Reviewerly/exHarmony huggingface dataset:
Teaching &amp; Learning in Virtual Reality: Metaverse Classroom Exploration

Virtual reality (VR) offers a potentially engaging environment for teaching college students. The faculty of an interaction design program is currently creating a college course where students are taught entirely in a VR classroom. The curriculum and study methodology are simultaneously in the development process and intend to measure the impact of such a course on students and instructors. The research questions in consideration involve understanding the needs for facilitating a classroom in a virtual world while ensuring learning occurs. The faculty plans to use the metaverse as a dual workspace and classroom for student participants. The metaverse is an online social experience where students interact through an internet connection in a seemingly lifelike digital space. Additional questions involve the adoption of a beta of the Meta Horizon Workrooms and whether the technology offers a viable platform for virtual classroom instruction. The proposed technology integrations include video conferencing, spatial audio, and additional app integrations for increasing student engagement and academic performance.
The Impact of Mobile-Assisted Language Learning on English as a Foreign Language Learners’ Vocabulary Learning Attitudes and Self-Regulatory Capacity

Over the past decades, English as a foreign language (EFL) learning has witnessed a heightened interest in the role of mobile-assisted language learning (MALL) in vocabulary learning. To shed more light on the impact of MALL on vocabulary learning, this study, employing a quantitative longitudinal design, aimed at examining the impact of a MALL programme on 139 EFL learners' vocabulary learning attitudes and self-regulatory capacity. To this end, this study investigated the latent change score models of the learners' vocabulary learning attitudes and self-regulatory capacity over time. Over the course of 1 year, various mobile applications were integrated into the regular English language instruction of the learners. The required data were collected via administering vocabulary learning attitude and self-regulating capacity in vocabulary learning scales. The data were analysed applying latent growth curve modelling to examine the participants' longitudinal trajectories and patterns of change in the two waves of collected data. The fit indices of the latent change models revealed an increase in both the EFL learners' vocabulary learning attitudes and their self-regulatory capacity during the 1-year MALL programme. The analysis of between-person differences also indicated that changes in both variables were positively correlated.
Hu-Fu

Data isolation has become an obstacle to scale up query processing over big data, since sharing raw data among data owners is often prohibitive due to security concerns. A promising solution is to perform secure queries over a federation of multiple data owners leveraging secure multi-party computation (SMC) techniques, as evidenced by recent federation work over relational data. However, existing solutions are highly inefficient on spatial queries due to excessive secure distance operations for query processing and their usage of general-purpose SMC libraries for secure operation implementation. In this paper, we propose Hu-Fu, the first system for efficient and secure spatial query processing on a data federation. The idea is to decompose the secure processing of a spatial query into as many plaintext operations and as few secure operations as possible, where fewer secure operators are involved and all secure operators are implemented dedicatedly. As a working system, Hu-Fu supports not only query input in native SQL, but also heterogeneous spatial databases ( e.g. , PostGIS, Simba, GeoMesa, and SpatialHadoop) at the backend. Extensive experiments show that Hu-Fu usually outperforms the state-of-the-arts in running time and communication cost while guaranteeing security.
Selective data acquisition in the wild for model charging

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world supervised machine learning (ML) tasks. In this paper, we study a new problem, namely selective data acquisition in the wild for model charging : given a supervised ML task and data in the wild (e.g., enterprise data warehouses, online data repositories, data markets, and so on), the problem is to select labeled data points from the data in the wild as additional train data that can help the ML task. It consists of two steps (Fig. 1). The first step is to discover relevant datasets ( e.g. , tables with similar relational schema), which will result in a set of candidate datasets. Because these candidate datasets come from different sources and may follow different distributions, not all data points they contain can help. The second step is to select which data points from these candidate datasets should be used. We build an end-to-end solution. For step 1, we piggyback off-the-shelf data discovery tools. Technically, our focus is on step 2, for which we propose a solution framework called AutoData. It first clusters all data points from candidate datasets such that each cluster contains similar data points from different sources. It then iteratively picks which cluster to use, samples data points ( i.e. , a mini-batch) from the picked cluster, evaluates the mini-batch, and then revises the search criteria by learning from the feedback ( i.e. , reward) based on the evaluation. We propose a multi-armed bandit based solution and a Deep Q Networks-based reinforcement learning solution. Experiments using both relational and image datasets show the effectiveness of our solutions.
Towards Trustworthy AI in Dentistry

Medical and dental artificial intelligence (AI) require the trust of both users and recipients of the AI to enhance implementation, acceptability, reach, and maintenance. Standardization is one strategy to generate such trust, with quality standards pushing for improvements in AI and reliable quality in a number of attributes. In the present brief review, we summarize ongoing activities from research and standardization that contribute to the trustworthiness of medical and, specifically, dental AI and discuss the role of standardization and some of its key elements. Furthermore, we discuss how explainable AI methods can support the development of trustworthy AI models in dentistry. In particular, we demonstrate the practical benefits of using explainable AI on the use case of caries prediction on near-infrared light transillumination images.
Polymetallic mineralization prospectivity modelling using multi-geospatial data in logistic regression: The Diapiric Zone, Northeastern Algeria

Prospecting and exploring minerals present major challenges in tectonically complex regions for sustainable development as in Northeastern Algeria. This area is promising for its mineral potential, especially the metallogenic province ‘The Diapiric Zone’. This study concerns mapping and predicting potential polymetallic mineralization locations by integration of remote sensing, gravity, and magnetic datasets. Several enhancement and processing methods have been applied on Landsat8_OLI and ASTER_1T remote sensed data to reduce uncertainty for achieving the best detection of hydrothermal alteration zones and lithological mapping. Furthermore, the Centre for Exploration Targeting grid analysis technique, the contact occurrence density and entropy orientation tools were employed on ground-gravity and aeromagnetic data to understand and visualize the pathways for hydrothermal fluids circulation of mineral deposits. The polymetallic mineralization prospective areas were produced using a logistic regression model on the resulting multifactor. High zones of lead-zinc cover most the area that has been confirmed by field investigation.
AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.
Driven by technology or sociality? Use intention of service robots in hospitality from the human–robot interaction perspective

The technology and sociality of service robots in the hospitality industry are integrated into iTAM from the perspective of human–robot interactions. The empirical results of the functional-social perceived model show that both functional and social perceptions mediate the relationship between human–robot interaction and the intention to use robots, and that the mediating effect of technology is stronger than that of social perception. In terms of functional perception, the mediating effect of perceived usefulness was stronger than that of perceived ease of use. In terms of social perception, the mediating effect of perceived enjoyment was significant, while the mediating effect of rapport and trust was not. Therefore, customers' perceived enjoyment is stronger than that of rapport and trust. • HRI is explored by integrating robots’ intelligence and users’ self-efficacy. • Both functional and social perception mediate use intention in HRI. • The mediating effect of perceived usefulness is stronger than ease of use. • Enjoyment plays mediating role significantly compared with rapport and trust.
Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation

• We analyzed the analytical quality of 36 commercial laboratories from Brazil and Paraguay. • Outlying attributes in spectral clusters detected by adjusted method to the data distribution. • Spectra-based systematic sampling had attributes equally distributed among the subsets. • Nearly 25% laboratories had the potential to reduce the need for laboratory analysis by at least 50%. We need innovative solutions to help laboratories to rapidly characterize the soils and adopt internal quality controls with lower costs and ecosystem impacts. Soil spectroscopy emerged as an alternative to wet chemistry. However, soil laboratories still do not widely use this technology in their routines, mainly due to the lack of: i) standards and protocols, ii) spectral libraries, iii) capacity in spectral methods, and iv) professionals with expertise in chemometrics. Therefore, we aimed to provide basic guidelines for the use of soil spectra in laboratory routines regarding internal quality control and samples selection for analysis and prediction. We used 350–2500 nm spectra and unsupervised random forests to compute proximity matrices and cluster spectrally similar soil samples for outlier detection using an adjusted method for skewed distribution, which allowed us to establish an internal quality control. Then, we used the most important wavelengths from unsupervised random forest to order soil samples by laboratory and subset them into different set sizes for training and testing prediction models for clay, sand, organic matter cation exchange capacity and base saturation. Our results indicated that internal quality control based on soil spectra and unsupervised analysis can be implemented for laboratory routines. Spectra-based soil samples selection and attributes predictions can reduce the need for laboratory analysis by half. Soil spectroscopy can become an important alternative to improve traditional analytical results, save time, reduce costs, and mitigate environmental impact with acceptable accuracy. Soil data and full codes from this study are publicly available at https://github.com/raulpoppiel/urssa .
Improving machine translation systems via isotopic replacement

Machine translation plays an essential role in people's daily international communication. However, machine translation systems are far from perfect. To tackle this problem, researchers have proposed several approaches to testing machine translation. A promising trend among these approaches is to use word replacement, where only one word in the original sentence is replaced with another word to form a sentence pair. However, precise control of the impact of word replacement remains an outstanding issue in these approaches.
ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping

Retailers such as grocery stores or e-marketplaces often have vast selections of items for users to choose from. Predicting a user's next purchases has gained attention recently, in the form of next basket recommendation (NBR), as it facilitates navigating extensive assortments for users. Neural network-based models that focus on learning basket representations are the dominant approach in the recent literature. However, these methods do not consider the specific characteristics of the grocery shopping scenario, where users shop for grocery items on a regular basis, and grocery items are repurchased frequently by the same user.
Generative Adversarial Framework for Cold-Start Item Recommendation

The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models perform badly for cold items which haven't emerged in the training set. The most common solutions are to generate the cold embedding for the cold item from its content features. However, the cold embeddings generated from contents have different distribution as the warm embeddings are learned from historical interactions. In this case, current cold-start methods are facing an interesting seesaw phenomenon, which improves the recommendation of either the cold items or the warm items but hurts the opposite ones. To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). By training the generator and the recommender adversarially, the generated cold item embeddings can have similar distribution as the warm embeddings that can even fool the recommender. Simultaneously, the recommender is fine-tuned to correctly rank the "fake'' warm embeddings and the real warm embeddings. Consequently, the recommendation of the warms and the colds will not influence each other, thus avoiding the seesaw phenomenon. Additionally, GAR could be applied to any off-the-shelf recommendation model. Experiments on two datasets present that GAR has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN-based model (improved by over 17.78%).
NeuronFair

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~ ×5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems. The code of NeuronFair is open-sourced at https://github.com/haibinzheng/NeuronFair.
A Temporal-Aware Relation and Attention Network for Temporal Action Localization

Temporal action localization is currently an active research topic in computer vision and machine learning due to its usage in smart surveillance. It is a challenging problem since the categories of the actions must be classified in untrimmed videos and the start and end of the actions need to be accurately found. Although many temporal action localization methods have been proposed, they require substantial amounts of computational resources for the training and inference processes. To solve these issues, in this work, a novel temporal-aware relation and attention network (abbreviated as TRA) is proposed for the temporal action localization task. TRA has an anchor-free and end-to-end architecture that fully uses temporal-aware information. Specifically, a temporal self-attention module is first designed to determine the relationship between different temporal positions, and more weight is given to features within the actions. Then, a multiple temporal aggregation module is constructed to aggregate the temporal domain information. Finally, a graph relation module is designed to obtain the aggregated graph features, which are used to refine the boundaries and classification results. Most importantly, these three modules are jointly explored in a unified framework, and temporal awareness is always fully used. Extensive experiments demonstrate that the proposed method can outperform all state-of-the-art methods on the THUMOS14 dataset with an average mAP that reaches 67.6% and obtain a comparable result on the ActivityNet1.3 dataset with an average mAP that reaches 34.4%. Compared with A2Net (TIP20), PCG-TAL (TIP21), and AFSD (CVPR21) TRA can achieve improvements of 11.7%, 4.4%, and 1.8%, respectively on the THUMOS14 dataset.
H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve

• A hybrid method (H-ProMed) is proposed for accurate and robust prostate segmentation in TRUS images. • An optimized closed polygonal segment method is newly proposed to obtain data sequences. • An improved dynamic storage-based differential evolution method is newly used to assist in finding the optimal caputo fractional-order backpropagation training network. • A smooth mathematical model of the prostate contour is developed to express the prostate contour. The purpose of this work is to develop a method for accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images. These images are difficult to segment due to missing/ambiguous boundary between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes. This paper develops a novel hybrid method for TRUS prostate segmentation by combining an improved principal curve-based method with an evolutionary neural network; the former for achieving the data sequences while and the latter for improving the smoothness of the prostate contour. Both qualitative and quantitative experimental results showed that our proposed method achieved superior segmentation accuracy and robustness as compared to state-of-the-art methods. The average Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) of prostate contours against ground-truths were 96.8%, 95.7%, and 96.4%, and the DSC of around 92% and 95% for other deep learning and hybrid methods, respectively.
Dense Coding and Quantum Memory Assisted Entropic Uncertainty Relations in a Two‐Qubit State Influenced by Dipole and Symmetric Cross Interactions

Abstract Optimal dense coding and entropic uncertainty relation of quantum memory (EUR‐QM) of the two‐qubit Heisenberg chain model influenced by atomic dipole and Kaplan–Shekhtman–Entin–Wohlman–Aharony interactions are studied. The temporal evolution of the two functions at the phase decoherence environment with the Werner state as the initial state is discussed. The results imply that the dense coding and EUR‐QM have a contrary relationship, and an increase in the phase decoherence rate reduces the behavior of the two functions. The growth in coupling dipole interaction plays a role in oscillating and increasing the minimum bounds of the dense coding capacity. On the other hand, the effect of a thermal bath on the two functions at finite temperatures is investigated. The coding capacity suffers from sudden death as the temperature increases. The possibility of restraining the sudden death induced by the temperature may be enhanced by increasing the strength of the symmetric cross interaction. As the coupling of dipole increases, the EUR‐QM decreases while the coding capacity rises to its maximum value.
Cross-Resolution Distillation for Efficient 3D Medical Image Registration

Images captured in clinic such as MRI scans are usually in 3D formats with high spatial resolutions. Existing learning-based models for medical image registration consume large GPU memories and long inference time, which is difficult to be deployed in resource-limited diagnosis scenarios. To address this problem, instead of shrinking the model size as in previous works, we turn to reducing the input resolution of existing registration models and boosting their performance through knowledge distillation. Specifically, we propose a cross-resolution distillation (CRD) scheme, which is designed to train low-resolution models under the guidance of corresponding high-resolution models. Nevertheless, due to the resolution gap between features in high/low-resolution models, straightforward distillation is difficult to apply. To overcome this challenge, we first introduce a feature-shifted teacher (FST) to shift and fuse features of high/low-resolution models. Then, we exploit this teacher model to guide the learning of the low-resolution student model with distillation losses on both features and deformation fields. Finally, we only need to use the distilled student model during inference. Experimental results on four 3D medical image datasets demonstrate that the low-resolution models trained through our CRD scheme use fewer than 20% GPU memories and less than 20% inference time while achieving competitive performance compared with corresponding high-resolution models.