Reviewerly/exHarmony huggingface dataset:
Adaptive Field-of-view Restriction: Limiting Optical Flow to Mitigate Cybersickness in Virtual Reality

Dynamic field-of-view (FOV) restriction is a widely used software technique to mitigate cybersickness in commercial virtual reality (VR) applications. The classical FOV restrictor is implemented using a symmetric mask that occludes the periphery in response to translational and/or angular velocity. In this paper, we introduce adaptive field-of-view restriction, a novel technique that responds dynamically based on real-time assessment of optical flow generated by movement through a virtual environment. The adaptive restrictor utilizes an asymmetric mask to obscure regions of the periphery with higher optical flow during virtual locomotion while leaving regions with lower optical flow visible. To evaluate the proposed technique, we conducted a gender-balanced user study (N = 38) in which participants completed in a navigation task in two different types of virtual scenes using controller-based locomotion. Participants were instructed to navigate through either close-quarter or open virtual environments using adaptive restriction, traditional symmetric restriction, or an unrestricted control condition in three VR sessions separated by at least 24 hours. The results showed that the adaptive restrictor was effective in mitigating cybersickness and reducing subjective discomfort, while simultaneously enabling participants to remain immersed for a longer amount of time compared to the control condition. Additionally, presence ratings were significantly higher when using the adaptive restrictor compared to symmetric restriction. In general, these results suggest that adaptive field-of-view restriction based on real-time measurement of optical flow is a promising approach for virtual reality applications that seek to provide a better cost-benefit tradeoff between comfort and a high-fidelity experience.
Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities

Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.
DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.
Tracking vision transformer with class and regression tokens

Aim of this work is to introduce a novel visual object tracking model based on siamese network and vision transformer. Tracking is performed by multiple tokens exploiting the learning and memorization capabilities of the vision transformers. Therefore, the tracking problem is divided into multiple sub-tasks and experiments by using multiple tokens for learning each individual sub-task. This makes possible to learn a robust characterization of the problem with an explainable architecture, understanding the motivation of the choice that the neural network does. This is due to the attention in the transformer that uses the representational capacity of tokens that allows one to identify, simply with respect to different architectures and methodologies, where all the interest is focused. Several experiments are performed on benchmark data proving to be among the most performing trackers compared with the state of the art in explainability, precision, robustness and speed.
A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.
Time-Optimal Trajectory Planning of 6-DOF Manipulator Based on Fuzzy Control

Currently, the teaching programming or offline programming used by an industrial manipulator can manually set the running speed of the manipulator. In this paper, to consider the running speed and stability of the manipulator, the time-optimal trajectory planning (TOTP) of the manipulator is transformed into a nonlinear optimal value search problem under multiple constraints, and a time-search algorithm based on fuzzy control is proposed, so that the end of the manipulator can run along the given path in Cartesian space for the shortest time, and the angular velocity and angular acceleration of each joint is within a limited range. In addition, a simulation model of a 6-DOF manipulator is established in MATLAB, taking a straight-line trajectory of the end of the manipulator in Cartesian space as an example, and the effectiveness and efficiency of the algorithm proposed in this paper are proved by comparing the execution time with the bisection algorithm and the traditional gradient descent method.
Measuring the Prevalence of Anti-Social Behavior in Online Communities

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.
Issues-Driven features for software fault prediction

Software systems are an integral part of almost every modern industry. Unfortunately, the more complex the software, the more likely it will fail. A promising strategy is applying fault prediction models to predict which components may be defective. Since features are essential to the prediction model’s success, extracting significant features can improve the model’s accuracy. Previous research studies used software metrics as features in fault prediction models. One disadvantage of these features is that they measure the code developed rather than the requirements. On the other hand, faults are frequently the result of a mismatch between the software’s behavior and its needs. We present a novel paradigm for constructing features that consider the requirements as well by combining novel requirement metrics, called Issues-Driven features, and traditional code metrics. We experimentally compare the performance of Issues-Driven features and state-of-the-art traditional features on 86 open-source projects from two organizations. The results show that Issues-Driven features are significantly better than state-of-the-art features and achieve an improvement of 6 to 13 percent in terms of AUC. The study concludes that integrating the requirements into fault prediction features overcomes the limitations of traditional software metrics that are agnostic to the requirements of the software.
Recaptured screen image identification based on vision transformer

Due to the copyright issues often involved in the recapture of LCD screen content, recaptured screen image identification has received lots of concerns in image source forensics. This paper analyzes the characteristics of convolutional neural network (CNN) and vision transformer (ViT) in extracting features and proposes a cascaded network structure that combines local-feature and global-feature extraction modules to detect the recaptured screen image from original images with or without demoiréing operation. We first extract the local features of the input images with five convolutional layers and feed the local features into the ViT to enhance the local perception capability of the ViT module, and further extract the global features of the input images. Through thorough experiments, our method achieves a detection accuracy rate of 0.9691 in our generated dataset and 0.9940 in the existing mixture dataset, both showing the best performance among the compared methods.
Automatic topology optimization of echo state network based on particle swarm optimization

The task of time series forecasting is to predict the future trend of data based on the collected historical data, providing theoretical and data support for human judgment and decision making. Randomization-based echo state networks (ESNs) are widely used in the research and application field of time series analysis for their simple structure and fast training speed. The core of the ESN is its dynamic reservoir, which the original reservoir are randomly generated and controlled only by parameter sparsity, often performing poorly on complex tasks and affecting the performance of networks. Manual design of the topology of reservoir is difficult, time-consuming and inconvenient to operate, which is not conducive to the development of ESNs. The construction of a suitable reservoir topology for practical application problems to enrich reservoir dynamics is a hot research point for researchers. In this paper, an automatic optimization method is introduced into the topology optimization of ESN (TP-ESN), and the particle swarm optimization algorithm is used to optimize the topological construction of the ESN. The connection structure between the reservoir neurons is first encoded and then iteratively optimized. The optimized structure is decoded and then the reservoir is initialized for ESN training. Prediction results on Mackey–Glass benchmark time series and two electroencephalogram (EEG) datasets demonstrate that TP-ESN method can have better adaptability, stronger prediction ability and stability than several other manually designed ESN reservoir topologies in the case of relatively complex tasks.
Exam Proctoring Classification Using Eye Gaze Detection

Currently, as there is a spike in the number of examinations being conducted online, there is also an increasing need for proctors, a resource that soon got overwhelmed. Proctors are supposed to monitor each and every examinee individually to ensure that the code of conduct is being followed by the examinee, contrary to the offline examination system in which a large group of students are overlooked by a handful of invigilators. But even then, the rate of unfair means being opted by the examinees in online examinations is quite higher in comparison to offline examinations. Thus, making the current process extensive, tiresome and wasteful. To make the process more coherent we came up with Eagle Eye. This system would be implemented and made efficient with the use of the technique of Eye gaze detection using Artificial Intelligence and Machine Learning. At the beginning, of the examination, the examinee would be required to undergo a calibration test to determine the situations around the examinee and to set a designated border-box area into which the examinee would be required to get their eyeball movements tested. This data gathered from the detection would be used to examine whether or not the examinee is looking outside or inside the box, which in turn would be used to classify the data as fraudulent and/or fair respectively. If detected to be fraudulent by the system, an alert will be sent to the examiner and the examinee both so that further actions can be taken according to the mandate. To achieve the best prediction results, we have made our own dataset with the help of volunteers so as to obtain unfiltered and more authentic samples to work with. Impact Statement — Whilst the shift of the education sector into the online mode has been rather swift than not. Nevertheless, there are a few areas in which some modifications or improvements could be made to improve efficiency and decrease the need for enormous human capital. One of such areas is Online Examinations, to be specific, the proctoring method. Even with the presence of individual proctors with every single examinee, the rate of unfair means being opted by the examinees in online examinations is quite higher in comparison to offline examinations wherein a single proctor keeps a track of a group of examinees. Thus, making the current process extensive, tiresome and wasteful. To make the process more coherent we came up with Eagle Eye. A system which would use less financial and human resources and capital investment.
SMT solving for the validation of B and Event-B models

Abstract ProB provides a constraint solver for the B-method written in Prolog and can make use of different backends based on SAT and SMT solving. One such backend translates B and Event-B operators to SMT-LIB using the Z3 solver. This translation uses quantifiers to axiomatize some operators, which are not well-handled by Z3. Several relational constraints such as the transitive closure are not supported by this translation. In this article, we substantially improve the translation to SMT-LIB by employing a more constructive rather than axiomatized style using Z3’s lambda function. Thereby, we are able both to translate more B and Event-B operators to SMT-LIB and improve the overall performance. We further extend ProB ’s interface to Z3 to run different solver configurations in parallel. In addition, we present a direct implementation of SMT solving in Prolog using ProB ’s constraint solver as a theory solver. We hereby aim to combine the strengths of conflict-driven clause learning for identifying contradictions with ProB ’s constraint solver for finding solutions. We deem this implementation to be worthwhile since ProB ’s constraint solver is tailored toward solving B and Event-B constraints, and we herewith avoid the dependency on an external SMT solver. Empirical results show that the new integration of Z3 has improved performance of constraint solving and enables to solve several constraints which cannot be solved by ProB ’s constraint solver. Furthermore, the direct implementation of SMT solving in ProB shows benefits compared to ProB ’s constraint solver and the integration of Z3.
Producing competent HPC graduates

Employers seek recruits who can apply the knowledge, skill, and culture they acquire in college to solve problems as soon as they enter the workforce.
A multitask recommendation algorithm based on DeepFM and Graph Convolutional Network

Abstract For a long time, the problems of cold start and sparse data have always been the key problems to be solved by the recommendation system. Researchers usually use auxiliary information to deal with the aforementioned problems, thereby achieving the purpose of enhancing the recommendation effect. For example, the multitask feature learning framework (MKR) uses knowledge graphs as auxiliary information to enhance recommendations. However, the MKR algorithm has the problem of insufficient semantic information representation which affect the recommendation results. Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed. The graph convolution network is used to deeply mine auxiliary entity information in the knowledge graph to supplement the sparse item semantics information in the recommendation task. Through the method of cross compression unit combined with Deep Neural Network to achieve feature sharing items and entities which to make up for the impact of insufficient feature representation. Then the DeepFM_GCN model utilizes DeepFM to deeply mine the interaction feature of users and items to avoid inaccurate items recommended to users. From the analysis of the experimental results, the DeepFM_GCN model can more fully explore user and item features, accordingly avoiding semantic ambiguity and improving prediction accuracy.
Quantum energy landscape and circuit optimization

We study the effects of entanglement and control parameters on the symmetry of the energy landscape and optimization performance of the variational quantum circuit. Through a systematic analysis of the Hessian spectrum, we characterize the local geometry of the energy landscape at a random point and along an optimization trajectory. We argue that decreasing the entangling capability and increasing the number of circuit parameters have the same qualitative effect on the Hessian eigenspectrum. Both the low-entangling capability and the abundance of control parameters increase the curvature of nonflat directions, contributing to the efficient search of area-law entangled ground states as to the optimization accuracy and the convergence speed.
A Secure Interoperability Management Scheme for Cross-Blockchain Transactions

Blockchain technology has recently attracted tremendous interest due to its potential to revolutionize the industry by achieving decentralization while increasing the number of data sources, transparency, reliability, auditability, and trustworthiness. However, one of the major barriers to the widespread adoption of blockchain applications is the lack of mutual consensus and management across blockchains. Cross-blockchain consensus refers to one blockchain network reaching a consensus with another blockchain network to provide the ability to interact and share data. In this paper, we propose a secure management scheme with symmetric cross-blockchain communication and certificateless signature primitives, in which two heterogeneous blockchains are linked by a relay chain to simultaneously deliver cross-blockchain transaction security, achieve compatibility among various blockchains, and ensure the consistency of data exchanged, in practice. Additionally, our evaluation and security analysis shows the practicability and security of our proposed management scheme and demonstrates that a common test platform based on Ethereum can achieve acceptable computation costs.
Occluded Object Detection for Autonomous Vehicles Employing YOLOv5, YOLOX and Faster R-CNN

Autonomous vehicles (AV s) are the future of trans-portation and are likely to bring countless benefits compared to human-operated driving. However, there are still a lot of advances to be made before these vehicles can be considered completely safe and before they can reach full autonomy. Perceiving the environment with the utmost accuracy and speed is crucial for autonomous vehicles. Hence, making this process more efficient and streamlined is of paramount importance. In order to perceive the environment, AV s need to classify and localize the different objects in the surrounding. For this research, we deal with the detection of occluded objects to help enhance the perception of AV s. We introduce a new dataset containing occluded instances of road scenes from the perspective of Bangladesh. We utilized transfer learning to train the YOLOv5, YOLOX, and Faster R-CNN models, using their respective pre-trained weights on the COCO dataset. We then evaluate and compare the performance of the three object detection algorithms on our dataset. YOLOv5, YOLOX, and Faster R-CNN achieved mean Average Precision (mAP) at 0.5 of 0.777, 0.849, and 0.688, and mAP at 0.5:0.95 of 0.546, 0.634, and 0.422, respectively, in our test set. Therefore, we find YOLOX to be the best performing model on our dataset, and its high mAP scores demonstrate the effectiveness of the model as well as the dataset.