Reviewerly/exHarmony huggingface dataset:
Hand Tracking with Vibrotactile Feedback Enhanced Presence, Engagement, Usability, and Performance in a Virtual Reality Rhythm Game

Recent hand tracking systems and wearable haptic devices have contributed to enhancing user experience in virtual environments. However, controllers are still prevalent in virtual reality (VR) games as a main interaction device. Also, haptic devices are rare and not widely accepted by users. In this article, we examined the effects of interaction methods and vibrotactile feedback on users’ sense of presence, engagement, usability, and task performance in a VR game. In our experiment, 36 participants wearing VR goggle played a rhythm game under three conditions: (1) VR controllers, (2) hand tracking without vibrotactile feedback, and (3) hand tracking with vibrotactile feedback at fingertips through the gloves we developed. Results showed that hand tracking improved presence, engagement, usability, and performance, compared to the VR controller. Furthermore, vibrotactile feedback enhanced presence and engagement even more clearly. Results are discussed with the components of VR user experience and practical design guidelines.
Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations

Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of param-eters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.
Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning

The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vulnerable to adversarial attacks. This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and implementing explainable AI techniques. In our experiments with the NSL-KDD dataset, the PGD adversarial-trained model was a more robust model than DeepFool adversarial-trained and FGSM adversarial-trained models, with a ROC-AUC of 0.87. The FGSM attack did not affect the PGD adversarial-trained model’s ROC-AUC, while the DeepFool attack caused a minimal 9.20% reduction in PGD adversarial-trained model’s ROC-AUC. PGD attack caused a 15.12% reduction in the DeepFool adversarial-trained model’s ROC-AUC and a 12.79% reduction in FGSM trained model’s ROC-AUC.
Solar Energy Resources and Photovoltaic Power Potential of an Underutilised Region: A Case of Alice, South Africa

Despite South Africa’s international recognition in solar energy investments, the country is struggling to meet its growing energy needs. In recent years, national blackouts and load shedding have been a recurring experience in the country. The high cost of electrification and the overstrained national grid have left several rural communities without access to electricity. This study aims to explore the solar energy resources and performance of a 3.8 kWp stand-alone residential photovoltaic (PV) power system in one of the underutilised regions in South Africa. The study mainly uses ground measured solar radiation data to evaluate the solar resources of Alice and compare them with those in other parts of the world with mega solar PV projects. The components of solar radiation considered are global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The average total daily GHI, DNI, and DHI of Alice were 4.98, 5.74, and 1.44 kWh/m2. Clear sky conditions were found to occur on 233 days in the monitoring year, resulting in an average total daily GHI of 6.13 kWh/m2, DNI of 6.73 kWh/m2 and DHI of 0.17 kWh/m2. The findings indicated that Alice possesses abundant solar resources for PV and concentrated solar power generation, and is comparable to other regions internationally.
An efficient and practical approach for privacy-preserving Naive Bayes classification

Nowadays, the development of machine learning has brought about tremendous benefits. Nevertheless, the process of building machine learning models can violate sensitive and private information in data, especially in some specific domains such as banking, medical, e-commerce. In this paper, we propose a novel privacy preservation solution for the Naive Bayes classification technique, a powerful machine learning algorithm. Because of being based on a new cryptographic protocol called secure multi-sum computation, the proposed solution not only protects data holders' privacy but also guarantees the classification model's accuracy. Moreover, the experimental results demonstrate that our solution outperforms the typical privacy-preserving Naive Bayes classifiers, and it is efficient in real-life applications.
Haze transfer and feature aggregation network for real-world single image dehazing

The absence of ground truth for hazy images in real scenes results in most dehazing models being trained only by synthesized datasets, which is not feasible for real hazy images due to the domain shift. To address this problem, a haze transfer and feature aggregation network, which includes a haze transfer subnetwork and a feature aggregation-based dehazing subnetwork, is developed in this study. In the haze transfer subnetwork, a discriminator group is designed to transfer haze information from real hazy images to clear images. The generated hazy image and its corresponding clear image form a training sample pair to solve the domain shift problem. The deep–shallow feature correlation has been considered by feature aggregation in the dehazing subnetwork. Information related to the haze-free image is highlighted, and the guidance of clear images can be fully used to prevent detailed information loss during feature extraction. The proposed method provides a new aspect of usage for real hazy images during training and improves the scalability of dehazing models. The experimental results show that the dehazing performance of the proposed method outperforms the state-of-the-art dehazing methods compared. The algorithm code is available at https://github.com/lhf12278/HTFA-Net.
Variational quantum evolution equation solver

Variational quantum algorithms offer a promising new paradigm for solving partial differential equations on near-term quantum computers. Here, we propose a variational quantum algorithm for solving a general evolution equation through implicit time-stepping of the Laplacian operator. The use of encoded source states informed by preceding solution vectors results in faster convergence compared to random re-initialization. Through statevector simulations of the heat equation, we demonstrate how the time complexity of our algorithm scales with the Ansatz volume for gradient estimation and how the time-to-solution scales with the diffusion parameter. Our proposed algorithm extends economically to higher-order time-stepping schemes, such as the Crank-Nicolson method. We present a semi-implicit scheme for solving systems of evolution equations with non-linear terms, such as the reaction-diffusion and the incompressible Navier-Stokes equations, and demonstrate its validity by proof-of-concept results.
Integration of Project-Based E-Learning with STEAM: An Innovative Solution to Learn Ecological Concept

The Science, Technology, Engineering, Art and Mathematics (STEAM) approach is increasingly used as it is believed to be capable of facilitating students to understand subjects and adjust to the 21 st century competency framework.Integrating a project-based learning (PjBL) model with STEAM is part of good teaching practice.The PjBL-STEAM learning is conducted online (e-learning) to facilitate students in accessing learning content due to the COVID-19 pandemic.The research analyzes the PjBeL-STEAM learning model on the improvement of students' concept mastery in ecological concepts.The learning emphasizes project activities by conducting an independent practicum and creating an animation of biogeochemical cycles.The research involves 72 students of Grade 10 in one of the senior high schools (SMA) in Jakarta.Instruments employed in the research include pretest and posttest of the mastery of ecological concepts.Data is analyzed using independent sample t-test.The research results indicate that the PjBeL-STEAM learning model improves students' mastery of ecological concepts.The research has a limitation related to the assessment of students' attitudes that is not conducted due to online learning.
Exploiting temporal coherence for self-supervised visual tracking by using vision transformer

Deep learning based fully-supervised visual trackers entail the requirement of large-scale and frame-wise annotation that needs a laborious and tedious data annotation process. To reducing the amount of labeled efforts, a self-supervised learning framework, the ETC, is proposed in this work that exploits temporal coherence as a self-supervised signal and uses visual transformer to capture the relationship among the unlabeled video frames. We design a cycle-consistent transformer architecture to cast self-supervised tracking as cycle prediction problems. With carefully-designed and targeted configurations for cycle-consistent transformer including temporal sampling strategies, tracking initialization and data augmentation, our approach is applicable for two tracking settings, i.e., the unlabeled sample (ULS) scene and the few labeled sample (FLS) scene. To learn richer and more discriminative representations, we not only utilize the inter-frame correspondence, but also conduct the intra-frame correspondence to effectively model the target-to-frame and long-range correspondence. Extensive experiments are conducted on the popular benchmark datasets OTB2015, VOT2018, UAV123, TColor-128, NFS and LaSOT, and the results show that our approach achieves competitive results in the ULS setting, and supplies a trade-off between performance and annotation cost in the FLS setting.
A Survey on Human Cancer Categorization Based on Deep Learning

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.
Adopting Voice Assistants in Online Shopping: Examining the Role of Social Presence, Performance Risk, and Machine Heuristic

Although voice commerce using voice assistants (VA) in shopping is burgeoning, little is known about the effect of the manner of interaction with VAs in shopping. To fill this gap, this study applied the concept of message interactivity by investigating the key factors that influence intention to use VAs embedded in smart speakers for online shopping. Specifically, we analyzed consumers' responses to the adoption of VAs with the mediating variable, such as social presence and performance risk as well as the moderating variable, machine heuristic using a lab experiment (N = 62). Our results showed that individuals who experienced contingent conversations with Alexa reported greater feelings of social presence, and less perceptions of performance risk. Moreover, these social presence and performance risk mediated the effect of message interactivity on perceived usefulness, which in turn, predicted the intention to use a VA for an online commerce platform. These results also suggested that voice commerce platforms can increase the positive impact of message interactivity by amplifying machine heuristic.
Cross-modality disentanglement and shared feedback learning for infrared-visible person re-identification

Infrared-visible person re-identification (IV-ReID) has become a research hotspot in the field of computer vision. Compared with traditional person re-identification, the IV-ReID task is still very challenging due to huge difference between modalities. Most existing approaches are designed to bridge the cross-modality gap through single feature-level constraints, but the results are not very satisfactory. To this end, a novel cross-modality disentanglement and shared feedback (CMDSF) learning framework is proposed. The framework consists of a cross-modality images disentanglement network (CMIDN) and a dual-path shared feedback learning network (DSFLN). Specifically, the former uses a pairing strategy to more efficiently disentangle the cross-modality features and constrain the feature distribution distances between modalities. It achieves modality-level alignment while maintaining specific identity-consistency. The latter adopts a dual-path shared module (DSM) to obtain discriminative mid-level feature information, and achieves feature-level alignment. Furthermore, a feedback scoring module (FSM) with a negative feedback mechanism is proposed to compensate for the weak supervision defect of objective loss during backpropagation. It optimizes model parameters by providing a strong feedback signal. In summary, we propose an efficient learning framework with two parts jointly trained and optimized in an end-to-end manner. Extensive experimental results on two cross-modality datasets demonstrate that our method achieves a competitive performance compared with the state-of-the-art methods. • The pairing strategy of cross-modality images disentanglement network (CMIDN) reduces the feature distribution distance between the infrared set and the visible set. The pairing strategy reduces the feature distribution distance between the infrared and visible sets. • A dual-path shared module is proposed to mine the middle-level discriminative information. • The feedback scoring module provides a strong feedback signal to optimize the model parameters. • The proposed CMIDN and DSFLN sub-networks achieve modality-level alignment and feature level alignment in an end-to-end manner. The proposed CMIDN and DSFLN sub-networks achieve modality-level and feature-level alignment in an end-to-end manner.
ZipZap: A Blockchain Solution for Local Energy Trading

In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.
Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements

The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) (2)error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) (2)error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.
Hyper-node Relational Graph Attention Network for Multi-modal Knowledge Graph Completion

Knowledge graphs often suffer from incompleteness, and knowledge graph completion (KGC) aims at inferring the missing triplets through knowledge graph embedding from known factual triplets. However, most existing knowledge graph embedding methods only use the relational information of knowledge graph and treat the entities and relations as IDs with simple embedding layer, ignoring the multi-modal information among triplets, such as text descriptions, images, etc. In this work, we propose a novel network to incorporate different modal information with graph structure information for more precise representation of multi-modal knowledge graph, termed as hyper-node relational graph attention (HRGAT) network. In HRGAT, we use low-rank multi-modal fusion to model the intra-modality and inter-modality dynamics, which transforms the original knowledge graph to a hyper-node graph. Then, relational graph attention (RGAT) network is used, which contains relation-specific attention and entity-relation fusion operation to capture the graph structure information. Finally, we aggregate the updated multi-modal information and graph structure information to generate the final embeddings of knowledge graph to achieve KGC. By exploring multi-modal information and graph structure information, HRGAT embraces faster convergence speed and achieves the state-of-the-art for KGC on the standard datasets. Implementation code is available at https://github.com/broliang/HRGAT.
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems

Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the cost of examining all the possible higher-order feature interactions is prohibitive (exponentially growing with the order increasing). Hence existing approaches only detect limited order (e.g., combinations of up to four features) beneficial feature interactions, which may miss beneficial feature interactions with orders higher than the limitation. In this paper, we propose a hypergraph neural network based model named HIRS. HIRS is the first work that directly generates beneficial feature interactions of arbitrary orders and makes recommendation predictions accordingly. The number of generated feature interactions can be specified to be much smaller than the number of all the possible interactions and hence, our model admits a much lower running time. To achieve an effective algorithm, we exploit three properties of beneficial feature interactions, and propose deep-infomax-based methods to guide the interaction generation. Our experimental results show that HIRS outperforms state-of-the-art algorithms by up to 5% in terms of recommendation accuracy.
Rolling bearing fault diagnosis based on quantum LS-SVM

Abstract Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect the state of the entire industrial system. Therefore, there is great engineering value to researching and improving the fault diagnosis technology of rolling bearings. However, with the involvement of the whole mechanical equipment, we need to have a large quantity of data to support the accuracy of fault diagnosis, while the efficiency of classical machine learning algorithms is poor in processing big data, and huge amount of computing resources is required. To solve this problem, this paper combines the HHL algorithm in quantum computing with the LS-SVM algorithm in machine learning and proposes a fault diagnosis model based on a quantum least square support vector machine (QSVM). Based on experiments simulated on analog quantum computers, we demonstrate that our fault diagnosis based on QSVM is feasible, and it can play a far superior advantage over the classical algorithm in the context of big data.