Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Virtual Footwear Try-On Applications in Virtual and Augmented Reality

Abstract Virtual try-on technology (VTO) in virtual reality (VR) and augmented reality (AR) has been developed for years to create novel shopping experiences for users by allowing them to virtually wear fashion products. Compared to garments or facial accessories, fewer studies have focused on virtual footwear try-on, regardless of user study or technical development. Thus, it is necessary to examine the effectiveness of existing VTO applications on the user's affective responses. In this study, we compared the user experience of three different footwear try-on methods (real, VR, and AR) with both physiological and psychological measures. Subjects conducted a try-on experiment on different pairs of sneakers. Each subject’s gaze trajectory was recorded using an eye tracker and analyzed to show his/her visual attention in each method. Afterward, the subjects completed questionnaires to assess the sense of presence, usability, and the user experience score for the try-on processes, and subsequently attended a think-aloud procedure to express their thoughts. The analysis results of the collected data showed that the user experience produced by the VR and AR try-on is not comparable to that of the real environment. The results also revealed factors that negatively affect the quality of the user's interaction with the processes. These findings may provide insights into further improvements in VTO technology.
An efficient deep learning‐based video captioning framework using multi‐modal features

Abstract Visual understanding has become more significant in gathering information in many real‐life applications. For a human, it is a trivial task to understand the content in a visual, however the same is a challenging task for a machine. Generating captions for images and videos for better understanding the situation is gaining more importance as they have wide application in assistive technologies, automatic video captioning, video summarizing, subtitling, blind navigation, and so on. The visual understanding framework will analyse the content present in the video to generate semantically accurate caption for the visual. Apart from the visual understanding of the situation, the gained semantics must be represented in a natural language like English, for which we require a language model. Hence, the semantics and grammar of the sentences being generated in English is yet another challenge. The captured description of the video is supposed to collect information of not just the objects contained in the scene, but it should also express how these objects are related to each other through the activity described in the scene, thus making the entire process a complex task for a machine. This work is an attempt to peep into the various methods for video captioning using deep learning methodologies, datasets that are widely used for these tasks and various evaluation metrics that are used for the performance comparison. The insights that we gained from our premiere work and the extensive literature review made us capable of proposing a practical, efficient video captioning architecture using deep learning which that will utilize the audio clues, external knowledge and attention context to improve the captioning process. Quantum deep learning architectures can bring about extraordinary results in object recognition tasks and feature extraction using convolutions.
Solution to Traveling Freelance Teacher Problem using the Simple K-Means Clustering

This article proposes viable solutions to the traveling freelance teacher problem, which is similar to the traveling salesman with the time window problem. Freelance teachers are usually paid higher than fully employed teachers, but they are often offered fewer hours and a less consistent schedule. However, freelancers can work at more than one private school, private students, and charity-based work at a local hall or religious places such as a mosque. Mosques in Indonesia are located both in urban areas, where many mosques are nearby and in remote villages, sparsely populated rural areas, where the travel time between mosques is significant. The traveling teacher problem is solved by clustering teaching locations using the K-means clustering approach, and clusters are joined to end the route with the shortest distance. This study used the K-means to automatically search for unknown clusters of learning centers similar to a teacher's geolocation. Results show that using K-Means, the approach can recommend a similar learning center to the teacher's requirement at a cluster number index of about 83%. This study also revealed that the best number of clusters was determined by cluster number index, cluster sum of squared errors, silhouette, and time taken.
A Novel Stacked Ensemble for Hate Speech Recognition

Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems.
Privacy-Driven Learning Analytics

Data privacy is a central theme in the global dialogue around the application of data science in education. Despite the growing need, research organisations and private companies working on education and learning analytics solutions still rely on ad hoc, red-tape-heavy and inconsistent approaches to privacy protection. This chapter outlines the substantial and growing body of work on data privacy risk measurement and reduction which can help address this problem and enable better use of online learning data with improved privacy risk management. The combination of privacy risk measurement and reduction tools with a sound privacy risk management framework has the potential to manage privacy risk reliably and consistently across all datasets in a pragmatic and cost-effective way as tools evolve to integrate with standard data management infrastructure.
Shared seagull optimization algorithm with mutation operators for global optimization

Seagull optimization algorithm (SOA) has the disadvantages of low convergence accuracy, weak population diversity, and tendency to fall into local optimum, especially for high dimensional and multimodal problems. To overcome these shortcomings, initially, in this study, a shared SOA (SSOA) is proposed based on the combination of a sharing multi-leader strategy with a self-adaptive mutation operator. In addition, seven new variants of the SSOA algorithm are proposed employing the Gaussian mutation operator, Cauchy mutation operator, Lévy flights mutation operator, improved Tent chaos mutation operator, neighborhood centroid opposition-based learning mutation operator, elite opposition-based learning mutation operator, and simulated annealing algorithm combined with other mutation operators, namely, GSSOA, CSSOA, LFSSOA, ITSSOA, ESSOA, NSSOA, and CMSSOA, respectively. Then, the performance of these variants was evaluated on 23 benchmark functions, and the various performances of the best variant were evaluated on a comprehensive set of 43 benchmark problems and three real-world problems compared to other optimizers. Experimental and statistical results demonstrate that the proposed CMSSOA algorithm outperforms other variants of the SSOA algorithm and competitor approaches.
Blockchain-Integrated Internet-of-Things Architecture in Privacy Preserving for Large-Scale Healthcare Supply Chain Data

The supply chain forms the backbone of healthcare industry operations. The design and development of healthcare information systems (HIS) help different types of decision-making at various levels of business operations. Business process management decision-making is a complex task requiring real-time data collection from different operational sources. Hence, information technology (IT) infrastructure for data acquisition and sharing affects the operational effectiveness of the healthcare industry. The internet of things (IoT) applications have drawn significant research interest in the service of the healthcare industry. IoT technology aims to simplify the distributed data collection in healthcare practice, sharing, and processing of information and knowledge across many collaborating partners using suitable enterprise information systems. However, implementing blockchain technology in IoT-based data communication networks demands extra research initiatives. This chapter presents a review of security-related issues in the context of a HIS consisting of IoT-based blockchain technology.
Responsible use of agile robots in public spaces

In late 2019, a quadruped agile robot, named Spot, was introduced by the robotics company Boston Dynamics for lease to the public. Spot found quick commercial success and social media fame with its sophisticated computer vision enabled inspection and environmental data collection capacity as well as its hypermobility. Spot was also leased by non-commercial organizations such as metropolitan police departments that integrated Spot into various aspects of their operations. Civil rights groups have criticized these deployments of Spot into public spaces based on its potential weaponization and its repeated use in poor and marginalized communities. Law enforcement organizations have responded to the conflict over their use of Spot in a variety of ways. This paper examines the issues raised by the increased use of agile robots through a combination of case studies and ethical design frameworks. We argue that while it may be too late to completely prohibit the use of this technology in public spaces, it is imperative that different stakeholder groups (i.e. technologists, policy makers, ethicists, etc.) must come together now to lay the groundwork for the responsible design, development, and deployment of agile robots. Several models for proactive computing ethics work are presented for stakeholder engagement around complex public safety, security, and equity concerns.
Bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain

Applying Machine Learning to solve or support complex tasks is growing in popularity in a lot of different contexts. One of these contexts is the medical domain. Through Machine Learning, specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc., can be eased by assisting physicians with useful models and their outcomes. However, understanding the application of Machine Learning and Artificial Intelligence algorithms requires expert knowledge and significant data science skills. This work presents a proposal for a user-friendly Machine Learning tool, focusing on providing a good user experience for physicians as well as an educative context for understanding the tasks involved in Machine Learning pipelines, their configuration, and their outputs.
A skewness reformed complex diffusion based unsharp masking for the restoration and enhancement of Poisson noise corrupted mammograms

Mammography is a proven imaging modality for the screening of breast cancer that helps to evaluate the existence of calcification, masses, tissue density, lump shape and edges. These features are generally corrupted by noise, distortion and artifacts during the image acquisition. The presence of these variability reduces the quality of image and limits the efficacy of radiologist's interpretation. To overcome these barriers, a skewness reformed complex diffusion based unsharp masking is proposed, where smoothing filter is changed with a skewness reformed complex diffusion (SRCD) in unsharp masking. To improve the efficacy of SRCD, edge threshold constraint of the diffusion coefficient has been automated instead of manual calculation. It has been automated by the skewness calculation of the image. It helps to detect the fine details, boundary and edges of image. In order to judge the proficiency of the proposed method, Mammography Image Analysis Society, Benign Breast Tumor IEEE dataset and phantom mammogram images are casted-off. The proposed method is compared with other state of the art with respect to qualitative and quantitative analysis. The quantitative analysis is measured in terms of mean squared error, peak signal to noise ratio, correlation parameter, normalized absolute error, universal quality index, structural similarity index, measure of enhancement, measure of enhancement by entropy, second derivative like measurement, average gradient and perceptual sharpness index. The obtained result confines the effectiveness of the proposed method and it provides removal of Poisson noise, enhancement of abnormalities, edge preservation and restoration of information in a single framework.
Security Principles and Challenges in Electronic Voting

An electronic voting system aims to cast and record votes of the legitimate voters while providing an adequate level of security in the way the votes are cast. These require eliminating the link between the voter and the vote, and at the same time, providing an audit trail for validating the counting and tallying processes. Voting is a critical principle of democracy, and to achieve an accurate vote recording, storing, and reporting, the designed system must be reliable, and tampering and fraud-resistant. Moreover, the architecture of the system must prevent user mistakes, and technical errors and drawbacks. This paper classifies the current critical security issues of the e-voting systems in a structured way, to help the e-voting researchers and designers evaluate their systems against them. To this end, it first reviews the system components, authorized user types, and potential threats, and then discusses the confidentiality, integrity, and availability (CIA) issues. The final contribution is introducing and prioritizing the potential security solutions.
An Exploration of ARM System-Level Cache and GPU Side Channels

Advanced RISC Machines (ARM) processors have recently gained market share in both cloud computing and desktop applications. Meanwhile, ARM devices have shifted to a more peripheral based design, wherein designers attach a number of coprocessors and accelerators to the System-on-a-Chip (SoC). By adopting a System-Level Cache, which acts as a shared cache between the CPU-cores and peripherals, ARM attempts to alleviate the memory bottleneck issues that exist between data sources and accelerators. This paper investigates emerging security threats introduced by this new System-Level Cache. Specifically, we demonstrate that the System-Level Cache can still be exploited to create a cache occupancy channel to accurately fingerprint websites. We redesign and optimize the attack for various browsers based on the ARM cache design, which can significantly reduce the attack duration while increasing accuracy. Moreover, we introduce a novel GPU contention channel in mobile devices, which can achieve similar accuracy to the cache occupancy channel. We conduct a thorough evaluation by examining these attacks across multiple devices, including iOS, Android, and MacOS with the new M1 MacBook Air. The experimental results demonstrate that (1) the System-Level Cache based website fingerprinting technique can achieve promising accuracy in both open (up to 90%) and closed (up to 95%) world scenarios, and (2) our GPU contention channel is more effective than the CPU cache channel on Android devices.
Gabor Log-Euclidean Gaussian and its fusion with deep network based on self-attention for face recognition

In this work, we proposed a face feature extraction method by Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component Analysis (called LGLG-WPCA). The proposed method extracts raw features from the multivariate Gaussian in the transform domain of Gabor wavelet and uses WPCA to get robust features. Because the space of Gaussian is a Riemannian manifold, it is difficult to incorporate the learning mechanism into the model. To address this issue, Log-Euclidean approach is used to embed the multivariate Gaussian into the linear space, and then use WPCA to learn discriminative face features. LGLG-WPCA is good at extracting the detail features of face image. Furthermore, another outstanding advantage of LGLG is that its features can be effectively integrated with the high-level features of deep learning network for face recognition in more complex environments. We presented the feature fusing approaches for face recognition based on Self-attention Network (SAN) and achieved obvious performance improvement to the-state-of-the-art deep networks including SENet and FaceNet. Experiments show the proposed method is robust under adverse conditions such as varying poses, skin aging and uneven illumination, and it is suitable for face image under small-scale datasets in complex environments, such as network-based or video-based person searching or tracking.
WhatsApp and Its Potential to Develop Communication Skills among University Students

Excellent communication skills among graduates are very crucial for their future and career development. WhatsApp application is seen to have the potential to develop these skills since it has robust functions and features, easy to use, and free. However, there are still issues in implementing this application as a tool in developing communication skills by utilizing this application for educational purposes among UTM students. Hence, this study was aiming to investigate the level of WhatsApp usage in education, the level of communication skills by utilizing WhatsApp for educational purposes among students, and their correlations. Student’s perception of using WhatsApp in education was also investigated. This study was based on quantitative research conducted using a survey design, where online questionnaires were distributed randomly among 400 UTM students based on simple random method. Findings showed that there is a high level of WhatsApp usage in education among UTM students and also a high level of communication skills by utilizing this application for educational purposes among UTM students. It was supported by a strong positive linear correlation that existed between these two variables. Students also showed positive perceptions of utilizing WhatsApp in education to develop their communication skills. Therefore, lecturers and students are encouraged to utilize WhatsApp application in education as it can help in fortifying communication skills among students who later become competent graduates that can meet the demand from the industries.
Quantum pattern recognition algorithms for charged particle tracking

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue 'Quantum technologies in particle physics'.
Path-constrained optimal trajectory planning for robot manipulators with obstacle avoidance

In this paper, we develop a novel path-constrained and collision-free optimal trajectory planning algorithm for robot manipulators in the presence of obstacles for the following problem: Given a desired sequence of discrete waypoints of robot configurations, a set of robot kinematic and dynamic constraints, and a set of obstacles, determine a time and jerk optimal and collision-free trajectory for the robot passing through the given waypoints with constant speed. Our approach in developing the robot path through the waypoints relies on the orthogonal collocation method where the states are represented with Legendre polynomials in the Barycentric form; the transcription process efficiently converts the continuous-time formulation of the optimal control problem (both time and jerk optimal) into a discrete non-linear program. In addition, we provide an efficient method for avoiding robot self-collisions (of joints and links) and collisions with workspace obstacles by modeling them as the union of spheres and cylinders in the workspace. The resulting collision free optimal trajectory provides smooth and constrained motion for the robot passing through all the waypoints in the given prescribed sequence. The proposed method is validated using numerical simulations and experiments on a six degree-of-freedom robot.
Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark

With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.