Reviewerly/exHarmony huggingface dataset:
Understanding User Identification in Virtual Reality Through Behavioral Biometrics and the Effect of Body Normalization

Virtual Reality (VR) is becoming increasingly popular both in the entertainment and professional domains. Behavioral biometrics have recently been investigated as a means to continuously and implicitly identify users in VR. Applications in VR can specifically benefit from this, for example, to adapt virtual environments and user interfaces as well as to authenticate users. In this work, we conduct a lab study (N = 16) to explore how accurately users can be identified during two task-driven scenarios based on their spatial movement. We show that an identification accuracy of up to 90% is possible across sessions recorded on different days. Moreover, we investigate the role of users’ physiology in behavioral biometrics by virtually altering and normalizing their body proportions. We find that body normalization in general increases the identification rate, in some cases by up to 38%; hence, it improves the performance of identification systems.
Usability evaluation of a virtual reality smartphone app for a living museum

Abstract This paper elaborates the empirical evidence of a usability evaluation of a VR and non-VR virtual tour application for a living museum. The System Usability Scale (SUS) was used in between participants experiments (Group 1: non-VR version and Group 2: VR version) with 40 participants. The results show that the mean scores of all components for the VR version are higher compared to the non-VR version, overall SUS score (72.10 vs 68.10), usability score (75.50 vs 71.70), and learnability (58.40 vs 57.00). Further analysis using a two-tailed independent t test showed no difference between the non-VR and VR versions. Additionally, no significant difference was observed between the groups in the context of gender, nationality, and prior experience (other VR tour applications) for overall SUS score, usability score, and learnability score. Α two-tailed independent t test indicated no significant difference in the usability score between participants with VR experience and no VR experience. However, a significant difference was found between participants with VR experience and no VR experience for both SUS score ( t (38) = 2.17, p = 0.037) and learnability score ( t (38) = 2.40, p = 0.021). The independent t test results indicated a significant difference between participant with and without previous visits to SCV for the usability score ( t (38) = −2.31, p = 0.027), while there was no significant differences observed in other components. It can be concluded that both versions passed based on the SUS score. However, the sub-scale usability and learnability scores indicated some usability issue.
Media forensics on social media platforms: a survey

Abstract The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future.
Self-Training With Progressive Representation Enhancement for Unsupervised Cross-Domain Person Re-Identification

In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two domains, it remains challenging to directly deploy a model trained on a labeled source domain to a target domain only with unlabeled data available. In this paper, a Self-Training with Progressive Representation Enhancement (PREST) framework, which comprises a multi-scale self-training method and a view-invariant representation learning module, is proposed to promote re-ID performance on the target domain in an unsupervised manner. More specifically, multi-scale representations, including the global body and local parts of pedestrian images, are utilized to obtain pseudo-labels. Then, some images are selected according to the pseudo-labels to create a new dataset for supervising the fine-tuning process, which is operated iteratively to progressively promote the performance. Furthermore, to mitigate the influence of different styles among sub-domains, in cases where a single sub-domain is captured by one camera, a classifier with a gradient reverse layer is first employed to learn view-invariant representation for pedestrian images with the same identity taken by different cameras; this can further enhance the reliability of the predicted labels and improve the cross-domain re-ID performance. Extensive experiments on three large-scale re-ID datasets demonstrate that our framework achieves significantly better performance than existing approaches.
Blockchain-Enabled Two-Way Auction Mechanism for Electricity Trading in Internet of Electric Vehicles

As people pay more attention to environment protection, the number of electric vehicles (EVs) is gradually increasing. Energy trading management for EVs is becoming a challenge. However, existing research has not considered the problem of information sharing between energy traders and issues surrounding the protection of user privacy. Therefore, in this article, we propose a vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) electricity trading architecture based on blockchain. All energy transactions of EVs can be recorded on the blockchain ledger to ensure privacy and smart contracts work as agents for pricing and optimal energy allocation. Furthermore, we introduce a two-way auction mechanism based on the Bayesian game and design a new price adjustment strategy. Finally, we propose a bidirectional auction mechanism based on the Bayesian game approach. We use extensive simulations to evaluate the performance of our proposed algorithm. Simulation results show that the social welfare and cost performance of our algorithm can be improved by up to 102.8% and 319%, respectively.
Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders

We propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE model learns to encode images in a categorical latent space. The prior distribution of latent codes is then modelled using an Auto-Regressive (AR) model. We found that the prior probability estimated by the AR model can be useful for unsupervised anomaly detection and enables the estimation of both sample and pixel-wise anomaly scores. The sample-wise score is defined as the negative log-likelihood of the latent variables above a threshold selecting highly unlikely codes. Additionally, out-of-distribution images are restored into in-distribution images by replacing unlikely latent codes with samples from the prior model and decoding to pixel space. The average L1 distance between generated restorations and original image is used as pixel-wise anomaly score. We tested our approach on the MOOD challenge datasets, and report higher accuracies compared to a standard reconstruction-based approach with VAEs.
Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation

Cold-start recommendation is a challenging problem due to the lack of user-item interactions. Recently, heterogeneous information network~(HIN)-based recommendation methods use rich auxiliary information to enhance users and items' connections, helping alleviate the cold-start problem. Despite progress, most existing methods model HINs under traditional supervised learning settings, ignoring the gaps between training and inference procedures in cold-start scenarios. In this paper, we regard cold-start recommendation as a missing data problem where some user-item interaction data are missing. Inspired by denoising auto-encoders that train a model to reconstruct the input from its corrupted version, we propose a novel model called Multi-view Denoising Graph Auto-Encoders~(MvDGAE) on HINS. Specifically, we first extract multifaceted meaningful semantics on HINs as multi-views for both users and items, effectively enhancing user/item relationships on different aspects. Then we conduct the training procedure by randomly dropping out some user-item interactions in the encoder while forcing the decoder to use these limited views to recover the full views, including the missing ones. In this way, the complementary representations for both users and items are more informative and robust to adjust to cold-start scenarios. Moreover, the decoder's reconstruction goals are multi-view user-user and item-item relationship graphs rather than the original input graphs, which make the features of similar users (or items) in the meta-paths closer together. Finally, we adopt a Bayesian task weight learner to balance multi-view graph reconstruction objectives automatically. Extensive experiments on both public benchmark datasets and a large-scale industry dataset WeChat Channel demonstrate that MvDGAE significantly outperforms the state-of-the-art recommendation models in various cold-start scenarios. The case studies also illustrate that MvDGAE has potentially good interpretability.
Video Object Segmentation Using Global and Instance Embedding Learning

In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into a binary segmentation task and tackle each instance independently. In contrast, we decompose the VOS task into two subtasks: global embedding learning that segments foreground objects of each frame in a pixel-to-pixel manner, and instance feature embedding learning that separates instances. The outputs of these two subtasks are fused to obtain the final instance masks quickly and accurately. Through using the relation among different instances per-frame as well as temporal relation across different frames, the proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner. Extensive experimental results on the challenging DAVIS[34] and Youtube-VOS [57] datasets show that our method achieves better performances than most counterparts in each case.
The Role of Business Intelligence Systems on Green Supply Chain Management: Empirical Analysis of FMCG in the UAE

This study empirically examines the effect of Business Intelligence Systems on internal greening processes (7Ps) and external greening practices (collaborations, social concerns, corporate environmental management, supply chain management) and measures their influences on firms' green performances. This current study is quantitative, and a survey was prepared. The study's primary data was collected using a convenient sampling method to accomplish the study objectives as the focus is on the empirical investigation of firms' green performances. A structured questionnaire was used, and measuring constructs were measured using 7 points Likert scale. Results revealed that Business Intelligence Systems and internal and external greenings collaboration positively impact firms' green performances, which in turn helps to enhance their positions.
Instance-dependent cost-sensitive learning for detecting transfer fraud

Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting fraudulent transactions is a binary classification problem. Classification models are commonly trained and evaluated in terms of statistical performance measures, such as likelihood and AUC, respectively. These measures, however, do not take into account the actual business objective, which is to minimize the financial losses due to fraud. Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model. In this article, an instance-dependent threshold is derived, based on the instance-dependent cost matrix for transfer fraud detection, that allows for making the optimal cost-based decision for each transaction. Two novel classifiers are presented, based on lasso-regularized logistic regression and gradient tree boosting, which directly minimize the proposed instance-dependent cost measure when learning a classification model. The proposed methods are implemented in the R packages cslogit and csboost, and compared against state-of-the-art methods on a publicly available data set from the machine learning competition website Kaggle and a proprietary card transaction data set. The results of the experiments highlight the potential of reducing fraud losses by adopting the proposed methods.
Quantum computing's potential for drug discovery: Early stage industry dynamics

Quantum computing (QC) is expected to revolutionize drug research by performing tasks classical supercomputers are not capable of. However, practically useful quantum computation is not yet a reality, and thus it is still unclear when and whether QC will be capable of solving real-world issues in drug discovery. By identifying the QC-related activities of pharmaceutical companies, startups, and academia in the field of drug discovery and development, we show that QC has gained traction across all of these stakeholder groups, that there is focus on developing utilities related to lead optimization and compound screening, and that there is a need for collaboration in the highly dynamic QC ecosystem.
Unsupervised Summarization Approach With Computational Statistics of Microblog Data

Microblogging, where millions of users exchange messages to share their opinions on different trending and non-trending topics, is one of the popular communication media in recent times. Several researchers are concentrating on these data due to a huge source of information exchanges in online social media. In platforms such as Twitter, dataset-generated lacks coherence, and manually extracting meaning or knowledge from them proves to be painstakingly difficult. It opens up the challenges to the researchers for knowledge extraction driven by a summarization approach. Therefore, automated summary generation tools are recommended to get a meaningful summary out of a given topic becomes crucial in the age of big data. In this work, an unsupervised, extractive summarization model has been proposed. For categorization of data, k-means algorithm has been used, and based on scoring of each document in the corpus, summarization model is designed. The proposed methodology achieves an improved outcome over existing methods, such as lexical rank, sum basic, LSA, etc. evaluated by rouge tool.
Multiscale generative adversarial network for <scp>real‐world super‐resolution</scp>

Summary Recently, most deep convolutional neural networks used for image super‐resolution have achieved impressive performance on ideal datasets. However, these methods always fail in real‐world super‐resolution, and the results are blurred and structurally deformed. In this paper, a multiscale generative adversarial network (MGAN) is proposed to alleviate these issues. The model's multiscale loss function can effectively reduce the solution space and obtain the best features to reconstruct the image. The degraded framework based on kernel estimation and noise injection is mainly applied to obtain LR images that share the same domain with real‐world pictures. Moreover, the gradient branch is presented to provide other structural priors for SR processing. Simultaneously, to obtain better visual effects, LPIPS is used for perceptual losses instead of Visual Geometry Group (VGG). The competitive results show that our MGAN model outperforms the state‐of‐the‐art methods, resulting in lower noise and better visual quality, and reflects the superiority in image structure restoration.
Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues that rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labeled data, which has little knowledge behind the instance-label pairs. When a deep network continually learns over time by accommodating new tasks, it usually quickly overwrites the knowledge learned from previous tasks. Referred to as the neural variability, it is well known in neuroscience that human brain reactions exhibit substantial variability even in response to the same stimulus. This mechanism balances accuracy and plasticity/flexibility in the motor learning of natural nervous systems. Thus, it motivates us to design a similar mechanism, named artificial neural variability (ANV), that helps artificial neural networks learn some advantages from "natural" neural networks. We rigorously prove that ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. This result theoretically guarantees ANV a strictly improved generalizability, robustness to label noise, and robustness to catastrophic forgetting. We then devise a neural variable risk minimization (NVRM) framework and neural variable optimizers to achieve ANV for conventional network architectures in practice. The empirical studies demonstrate that NVRM can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
Construction of Video Courses of Physical Education and Health Education in Colleges and Universities under the MOOC Platform

In the traditional construction process of educational video courses for physical and health education in universities, numerous problems are encountered, such as small storage capacity, significant delay error, and decreased information throughput rate. Therefore, an educational video course for physical and health education in universities based on the Massive Open Online Course (MOOC) platform is constructed. The course is built by constructing a creative environment for video courses, establishing a video course model under the MOOC platform, and the caching code design for video courses. Upon numerous modifications and test runs, with the campus network’s help, the construction of a network teaching video for physical and health theory courses is realized. Our proposed approach’s experimental result shows that the designed course’s frame rate is 10% higher than that of the traditional course designing. The node’s storage capacity is up to 130%, the delay error is significantly reduced, and the information throughput rate is not affected by the increase of network load.
A robust multiobjective Harris’ Hawks Optimization algorithm for the binary classification problem

The Harris’ Hawks Optimization (HHO) is a recent metaheuristic inspired by the cooperative behavior of the hawks. These avians apply many intelligent techniques like surprise pounce (seven kills) while they are catching their prey according to the escaping patterns of the target. The HHO simulates these hunting patterns of the hawks to obtain the best/optimal solutions to the problems. In this study, we propose a new multiobjective HHO algorithm for the solution of the well-known binary classification problem. In this multiobjective problem, we reduce the number of selected features and try to keep the accuracy prediction as maximum as possible at the same time. We propose new discrete exploration (perching) and exploitation (besiege) operators for the hunting patterns of the hawks. We calculate the prediction accuracy of the selected features with four machine learning techniques, namely, Logistic Regression, Support Vector Machines, Extreme Learning Machines, and Decision Trees. To verify the performance of the proposed algorithm, we conduct comprehensive experiments on many benchmark datasets retrieved from the University of California, Irvine (UCI) Machine Learning Repository. Moreover, we apply it to a recent real-world dataset, i.e., a Coronavirus disease (COVID-19) dataset. Significant improvements are observed during the comparisons with state-of-the-art metaheuristic algorithms. • We propose a multiobjective Harris’ Hawks Optimization algorithm (MHHO). • MHHO minimizes the number of features and maximizes the classification accuracy. • New discrete optimization operators are proposed for exploration and exploitation. • Experiments are performed on 13 benchmark UCI datasets and a COVID-19 dataset. • Solutions of MHHO are competitive with the state-of-the-art metaheuristics.
Multi-UAV Collaborative Transportation of Payloads With Obstacle Avoidance

Control barrier functions have been widely studied and applied to safety-critical systems, including multi-agent obstacle avoidance problems. In this work, we apply control barrier functions to a collaborative transportation problem involving two unmanned aerial vehicles (UAVs) moving a payload around obstacles as they deliver it to a target location. We develop a target-tracking controller for the UAVs, which is constrained to meet the requirements of payload dynamics and obstacle avoidance. We also present simulation results to demonstrate the benefits of the proposed problem formulation for a multi-obstacle environment.