Reviewerly/exHarmony huggingface dataset:
Research of Educational Programs in the Trending Areas of Economics for University Integration to the International Educational Space: Case of the Ural Federal University named after the First President of Russia B.N. Yeltsin

The article is devoted to the monitoring study of educational programs offered by the world's leading universities in the most popular fields today: energy, computer and engineering sciences, human resource management. The master's degree programs of universities included in the QS World University Rankings and additional educational courses presented on the international Coursera platform were selected as the basis for comparative analysis. The role of internationalization in university practice is considered from the point of view of the strategic direction of development and increasing the competitiveness of the university in the world market of educational services. The features of educational programs successfully implemented in the conditions of internationalization of education in Russia and various parts of the world – Europe, Asia, America, Australia – are determined. It is revealed that the development of internationalization of Russian education takes into account the peculiarities of domestic regions and is carried out in line with global trends, which allows maintaining and developing competitiveness among foreign universities. Practical recommendations are formulated for the formation of a strategy for the integration of the Ural Federal University named after the first President of Russia B. N. Yeltsin into the international educational space, maintaining and improving its competitiveness within the framework of the internationalization of educational programs.
Design and Implementation of a Personalizable Alternative Mouse and Keyboard Interface for Individuals with Limited Upper Limb Mobility

People with neuromuscular diseases often experience limited upper limb mobility, which makes the handling of standard computer mice and keyboards difficult. Due to the importance of computers in private and professional life, this work aims at implementing an alternative mouse and keyboard interface that will allow for their efficient use by people with a neuromuscular disease. Due to the strongly differing symptoms of these diseases, personalization on the hardware and software levels is the focus of our work. The presented mouse alternative is based on a spectacle frame with an integrated motion sensor for head tracking, which enables the control of the mouse cursor position; the keyboard alternative consists of ten keys, which are used to generate word suggestions for the user input. The interface was tested in a user study involving three participants without disabilities, which showed the general functionality of the system and potential room for improvement. With an average throughput of 1.56 bits per second achieved by the alternative mouse and typing speeds of 8.44 words per minute obtained using the alternative keyboard, the proposed interface could be a promising input device for people with limited upper limb mobility.
On the Vulnerability of Graph Learning-based Collaborative Filtering

Graph learning-based collaborative filtering (GLCF), which is built upon the message-passing mechanism of graph neural networks (GNNs), has received great recent attention and exhibited superior performance in recommender systems. However, although GNNs can be easily compromised by adversarial attacks as shown by the prior work, little attention has been paid to the vulnerability of GLCF. Questions like can GLCF models be just as easily fooled as GNNs remain largely unexplored. In this article, we propose to study the vulnerability of GLCF. Specifically, we first propose an adversarial attack against CLCF. Considering the unique challenges of attacking GLCF, we propose to adopt the greedy strategy in searching for the local optimal perturbations and design a reasonable attacking utility function to handle the non-differentiable ranking-oriented metrics. Next, we propose a defense to robustify GCLF. The defense is based on the observation that attacks usually introduce suspicious interactions into the graph to manipulate the message-passing process. We then propose to measure the suspicious score of each interaction and further reduce the message weight of suspicious interactions. We also give a theoretical guarantee of its robustness. Experimental results on three benchmark datasets show the effectiveness of both our attack and defense.
Fake and Dishonest Participant Immune Secret Image Sharing

Secret image sharing (SIS) has received increased attention from the research community because of its usefulness in multiparty secure computing, access control, blockchain distributive storage and other security-oriented applications. Prevention of fake and dishonest participants is a key issue that has spurred interest in practical applications of SIS. Unfortunately, most previous SIS schemes failed to detect and locate fake or dishonest participants. In this article, an SIS for a ( k,n )-threshold without pixel expansion is presented, which can detect and locate both fake and dishonest participants. Using a screening operation, the proposed approach fuses the benefits of polynomial-based SIS, visual cryptographic scheme (VCS) , and hash functions to authenticate separate participants both with and without a dealer. In addition, the proposed approach achieves lossless rebuilding of the secret image. Analyses and experiments are conducted in this study to establish the effectiveness of the presented approach.
ProposalVLAD with Proposal-Intra Exploring for Temporal Action Proposal Generation

Temporal action proposal generation aims to localize temporal segments of human activities in videos. Current boundary-based proposal generation methods can generate proposals with precise boundary but often suffer from the inferior quality of confidence scores used for proposal retrieving. In this article, we propose an effective and end-to-end action proposal generation method, named ProposalVLAD, with Proposal-Intra Exploring Network (PVPI-Net). We first propose a ProposalVLAD module to dynamically generate global features of the entire video, then we combine the global features and proposal local features to generate the final feature representations for all candidate proposals. Then, we design a novel Proposal-Intra Loss function (PI-Loss) to generate more reliable proposal confidence scores. Extensive experiments on large-scale and challenging datasets demonstrate the effectiveness of our proposed method. Experimental results show that our PVPI-Net achieves significant improvements on two benchmark datasets (i.e., THUMOS’14 and ActivityNet-1.3) and sets new records for temporal action detection task.
Research on adaptive enhancement of robot vision image based on multi-scale filter

Contrast enhancement and histogram equalisation are two image enhancement methods, which can lead to changes in the edge position of the resulting image, blurring or even loss of details. Therefore, this paper introduces a multi-scale filter to adaptively enhance the robot visual image, improve the brightness of the robot visual image, enrich the image details and reduce the image enhancement time. According to Retinex theory, the characteristic information of robot visual image is obtained, the logarithmic domain operation form of Retinex algorithm is obtained, the robot visual reflection image of high-frequency part is determined, the robot illumination visual image is estimated by multiscale filter, and the scale constant of Gaussian filter is obtained; According to the Retinex algorithm of weighted guided filtering, the robot visual image enhancement process is designed. The experimental results show that the average value of the robot visual image enhanced by this method is 88.63, the standard deviation is 62.78, the information entropy is 8.18, the robot visual image enhancement time is only 5.9s, and the PSNR of the robot visual image is up to 39.92, which proves that the robot visual image enhancement effect of this method is good.
A restrictive, parsimonious theory of footing in directional Harmonic Serialism

Abstract This paper develops a theory of footing in Harmonic Serialism (HS; Prince &amp; Smolensky 1993/2004; McCarthy 2000, 2016) where Con contains only directionally evaluated constraints (Eisner 2000, 2002; Lamont 2019, 2022 a , 2022 b ). Directional constraints harmonically order candidates by the location of violations rather than the total number of violations. A central result of adopting directional evaluation is that the constraint Parse ( $\sigma$ ) not only motivates iterative footing but also determines where feet surface. This obviates the need for alignment constraints (McCarthy &amp; Prince 1993; McCarthy 2003; Hyde 2012 a , 2016), which determine where feet are parsed in HS with constraints that count loci (Pruitt 2010, 2012). The theory uses fewer constraints, is empirically adequate, and makes more restrictive predictions than HS with counting constraints and parallel Optimality Theory (Prince &amp; Smolensky 1993/2004) with directional constraints.
A conceptual model supporting decision-making for test automation in Agile-based Software Development

With the rise of agile development notion, software test automation is widely adopted by software organizations to reduce the testing time and costs during the software development process. However, it is essential to carefully implement the test automation to ensure the project’s success. In the literature, several studies have indicated that decision-making is crucial for the adoption of test automation. For deciding about test automation adoption, several factors, including the skill level of testers, test tool, and development process, need to be considered since automated testing performance heavily depends on the mentioned factors and their interactions. Thus, it is of vital importance to ascertain the factors that influence test automation in the context of Agile-based Software Development (ASD). To accomplish this, we perform a systematic literature review and a questionnaire-based survey. In total, 21 factors are identified that significantly impact test automation in the ASD context. Additionally, the identified factors are categorized into five main classes. Following the identified factors’ assessment, this work proposes a conceptual model that supports agile practitioners in deciding about the adoption of test automation in the context of ASD. Finally, this work validates the proposed model to assess its practical applicability. The validation of the proposed model is accomplished by using an expert-based validation approach. The attained results show that the proposed model supports agile practitioners in estimating the possibility of success when implementing test automation in the ASD context.
Nonlinear kernel based feature maps for blur‐sensitive unsharp masking of JPEG images

In this paper, a method for estimating the blur regions of an image is first proposed, resorting to a mixture of linear and nonlinear convolutional kernels. The blur map obtained is then utilized to enhance images such that the enhancement strength is an inverse function of the amount of measured blur. The blur map can also be used for tasks such as attention-based object classification, low light image enhancement, and more. A CNN architecture is trained with nonlinear upsampling layers using a standard blur detection benchmark dataset, with the help of blur target maps. Further, it is proposed to use the same architecture to build maps of areas affected by the typical JPEG artifacts, ringing and blockiness. The blur map and the artifact map pair permit to build an activation map for the enhancement of a (possibly JPEG compressed) image. Extensive experiments on standard test images verify the quality of the maps obtained using the algorithm and their effectiveness in locally controlling the enhancement, for superior perceptual quality. Last but not least, the computation time for generating these maps is much lower than the one of other comparable algorithms.
Approaching “Data Feminism”: Visualizations to Shed Light on Inequality

Data feminism theory believes data visualization that embraces feminism can be used to reshape the world and powerfully reveal inequalities around women, people of color, and other disadvantaged groups. This paper seeks to find visualization works that are close to the thrust of data feminism and discusses their rhetorical, narrative, and other dimensions. From concrete works of practice, rather than guiding philosophies, it may be possible to present the current development of data feminism and respond to the call for “visualization as a starting point for data feminism”.
Quantum occupation time

Abstract We present a possible quantum analogue of occupation time, a random variable studied in random processes, using a quasi-probability distribution over the sample space of quantum particle paths defined by a generalized weak measurement scheme. Taking this approach, a broad range of statistical properties of quantum occupation time can be studied. We illustrate the theory on a discrete-time quantum walk on a line, where we derive two novel results. We introduce the notions of weak recurrence, weak Pólya number, and weak occupation time plateau.
Stretching immersion in virtual reality: How glitches reveal aspects of presence, interactivity and plausibility

Virtual reality (VR) immerses users in others’ lives, creating empathy and understanding long after the VR scenario has finished. As VR technology has matured, VR scenarios have begun to be used in complex real-world areas such as education, health and organisational change. These scenarios can be of variable technical quality, with limited interactive capacity, unrealistic environments and clunky or absent avatars. In this study, three scenarios related to gender inequality training were constructed with glitches in the core immersive qualities of presence, interactivity and plausibility in order to understand their effect on the immersive experience. Using a multi-step in-depth series of qualitative interviews to examine the whole immersive process, the results show that immersion is not compromised but changed by glitches. Limited interactivity led to uncomfortable interactions that allowed participants to process difficult emotions; implausible situations surfaced buried norms and prejudices; and avatar variation gave rise to a sense presence that also included distance, which gave the user opportunities for critical reflection. These results point towards immersion as a robust and richly textured concept, while interactivity, plausibility and presence can best be understood as dimensions rather than goals. Totally seamless and immersive experiences may not only be utopian but also unnecessary. The glitches in low-end productions can produce powerful communication without expensive technology.
Android App for Sport Academy

Abstract: The idea of the app is to connect golf, cricket, kabaddi, kho-kho, holly ball, running, and many others players with coaches. And provides the all information about “INTERNATIONAL SPORT”. Provide facility to those students who want registered with academy and improve knowledge of about the sport. Providing information about the multiple “INTERNATIONAL SPORTS “sports player and all. If student registered for joining academy, they have to pay for training payment option is available for paying training fees. We provide training demo video for those students who want to join our academy Also provide all updates related to sport, ongoing and upcoming events information for those students who want play State level, National and International level.
Interactive Chinese Character Learning in Virtual Reality

Learning Chinese characters and characters of Chinese origin is a challenging and time-consuming task for native speakers and second language learners alike. Traditional methods to acquire Chinese characters include repeatedly writing the characters on paper or air writing. In scientific research, computer- and mobile-assisted approaches have been implemented that allow users to be interactively guided through the learning process. The recent advancements and availability of virtual reality (VR) headsets with six degrees of freedom tracking and controllers provide an interesting new platform for education applications. The proposed VR application utilizes novel interaction and visualization methods tailored for VR with guided repetition as well as sound and haptic feedback. The augmentation of air writing further enables motor-based learning and allows for the application to be used as an educational exergame.
Ameliorated equilibrium optimizer with application in smooth path planning oriented unmanned ground vehicle

To enhance the performance of equilibrium optimizer (EO) and expand its application for smooth path planning of the unmanned ground vehicle (UGV), an ameliorated equilibrium optimizer (AEO) is developed and applied to the UGV smooth path planning problem. The main characteristics of AEO are the population initialization by an opposition-based learning (OBL) strategy, the concentration updating by a centroid opposition-based learning (COBL) strategy, and the concentration updating by a proposed self-learning strategy. The three improvement strategies in the AEO enhance the optimization performance of EO through utilizing the information of the opposite space, the neighborhood space, and the whole population. The performance of AEO is examined by comparing it with several well-known algorithms on 29 commonly used benchmark functions. The comparison results show that the AEO is superior to the compared algorithms and ranks first in performance evaluation. Furthermore, a smooth path planning method AEO-HB is proposed by optimizing the control points from high-order Bezier curve based on the AEO. Simulation experiment results manifest that the AEO-HB solves the smooth path planning problem and ranks first among the compared algorithms for the performance evaluation in three different cases. The above results of numerical experiments and smooth path planning experiments indicate that the proposed improvement strategies in the AEO enhance the performance of solving global optimization problems, which makes the AEO have the potential to deal with global optimization problems in more types of application scenarios.
Semi‐supervised learning dehazing algorithm based on the OSV model

Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real-world images. To address the aforementioned problems, a semi-supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi-supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real-world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real-world hazy images.
RETRACTED: Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. The journal was alerted about a post stating that the article contains several tortured phrases that make some passages hard to parse and that an advert was placed selling authorship of a paper with a very similar title, as detailed here: PubPeer - Biometric authentication integrated with wireless communicat.... The journal requested the authors to provide an explanation of these concerns, as well as raw data and evidence of the authors' contribution and collaboration, but the corresponding author did not satisfactorily address the editor’s concerns. The Editor-in-Chief assessed the case and decided to retract the article.
A New Web Application Extensibility Platform

We propose a novel platform for extensibility of web applications based on the design principle of Separation of Concerns. The major features of the proposed platform are Integration Simplicity, UI Consistency via a library of CSS styles, Code Transparency, Zero-effort upgrade and deployment, and Seamless two-directional data exchange using standard single sign-on protocols. The proposed platform has the potential to greatly simplify and standardize the development of web applications, and to significantly reduce the total cost of ownership for web applications.
Federated Cyberattack Detection for Internet of Things-Enabled Smart Cities

While attack detection is key to realize trustworthy smart cities, the use of large amounts of network traffic data by machine learning techniques can lead to privacy issues for citizens. To face this issue, we propose a federated learning approach in the context of Internet of Things-enabled smart cities integrating the Threat and Manufacturer Usage Description files as a prevention/mitigation approach.
Developing the Earthquake Early Warning and Evacuation Systems (EEWES) for Schools in Ranau District

This article presents the main development components Early Earthquake Warning and Evacuation System (EEWES) for the Ranau District of Sabah. Many early earthquake warning systems have been developed since the last major earthquake in Ranau in 2015, but none has appropriately investigated the school children evacuation processes/exercises. The system was developed not just to expedite the evacuation process at these schools but also to help evacuate the school children and to get in touch with the children’s parents who are outstation. Those parents will be updated on the evacuation initiative that is taking place on a real-time basis. The system may also transfer the evacuation details to the local and federal emergency services and other agencies concerned with the evacuation exercises. The system mainly utilised the APIs released by regional disaster centres, which may deliver speedy transmission alerts. The future research direction is to incorporate the local tremor sensor details into the system.