Reviewerly/exHarmony huggingface dataset:
Exploring the Multilingual Applications of ChatGPT

ChatGPT's ability to realistically mimic human conversation and its high level of ability to handle linguistic ambiguity opens new and exciting avenues in language learning. Building upon the technical affordances of ChatGPT, this study explores the perceptions of educational affordances when incorporating ChatGPT across languages discussed by language communities on YouTube and identifies best practices for its effective use in language education. Through inductive content analysis, this study discussed 18 languages categorized into four groups: (1) romanized languages with high resources, (2) non-romanized languages with high resources, (3) languages with medium resources, and (4) less frequently used languages. The findings reveal consensus that (a) ChatGPT is a valuable and remarkable tool for language teaching and, (b) learning and it cannot fully replace teachers, as humor, wit, and sympathy cannot be programmed. Two potentially significant issues or two gaps were identified and discussed: namely, the learning optimization gap and the knowledge comprehension gap.
A modified Bayesian boosting algorithm with weight-guided optimal feature selection for sentiment analysis

Sentiment analysis is crucial in understanding and analyzing public opinions, feedback, and social media data. In this study, we propose a modified Bayesian Boosting algorithm with weight-guided optimal feature selection for sentiment analysis. The goal is to improve the accuracy and efficiency of sentiment classification by selecting the most informative features and utilizing an optimized boosting algorithm. The proposed algorithm leverages the strength of the Bayesian framework and boosting techniques to handle the challenges of sentiment analysis effectively. It incorporates a feature selection mechanism that assigns weights to individual features based on their importance in sentiment classification. This weight-guided feature selection approach helps identify the most relevant and discriminative features, enhancing classification performance. The algorithm employs a modified Bayesian boosting algorithm to optimize the boosting process. It dynamically adjusts the weights assigned to weak classifiers during the boosting iterations, emphasizing the misclassified instances and providing more focused learning for accurate sentiment prediction. The proposed algorithm is evaluated on a benchmark sentiment analysis dataset, comparing it with other state-of-the-art algorithms. The results demonstrate that the modified Bayesian Boosting algorithm with weight-guided optimal feature selection achieves superior accuracy, precision, recall, and F1 score. The algorithm shows its effectiveness in capturing the sentiment patterns and improving the overall sentiment classification performance. The results demonstrated that the proposed model had gained an accuracy level of 98.49%, even the Random Forest algorithm gained an accuracy level of 78.6%, and the C4.5 model achieved an accuracy level of 68.58% individually. The proposed modified Bayesian Boosting algorithm with weight-guided optimal feature selection presents a promising approach for sentiment analysis. It effectively addresses the challenges of feature selection and boosting, leading to improved sentiment classification accuracy. The algorithm’s ability to adjust weights and select informative features adaptively enhances its sentiment analysis tasks performance. This research opens up new avenues for advancing sentiment analysis techniques and contributes to developing more accurate and efficient sentiment analysis models.
Central Bank Digital Currency in India

This chapter explores the benefits and issues surrounding the digital Rupee, also known as the eRupee or the central bank digital currency in India. The study found that Indian people who were interested in ‘cryptocurrency' information were also interested in ‘central bank digital currency' information. The study also showed that the introduction of CBDC has potential benefits such as reduced dependency on cash, higher seigniorage due to lower transaction costs, and reduced settlement risk. However, the India CBDC has associated risks that need to be carefully evaluated against the potential benefits. The introduction of a digital Rupee or CBDC in India will require legal and regulatory changes to make the phased CBDC implementation possible.
SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses

Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility.
Toss a Fault to Your Witcher: Applying Grey-box Coverage-Guided Mutational Fuzzing to Detect SQL and Command Injection Vulnerabilities

Black-box web application vulnerability scanners attempt to automatically identify vulnerabilities in web applications without access to the source code. However, they do so by using a manually curated list of vulnerability-inducing inputs, which significantly reduces the ability of a black-box scanner to explore the web application’s input space and which can cause false negatives. In addition, black-box scanners must attempt to infer that a vulnerability was triggered, which causes false positives.To overcome these limitations, we propose Witcher, a novel web vulnerability discovery framework that is inspired by grey-box coverage-guided fuzzing. Witcher implements the concept of fault escalation to detect both SQL and command injection vulnerabilities. Additionally, Witcher captures coverage information and creates output-derived input guidance to focus the input generation and, therefore, to increase the state-space exploration of the web application. On a dataset of 18 web applications written in PHP, Python, Node.js, Java, Ruby, and C, 13 of which had known vulnerabilities, Witcher was able to find 23 of the 36 known vulnerabilities (64%), and additionally found 67 previously unknown vulnerabilities, 4 of which received CVE numbers. In our experiments, Witcher outperformed state of the art scanners both in terms of number of vulnerabilities found, but also in terms of coverage of web applications.
YOLOv5s FMG: An Improved Small Target Detection Algorithm Based on YOLOv5 in Low Visibility

Accurate and real-time detection of small targets of pedestrians and cars in video images is indeed crucial for various applications such as autonomous driving and urban management. Existing detection algorithms face challenges related to small targets and low visibility, resulting in issues such as low accuracy, missed detection and reduced detection efficiency. This paper proposes an improved YOLOv5s FMG (Fine-tuning Slice, Multi-spectral Channel Attention, Ghost Bottleneck) detection method based on YOLOv5, which firstly introduces fine-tuning slicing aided hyper inference (SAHI) to generate small target objects by slicing the pictures into the network. Secondly, the multi-spectral channel attention (MCA) module is integrated into the feature extraction network, which enhances the information dissemination among features and strengthens the network’s ability to distinguish between foreground and background. Then, the network uses the convolution network to extract features instead of the full connection layer, and uses the lightweight Ghost Bottleneck instead of bottleneck structure. Finally, the prediction part adopts the complete intersection over union (CIoU) loss function to achieve accurate bounding box regression. Based on the experimental results conducted on the self-made dataset, compared to YOLOv5s, the mAP (0.5) of YOLOv5s FMG on the dataset is improved by 9.3%, and the mAP (0.5:0.95) is improved by 2%. At the same time, the frames per second (FPS) is increased by 41.8%, and the number of parameters has been reduced by 18.5%. The proposed method demonstrates successful detection of small targets of pedestrians and vehicles, ensuring its effective applicability under conditions of low visibility.
Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model

The need for an earthquake early-warning system (EEWS) is unavoidable in order to save lives. In terms of managing earthquake disasters and achieving effective risk mitigation, the quick identification of the earthquake’s intensity is a valuable factor. In light of this, the on-site intensity measurement can be transmitted over an Internet of Things (IoT) network. In this regard, a machine learning (ML) strategy based on numerous linear and non-linear models is proposed in this study for a quick determination of earthquake intensity after two seconds from the P-wave onset. We call this model an on-site two-second ML model-based earthquake intensity determination (2S-ML-EIOS). The utilized dataset INSTANCE for this model is observed by the number of 386 stations from the Italian national seismic network. Our model has been trained on 50,000 occurrences (150 thousand of 2s-three-component seismic windows). The model has the ability to deal with limited features of the waveform traces leading to reliable estimation of the earthquake intensity. The suggested model has a 98.59% accuracy rate in predicting earthquake intensity. The suggested 2S-ML-EIOS model can be used with a centralized IoT system to promptly send the alarm, and the IoT system will then instruct the affected administration to take the appropriate action. The 2S-ML-EIOS results are contrasted with those from the traditional manual solution approach, which corresponds to the ideal solution mean. Based on the extreme gradient boosting (XGB) model, the 2S-ML-EIOS can achieve the best intensity determination, and this improved performance demonstrates the methodology’s efficacy for EEWS.
A Hybrid Approach Based on GAN and CNN-LSTM for Aerial Activity Recognition

Unmanned aerial vehicles (UAVs), known as drones, have played a significant role in recent years in creating resilient smart cities. UAVs can be used for a wide range of applications, including emergency response, civil protection, search and rescue, and surveillance, thanks to their high mobility and reasonable price. Automatic recognition of human activity in aerial videos captured by drones is critical for various tasks for these applications. However, this is difficult due to many factors specific to aerial views, including camera motion, vibration, low resolution, background clutter, lighting conditions, and variations in view. Although deep learning approaches have demonstrated their effectiveness in a variety of challenging vision tasks, they require either a large number of labelled aerial videos for training or a dataset with balanced classes, both of which can be difficult to obtain. To address these challenges, a hybrid data augmentation method is proposed which combines data transformation with the Wasserstein Generative Adversarial Network (GAN)-based feature augmentation method. In particular, we apply the basic transformation methods to increase the amount of video in the database. A Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is used to learn the spatio-temporal dynamics of actions, then a GAN-based technique is applied to generate synthetic CNN-LSTM features conditioned on action classes which provide a high discriminative spatio-temporal features. We tested our model on the YouTube aerial database, demonstrating encouraging results that surpass those of previous state-of-the-art works, including an accuracy rate of 97.83%.
Blockchain based resource allocation in cloud and distributed edge computing: A survey

Cloud computing and distributed edge computing provide computing resources to meet the surging demands for computing caused by developments in technologies such as the Internet of Things (IoT) and Mobile communication (5G). Centralized resource allocation approaches in both computing paradigms suffer from single-point failure, tampering, modification in allocation results, and biased actions. Recently, blockchain has become popular in designing decentralized systems because of its features, including transparency, decentralization, and anti-tamper. In this paper, we provide a comprehensive survey of the research works applying blockchain in resource allocation in both computing paradigms, addressing the issues in centralized resource allocation approaches. Firstly, we identify several key research questions acting as motivation. To provide background knowledge, first, we discuss the centralized resource allocation in both computing paradigms and associated challenges. Then we discuss blockchain, its structure, working, characteristics and types, followed by its benefits to resource allocation. We identify several metrics to provide a comparative study of the works. We present a depth overview of blockchain-based resource allocation works in three domains: cloud computing, distributed edge computing and integrated edge and cloud computing. In each domain, works are summarized from three aspects: works using blockchain platforms, works providing blockchain frameworks and works advocating blockchain. We discuss consensus mechanisms in the works related to blockchain-based resource allocation, as the consensus mechanism is a fundamental part of the blockchain. Further, we provide key challenges requiring our attention. Finally, we conclude the survey.
A multi-perspective revisit to the optimization methods of Neural Architecture Search and Hyper-parameter optimization for non-federated and federated learning environments

The use of Neural Architecture Search (NAS) techniques is becoming more widespread. NAS methods have been essential for automating and accelerating the laborious and erroneous process of designing and tuning new Deep Learning (DL) approaches. The past few years have seen increasing number of research works on NAS and associated hyper-parameter optimization (HPO). They have arguably had the biggest influence on classification and object detection tasks, where they have produced state-of-the-art results. Despite the notable success to date, it is still difficult and not always practicable to apply NAS to real-world situations. In general, the structure of Convolution Neural Networks (CNNs) is too sophisticated to be used in platforms with limited resources, such as IoT, mobile, and embedded systems. In this paper, we revisit several state-of-the-art NAS and HPO methods from a multi-perspective view both in non-federated and distributed federated learning environments. Earlier attempts to critically analyze the components of NAS or HPO are only limited to non-federated environment, where NAS and HPO related computations need to be performed on a single CPU/ multiple GPUs and where all the data was readily accessible for performing the task of learning. In this article, we extend the discussion and critically analyze the important components of NAS and HPO methods in federated learning environment, where computations need to be performed on multiple computational units in federation and where only local data can be used by a single computational unit. Due to unavailability of entire data in federated learning environment, the earlier NAS methods (i.e., non-federated NAS methods) and the earlier HPO methods (i.e., non-federated HPO methods) cannot be used directly without modifications. We discuss these modifications made by researchers in non-federated NAS and HPO methods to be used in federated learning environment and provide discussion for future avenues.
LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications

The recently introduced Artificial Hummingbird Algorithm (AHA) exhibits competitive performance in developing optimization concerns. However, AHA has an imbalance between exploration and utilization abilities, often prematurely converging with low precision. Therefore, in this paper, a multi-strategy boosted hybrid artificial hummingbird algorithm called LCAHA combined with sinusoidal chaotic map strategy, Lévy flight, cross, and update foraging strategy is proposed. Firstly, LCAHA is initialized by the sinusoidal chaotic map strategy to obtain a population with better ergodicity. Secondly, introducing the Lévy flight can boost the diversity of the population, control premature convergence and boost the stability of the population. Then, two new strategies, cross foraging and update foraging, are introduced. The introduction of new foraging strategies further enhances the exploration and developmental capabilities. These three strategies work together to improve the overall performance of the AHA. Finally, the performance of the LCAHA is examined on 23 classical test suites, the CEC2017, CEC2019, and CEC2020 test suites, and six engineering optimization cases. The numerical experimental results show that LCAHA provides very promising numerical results in solving challenging optimization problems. The algorithm is applied to two spacecraft trajectory optimization cases. The experimental results demonstrate the applicability and potential of the LCAHA in solving practical applications.
Evaluation of COVID-19 m-Health apps: An analysis of the methods of app usability testing during a global pandemic

As the COVID-19 outbreak expanded over the world, governments looked for smartphone-based technological solutions to reduce the disease's dangers and prevent it. Most leading governments initially sought to use new smartphone-based applications (apps). In this study, we review articles that have evaluated these official government apps. The objective of this review is to answer the following question: “In evaluating or analyzing governmental apps for COVID-19, which methods have researchers used?”. This study reviews existing scholarly literature, to identify and analyze the investigations into the usability evaluation of official (governmental) mobile apps developed in the COVID-19 era. Study parameters specified that articles must be originally published in peer-reviewed journals or short articles, written in English, and must be published between 2019 and 2022 and indexed in PubMed, OVID, EMBASE, Web of Science, and Scopus. Additionally, the articles had to analyze and evaluate at least one mobile app that was launched and/or supported by a government. Reports, letters to the editor, review articles, and meta-analyses were excluded; also excluded were articles processing non-governmental apps. 11 studies were identified that met the inclusion criteria. These studies evaluate a total of 40 governmental, “official”, mobile apps, developed in 41 countries from the five continents. This study finds that the heuristic method, thematic analysis, and comparative analysis are the most popular research methods used for evaluating or analyzing governmental apps for COVID-19. This review of articles developed by various governments to combat COVID-19 leads the authors to conclude that most researchers sought to emphasize the strengths of these apps as opposed to limitations. Based on this review, we find that existing literature can sufficiently cover the effectiveness of these mobile apps.
Role of Artificial Intelligence (AI) to Enhance the Security and Privacy of Data in Smart Cities

Because smart cities are complex and interdependent, managing them involves dealing with political, technological, and socioeconomic challenges for many organizations. Since a few years back, a lot of literary works have concentrated on privacy, dangers, security and problems with the architecture of smart cities. These all factors mainly concern data processing and its management. This research primarily focuses on important difficulties, offers a thorough analysis of the literature, and makes recommendations for creating a framework for smart cities that primarily highlights the security and privacy elements. The infrastructure needed for smart cities, the protocols used to advance security and privacy, operational dangers to smart cities, and the best use of social media are only a few of the important topics covered in this study. It also offers a helpful perspective on many important issues and suggests a course for future research. The lessons learned might provide academics and researchers with a beneficial research foundation and a point of reference.
Smart-City Ecosystem Using Block-Chain Technology

With cities' continued growth and development, the implementation of incorporated, compatible, secure online services may improve the level of life for urban residents. Governmental organizations are aware of how digital currencies might be used to solve social challenges.. The concept of block-chain, initially associated with the digital currency bitcoin, offers a novel approach to the organization of smart-cities and a more transparent economic framework for the management of resources. The present research endeavors to examine the prospective advantages of utilizing block-chain-based services to facilitate the expansion of smart cities. Additionally, a Smart-city ecosystem model is suggested, which is founded on smart contracts established between firms, inhabitants, and government agencies. Additionally, it provides a synopsis of the potential application domains of the technology. The results of this study could potentially facilitate the implementation of blockchain technology as a means of facilitating interactions and transactions within the public sector at a regional level.
Teachers’ prospective attitudes towards the adoption of extended reality technologies in the classroom: interests and concerns

Abstract Recent international empirical studies have demonstrated positive results when applying extended reality (XR) technologies such as augmented reality, virtual reality, or mixed reality in teaching and learning. Thus, the preconditions and challenges of use must be investigated from teachers’ perspectives prior to implementing these technologies in Saudi schools and higher educational institutes. Therefore, this study examines the feasibility of applying XR tools and platforms in the classroom by understanding teachers’ opinions, especially regarding creating or acquiring XR educational content. A qualitative study is conducted using semi-structured interviews with ten educators in Saudi Arabia. As a result, several themes are identified: (1) an XR level of awareness among teachers, (2) an XR-learning content-acquiring approach, (3) teachers’ readiness for XR, (4) students’ readiness for XR, and (5) XR challenges for schools, including social aspects that are often overlooked. These themes highlight teachers’ interest in immersive technologies with their concerns about their possible use in the classroom. Hence, the study provides greater insights for exploration and application for future studies in this area. Moreover, it draws a realistic picture for policymakers and administrators, aiding them in creating an achievable adoption strategy for XR applications in the educational sector.