Reviewerly/exHarmony huggingface dataset:
DIMBA: discretely masked black-box attack in single object tracking

Abstract The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain a deeper understanding of the vulnerability of neural networks, and provide robustness to deep learning against miscellaneous adversaries. Despite extensive studies focusing on the robustness of image, audio, and NLP, works on adversarial examples of visual object tracking—especially in a black-box manner—are quite lacking. In this paper, we propose a novel adversarial attack method to generate noises for single object tracking under black-box settings, where perturbations are merely added on initialized frames of tracking sequences, which is difficult to be noticed from the perspective of a whole video clip. Specifically, we divide our algorithm into three components and exploit reinforcement learning for localizing important frame patches precisely while reducing unnecessary computational queries overhead. Compared to existing techniques, our method requires less time to perturb videos, but to manipulate competitive or even better adversarial performance. We test our algorithm in both long-term and short-term datasets, including OTB100, VOT2018, UAV123, and LaSOT. Extensive experiments demonstrate the effectiveness of our method on three mainstream types of trackers: discrimination, Siamese-based, and reinforcement learning-based trackers. We release our attack tool, DIMBA, via GitHub https://github.com/TrustAI/DIMBA for use by the community.
Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest

In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.
A combined deformable model and medical transformer algorithm for medical image segmentation

Deep learning-based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance.
An Active Contour Model Based on Retinex and Pre-Fitting Reflectance for Fast Image Segmentation

In the present article, this paper provides a method for fast image segmentation for computer vision, which is based on a level set method. One dominating challenge in image segmentation is uneven illumination and inhomogeneous intensity, which are caused by the position of a light source or convex surface. This paper proposes a variational model based on the Retinex theory. To be specific, firstly, this paper figures out the pre-fitting reflectance by using an algorithm in the whole image domain before iterations; secondly, it reconstructs the image domain using an additive model; thirdly, it uses the deviation between the global domain and low-frequency component to approximate the reflectance, which is the significant part of an energy function. In addition, a new regularization term has been put forward to extract the vanishing gradients. Furthermore, the new regularization term is capable of accelerating the segmentation process. Symmetry plays an essential role in constructing the energy function and figuring out the gradient flow of the level set.
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Practical quantum computing holds clear promise in addressing problems not generally tractable with classical simulation techniques, and some key physically interesting applications are those of real-time dynamics in strongly coupled lattice gauge theories. In this article, we benchmark the real-time dynamics of $\mathbb{Z}_2$ and $U(1)$ gauge invariant plaquette models using noisy intermediate scale quantum (NISQ) hardware, specifically the superconducting-qubit-based quantum IBM Q computers. We design quantum circuits for models of increasing complexity and measure physical observables such as the return probability to the initial state, and locally conserved charges. NISQ hardware suffers from significant decoherence and corresponding difficulty to interpret the results. We demonstrate the use of hardware-agnostic error mitigation techniques, such as circuit folding methods implemented via the Mitiq package, and show what they can achieve within the quantum volume restrictions for the hardware. Our study provides insight into the choice of Hamiltonians, construction of circuits, and the utility of error mitigation methods to devise large-scale quantum computation strategies for lattice gauge theories.
Cybersecurity Awareness and Training (CAT) Framework for Remote Working Employees

Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations' critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees' capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies' results showed that the proposed CAT framework can identify employees' capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations.
Enriching Traditional Higher STEM Education with Online Teaching and Learning Practices: Students’ Perspective

In this paper, we aim to identify online teaching and learning practices that would be beneficial for blended and traditional on-campus education within STEM (Science, Technology, Engineering, and Mathematics) courses. Our university, as well as the majority of higher education institutions worldwide, has had few to no experience in delivering full online courses before 2020. The teaching process was, however, severely affected and modified by the COVID-19 pandemic, forcing an abrupt and unprepared shift towards online education. In this work, we look at the pandemic as causing a very favorable side effect that forced the university to study, test, apply, and evaluate the benefits and drawbacks of online education and assessment methods. The study is a result of joint efforts from different departments at the University of Aveiro, Portugal, connected to STEM undergraduate and graduate programs and is based on a questionnaire targeted towards students. In total, 167 valid STEM students’ answers have been collected and analyzed, both quantitatively and qualitatively. As the result, the best teaching and learning practices are identified and the main difficulties and obstacles experienced by students are detected. Some of the problems are common to many higher education institutions, such as the lack of teacher preparation in delivering quality online synchronous and asynchronous classes, technical limitations (network bandwidth/weak equipment), ineffective communication during synchronous classes, gaps in student skills, and low activity of some students and even teachers. We believe that the presented results would allow for improving future on-campus, distance, and blended learning courses, particularly through avoiding less effective teaching and assessment methods and favoring those techniques that students consider more efficient. This ultimately would lead to a more rewarding teaching/learning experience.
TSSTNet: A Two-Stream Swin Transformer Network for Salient Object Detection of No-Service Rail Surface Defects

The detection of no-service rail surface defects is important in the rail manufacturing process. Detection of defects can prevent significant financial losses. However, the texture and form of the defects are often very similar to the background, which makes them difficult for the human eye to distinguish. How to accurately identify rail surface defects thus poses a challenge. We introduce salient object detection through machine vision to deal with this challenge. Salient object detection locates the most “significant” areas of an image using algorithms, which constitute an integral part of machine vision inspection. However, existing saliency detection networks suffer from inaccurate positioning, poor contouring, and incomplete detection. Therefore, we propose an innovative deep learning network named Two-Stream Swin Transformer Network (TSSTNet) for salient detection of no-service rail surface defects. Specifically, we propose a two-stream encoder—one stream for feature extraction and the other for edge extraction. TSSTNet also includes a three-stream decoder, consisting of a saliency stream, edge stream, and fusion stream. For the problem of incomplete detection, we innovatively introduce the Swin Transformer to model global information. For the problem of unclear contours, we expect to deepen the understanding of the difference in depth between the foreground and background through the learning of contour maps, so the contour alignment module (CAM) is created to deal with this problem. Moreover, to make the most of multimodal information, we suggest a multi-feature fusion module (MFFM). Finally, we conducted comparative experiments with 10 state-of-the-art (SOTA) approaches on the NRSD-MN datasets, and our model performed more competitively than others on five metrics.
Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.
A Typology of Metaverses

Diverse types of metaverses exist. We present a typology of metaverses to elaborate how different features and technologies are being incorporated in metaverse solutions that are targeted at different groups of users and the benefits they are looking for.
Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.
Blockchain-based decentralized data dissemination scheme in smart transportation

Data dissemination of safety messages in smart transportation allows vehicles to broadcast safety-related information about their surroundings to other vehicles within their proximity for early warnings of endangering events. Safety can only be achieved if the data transmitted are reliable. However, this may violate the privacy of a vehicle. At the same time, malicious or defective vehicles should be held accountable when misbehavior occurs. Utilizing blockchain technology, we propose a secure data dissemination scheme for smart transportation that solves the conflicting security requirements of reliability, privacy and accountability simultaneously, which provides a desirable level of security through its decentralized system. The analysis evaluation and simulation experiments signify our proposed scheme achieves security and performance efficiency, robust against adversarial attacks and practical for real world deployment.
QuISP: a Quantum Internet Simulation Package

We present an open-source event-driven simulation package called QuISP built on top of the OMNeT++ discrete event simulation framework. Behavior of quantum devices in small quantum networks has been the focus of recent studies, it is still an open question how they will work in networks of practical size. QuISP is designed to simulate large-scale quantum networks to investigate their behavior under realistic, noisy and heterogeneous configurations. Our confidence in the simulator is supported by comparing its output to analytic results for a small network. QuISP can handle thousands of qubits in hundreds of nodes on a laptop computer, preparing for full Quantum Internet simulation. This simulator promotes the development of protocols for larger and complex quantum networks.
Evaluating the Effect of Organizational Justice on Turnover Intention in the Public Hospitals of Jordan: Mediated-Moderated Model of Employee Silence, Workplace Bullying, and Work Stress

Purpose: The goal of this study is to determine how organizational justice (OJ), employee silence (ES), and workplace bullying (WB) affect turnover intention (TI). ES and WB play mediating roles in this study, and work stress (WS) acts as a moderator between ES, WB, and IT. Design/methodology/approach: The study has selected for a quantitative method of analysis in order to meet its goal. 370 nurses from Jordan's public hospitals filled out questionnaires based on a random sample. Smart PLS 3.3.3 was used to evaluate the data. Findings: Results show that OJ is highly impacted by IT. OJ also has a close relationship with ES and WB. ES and WB hence have a substantial relationship with IT. More significantly, the study showed that the OJ and IT were mediated through the ES and WB. Additionally, WS acts as a mediator between WB and IT. Practical implications: This paper adds to the body of theoretical and applied research on turnover intention analysis. This study's theoretical framework should make it easier for individuals to understand the difficulties that come with the intention to turnover in Jordan's public hospitals. However, this study offers Jordanian decision-makers with useful guidance on how to encourage Jordanians to lessen their intentions to turnover by thoroughly examining the critical factors that influence turnover intention. Originality/value: It is important to take into account the rising rate of employee turnover at Jordan's public hospitals. Turnover has become a significant concern for management on a global scale.
A SYSTEMATIC REVIEW ON SECURITY VULNERABILITIES TO PREVENY TYPES OF ATTACKS IN IOMT

And here's a summary of latest developments in Internet of Things (IoT) integrated devices, wireless connections, and biosensing which have contributed in the fast development of wearable sensor implantation. This study also discusses the applications of the internet of medical things (IoMT), which now has attracted considerable interest as an environment of networked clinical systems, computational capabilities, and medical sensors designed to improve the quality of healthcare services. The perspective of healthcare and lifestyle can indeed be totally transformed by AI technology based on 5G. The aim of this proposed research design is to investigate risks which might undermine the credibility, confidentiality, and security of IoMT platforms in consideration of the relevance of IoT platforms and 5G networks.&#x0D; Moreover, there have been cutting-edge blockchain-based techniques which can aid in enhancing IoMT network security. IoMT has indeed been discovered to be vulnerable to a range of attacks, notably malware, DoS attacks, and wiretapping attacks. IoMT is additionally prone to a range of issues, involving safety, privacy, and anonymity. There are revolutionary cryptography solutions, such as password protection, authentication protocols, and data encryption, which can aid in improving the security and trustworthiness of IoMT devices despite the different of security risks.
Facial beauty prediction fusing transfer learning and broad learning system

Abstract Facial beauty prediction (FBP) is an important and challenging problem in the fields of computer vision and machine learning. Not only it is easily prone to overfitting due to the lack of large-scale and effective data, but also difficult to quickly build robust and effective facial beauty evaluation models because of the variability of facial appearance and the complexity of human perception. Transfer Learning can be able to reduce the dependence on large amounts of data as well as avoid overfitting problems. Broad learning system (BLS) can be capable of quickly completing models building and training. For this purpose, Transfer Learning was fused with BLS for FBP in this paper. Firstly, a feature extractor is constructed by way of CNNs models based on transfer learning for facial feature extraction, in which EfficientNets are used in this paper, and the fused features of facial beauty extracted are transferred to BLS for FBP, called E-BLS. Secondly, on the basis of E-BLS, a connection layer is designed to connect the feature extractor and BLS, called ER-BLS. Finally, experimental results show that, compared with the previous BLS and CNNs methods existed, the accuracy of FBP was improved by E-BLS and ER-BLS, demonstrating the effectiveness and superiority of the method presented, which can also be widely used in pattern recognition, object detection and image classification.