Reviewerly/exHarmony huggingface dataset:
Remote Proctoring in Language Testing: Implications for Fairness and Justice

In the wake of the COVID-19 boom in remote administration of language tests, it appears likely that remote administration will be a permanent fixture in the language testing landscape. Accordingly, language test providers, stakeholders, and researchers must grapple with the implications of remote proctoring on valid, fair, and just uses of tests. Drawing on an argument-based approach to fairness and justice, which subsumes validity, we articulate key sub-claims, warrants, rebuttals, and relevant backing related to the use of remote proctoring in language tests. With respect to meaningfulness as a core element of fairness, we focus on how remote proctoring is both a bulwark against construct-irrelevant responses (cheating) and a potential source of construct-irrelevant variance due to inauthentic constraints on test-taking conditions. Other fairness concerns relate to technological biases across racial/ethnic groups and access to suitable technology and physical space for remote proctoring. For justice, we consider the consequences and social values of remote-proctored language tests (Coghlan et al., 2021). We propose that these articulations of remote proctoring issues within Kunnan’s fairness and justice framework can usefully motivate and guide research on as well as critique of testing procedures and test uses.
Zero-Inflated Text Data Analysis using Generative Adversarial Networks and Statistical Modeling

In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documents and terms for row and column, respectively. Each element of this matrix is an occurred frequency of term in a document. Most elements of the matrix are zeros, because the number of columns is much larger than the rows. This problem is a cause of decreasing model performance in text data analysis. To overcome this problem, we propose a method of zero-inflated text data analysis using generative adversarial networks (GAN) and statistical modeling. In this paper, we solve the zero-inflated problem using synthetic data generated from the original data with zero inflation. The main finding of our study is how to change zero values to the very small numeric values with random noise through the GAN. The generator and discriminator of the GAN learned the zero-inflated text data together and built a model that generates synthetic data that can replace the zero-inflated data. We conducted experiments and showed the results, using real and simulation data sets to verify the improved performance of our proposed method. In our experiments, we used five quantitative measures, prediction sum of squares, R-squared, log-likelihood, Akaike information criterion and Bayesian information criterion to evaluate the model’s performance between original and synthetic data sets. We found that all performances of our proposed method are better than the traditional methods.
Application and challenges of a metaverse in medicine

Metaverse has been confirmed as a relatively amorphous concept of innovation, which refers to technological advancement. Metaverse, i.e., a coalition between reality world and virtual world, has created significant significance and convenience in education, communication, economy, etc. The COVID-19 outbreak has stimulated the growth of metaverse applications in medicine. The above-mentioned technology has broad applications while comprising online remote medical treatment, online conferences, medical education, preparation of surgical plans, etc. Moreover, technical, security, and financial challenges should be tackled down by the future widespread use of metaverse. Metaverse is limitlessly promising, and it will exert a certain effect on future scientific and technological advancements in the medical industry. The review article primarily aims to summarize the application of the metaverse in medicine and their challenge in the future of medicine.
Perspective Taking Reflects Beliefs About Partner Sophistication: Modern Computer Partners Versus Basic Computer and Human Partners

Abstract We investigate partner effects on spatial perspective taking behavior in listeners, comparing behavior with a human versus a computer partner (Experiments 1 and 2), and with computer partners of different perceived capabilities (Experiment 3). Participants responded to spoken instructions from their partner which could be interpreted egocentrically (from their own perspective) or othercentrically (from their partner's perspective). In contrast to earlier work, we found that participants were more egocentric with a computer than a human partner. Participants were also more egocentric with a computer partner that appeared more modern and capable, compared to one that appeared outdated and limited in ability. Our results show that perspective taking behavior is sensitive to information about one's partner; in particular, listeners consider their partner's potential ability to collaborate, adjusting their egocentric tendencies accordingly. Moreover, we highlight what appears to be a shift in listeners' expectations regarding computers' collaborative capabilities, leading to greater willingness to push the burden of perspective taking onto a computer partner.
Prospect of optical chirality logic computing

In this Perspective, we summarize the current state-of-the-art and the challenges of optical chirality logic computing. We discuss the prospects of its applications in integrated photonics, quantum technologies, and other multifunctional optoelectronics for ultrafast data processing.
Efficient Human Motion Reconstruction from Monocular Videos with Physical Consistency Loss

Vision-only motion reconstruction from monocular videos often produces artifacts such as foot sliding and jittering. Existing physics-based methods typically either simplify the problem to focus solely on foot-ground contacts, or they reconstruct full-body contacts within a physics simulator, necessitating the solution of a time-consuming bilevel optimization problem. To overcome these limitations, we present an efficient gradient-based method for reconstructing complex human motions (including highly dynamic and acrobatic movements) with physical constraints. Our approach reformulates human motion dynamics through a differentiable physical consistency loss within an augmented search space that accounts both for contacts and camera alignment. This enables us to transform the motion reconstruction task into a single-level trajectory optimization problem. Experimental results demonstrate that our method can reconstruct complex human motions from real-world videos in minutes, which is substantially faster than previous approaches. Additionally, the reconstructed results show enhanced physical realism compared to existing methods.
Perceptually Adaptive Real-Time Tone Mapping

Tone mapping operators aim to remap content to a display's dynamic range. Virtual reality is a popular new display modality that has significant differences from other media, making the use of traditional tone mapping techniques difficult. Moreover, real-time adaptive estimation of tone curves that faithfully maintain appearance remains a significant challenge. In this work, we propose a real-time perceptual contrast-matching framework, that allows us to optimally remap scenes for target displays. Our framework is optimized for efficiency and runs on a mobile Quest 2 headset in under 1ms per frame. A subjective study on an HDR-VR prototype demonstrates our method's effectiveness across a wide range of display luminances, producing imagery that is preferred to alternatives tone mapped at peak luminances an order of magnitude higher. This result highlights the importance of good tone mapping for visual quality in VR.
Challenges of Blockchain adoption in financial services in China's Greater Bay Area

In China's Greater Bay Area (Guangdong-Hong Kong-Macao), the increasing use of Blockchain technology in financial services has the potential to generate benefits for many stakeholders. Blockchains are known for their distinctive features, such as decentralized architecture, tamper-proof data structures, and traceable transactions. These features make Blockchain a preferred choice of platform for developing applications in financial service areas. Meanwhile, some questions have been raised regarding Blockchain's suitability to compete with or even replace existing financial systems. This paper provides insights into the current progress of Blockchain applications in insurance, banking, payments, asset trading, loans, remittances, the Internet of Things (IoT) for the finance industry, financial inclusions, and enterprise-level interaction in finance and governance. We review the barriers to widespread Blockchain adoption, especially the risks when transaction fees dominate mining rewards. By comparing the emerging Blockchain technologies and incentive issues related to real-world applications, we hope that this paper can serve as a valuable source of reference for Blockchain researchers and developers in financial service areas.
Exploring Semantic vs. Syntactic Features for Unsupervised Learning on Application Log Files

This paper explores the effect of traditional TF-IDF and state-of-the-art Large Language models towards anomaly detection in application log files. Four unsupervised learning algorithms are used on three application log files using these models. Results show that each model has advantages and disadvantages depending on the log file used.
ItoV: Efficiently Adapting Deep Learning-Based Image Watermarking to Video Watermarking

Robust watermarking tries to conceal information within a cover imag e/video imperceptibly that is resistant to various distortions. Recently, deep learning-based approaches for image watermarking have made significant advancements in robustness and invisibility. However, few studies focused on video watermarking using deep neural networks due to the high complexity and computational costs. Our paper aims to answer this research question: Can well-designed deep learning-based image watermarking be efficiently adapted to video watermarking? Our answer is positive. First, we revisit the workflow of deep learning-based watermarking methods that leads to a critical insight: temporal information in the video may be essential for general computer vision tasks but not for specific video watermarking. Inspired by this insight, we propose a method named ITOV for efficiently adapting deep learning-based Image watermarking to Video watermarking. Specifically, ITOV merges the temporal dimension of the video with the channel dimension to enable deep neural networks to treat videos as images. We further explore the effects of different convolutional blocks in video watermarking. We find that spatial convolution is the influential primary component in video watermarking, and depthwise convolutions significantly reduce computational costs with negligible impact on performance. In addition, we propose a new frame loss to constrain that the watermark intensity in each video clip frame is consistent, significantly improving the invisibility. Extensive experiments show the superior performance of the adapted video watermarking method compared with the state-of-the-art methods on Kinetics-600 and Inter4K datasets, which demonstrates the efficacy of our method ITOV.
Distributed Quantized Transmission and Fusion for Federated Machine Learning

Federated machine learning (FL) is a powerful technology which can be implemented to exploit the sheer amount of geographically distributed data for enhanced computation. Exploiting the impending proliferation of wireless devices, in this paper, we incorporate distributed quantized transmissions for reliable connectivity to a remote FL server. We develop a novel theoretical framework for the convergence analysis of the proposed network under joint impact of communication bit error rate (BER), and model quantization, and participation control. We show that the convergence rate of the network is affected by the BER and it can be improved via participation control. Through simulation, we demonstrate that our proposed model can provide the same performance as the conventional FL networks based on point-to-point communication while the energy consumption is divided across the distributed nodes.
Store of value or speculative investment? Market reaction to corporate announcements of cryptocurrency acquisition

Abstract In this study, we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions, selling, or acceptance as a means of payment. Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency. We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows. However, when we perform heterogeneity analyses, we observe significant differences in market reaction between events with high (larger CARs) and low cryptocurrency exposure (lower CARs). Multivariate regressions show that the level of exposure to cryptocurrency ("skin in the game") is a critical factor underlying abnormal returns around the event. Further analyses reveal that economically meaningful acquisitions of BTC or ETH (relative to firm's total assets) drive the observed effect. Our findings have important implications for managers, investors, and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.
Model of Developing Improvisation Skills of a Future Vocal Artist in Solo Singing Classes

In the contemporary system of higher arts education, teachers of culture and arts, in particular solo singing, face an important task — to develop music piece analysis and interpretation skills and vocal and motor skills in future vocal artists, as the student in the course of learning must perfectly master not only different vocal techniques, but also must create an artistic image of the piece through facial expressions, gestures and movements. The development of improvisation skills is an important component of professional training of future vocal artists. The paper considers the feasibility of the model of developing jazz improvisation skills of future vocal artists in solo singing classes. When designing the model, we established and analyzed the interrelations between the structural components of the process of developing jazz improvisation skills of future vocal artists in solo singing classes. The conducted analysis made it possible to identify three structural components of our model: methodological (implies the purpose, approaches and principles of the process of jazz improvisation skills development), content (includes the structural components of jazz improvisation of future vocal artists) and organizational/technological component (includes the methodology of diagnosing the levels of development of jazz improvisation skills of future vocal artists, criteria, indicators and levels of jazz improvisation skills development). The process of developing jazz improvisation skills of future vocal artists in solo singing classes is complex and multifaceted, and students’ ability to jazz improvisation is one of the special competencies that characterize a future vocal artist as a specialist in their field.
QTN-VQC: an end-to-end learning framework for quantum neural networks

Abstract This work focuses on investigating an end-to-end learning approach for quantum neural networks (QNN) on noisy intermediate-scale quantum devices. The proposed model combines a quantum tensor network (QTN) with a variational quantum circuit (VQC), resulting in a QTN-VQC architecture. This architecture integrates a QTN with a horizontal or vertical structure related to the implementation of quantum circuits for a tensor-train network. The study provides theoretical insights into the quantum advantages of the end-to-end learning pipeline based on QTN-VQC from two perspectives. The first perspective refers to the theoretical understanding of QTN-VQC with upper bounds on the empirical error, examining its learnability and generalization powers; The second perspective focuses on using the QTN-VQC architecture to alleviate the Barren Plateau problem in the training stage. Our experimental simulation on CPU/GPUs is performed on a handwritten digit classification dataset to corroborate our proposed methods in this work.
Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems.
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.
On-Board Real-Time Pedestrian Detection for Micro Unmanned Aerial Vehicles Based on YOLO-v8

Nowadays, allowing unmanned aerial vehicles (UAVs) to accompany humans in daily life has become a hot topic. Pedestrian detection plays an important role on this application with its accuracy and real-time detection. In this paper, we design an on-board real-time pedestrian detection method for micro UAVs based on YOLO-v8 network. More specifically, several custom network models of small scales are fine-tuned based on YOLO-v8 to detect pedestrians in real-time. Besides, our detection methods are implemented and deployed on a custom micro UAV. Through simulation and real-world experiments, the results show that the custom detection network models based on YOLO-v8 can accurately detect pedestrians at up to 34 FPS on Jetson Xavier NX.
Deep learning-based medical image analysis with explainable transfer learning

Along with the remarkable progress of deep learning-based medical image analysis (DLB-MIA), deep learning models are widely deployed for computer-aided diagnosis (CAD). However, Data scarcity and model interpretability pose noteworthy challenges to DLB-MIA application. Explainable artificial intelligence (XAI) can be applied in transfer learning to address the aforementioned problems, which makes explainable transfer learning a promising methodology. The utilization of transfer learning combined with XAI techniques is therefore surveyed. The current status of explainable transfer learning is summarized. The application of explainable transfer learning in DLB-MIA is investigated respectively on convolutional neural networks (CNNs) and transformers.
Sem@K: Is my knowledge graph embedding model semantic-aware?

Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w.r.t. domain and range constraints. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@K. We also perform an extensive study to qualify the abilities of KGEMs as measured by our metric. Our experiments show that Sem@K provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offers different conclusions on the predictive power of models. Regarding Sem@K, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.
A Survey on Blockchain-Based Federated Learning

Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.