hindering other clustering techniques. Our evaluation shows that K-Means has a weakness of inconsistent clustering results and weak clustering performance when the data dimensionality increases. Unlike K-Means clustering, Artificial Neural Networks (ANN) models such as Self-Organizing Maps (SOM) demonstrated higher accuracy and efficiency in clustering even with high dimensional datasets. In this paper, we introduce a new clustering model based on an optimization technique called Grey Wolf Optimization (GWO) used conjointly with SOM clustering to enhance its clustering performance and accuracy. The evaluation results of our proposed technique show an improvement in the effectiveness and efficiency in comparison with state-of-the-art approaches.
Mobile Learning in Higher Education: A Systematic Literature Review

Mobile learning (M-Learning) has become a popular and effective method of education that leverages the ubiquity of mobile devices. M-Learning has digitally transformed the process of teaching and learning. It has tremendous potential to empower all sections of society through education and training. This study presents a systematic literature review of M-Learning. The articles were retrieved from Scopus and Web of Science databases. After applying inclusion and exclusion criteria, a final selection of 161 articles published between 2016 and 2022 was included in the review. To analyze the articles, the researchers employed the TCCM (Theory, Context, Characteristics, Methods) framework, which facilitated addressing the research questions. This review identified various theories, such as behaviorism, constructivism, cognitivism, situated learning, problem-based learning, context awareness learning, socio-cultural theory, collaborative learning, conversational learning, lifelong learning, informal learning, activity theory, connectivism, navigation, and location-based learning, that are used to support and guide the implementation of M-Learning. In terms of context, developing countries contributed to 70.8% of the studies, while developed countries contributed to 29.1%. Further, a majority of the studies, 93%, involved students followed by faculty members and only two studies involved staff from higher education management. A total of 19 unique characteristic factors have been identified, such as personal, intention, attitude, usage, utility, ease of use, learnability, social, technological, pedagogical, anxiety, enjoyment, accessibility, knowledge, experience, trust, price, and habit. A quantitative research design was used in 90% of the studies, followed by mixed methods research design in 7% of the studies, and qualitative research design in only 3% of the studies. Further, this article synthesizes previous research findings and highlights gaps for future research. Overall, this review contributes to the understanding and advancement of M-Learning as a valuable educational platform.
An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.
Prototyping a Hyperledger Fabric-Based Security Architecture for IoMT-Based Health Monitoring Systems

The Internet of Medical Things (IoMT) has risen significantly in recent years and has provided better quality of life by enabling IoMT-based health monitoring systems. Despite that fact, innovative security mechanisms are required to meet the security concerns of such systems effectively and efficiently. Additionally, the industry and the research community have anticipated that blockchain technology will be a disruptive technology that will be able to be integrated into innovative security solutions for IoMT networks since it has the potential to play a big role in: (a) enabling secure data transmission, (b) ensuring IoMT device security, and (c) enabling tamper-proof data storage. Therefore, the purpose of this research work is to design a novel lightweight blockchain-based security architecture for IoMT-based health monitoring systems leveraging the features of the Hyperledger Fabric (HF) Platform, its utilities. and its lightweight blockchain nature in order to: (i) ensure entity authentication, (ii) ensure data confidentiality, and (iii) enable a more energy-efficient blockchain-based security architecture for IoMT-based health monitoring systems while considering the limited resources of IoMT gateways. While security mechanisms for IoT utilizing HF do exist, to the best of our knowledge there is no specific HF-based architecture for IoMT-based health monitoring systems.
Effectiveness of online training on digital pedagogical skills of remote area teachers in Nepal

Pedagogical skills are fundamental requirements for all teachers for the pedagogical shift in day-to-day teaching and learning activities. Therefore, the digital pedagogical skills training was organized for enhancing the pedagogical skill of the teachers. The aim of this research was to find the effect of that virtual training on the digital pedagogical skills provided to teachers. The quasi-experimental research design was employed among 44 school teachers in Karnali Province. Mean, standard deviation, t-test, ANOVA, and linear regression were major statistical techniques used in the research. The finding indicates that the intervention was highly effective to enhance the digital pedagogical skills of the teachers in remote areas. Gender, qualification, and teaching level are contributing factors to determine the digital pedagogical skills of teachers. The training was found to be more effective in the use of writing tools, email, file sharing, internet surfing, subject-related mobile applications and online resources, and video conferencing tools.
Emerging Trends and Future Prospects of Cybersecurity Technologies: Addressing Challenges and Opportunities

Cybersecurity is an ever-evolving domain, continuously challenged by sophisticated threats and rapidly advancing technologies. To safeguard digital assets and privacy in the face of emerging risks, it is imperative to explore and embrace the latest trends and innovations in cybersecurity technologies. This research paper investigates the most promising emerging trends in cybersecurity and their potential implications for the future landscape of cyber defense. By addressing the challenges and opportunities presented by these technologies, this paper aims to shed light on how organizations and policymakers can proactively strengthen their cybersecurity posture.
Image Enhancement using Adaptive Median Filter

Impulse noise frequently taints digital images as a result of errors produced by noisy sensors, faults during the analog-to-digital conversion process, and errors produced in communication channels. This persistent bug modifies the brightness of some pixels while keeping the brightness of other pixels unaltered. We looked at the Median Filter, Weighted Median Filter, and Multi-Stage Median Filter to lessen impulsive noise and enhance the quality of the impacted image. These filters enhance the image quality by removing or successfully suppressing impulse noise in the image while maintaining the information about the image's edges. In MATLAB simulations, the effectiveness of these filters was assessed on a picture that has undergone different types of impulse noise corruption (Salt-and-Pepper and Gaussian noise).
Subject Review: Various Techniques for Image Compression

Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy &amp; lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.
Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach

Pre-trained language model-based neural rankers have been widely applied in information retrieval (IR). However, the robustness issue of current IR models has not received sufficient attention, which could significantly impact the user experience in practical applications. In this study, we focus on the defensive ability of IR models against query attacks while guaranteeing their retrieval performance. We discover that improving the robustness of IR models not only requires a focus on model architecture and training methods but is also closely related to the quality of data. Different from previous research, we use large language models (LLMs) to generate query variations with the same intent, which exhibit richer and more realistic expressions while maintaining consistent query intent. Based on LLM-generated query variations, we propose a novel contrastive training framework that substantially enhances the robustness of IR models to query perturbations. Specifically, we combine the contrastive loss in the representation space of query variations with the ranking loss in the retrieval training stage to improve the model’s ability to understand the underlying semantic information of queries. Experimental results on two public datasets, WikiQA and ANTIQUE, demonstrate that the proposed contrastive training approach effectively improves the robustness of models facing query attack scenarios while outperforming baselines in retrieval performance. Compared with the best baseline approach, the improvements in average robustness performance of Reranker IR models are 24.9%, 26.5%, 27.0%, and 75.0% on WikiQA and 8.7%, 1.9%, 6.3%, and 13.6% on ANTIQUE, in terms of the MAP (Mean Average Precision), MRR (Mean Reciprocal Rank), nDCG@10 (Normalized Discounted Cumulative Gain) and P@10 (Precision), respectively.
Enhancing Underwater Images via Color Correction and Multiscale Fusion

Color distortion, low contrast, and blurry details are the main features of underwater images, which can have adverse effects on their quality. To address these issues, a novel enhancement method based on color correction and multiscale fusion is proposed to improve underwater image quality, achieving color correction, contrast enhancement, and detail sharpening at different stages. The method consists of three main steps: color correction using a simple and effective histogram equalization-based method to correct color distortion, decomposition of the V channel of the color-corrected image into low- and high-frequency components using a guided filter, enhancement of the low-frequency component using a dual-interval histogram based on a benign separation threshold strategy, and a complementary pair of gamma functions; the fusion of the two versions of the low-frequency component to enhance image contrast; and finally, the design of an enhancement function to highlight image details. Comparative analysis with existing methods demonstrates that the proposed method achieves high-quality underwater images and favorable qualitative and quantitative evaluations. Compared to the method with the highest score, the average UIQM score of our method exceeds 6%, and the average UCIQE score exceeds 2%.
IMPLEMENTASI ALGORITMA FAST CORNER DETECTION DALAM PERANCANGAN APLIKASI PENGELAN HEWAN BERBASIS AR

The purpose of this study was to present findings on endangered species, specifically focusing on endangered animals worldwide. Several species were selected for investigation, including orangutans, Komodo dragons, Kakapo (Strigops habroptilus), Javan rhinos, Amur leopards, and vaquitas (Phocoena sinus). Augmented reality, a technology that merges the real and virtual worlds through computer devices, was employed by the author to develop an animal recognition application. The aim was to create an engaging educational tool, particularly targeting school children, to facilitate learning about endangered animals. The research progressed through three stages: gathering information on endangered animals globally, examining existing augmented reality applications, and designing markers for detection using the fast corner detection algorithm. Android, a Linux-based mobile operating system, offered an open platform for developers to create applications compatible with various mobile devices. Consequently, the outcome of this study manifested as an augmented reality-based application that delivers comprehensive information on endangered species. The application's potential to serve as a captivating medium for education and knowledge acquisition is highly anticipated.
Understanding Rug Pulls: An In-depth Behavioral Analysis of Fraudulent NFT Creators

The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles and an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, the fraudulent schemes that steal investors’ funds. From a curated dataset of 760 rug pulls across 10 NFT marketplaces, we examine these schemes’ structural and behavioral properties, identify the characteristics and motivations of rug pullers, and classify NFT projects into 20 groups based on creators’ association with their accounts. Our findings reveal that repeated rug pulls account for a significant proportion of the rise in NFT-related cryptocurrency crimes, with one NFT creator attempting 37 rug pulls within 3 months. Additionally, we identify the largest group of creators influencing the majority of rug pulls and demonstrate the connection between rug pullers of different NFT projects using the same wallets to store and move money. Our study contributes to understanding NFT market risks and provides insights for designing preventative strategies to mitigate future losses.
Digital Assessment and the “Machine”

In this chapter, we explore assessment that is performed automatically by the digital environment, what might be called assessment “through technology.” Based on experience with the design of three innovative content-specific automatic assessment platforms, the main goal of the chapter is to exemplify design considerations to map the development of digital assessments. These digital platforms use various methods to assess mathematics through student’s interaction with digital learning resources. We address the fit between an assessment’s task design and its goals, the analysis of student work by the platform, and the report that is then produced. We want tasks to offer opportunities for students to express mathematical ideas, to take advantage of the opportunities provided by automatic assessment, as well as to meet the goals of assessments. In writing tasks, designers must also take into account two other design considerations: How can the digital assessment platform interpret and analyze student work in variable and flexible ways? How can the platform “make sense” of student work, so as to be able to generate feedback or to report on learning achievements? What are the ways in which insights from this analysis will be made accessible and to whom? Taken together, examination of the design of the tasks given to students to collect data, how that data is analyzed by the machine, and how the machine reports on that analysis allows us to map current digital assessment practices. We close by emphasizing the importance of continued engagement of the mathematics education community with the design of digital assessment platforms because mathematics education stakeholders bring with them a content-specific focus on higher-level thinking in mathematics and on students’ conceptions and misconceptions.
Politics of Digital Resources in Mathematics Education

In this chapter, we ask whether digital resources in the mathematics classroom can be said to have a political agenda or political consequences. Of course, resources can be used for political purposes – but do they serve a political goal? We theoretically explore this question by relating to mainstream trends in mathematics education literature as political, the literature on digital resources in the mathematical classroom, and recent broad concerns about the political consequences of technology. Then we introduce a framework from science studies that describe the political influences of artifacts as happening on three levels: (1) the use of artifacts to meet human decided ends, (2) unforeseen consequences by use of artifacts, and (3) larger changes to society and sociotechnical systems that are fertilized by the existence or broad use of an artifact. This framework discusses two types of digital resources and their political consequences, namely, the use of computer algebra systems in the teaching of mathematics and the use of Internet-based communication for mathematics instruction. We end the chapter by discussing what type of reflections the perspective of resources as political demands of mathematics educators.
Knowledge Mapping of Human Activity Recognition Techniques for Assistive Living

Purpose: Human Activity Recognition (HAR) is a subject of research that identifies an individual’s activities for assistive living. The proliferation of ICT and sensor technology prompted HAR to flourish beyond unfathomable levels, having immense human-centric applications. The development of accurate HAR systems involves complex statistical and computational tasks from signal acquisition to activity classification. This research aims to conduct a systematic review of recent techniques proposed for each stage of HAR application development. Methodology: The review is conducted following Kitchenham principles, using Scopus and Web of Science databases. Firstly, research questions were formulated, followed by the search strategy definition. Based on assessment criteria, 193 papers are shortlisted and thoroughly analyzed to extract research- related information. Results: The techniques identified in 193 articles are comprehensively mapped from four aspects: data acquisition, data preprocessing and feature engineering, learning algorithm, and evaluation. Each technique is examined for its strengths and limitations to assist application developers in selecting the best one for their needs. The prevailing challenges and upcoming research opportunities are thoroughly explored. Conclusion: The ever-expanding literature in the field necessitated an update to the status of HAR literature. Compared to other reviews that focused on specific methods, fields of application, and datatypes, to the best of our understanding, this is the first evaluation of its kind that provides a broader mapping of HAR approaches. The findings of this analysis will provide researchers and newcomers in the field an up-to-date and holistic view of the complete body of work in this area.