Reviewerly/exHarmony huggingface dataset:
Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.
Prediction of network public opinion based on bald eagle algorithm optimized radial basis function neural network

Purpose In the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments. Design/methodology/approach Aiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion. Findings The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments. Originality/value A method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.
Deep Reinforcement Learning at the Edge of the Statistical Precipice

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field. We illustrate this point using a case study on the Atari 100k benchmark, where we find substantial discrepancies between conclusions drawn from point estimates alone versus a more thorough statistical analysis. With the aim of increasing the field's confidence in reported results with a handful of runs, we advocate for reporting interval estimates of aggregate performance and propose performance profiles to account for the variability in results, as well as present more robust and efficient aggregate metrics, such as interquartile mean scores, to achieve small uncertainty in results. Using such statistical tools, we scrutinize performance evaluations of existing algorithms on other widely used RL benchmarks including the ALE, Procgen, and the DeepMind Control Suite, again revealing discrepancies in prior comparisons. Our findings call for a change in how we evaluate performance in deep RL, for which we present a more rigorous evaluation methodology, accompanied with an open-source library rliable, to prevent unreliable results from stagnating the field.
Leading isospin breaking effects in the HVP contribution to $a_{\mu}$ and to the running of $\alpha$

The anomalous magnetic moment of the muon a µ and the running of the electromagnetic coupling α play a fundamental role in beyond Standard Model (SM) physics searches.Non-perturbative hadronic contributions to both quantities, which are related to the hadronic vacuum polarization (HVP) function consisting of two electromagnetic currents, are a main source of uncertainty in the SM prediction.We compute the HVP function in lattice QCD+QED applying the timemomentum representation method.We expand the relevant correlation functions around the isosymmetric limit.In particular, we focus on leading isospin breaking effects taking quarkconnected contributions into account, which we evaluate on isosymmetric N f = 2 + 1 QCD gauge ensembles generated by the CLS initiative with non-perturbatively O(a)-improved Wilson fermions.
Improving Arithmetic Optimization Algorithm Through Modified Opposition-based Learning Mechanism

This study aims to present a novel hybrid metaheuristic algorithm through improving the performance of the arithmetic optimization algorithm (AOA). A modified version of opposition-based learning mechanism (mOBL) has been used to provide the improvement. The greater performance of the improved version of the arithmetic optimization algorithm (mOBL-AOA) has been demonstrated through statistical and non-parametric tests by using benchmark functions of Schwefel 2.22, Rosenbrock, Step, Schwefel, Ackley and Penalized. The results were demonstrated comparatively by using sine cosine, Lévy flight distribution and the original arithmetic optimization algorithms. The performed comparative analyses have confirmed the highly competitive performance of the mOBL-AOA algorithm in terms of tackling with the the optimization problems.
Implementation of Tutorials with Interactive Elements for the Study of General Technical and Electrical Engineering Disciplines in the E-environment

The article presents the technology of implementation of tutorials with interactive elements for the study of general technical and electrical engineering disciplines in the e-environment. The levels of study of these disciplines are described. The types of interactive audiovisual online tools according to the types of classes are presented: lectures, laboratory and practical works and independent tasks. At the initial level, it is proposed to use multimedia online lectures, video instructions for practical and laboratory work in general technical and electrical engineering disciplines, test simulators. Audiovisual lectures, online calculators for practical works, online laboratory works, graphic training simulators are used at a sufficient level. The average level involves the use of interactive lectures, video tutorials for studying engineering programs, online laboratory work with the presentation of results in an e-environment, gamified training simulators. At a high level it is proposed to use interactive lectures with a task for reflection, an engineering project in general technical and electrical engineering disciplines in an e-environment, online laboratory work with discussion in a thematic forum, report on a webinar with the presentation of engineering developments. Tutorials for future electrical engineers in general technical disciplines "Mechanics of Materials and Constructions", "Theory of Mechanisms and Machines", "Engineering and Computer Graphics" and electrical engineering disciplines "Theoretical Foundations of Electrical Engineering", "Electrical Technologies" for studying in the e-environment were created and tested. Such tutorials contain interactive audiovisual online tools for performing tasks in an e-environment that are presented with the help of QR-codes. The obtained results were checked according to the Kolmogorov-Smirnov criterion.
Risk-Based Adaptive User Authentication for Mobile Passenger ID Devices for Land/Sea Border Control

New services and products are increasingly becoming integral parts of our daily lives rising our technological dependence, as well as our exposure to risks from cyber. Critical sectors such as transport are progressively depending on digital technologies to run their core operations and develop novel solutions to exploit the economic strengths of the European Union. However, despite the fact that the continuously increasing number of visitors, entering the European Union through land-border crossing points or seaports, brings tremendous economic benefits, novel border control solutions, such as mobile devices for passenger identification for land and sea border control, are essential to accurately identify passengers “on the fly” while ensuring their comfort. However, the highly confidential personal data managed by these devices makes them an attractive target for cyberattacks. Therefore, novel secure and usable user authentication mechanisms are required to increase the level of security of this kind of devices without interrupting border control activities. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Besides that, a novel risk-based adaptive user authentication mechanism is proposed for mobile passenger identification devices used by border control officers at land and sea borders.
RFaceID

Face recognition (FR) has been widely used in many areas nowadays. However, the existing mainstream vision-based facial recognition has limitations such as vulnerability to spoofing attacks, sensitivity to lighting conditions, and high risk of privacy leakage, etc. To address these problems, in this paper we take a sparkly different approach and propose RFaceID, a novel RFID-based face recognition system. RFaceID only needs the users to shake their faces in front of the RFID tag matrix for a few seconds to get their faces recognized. Through theoretical analysis and experiment validations, the feasibility of the RFID-based face recognition is studied. Multiple data processing and data augmentation techniques are proposed to minimize the negative impact of environmental noises and user dynamics. A deep neural network (DNN) model is designed to characterize both the spatial and temporal feature of face shaking events. We implement the system and extensive evaluation results show that RFaceID achieves a high face recognition accuracy at 93.1% for 100 users, which shows the potential of RFaceID for future facial recognition applications.
Quantum private comparison of arbitrary single qubit states based on swap test

By using swap test, a quantum private comparison (QPC) protocol of arbitrary single qubit states with a semi-honest third party is proposed. The semi-honest third party (TP) is required to help two participants perform the comparison. She can record intermediate results and do some calculations in the whole process of the protocol execution, but she cannot conspire with any of participants. In the process of comparison, the TP cannot get two participants’ private information except the comparison results. According to the security analysis, the proposed protocol can resist both outsider attacks and participants’ attacks. Compared with the existing QPC protocols, the proposed one does not require any entanglement swapping technology, but it can compare two participants’ qubits by performing swap test, which is easier to implement with current technology. Meanwhile, the proposed protocol can compare secret integers. It encodes secret integers into the amplitude of quantum state rather than transfer them as binary representations, and the encoded quantum state is compared by performing the swap test. Additionally, the proposed QPC protocol is extended to the QPC of arbitrary single qubit states by using multi-qubit swap test.
i-DERASSA: e-learning Platform based on Augmented and Virtual Reality interaction for Education and Training

With the Coronavirus 2019 disease (COVID-19) spread, causing a world pandemic, e-learning can provide an optimal education and training solution. COVID-19 has wholly disrupted the education system. Switching to e-learning could be the enabler to create a new, more effective method of educating students when correctly applied. This paper proposes an elearning platform based on 3D interaction using augmented reality (AR) and virtual reality (VR) designed to meet particular learning objectives divided into levels and subjects, which aims to facilitate the teaching process and administrative workload in schools. We provide details on the platform, VR and AR courses/exercises conception and implementation. Many concepts may be hard to explain in a classroom or visualise in a textbook, e.g., anatomical concepts, molecular structures, space phenomena, or complex abstract topics. The AR and VR technologies make it much easier to achieve, creating a rich, interactive experience that combines real and virtual worlds. VR that provides information through realistic 3D models in immersive environments can better present concepts and skills. Our main contribution is the integration of AR and VR interaction in the web; we develop courses/ exercises selected from the Algerian Ministry of Education and Teaching learning program based on 3D interaction in virtual and augmented scenes. We hope by the integration of VR and AR into education attempts to increase the level of participation and maybe even the understanding of abstract and complex concepts.
Affecting Explicit and Implicit Body Image with Thin-Idealized Avatars in Virtual Reality: The Role of Sense of Embodiment

A large number of previous studies have examined the effects of thin-ideal avatars on body image in virtual reality (VR), reporting mixed results. A possible explanation is that individuals' sense of embodiment (SoE) can influence the effects of avatars. Using the body discontinuity paradigm, a paradigm rarely used in prior studies, this study explores how SoE affects users' body image when using thin-ideal avatars in VR. A group of 77 female participants were randomly assigned to one of the SoE conditions to complete a VR task using a thin-ideal avatar. The body image was assessed not only using explicit but also with implicit measures. The result shows that participants in a high SoE condition were more likely to have a more positive actual body image than others in a low SoE condition, regardless of whether implicit or explicit measures were used. Furthermore, the ownership subcomponent of the SoE significantly mediated the relationship between thin-ideal avatars exposure and the actual body image using implicit measures. However, no substantial difference in ideal body image was found. These findings extend our understanding of avatar effects in VR, thereby providing additional evidence for using avatars to explore body image-related phenomena and to use them in psychotherapeutic applications such as treatments for eating disorders.
Integration Mechanisms of Universities and Real Economy Sector

In the modern world, the role of universities is changing dramatically and a new model of their participation in the economy is being formed. The essence of the new model is an immeasurably closer integration of universities with enterprises and organizations of the real sector of the economy. Universities that reject this model will no longer be able to be full participants in the higher education market. In this regard, many Russian universities are forced to look for new mechanisms of interaction with economic entities of the regional economy. The article summarizes and systematizes the most successful practices of Russian universities on the creation and use of integration mechanisms with companies, enterprises and departments of the regions. For this purpose, examples of such promising institutions are considered, such as research and educational centers and infrastructure initiatives; supporting universities and network initiatives; targeted training and practice-oriented programs; integration of production and education on the site of universities (training centers); partial integration of production and education on the site of enterprises (basic departments); full integration of production and education at the enterprise site (outsourcing); entrepreneurial universities and a model of global academic partnership; the institute of unique scientific collaborations and Collective Use Centers. The analysis of this experience allowed us to draw the general contours of a new model of a practice-oriented university.
Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework

The silent transmission of COVID-19 has led to an exponential growth of fatal infections. With over 4 million deaths worldwide, the need to control and stem transmission has never been more critical. New COVID-19 vaccines offer hope. However, administration timelines, long-term protection, and effectiveness against potential variants are still unknown. In this context, contact tracing and digital contact tracing apps (CTAs) continue to offer a mechanism to help contain transmission, keep people safe, and help kickstart economies. However, CTAs must address a wide range of often conflicting concerns, which make their development/evolution complex. For example, the app must preserve citizens' privacy while gleaning their close contacts and as much epidemiological information as possible.In this study, we derived a compare-and-contrast evaluative framework for CTAs that integrates and expands upon existing works in this domain, with a particular focus on citizen adoption; we call this framework the Citizen-Focused Compare-and-Contrast Evaluation Framework (C3EF) for CTAs.The framework was derived using an iterative approach. First, we reviewed the literature on CTAs and mobile health app evaluations, from which we derived a preliminary set of attributes and organizing pillars. These attributes and the probing questions that we formulated were iteratively validated, augmented, and refined by applying the provisional framework against a selection of CTAs. Each framework pillar was then subjected to internal cross-team scrutiny, where domain experts cross-checked sufficiency, relevancy, specificity, and nonredundancy of the attributes, and their organization in pillars. The consolidated framework was further validated on the selected CTAs to create a finalized version of C3EF for CTAs, which we offer in this paper.The final framework presents seven pillars exploring issues related to CTA design, adoption, and use: (General) Characteristics, Usability, Data Protection, Effectiveness, Transparency, Technical Performance, and Citizen Autonomy. The pillars encompass attributes, subattributes, and a set of illustrative questions (with associated example answers) to support app design, evaluation, and evolution. An online version of the framework has been made available to developers, health authorities, and others interested in assessing CTAs.Our CTA framework provides a holistic compare-and-contrast tool that supports the work of decision-makers in the development and evolution of CTAs for citizens. This framework supports reflection on design decisions to better understand and optimize the design compromises in play when evolving current CTAs for increased public adoption. We intend this framework to serve as a foundation for other researchers to build on and extend as the technology matures and new CTAs become available.
Assessing Rasch measurement estimation methods across R packages with yes/no vocabulary test data

Instrument measurement conducted with Rasch analysis is a common process in language assessment research. A recent systematic review of 215 studies involving Rasch analysis in language testing and applied linguistics research reported that 23 different software packages had been utilized. However, none of the analyses were conducted with one of the numerous R-based Rasch analysis software packages, which generally employ one of the three estimation methods: conditional maximum likelihood estimation (CMLE), joint maximum likelihood estimation (JMLE), or marginal maximum likelihood estimation (MMLE). For this study, eRm, a CMLE-based R package, was utilized to conduct a dichotomous Rasch analysis of a Yes/No vocabulary test based on the academic word list. The resulting parameters and diagnostic statistics were compared with the equivalent results from four other R-based Rasch measurement software packages and Winsteps. Finally, all of the packages were utilized in the analysis of 1000 simulated datasets to investigate the extent to which results generated from the contrasting estimation methods converged or diverged. Overall, the differences between the results produced with the three estimation methods were negligible, and the discrepancies observed between datasets were attributable to the software choice as opposed to the estimation method.
A differentially private matrix factorization based on vector perturbation for recommender system

Matrix factorization (MF) techniques have yielded immense success in recommender systems (RSs). Since a huge amount of user data is collected and used in RS, it raises concerns about data privacy. As a strict privacy protection framework, many efforts attempt to apply Differential Privacy (DP) to MF. However, there are still some challenges or problems in designing MF with privacy preservation, such as error accumulation in the multiple iterations of MF, introduction of unnecessary noises, and difficult sensitivity analysis. To overcome these problems, we devise a vector perturbation-based differentially private matrix factorization (VP-DPMF). Our scheme can prevent error accumulation by perturbing the objective function of MF rather than its factorization process or results. It also addresses the difficulty of analyzing sensitivity by exploiting the polynomial representation of the objective function. Furthermore, our scheme can reduce unnecessary noises by controlling the perturbation within the vector term of the polynomial, and can preserve the convexity property of the original function. Theoretical analysis demonstrates that our scheme can achieve good performance in a large-scale recommender system. Experimental results on some benchmark datasets show that the proposed scheme can provide both rigid privacy guarantee and satisfactory recommendation quality.