Reviewerly/exHarmony huggingface dataset:
AR and VR Enhances Learning

AR and VR technologies hold significant potential for enhancing learning by creating captivating and interactive environments. AR overlays digital information onto the real world, while VR generates fully simulated settings, offering realistic scenarios and simulations that are challenging to replicate physically. This improves engagement and information retention. Furthermore, AR and VR personalize learning experiences based on individual needs and styles, and they facilitate collaborative learning by connecting students across locations, fostering communication and exposure to diverse perspectives. The integration of AR and VR in education can revolutionize the learning process, making it interactive, engaging, and effective. As advancements continue and accessibility increases, we can expect more innovative and impactful applications of AR and VR in enriching learning experiences.
Financial Fraud in the Age of FinTech

Fintech is a vast domain that includes a wide range of activities, from data security to financial service delivery, which helps companies automate their business operations and introduce the latest financial products and services. The problem of financial fraud has a significant impact on the finance sector and needs behavioral understanding from the perspective of both the fraudster and the consumer. The purpose of the chapter is to identify and analyze the various aspects of financial fraud in the age of Fintech and highlight the emerging research areas and trends. Co-occurrence and co-citation analyses were used to identify emerging trends and research directions related to fraud detection in Fintech research. The cluster analysis revealed five clusters which have been explained further. The research employs thematic clusters that identify fundamental, various themes based on their centrality and density. These themes and streams are intended to guide researchers, scholars, policymakers, and practitioners towards more precise, distinctive, and important areas of focus.
Chinese college students collaborative mobile-assisted language learning experience and flow as a key factor for further adoption

In recent years, the widespread shift toward online learning in higher education has led to a notable increase in the utilization of collaborative mobile-assisted language learning (MALL). However, the efficacy and implementation of MALL in college settings remain subjects of ongoing scholarly debate. To gain deeper insights into the experiences of Chinese college students with collaborative MALL and investigate factors that may influence their intentions for further adoption, this study proposed a comprehensive model that integrates the updated Unified Theory of Acceptance and Use of Technology (UTAUT) and flow theory.The model aimed to assess the relationship between flow and various antecedents, including perceived cost, social influences, perceived mobility, collaboration, and knowledge sharing, which shape students' intentions to adopt collaborative MALL. A survey was conducted among a sample of 831 students from 32 provinces and autonomous regions.The data analysis revealed that while 73% of participants reported having experienced collaborative MALL, overall adoption levels among Chinese college students are still in its initiative stage of adoption. Furthermore, variations were observed in the experiences of students from different majors and level of education. Importantly, the assessment of the proposed Mobile Collaborative Language Learning (MCLL) Model demonstrated the significant role of flow in predicting the adoption of collaborative MALL among Chinese college students.The study concludes with suggestions for future research opportunities based on the research findings, aiming to enhance our understanding and application of collaborative MALL in higher education contexts.
Towards Health-Aware Fairness in Food Recipe Recommendation

Food recommendation systems play a crucial role in promoting personalized recommendations designed to help users find food and recipes that align with their preferences. However, many existing food recommendation systems have overlooked the important aspect of healthy-food and nutritional value of recommended foods, thereby limiting their effectiveness in generating truly healthy recommendations. Our preliminary analysis indicates that users tend to respond positively to unhealthy food and recipes. As a result, existing food recommender systems that neglect health considerations often assign high scores to popular items, inadvertently encouraging unhealthy choices among users. In this study, we propose the development of a fairness-based model that prioritizes health considerations. Our model incorporates fairness constraints from both the user and item perspectives, integrating them into a joint objective framework. Experimental results conducted on real-world food datasets demonstrate that the proposed system not only maintains the ability of food recommendation systems to suggest users’ favorite foods but also improves the health factor compared to unfair models, with an average enhancement of approximately 35%.
Hybrid Gate-Pulse Model for Variational Quantum Algorithms

Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces significant redundancy when quantum gates are eventually transformed into control signals and applied on quantum devices. For superconducting quantum computers, the control signals are microwave pulses. Therefore, pulse-level optimization has gained more attention from researchers due to their advantages in terms of circuit duration. Recent works, however, are limited by their poor scalability brought by the large parameter space of control signals. In addition, the lack of gate-level "knowledge" also affects the performance of pure pulse-level frameworks. We present a hybrid gate-pulse model that can mitigate these problems. We propose to use gate-level compilation and optimization for "fixed" part of the quantum circuits and to use pulse-level methods for problem-agnostic parts. Experimental results demonstrate the efficiency of the proposed framework in discrete optimization tasks. We achieve a performance boost at most 8% with 60% shorter pulse duration in the problem-agnostic layer.
Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review

Reviews are a person's way of expressing feedback on something in the form of criticism and ideas. Reviews of mobile apps are a type of user feedback that focuses on the performance and look of a mobile application and is typically featured on the download page of a mobile application, such as in the Apps Store. Because it comprises a person's feelings and emotions, whether they are joyful, sad, hostile, or indifferent toward a mobile application, the review data is textual and may be gathered and utilized as material for creating a textual dataset. This work creates a multi-label multi-class Indonesian-language dataset based on public reviews of mobile applications with sentiment and emotional values. Another factor supporting the creation of this dataset is the fact that there is still a limited number of textual datasets based on the Indonesian language that are multi-label multiclass for performing sentiment analysis tasks, particularly those linked to text classification tasks. The data generated by this research was cleaned and handled during the pre-processing step and was annotated with 3 sentiments, namely positive, negative, and neutral, as well as 6 emotions, namely anger, fear, sad, happy, love, and neutral.
Chinese named entity recognition of apple diseases and pests based on iterative dilated convolution

In order to solve the problems of Chinese named entity recognition in the field of apple diseases and pests, including the difficulty of identifying long entities and polysemous words, we proposed a Chinese named entity recognition model based on iterative dilated convolution. Specifically, the Bert pre-trained model was used as the embedding layer to enhance the semantic representation of the text by integrating the text position information. The iterative dilated convolutional neural network (IDCNN) was used to extract text context features and output all possible sequences labels to the conditional random field (CRF) to get the optimal sequence label. To verify the validity of the model, we constructed a corpus containing 6 entity categories of apple diseases and pests, and experiments were performed on the corpus. The experimental results showed that the precision, recall and F1 score of the proposed model are 93.29%, 93.68% and 93.49%, respectively, which are superior to the other three conventional models. It provides a new idea for the recognition of complex named entities in the field of agriculture.
JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods.
Cryogenic ion trap system for high-fidelity near-field microwave-driven quantum logic

Abstract We report the design, fabrication, and characterization of a cryogenic ion trap system for the implementation of quantum logic driven by near-field microwaves. The trap incorporates an on-chip microwave resonator with an electrode geometry designed to null the microwave field component that couples directly to the qubit, while giving a large field gradient for driving entangling logic gates. We map the microwave field using a single 43 Ca + ion, and measure the ion trapping lifetime and motional mode heating rates for one and two ions.
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation

Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations.
A Novel User Experience Cloud Computing Model for Examining Brand Image Through Virtual Reality

This research paper presents a novel Cloud Computing User Experience (CCUE) approach to reconstructing the brand image of traditional Shanghai cosmetic brands by leveraging virtual reality (VR) technology and user experience (UX) research. Traditional Shanghai cosmetic brands possess rich cultural heritage and unique product offerings, but often face challenges in maintaining relevance in the modern market. The proposed CCUE uses the VR technology to create immersive and interactive experiences that allow consumers to explore and engage with the brand in a virtual environment. The developed CCUE model integrates the Artificial Intelligence (AI) integrated Imperialist Competitive Algorithm (ICA) for the user-machine interaction. With the CCUE a combination of VR simulations, product showcases, and interactive storytelling, users can experience the essence and history of traditional Shanghai cosmetic brands, fostering a deep connection and emotional attachment. Additionally, UX research techniques are employed to gather user feedback and insights, enabling the refinement and optimization of the VR experience. The findings of this CCUE contribute to the field of brand reconstruction and provide practical insights for traditional brands seeking to revitalize their image in a rapidly evolving market.
QSMAH: A novel quantum-based secure cryptosystem using mutual authentication for healthcare in the internet of things

Healthcare in IoT provides many benefits, such as real-time data transfer and decision-making based on the information received from the patient's Body Sensor Nodes (BSNs). Many healthcare centers have adopted IoT-based devices such as heart monitoring implants and Electrocardiograms (ECG) for continuous patient monitoring in and out of hospitals. However, due to open Internet connectivity and lack of authentication, these devices may lead to several life-threatening risks. Recent advancements in Quantum Computing threatened the security of classical cryptographic primitives such as the RSA algorithm. The security and privacy of patient data are of utmost importance and are to be protected from existing vulnerabilities and futuristic Quantum attacks. Motivated by the abovementioned issues, we proposed the QSMAH protocol which ensures secure Key Agreement (KA) and Mutual Authentication (MA) based on Quantum Cryptography. Unlike classical authentication schemes, in our protocol, Quantum Teleportation and Quantum Entanglement are effectively utilized for secure data transfer among entities. The security of the proposed QSMAH protocol relies on a Quantum Key and Greenberger–Horne–Zeilinger (GHZ) states to achieve strong authentication. An extensive formal security analysis using BAN logic is provided to prove the goal of our protocol. The simulation of the proposed protocol is provided using Automated Validation of Internet Security Protocols and Applications (AVISPA). The results reflect that the security of our protocol cannot be tampered with by Quantum Shor's and Grover's algorithms. The security analysis proves that QSMAH is also resistant to classical attacks and futuristic Quantum attacks on cryptographic schemes.
Mapping copper concentrations in tropical open cast mining fronts from X-ray fluorescence and FAAS techniques: A comparative study

Determining copper (Cu) contents and their spatial distribution in tropical open cast mining fronts is crucial for short-term exploitation planning. However, this evaluation in a fast and accurate way can be directly impaired by methods applied. The aim of this work was to assess the analytical performance of portable X-Ray fluorescence (pXRF) for Cu analysis in the mining fronts at the Cu Sossego Mine, in the Carajás Mineral Province, State of Pará, Brazil. For this, 143 drilling samples were collected in mining fronts at the Sequeirinho pit, an area chosen as representative of the heterogeneity of the Cu levels in the deposit of Sossego Mine. The samples were analyzed with three different techniques to determine the Cu levels as follows: pXRF; wavelength-dispersive X-Ray fluorescence (WDXRF); and flame atomic absorption spectrometry (FAAS) after acid digestion with aqua regia (reference method). Results of the three analytical methods were used to generate block models through Leapfroag Geo 6.0 software. Portable XRF data presented a strong positive correlation (>0.70) with the results obtained by the FAAS. In addition, pXRF performed better than WDXRF, with less dispersed values compared to FAAS. Similar distribution maps were generated using the three methods of analysis. After correction, the average Cu content from the pXRF-generated block model was only 0.15% lower than that derived from FAAS data. Therefore, pXRF is a fast, portable, non-destructive, low-cost, accurate and recommendable method for mining front sample analysis and to assist the quality control and geoscience teams in decision-making for feeding the mineral processing plant.
Accuracy Comparison of CNN, LSTM, and Transformer for Activity Recognition Using IMU and Visual Markers

Human activity recognition (HAR) has applications ranging from security to healthcare. Typically these systems are composed of data acquisition and activity recognition models. In this work, we compared the accuracy of two acquisition systems: Inertial Measurement Units (IMUs) vs Movement Analysis Systems (MAS). We trained models to recognize arm exercises using state-of-the-art deep learning architectures and compared their accuracy. MAS uses a camera array and reflective markers. IMU uses accelerometers, gyroscopes, and magnetometers. Sensors of both systems were attached to different locations of the upper limb. We captured and annotated 3 datasets, each one using both systems simultaneously. For activity recognition, we trained 8 architectures, each one with different operations and layers configurations. The best architectures were a combination of CNN, LSTM, and Transformer achieving test accuracy from 89% to 99% on average. We evaluated how feature selection reduced the sensors required. We found IMU and MAS data were able to distinguish correctly the arm exercises. CNN layers at the beginning produced better accuracy on challenging datasets. IMU had advantages over other aquisition systems for activity recognition. We analyzed the relations between models accuracy, signal waveforms, signals correlation, sampling rate, exercise duration, and window size. Finally, we proposed the use of a single IMU located at the wrist and a variable-size window extraction.
BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing

In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain.
Differential evolution with alternation between steady monopoly and transient competition of mutation strategies

Real parameter single objective optimization has been studied for decades. In recent, long-term search is emphasized based on the consideration that, in the field, solving difficulty often scales exponentially with the increase of function dimensionality. For long-term search, Differential Evolution (DE) still performs outstanding among types of population-based metaheuristics. In this paper, based on IMODE – a DE algorithm with three mutation strategies, we propose AMCDE – Differential Evolution with Alternation between steady Monopoly and transient Competition of mutation strategies. Our algorithm has two states. In the steady state – monopoly, a selected mutation strategy controls the whole population. Once improvement in fitness becomes difficult, the transient state – competition – arises. In the competition state, similar with IMODE, each of the mutation strategies controls a proportion of positions in the population and competes with the others. For enhancement, we propose that positions controlled by the winner among the three mutation strategies continue to be controlled by the mutation strategy in the next generation. Besides, in the competition state, the original selection strategy of IMODE is revised by us for diversification, while adaptation of the crossover rate is updated. Our experiment is based on three CEC benchmark test suites and the CEC 2011 suite of real world optimization problems. AMCDE is compared with seven peers. Based on experimental results, our algorithm demonstrates superior or at the very least comparable performance for long-term search compared to the peers. Moreover, we do experimental observation on AMCDE.
Spatial constraint for efficient semi-supervised video object segmentation

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation; the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems.