Reviewerly/exHarmony huggingface dataset:
Virtual reality and perceived learning effectiveness in accounting studies: the mediating role of task-technology fit

The use of virtual reality (VR) for the improvement of learning outcomes for accounting studies is an area in which the research is still limited. Technology-based accounting learning is very important because, currently, the accounting profession is very much affected by rapid technological change which means that it must adapt to remain relevant in the business world. This study aims to examine the role of the mediating effect of task-technology fit (TTF) on the relationship between the use of VR and learning outcomes for accounting studies. This research model states that the use of VR will be able to increase TTF, technology quality and accessibility, and then increase reflective thinking and reduce cognitive overload thereby increasing perceived learning effectiveness (PLE). The hypotheses derived from this model were tested empirically using survey data from 199 users of VR engaged in accounting studies. The data analysis uses partial least squares-structural equation modeling (PLS-SEM), and the results support the hypothesis that TTF mediates the relationship between VR use, learning behavior, and PLE.
Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class imbalance learning problems, the classification model will pay too much attention to the majority class samples and cannot guarantee the classification performance of the minority class samples, which might be more valuable. By synthesizing the minority class samples and changing the data distribution, unbalanced datasets can be optimized. Traditional oversampling algorithms have problems of blindness and boundary ambiguity when synthesizing new samples. A modified reclassification algorithm based on Gaussian distribution is put forward. First, the minority class samples are reclassified by the KNN algorithm. Then, different synthesis strategies are selected according to the combination of the minority class samples, and the Gaussian distribution is used to replace the uniform random distribution for interpolation operation under certain classification conditions to reduce the possibility of generating noise samples. The experimental results indicate that the proposed oversampling algorithm can achieve a performance improvement of 2∼8% in evaluation metrics, including G-mean, F-measure, and AUC, compared to traditional oversampling algorithms.
Requirement Engineering Methods for Virtual Reality Software Product Development - A Mapping Study

Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze and specify the requirements of a enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, etc. across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for requirement engineering for developing VR products requires a detailed study. This paper presents a mapping study on requirement engineering methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific requirement engineering methods in various fields of interest. We also discuss future directions in requirement engineering for VR products.
Minimal qubit representations of Hamiltonians via conserved charges

In the last years, we have been witnessing a tremendous push to demonstrate that quantum computers can solve classically intractable problems. This effort, initially focused on the hardware, progressively included the simplification of the models to be simulated. We consider Hamiltonians written in terms of Pauli operators and systematically cut all qubits that are not essential to simulate the system. Our approach is universally applicable and lowers the complexity by first ensuring that the largest possible portion of the Hilbert space becomes irrelevant, and then by finding and exploiting all conserved charges of the system, i.e., symmetries that can be expressed as Pauli operators. Remarkably, both processes are classically efficient and optimal. To showcase our algorithm, we simplify chemical molecules, lattice gauge theories, and the Hubbard and Kitaev models.
A novel framework for multiple disease prediction in telemedicine systems using deep learning

Telemedicine systems are gaining popularity due to their ability to provide remote medical services. These systems produce a lot of data, which may be used for a variety of purposes, including quality improvement, decision-making, and predictive analytics. Deep learning is an effective data mining method that may be applied to this data to bring out significant findings. Telemedicine systems, which allow patients to receive medical consultation and treatment remotely, generate vast amounts of data. Analyzing this data can provide valuable insights for improving patient care and optimizing the telemedicine system. Data mining techniques can be incredibly valuable for telemedicine systems, as they can help to identify patterns and insights in large amounts of patient data. Data mining techniques can assist telemedicine systems in making better decisions and offer better care to patients. In this paper, a novel framework for multiple disease prediction in telemedicine system using an effective deep learning algorithm was developed. The proposed multiple disease prediction system is composed of Long Short Term Memory (LSTM) unit.The experimental results revealed that the suggested disease prediction model exceeded the present models with an accuracy of 98.51%.
Accelerated variational quantum eigensolver with joint Bell measurement

The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamiltonians in quantum chemistry. However, due to the noncommutativity of the Pauli operators in the Hamiltonian, the number of measurements required on quantum computers increases significantly as the system size grows, which may hinder practical applications of VQE. In this work, we present a protocol termed joint Bell measurement VQE (JBM-VQE) to reduce the number of measurements and speed up the VQE algorithm. Our method employs joint Bell measurements, enabling the simultaneous measurement of the absolute values of all expectation values of Pauli operators present in the Hamiltonian. In the course of the optimization, JBM-VQE estimates the absolute values of the expectation values of the Pauli operators for each iteration by the joint Bell measurement, while the signs of them are measured less frequently by the conventional method to measure the expectation values. Our approach is based on the empirical observation that the signs do not often change during optimization. We illustrate the speed-up of JBM-VQE compared to conventional VQE by numerical simulations for finding the ground states of molecular Hamiltonians of small molecules, and the speed-up of JBM-VQE at the early stage of the optimization becomes increasingly pronounced in larger systems. Our approach based on the joint Bell measurement is not limited to VQE and can be utilized in various quantum algorithms whose cost functions are expectation values of many Pauli operators.
Attention-based speech feature transfer between speakers

In this study, we propose a simple yet effective method for incorporating the source speaker's characteristics in the target speaker's speech. This allows our model to generate the speech of the target speaker with the style of the source speaker. To achieve this, we focus on the attention model within the speech synthesis model, which learns various speaker features such as spectrogram, pitch, intensity, formant, pulse, and voice breaks. The model is trained separately using datasets specific to the source and target speakers. Subsequently, we replace the attention weights learned from the source speaker's dataset with the attention weights from the target speaker's model. Finally, by providing new input texts to the target model, we generate the speech of the target speaker with the styles of the source speaker. We validate the effectiveness of our model through similarity analysis utilizing five evaluation metrics and showcase real-world examples.
Cyber Threat Intelligence on Blockchain: A Systematic Literature Review

Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.
<scp>Tech‐infused</scp> classrooms: A comprehensive study on the interplay of mobile learning, <scp>ChatGPT</scp> and social media in academic attainment

Abstract Mobile learning (M‐learning), ChatGPT and social media are integral to university education, improving accessibility, personalization and interactive engagement in the learning process. This paper aimed to investigate the role of M‐learning, ChatGPT and social media in university students' academic performance. It was a cross‐sectional quantitative investigation, and data were collected from 600 university students through a self‐developed questionnaire having four subscales (M‐learning, ChatGPT, social media and academic performance). Simple linear regression and process macro were deployed to test study hypotheses. The analysis revealed that M‐learning is a significant contributor to students' academic performance at the university level ( β = .460, t (598) = 12.663, p &lt; .001). Social media were found to be significantly mediating the relationship between M‐learning and academic performance with coefficient B = .267, SE = .038, t (597) = 7.08, p &lt; .001, 95% CI [0.193, 0.341]. ChatGPT was significantly contributing to academic performance ( B = .288, t (596) = 2.50, p = .013), but the interaction term had an insignificant effect on the relationship between M‐learning and academic performance ( β = .292, t (596) = −0.43, p = .668). The study suggests that M‐learning can improve students' performance. The influence of social media and the non‐moderating role of ChatGPT indicate the need for strategic integration of these tools to enhance university students' outcomes. It is recommended that university students should judiciously use social media to enhance M‐learning. In contrast, universities should foster this integration to achieve educational aims and explore how AI tools like ChatGPT can be optimally deployed to improve learning.
A Time-Series-Based Sample Amplification Model for Data Stream with Sparse Samples

Abstract The data stream is a dynamic collection of data that changes over time, and predicting the data class can be challenging due to sparse samples, complex interdependent characteristics between data, and random fluctuations. Accurately predicting the data stream in sparse data can create complex challenges. Due to its incremental learning nature, the neural networks suitable approach for streaming visualization. However, the high computational cost limits their applicability to high-speed streams, which has not yet been fully explored in the existing approaches. To solve these problems, this paper proposes an end-to-end dynamic separation neural network (DSN) approach based on the characteristics of data stream fluctuations, which expands the static sample at a given moment into a sequence of sample streams in the time dimension, thereby increasing the sparse samples. The Temporal Augmentation Module (TAM) can overcome these challenges by modifying the sparse data stream and reducing time complexity. Moreover, a neural network that uses a Variance Detection Module (VDM) can effectively detect the variance of the input data stream through the network and dynamically adjust the degree of differentiation between samples to enhance the accuracy of forecasts. The proposed method adds significant information regarding the data sparse samples and enhances low dimensional samples to high data samples to overcome the sparse data stream problem. In VDM the preprocessed data achieve data augmentation and the samples are transmitted to VDM. The proposed method is evaluated using different types of data streaming datasets to predict the sparse data stream. Experimental results demonstrate that the proposed method achieves a high prediction accuracy and that the data stream has significant effects and strong robustness compared to other existing approaches.
Unsupervised Conditional Diffusion Models in Video Anomaly Detection for Monitoring Dust Pollution

Video surveillance is widely used in monitoring environmental pollution, particularly harmful dust. Currently, manual video monitoring remains the predominant method for analyzing potential pollution, which is inefficient and prone to errors. In this paper, we introduce a new unsupervised method based on latent diffusion models. Specifically, we propose a spatio-temporal network structure, which better integrates the spatial and temporal features of videos. Our conditional guidance mechanism samples frames of input videos to guide high-quality generation and obtains frame-level anomaly scores, comparing generated videos with original ones. We also propose an efficient compression strategy to reduce computational costs, allowing the model to perform in a latent space. The superiority of our method was demonstrated by numerical experiments in three public benchmarks and practical application analysis in coal mining over previous SOTA methods with better AUC, of at most over 3%. Our method accurately detects abnormal patterns in multiple challenging environmental monitoring scenarios, illustrating the potential application possibilities in the environmental protection domain and beyond.
PLATINUM GROUP MINERALS OF THE BAIMKA GOLD PLACER CLUSTER, WESTERN CHUKOTKA: THE NEW DATA

The results of the study of the platinum group minerals of the Baimka gold placer cluster, Western Chukotka, Russia, are presented. Platinum group minerals belong to the iridium-platinum and platinum miner- alogical-geochemical types with the Late Jurassic cumulative pyroxenite-gabbro complexes as a probable source. Platinum group minerals came to alluvial gold placers primarily from intermediate reservoirs, which is the Volgian volcanic-sedimentary sequence. Rounded silicate glass inclusions are a specific feature of platinum minerals from the Baimka placer cluster.
Integrative Approach for High-Speed Road Surface Monitoring: A Convergence of Robotics, Edge Computing, and Advanced Object Detection

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.
The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings

The development of noninvasive and cost-effective methods of detecting Alzheimer's disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system.
Privacy-Preserving Classification on Deep Learning with Exponential Mechanism

Abstract How to protect the privacy of training data in deep learning has been the subject of increasing amounts of related research in recent years. Private Aggregation of Teacher Ensembles (PATE) uses transfer learning and differential privacy methods to provide a broadly applicable data privacy framework in deep learning. PATE combines the Laplacian mechanism and the voting method to achieve deep learning privacy classification. However, the Laplacian mechanism may greatly distort the histogram vote counts of each class. This paper proposes a novel exponential mechanism with PATE to ensure the privacy protection. This proposed method improves the protection effect and accuracy through the screening algorithm and uses the differential privacy combination theorems to reduce the total privacy budget. The data-dependent analysis demonstrates that the exponential mechanism outperforms the original Laplace mechanism. Experimental results show that the proposed method can train models with improved accuracy while requiring a smaller privacy budget when compared to the original Pate framework.
Real-Time Billiard Shot Stability Detection Based on YOLOv8

This book chapter presents a real-time system for detecting the stability of a player's billiard shot, based on the YOLOv8 neural network. The system comprises a real-time object detection model and a real-time slope monitoring system. The model focuses on detecting four classes: The cue ball, hand, cue stick tip, and the bridge hand (hand support point). The project involved iterative model training on a custom dataset, eventually achieving a YOLOv8 model with 95% accuracy. The stability of a player's shot is detected by simulating slope change of cue stick during aiming, using the cue stick tip and bridge hand. Overall, the project highlights the immense potential of YOLOv8 in sports applications.
Robust Adversarial Deep Reinforcement Learning

Deep reinforcement learning has shown remarkable results across various tasks. However, recent studies highlight the susceptibility of DRL to targeted adversarial disruptions. Furthermore, discrepancies between simulated settings and real-world applications often make it challenging to transfer these DRL policies, particularly in situations where safety is essential. Several solutions have been proposed to address these issues to enhance DRL's robustness. This chapter delves into the significance of adversarial attack and defense strategies in machine learning, emphasizing the unique challenges in adversarial DRL settings. It also presents an overview of recent advancements, DRL foundations, adversarial Markov decision process models, and comparisons among different attacks and defenses. The chapter further evaluates the effectiveness of various attacks and the efficacy of multiple defense mechanisms using simulation data, specifically focusing on policy success rates and average rewards. Potential limitations and prospects for future research are also explored.