Reviewerly/exHarmony huggingface dataset:
An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.
Novel Blockchain and Zero-Knowledge Proof Technology-Driven Car Insurance

It is crucial to ensure the privacy and authenticity of the owner’s information in car insurance claims. However, the current traditional car insurance claims scenario suffers from inefficiency, complex service, unreliable data, and data leakage. Therefore, considering the privacy and sensitivity of insurance information and car owner data, we can use blockchain, smart contracts, and zero-knowledge proof technology to improve the current problems. This paper proposes a novel car insurance claim scheme based on smart contracts, blockchain, and zero-knowledge proof. Our scheme focuses on preserving privacy in the car insurance authorization and claim process. We design a private smart contract for the creation and revocation of car insurance and public smart contract for the authorization and validation of car insurance. By using ZoKrates, generating zero-knowledge proofs off chain and verifying the proofs on chain reduces the amount of data storage and computation on chain and provides privacy protection for sensitive information. Experimental results confirm the efficacy of our scheme in terms of security and performance.
Metallogenic Prediction of the Zaozigou Gold Deposit Using 3D Geological and Geochemical Modeling

Deep-seated mineralization prediction is an important scientific problem in the area of mineral resources exploration. The 3D metallogenic information extraction of geology and geochemistry can be of great help. This study uses 3D modeling technology to intuitively depict the spatial distribution of orebodies, fractures, and intrusive rocks. In particular, the geochemical models of 12 elements are established for geochemical metallogenic information extraction. Subsequently, the front halo element association of As-Sb-Hg, the near-ore halo element association of Au-Ag-Cu-Pb-Zn, and the tail halo element association of W-Mo-Bi are identified. Upon this foundation, the 3D convolutional neural network model is built and used for deep-seated mineralization prediction, which expresses a high performance (AUC = 0.99). Associated with the metallogenic regularity, two mineral exploration targets are delineated, which might be able to serve as beneficial achievements for deep exploration in the Zaozigou gold deposit.
Review of Algorithms for Securing Data Transmission in Mobile Banking

With the dawn of mobile banking applications, bank customers can now register for M-banking and download applications that aid them to access services from the bank server remotely from their mobile devices. The conversation between the bank's server and the client application requires a secure connection. However, M-banking is often conducted via unsecure wireless networks on which adversaries can use available techniques to hack into system to steal sensitive financial information including money. This paper's objective is to review the state-of-the-art algorithms that secure data on transit in M-banking. Thus, we document the strengths and weaknesses of these mechanisms and report on structure of their operation. The study reviewed various encryption algorithms such as Rivest-Shamir-Adleman Algorithm, Elliptic Curve Cryptography, Digital Signature Algorithm, Blowfish algorithm, Advanced Encryption Standard, Data Encryption Standard and Tripple Data Encryption Standard. In addition, the study reviewed steganography and hybrid algorithms. From this study, we show that Advanced Encryption Standard is the most preferred standard for M-banking because there are no specific attacks against it so far. However, since technology is changing fast, Advanced Encryption Standard might not provide security in M-banking for long. Therefore, this study shows and recommends utilization of a combination of Advanced Encryption Standard algorithm and Least Significant Bit steganography to produce a robust hybrid algorithm that is tamperproof from flaws existing in current cryptosystems.
Dancing in virtual reality as an inclusive platform for social and physical fitness activities: a survey

Abstract Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing ( $$N=292$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>292</mml:mn> </mml:mrow> </mml:math> ); we study the users’ motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user’s motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications.
Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.
Designing a Chatbot for Contemporary Education: A Systematic Literature Review

A chatbot is a technological tool that can simulate a discussion between a human and a program application. This technology has been developing rapidly over recent years, and its usage is increasing rapidly in many sectors, especially in education. For this purpose, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to analyze the developments and evolutions of this technology in the educational sector during the last 5 years. More precisely, an analysis of the development methods, practices and guidelines for the development of a conversational tutor are examined. The results of this study aim to summarize the gathered knowledge to provide useful information to educators that would like to develop a conversational assistant for their course and to developers that would like to develop chatbot systems in the educational domain.
Enhancing Counterfeit Detection with Multi-Features on Secure 2D Grayscale Codes

Counterfeit products have become a pervasive problem in the global marketplace, necessitating effective strategies to protect both consumers and brands. This study examines the role of cybersecurity in addressing counterfeiting issues, specifically focusing on a multi-level grayscale watermark-based authentication system. The system comprises a generator responsible for creating a secure 2D code, and an authenticator designed to extract watermark information and verify product authenticity. To authenticate the secure 2D code, we propose various features, including the analysis of the spatial domain, frequency domain, and grayscale watermark distribution. Furthermore, we emphasize the importance of selecting appropriate interpolation methods to enhance counterfeit detection. Our proposed approach demonstrates remarkable performance, achieving precision, recall, and specificities surpassing 84.8%, 83.33%, and 84.5%, respectively, across different datasets.
Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction

While generative artificial intelligence has gained popularity, e.g., for the creation of images, it can also be used for the creation of synthetic tabular data. This bears great potential, especially for the healthcare industry, where data are often scarce and underlie privacy restrictions. For instance, the creation of synthetic electronic health records (EHR) promises to improve the usage of machine learning algorithms, which usually work with large amounts of data. This also applies for the prediction of the patient length of stay (LOS), a key measure for hospitals. Thereby, the LOS represents one of the core tools for decision makers to plan the allocation of resources. Thus, this paper aims to add to the still-young research concerning the application of generative adversarial nets (GAN) on tabular EHR. It does that with the intention to leverage the advantages of synthetic data for the prediction of the LOS in order to contribute to the efficiency-enhancing and cost-saving aspirations of hospitals and insurance companies. Therefore, the applicability of synthetic data that is generated using GANs as a proxy for scarce real-world EHR for the patient LOS multi-class classification task is examined. In this context, the Conditional Tabular GAN (CTGAN) and the Copula GAN are selected as the underlying models as they are state-of-the-art GAN architectures designed for generating synthetic tabular data. The CTGAN is found to be the superior model for the underlying use case. Nevertheless, the paper shows that there is still room for improvement when applying state-of-the-art GAN architectures to clinical healthcare data.
Herding behavior in the cryptocurrency market: the case of the Russia–Ukraine conflict

Abstract This study investigates the herding behavior in the cryptocurrency market during the period of the Russia and Ukraine conflict using intraday cryptocurrency price data of the five largest cryptocurrencies in terms of market capitalization. The empirical results indicate an anti-herding behavior during the whole period of the conflict, especially after the conflict officially happens. The research contributes to the growing literature on herding behavior in the cryptocurrency market by using intraday data and examining the Russia–Ukraine conflict period.
Unlocking the Potential of E-Commerce in Yemen: Identifying Key Impacting Factors and Exploring Strategic Solutions

This study offers a comprehensive analysis of the e-commerce ecosystem in Yemen, identifying key opportunities and impacting factors to growth, and suggesting suitable strategies for e-commerce startups. The study employs an integrated methodical approach, combining a literature review, and data analysis using Grey Analytic Hierarchy Process (Grey AHP) and Grey Technique for Order of Preference by Similarity to Ideal Solution (Grey TOPSIS). The literature review identifies opportunities, challenges, and strategic solutions. The Grey AHP assesses the relative importance of opportunities and challenges. The Grey TOPSIS ranks the best strategies that e-commerce startups in Yemen can adopt to leverage these opportunities and manage the negative effects of the barriers. The results show that large market potential, the underserved market, and the growing middle class represent the most significant opportunities. In contrast, economic and political instability, logistical challenges, and the lack of trust in online payments are the most significant impacting factors. To overcome these challenges, the top three strategies recommended are building strategic partnerships, offering secure payment and delivery options, and targeting the underserved market. By implementing these strategies, the e-commerce ecosystem in Yemen can prosper, take advantage of opportunities, and contribute to the country’s economic development.
Automatic Short Text Summarization Techniques in Social Media Platforms

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication.
Architecture of the Slurm Workload Manager

Slurm is an open source, fault-tolerant, and highly scalable workload manager used on many of the world's supercomputers and computer clusters. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources for some duration of time. Second, it provides a framework for starting, executing, and monitoring work on the allocated resources. Finally, it arbitrates contention for resources by managing queues of pending work and enforcing administrative policies. This paper describes the current design and capabilities of Slurm.
FFENet: frequency-spatial feature enhancement network for clothing classification

Clothing analysis has garnered significant attention, and within this field, clothing classification plays a vital role as one of the fundamental technologies. Due to the inherent complexity of clothing scenes in real-world environments, the learning of clothing features in such complex scenes often encounters interference. Because clothing classification relies on the contour and texture information of clothing, clothing classification in real scenes may lead to poor classification results. Therefore, this paper proposes a clothing classification network based on frequency-spatial domain conversion. The proposed network combines frequency domain information with spatial information and does not compress channels. It aims to enhance the extraction of clothing features and improve the accuracy of clothing classification. In our work, (1) we combine the frequency domain information and spatial information to establish a clothing feature extraction clothing classification network without compressed feature map channels, (2) we use the frequency domain feature enhancement module to realize the preliminary extraction of clothing features, and (3) we introduce a clothing dataset in complex scenes (Clothing-8). Our network achieves a top-1 model accuracy of 93.4% on the Clothing-8 dataset and 94.62% on the Fashion-MNIST dataset. Additionally, it also achieves the best results in terms of top-3 and top-5 metrics on the DeepFashion dataset.
Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.
Mobile learning in university science education: a systematic literature review

ABSTRACTThis study adopts a robust systematic literature review (SLR) to investigate the use of mobile devices to support learning (mobile learning) in university science education. It analyses 24 high quality studies over the past decade to generate insights into contemporary mobile learning developments in relation to settings, applications and pedagogical approaches, as well as trends in research methodologies and outcomes. The results show that the use of mobile devices is providing university science learners with peer learning opportunities and supporting their networked interactions with the science community and resources. However, the study also uncovers more constrained use of mobile devices associated with traditional didactic approaches, often in formal settings. The study identifies key differences from other SLR findings on mobile learning in school science education, most notably with respect to adopted pedagogies. It suggests future research directions, including the need for more qualitative studies of mobile learning in university science education.Keywords: Mobile learninguniversity science educationsystematic literature reviewdigital pedagogies AcknowledgementWe would like to acknowledge Emeritus Professor Didar Zowghi’s valuable contributions to the conceptualisation and development of this study.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data in the findings of this study is available from the corresponding author with reasonable request.Additional informationNotes on contributorsLe Quan LyLe Quan Ly is a researcher in the Faculty of Arts and Social Sciences (FASS) at the University of Technology Sydney (UTS), Australia. Her research focuses on the use of innovative digital learning in university science education.Matthew KearneyMatthew Kearney is a Professor of Educational Technology in the Faculty of Arts and Social Sciences (FASS) at the University of Technology Sydney (UTS), Australia. His research focuses on how digital technologies can be used in pedagogically transformational ways, particularly in school and teacher education contexts.
Free Press Under Pressure? Experiences and Consequences of Hateful Harassment on Journalists in Germany

The rise of populist movements all over the world and various global crises in recent years have led to a sharp increase in distrust in news and the media. Although this development is tangible globally, it seems pertinent to take a look at a Western liberal democracy with a comparatively good journalistic infrastructure such as Germany, where hateful harassment and attacks on journalists are on the rise. These issues have been widely discussed publicly in Germany. However, it would be useful to take into account the perspective of those affected by these phenomena. To contribute to the discourses on hate against journalists, the current contribution presents data from a survey of active journalists in Germany (&lt;em&gt;n&lt;/em&gt; = 322). The questionnaire comprised both standardized and open-ended questions, focusing specifically on hateful harassment and attacks experienced by journalists, including the ways through which they are transported and whether hate can be politically localized. The results reveal that hate and attacks are mainly attributed to right-wing individuals and groups. In addition to verbal hate, various responding journalists reported having been physically attacked or having received death threats. Given the frequency of experienced hateful harassment, most respondents fear that the freedom of press in Germany is in jeopardy (62%), and about half have considered self-censorship to avoid being the target of hate. The severity of experienced hate is illustrated by open-ended questions in the form of personal accounts that are analyzed using qualitative content analysis. The results are discussed in light of the role of a free press in modern democracies, as well as recent research on devaluing attitudes towards the free press in Germany.