Reviewerly/exHarmony huggingface dataset:
A HEVC Steganalysis Algorithm Based on Relationship of Adjacent Intra Prediction Modes

Currently, many High Efficiency Video Coding (HEVC) video steganography algorithms based on Intra Prediction Mode (IPM) have been proposed. However, the existing IPM-based steganalysis algorithms designed for H.264/AVC videos are hard to be effectively applied to HEVC. Thus, it is of significant value to study IPM-based steganalysis for HEVC videos. In this paper, the distortion process of IPM-based HEVC steganography is first modeled. We find that the basic distortion exists in the change of the relationships between each embedded IPM and the adjacent IPMs. By exploiting these characteristics, we propose a novel IPM steganalysis based on the Relationship of Adjacent IPMs (RoAIPM) feature. In detail, the designed RoAIPM feature is constructed by generating different directional Gray-Level Co-occurrence Matrices (GLCMs) and texture characteristics of three refilled matrices: MPM-IPM matrix, Left-IPM matrix and Up-IPM matrix. Experiments prove that the proposed RoAIPM feature is very sensitive to the slight change introduced by IPM-based steganography. In various coding conditions, regardless of whether the feature is after dimension reduction or not, compared with state-of-the-art works, the proposed steganalysis can both present a well higher detection accuracy against the latest IPM-based HEVC steganography methods.
Human Activity Recognition System Using Artificial Neural Networks

Population aging and the increasing costs of health care, especially for the elderly affected by chronic diseases, requires new medical assistance strategies that makes it possible to monitor these people remotely and provide reliable information on their routines. In this context, human activity recognition (HAR) systems are an important element to overcoming the problem. Therefore, this paper proposes a HAR system prototype containing a multilayer perceptron (MLP) as a classifier. The model hyperparameters were selected using a publicly available dataset. Then, data was collected from accelerometers and gyroscopes embedded in wearable devices of 15 subjects while performing six basic activities (walking, sitting, lying down, standing, walking upstairs and walking downstairs). The system reached an average accuracy of 90.74% and weighted F-measure of 90.03% based on leave-one-subject-out cross-validation.
The logical essence and practical path of collaborative innovation governance between universities and communities in the new era

Under the background of "promoting the modernization of national governance system and governance capacity", collaborative innovative governance between universities and communities has become a future paradigm of modern social governance and an important part of governance innovation. Starting from the theoretical logic of collaborative governance between universities and communities, this paper analyzes the requirements of collaborative innovative governance between universities and communities in the new era by examining the reality of collaborative governance. On this basis, it is proposed that the collaborative innovation governance between universities and communities should form a collaborative governance practice path in terms of mechanism synergy, resource synergy, technology synergy and external environmental forces.
DISTRIBUTED KEY SYSTEMS: ENHANCING SECURITY, FAULT TOLERANCE AND DISASTER RECOVERY IN CLOUD COMPUTING

Cloud computing has increased the need for sound and sophisticated security strategies.Although encryption is the primary mechanism to provide security in data transmission, cloud computing offers both advantages and disadvantages to enhancing the encryption process.Encryption algorithms currently available are robust, but are well known and require a "key" to make a session unique.Therefore, if the key is compromised the encryption can be unlocked no matter how robust the algorithm.In the classical model, a node in a network contains the entire key, and if compromised, then potentially the entire cloud's security could be breached.Also, if the key were destroyed then all the data it protects could be lost, because the data couldn't be unencrypted.Therefore, splitting the key and storing portions on multiple nodes within a cloud would make it more difficult to compromise or lose the key.Further, the key could be striped (like a RAID, Redundant Array of Independent Disks) so if distributed across multiple nodes some could fail and the key could still be recovered.This methodology could create greater security, while providing greater fault tolerance by allowing retrieval of the entire key even if some nodes were compromised or destroyed.
Visual Question Answering System for Relational Networks

Visual question answering (VQA) is a multi-modal research area combining deep learning, natural language processing, and computer vision. It is a multi-modal or cross-media problem where the computer system is built to answer the questions about an input image. The answers may be open-ended or multiple choice. For descriptive answers, the structure of the answer sentence generated is also verified for correctness. This project proposes to model a VQA system for relation network. The major goal of the relation network is to explore the spatial or logical relation among objects present in an image through the questions. It mainly concentrates on answering questions where the position or query is placed through relation to another object or region within the image. The proposed model aims on exploring the object-to-object-based or feature-to-feature-based relations in an image. The main applications of VQA include assistance for visually impaired people, enhancing e-learning capabilities, and improving image retrieval systems.
A word alignment study to improve the reliability of the statistical and neural translation system

Word alignment is an essential task for numerous natural language processing applications, including machine translation. The performance of the statistical machine translation systems is directly impacted by the performance of their alignment modules. However, such alignment models perform worse and induce low machine translation performance when translating morphological rich or low resource languages. The first objective of this paper is to examine the impact of incorporating some morphosyntactic features on the statistical alignment models and on the associated translation systems for the (Arabic, English) language pair, and to identify which of these features is most suitable. Although the neural machine translation system does not directly include a concept of word alignment, we propose, in the second part of this work, a method of adjusting the attention mechanism of these systems by the statistical alignments. Experimental results show that the proposed approaches significantly improve the alignment and the translation performances.
Multimedia library-user interaction

To promote intellectual property concepts to wider public and to support the inventive activity, the All-Russian Patent and Technical Library of the Federal Institute for Industrial Property has been developing multimedia materials for the users. The target groups and user-specific services are described. The multimedia are based on the documents of the State Patent Fund, one of the most complete collection of patent documents. The Library provides free access to its materials. Advantages and drawbacks of library-user interaction are determined.
Improving Motor Imagery Training Through Combining Kinesthetic Motor Imagery with Immersive Virtual Reality

Several approaches to enhance motor imagery (MI), the mental practice of movement without concomitant motor execution, have been implemented in the past including observation of action through immersive virtual-reality (VR) and kinesthetic rehearsal of the movements. Although differences between VR-based motor imagery and kinesthetic motor imagery (KMI) in eliciting brain activity have been studied using electroencephalography (EEG), no investigation on the combined effect of VR and KMI has been made. Previous research has shown that observing actions through VR provides visual information as well as embodiment, or the concept of feeling one’s observation of another entity as part of oneself, that play an essential role in enhancing MI. Furthermore, KMI has been shown to elicit similar brain activity to actually performing the same task. We therefore hypothesized that applying immersive VR to provide a rich immersive visual scenario while subjects performed kinesthetic motor imagery will result in significant improvement in MI training. 15 subjects (9 male, 6 female) participated in this study where subjects performed kinesthetic motor imagery of three different hand tasks, namely drinking, flexion-extension of the wrist, and grabbing with and without visual information provided by VR. The experimental results provide evidence that VR, when combined with KMI, improved brain rhythmic patterns and provided better discriminating features between the tasks compared to KMI without any visual information. The findings suggest that the use of immersive virtual reality headsets can effectively improve the effects of kinesthetic motor imagery in enhancing motor imagery performance.
The Effect of Hands Synchronicity on Users Perceived Arms Fatigue in Virtual Reality Environment

The use of virtual reality (VR) system has become more common with the arrival of a new generation of headsets. This increase in accessibility has in turn led to VR being used for applications which require longer sessions, such as video games, artistic expression, rehabilitation, and so on. Taking muscular fatigue in account for the VR applications is therefore becoming essential to ensure a comfort of usage and also to avoid injuries. In addition, usual interactions in these diverse applications are very varied and may require the use of one or two hands, synchronized or not.However, little research has focused on fatigue produced by one and two handed interactions especially during mid-air interaction in VR environment. In this paper, we examine the effect of hands synchronicity on the user perceived arm fatigue in VR environment during a simple (one line of targets) and a composite (two lines of targets) pointing tasks performed using both controlled and free hands synchronicity. We show that users are moderately consistent in their assessments of the fatigue. We correlate the values of the objective performance with users' subjective assessments of perceived arm fatigue and report covered distance and the elapsed time from the beginning of the task as the highest correlators. We report new findings on user performance and on users strategies to reduce the perceived fatigue. We finally, discuss the implications of our findings for one and two-handed interaction in VR environment.
Increasing the Demand for Workers with a Criminal Record

State and local policies increasingly restrict employers’ access to criminal records, but without addressing the underlying reasons that employers may conduct criminal background checks. Employers may thus still want to ask about a job applicant’s criminal record later in the hiring process or make inaccurate judgments based on an applicant’s demographic characteristics. In this paper, we use a field experiment conducted in partnership with a nationwide staffing platform to test policies that more directly address the reasons that employers may conduct criminal background checks. The experiment asked hiring managers at nearly a thousand U.S. businesses to make incentive-compatible decisions under different randomized conditions. We find that 39% of businesses in our sample are willing to work with individuals with a criminal record at baseline, which rises to over 50% when businesses are offered crime and safety insurance, a single performance review, or a limited background check covering just the past year. Wage subsidies can achieve similar increases but at substantially higher cost. Based on our findings, the staffing platform relaxed the criminal background check requirement and offered crime and safety insurance to interested businesses.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Digitalization of Educational Space: Emotional Risks and Effects

The article is devoted to the systemic problem of digitalization of the educational space, understanding its emotional risks and effects. The authors identified the fol­lowing emotional risks of digitalization of the educational space: 1) emotional alienation of students from classmates and teachers; 2) emotional deprivation caused by the “digital divide”; 3) emotional exhaustion due to information over­load and the dominance of visual-figurative perception of educational material; 4) emotional anxiety due to the totalization of digital control over the results of ed­ucational and extracurricular activities of students; 5) emotional deformations caused by the instability of students’ self-esteem. The article emphasizes the proba­bilistic nature of these emotional risks. The authors also pay special attention to identifying a number of emotional effects of the digitalization of the educational space: 1) emotional euphoria from wide access to digital learning among those who previously, for various reasons, did not have a full opportunity to receive a classical education; 2) emotional involvement of people with developmental dis­abilities in the digital educational process; 3) emotional positivity of students from the expansion of opportunities for entering secondary specialized and higher edu­cational institutions through digital technologies; 4) emotional connection with the participants of educational forums and other electronic educational resources; 5) emotional inspiration of students when they receive opportunities to form indi­vidual learning paths through digital technologies. The result of the study is propos­als on ways to level the emotional risks of digitalization of the educational space.
Conditional independence between Markov random fields

In this paper, we propose to use the pseudo-Bayesian information criterion (PIC), created by [1] to investigate the conditional independence between Markov random fields. The methodology is based on the work of [2] in which the Bayesian information criterion (BIC) was used for investigating the conditional independence between Markov processes.
Content Coverage and Readability of Science Textbooks in Use in Nigerian Secondary Schools

This study evaluated the content coverage and readability of science textbooks in use in Nigerian secondary schools.The study utilized an evaluation design.The study was limited to the core sciences studied at both the junior and senior secondary schools.They are Physics, Chemistry, Biology and Basic Science.A total of one thousand eight hundred and forty-eight research subjects comprising one thousand eight hundred students and forty-eight science teachers were used for the study.Two research questions and one null hypothesis guided the study.The research questions were answered using the quantitative model for textbook evaluation developed by Emerole (2008) while the hypothesis was tested at 0.05 level of significance using Chi-square test of goodness of fit.The finding revealed that all the science textbooks evaluated covered the contents of the core curriculum.The result of data analysis revealed that three out of four evaluated textbooks in biology are readable.Modern Biology for Senior Secondary Schools had readability mean score of 38.41%, Essential Biology for Senior Secondary Schools had 72.4%, College Biology for Senior Secondary Schools had 66.29% while Comprehensive Biology for Senior Secondary Schools had readability index of 60.1%.The data on readability for physics, chemistry and basic science indicate that they are readable.Based on the findings the researchers made specific recommendations with respect to the textbooks recommended for use in the four subject areas in Nigerian secondary schools.This will provide a template and guide for ministry of education in recommendation of science textbooks for secondary schools.In addition, it will serve as a basis and guide for review of science textbooks in use in both junior and senior secondary schools.
Blockchain-Based Real Time Healthcare Emergency

Healthcare Real-time emergency systems have attracted a great deal of interest from both academia and industry. It is important to provide timely medical services at remote areas through an effective communication between healthcare providers. The healthcare stakeholders (including the doctors, nurses, etc.) are facing tremendous problems to track the conditions of the patients, trace them, and coordinate their interventions. In several cases, the healthcare stakeholders are acting without or with little coordination since they do not have the same information about the patient. They do not have also the same trace about patient. In order to overcome these problems, we propose in this paper a Blockchain-based Real Time Healthcare Emergency (BRTHE-care) solution that will serve as a common platform for the healthcare stakeholders to provide immediate or real-time tracking and management of the conditions of the patients. The platform will also help Healthcare providers to interact with medical history records. Furthermore, we propose an intelligent agent to release the doctors from requesting updates about historical records and to help doctors in diagnosing disease. We run simulations using the Hyperledger Fabric Blockchain tool. Results show the efficiency of our solution in terms of communication failures, latency measurements, and the sending rates.
Pedagogical ethics and pedagogical tact

The article considers pedagogical ethics as a set of norms and rules of behavior of a teacher, which ensures the moral nature of pedagogical activity and as a science that studies the origin and nature, structure, functions and features of the manifestation of morality, and pedagogical tact as a sense of norm or observance by the teacher of the rules of behavior and etiquette, expressing its moral character and as a means of organizing interaction between the teacher and pupils.
About the Fate of Enlightenment

The article examines the objections expressed in the literature to the logic of En­lightenment thinking, which, in the opinion of many thinkers, leads to the stan­dardization of life, suppression of individuality, and the development of totalitar­ian regimes. At the same time, it is shown that the modern stage of development of technologies associated with the widespread use of creative labor does not fit into the standard, traditional methods of control, and even where the behavior of the standard seems necessary, based on the consent of people, this does not al­ways lead to a positive effect. Therefore, the impact on human behavior of virtue ethics cannot be eliminated from the modern theory of morality. New approaches to understanding education are considered, associated with understanding it as embedded in modern global processes. When Enlightenment, on the one hand, turns out to be associated with the understanding of all the features of civiliza­tional development, and on the other hand, it serves to justify expansion from developed countries, subordination their own aims to disseminate knowledge and reform in developing countries. As a methodology, the system method, the method of comparative analysis, and the historical method are used. The novelty lies in the fact that it shows the reasons for unjustified, ideological criticism of Enlightenment and ways to overcome the shortcomings of the traditional logic of one-line systematic thinking.
Effective Motion Artefacts Reduction in ECG Signals Using Sign Adaptive Filters with Knowledge-Based Genetic Algorithm

Objective: Motion artefact (MA) is the most difficult noise to remove during electrocardiogram (ECG) signal preparation. Its presence can cause incorrect detection of QRS complexes, P and T wave distortion, and, as a result, misdiagnosis. To date, the adaptive noise canceler approach is the best practical way to remove MA, although its effectiveness relies on parameter choices (step-size and filter length). The goal of this research is to develop an automated approach for selecting the best settings for MA removal.Methods: We suggested a two-step knowledge-based genetic algorithm (k-ga) that includes knowledge acquisition and filter optimization. On dataset 1, an exhaustive search technique was utilized in the knowledge acquisition step. The purpose was to decrease the parameter's search space in order to speed up subsequent optimization. In the filter optimization step, the genetic algorithm (GA) is then initialized using the previously gained knowledge. We used dataset 2 and the sign filters: sign LMS (SLMS), sign-sign LMS (SSLMS), and sign data LMS in this experiment (SDLMS). We used ECG signals from the MIT-BIH arrhythmia database and noise from the Noise Stress Test Database to create the datasets.Results: Compared to the LMS filter and wavelet transform optimized by GA (GA-DWT), our results show that the proposed method (k-gaSDLMS) gives higher improvement in signal-to-noise ratio, lower root-mean-square-error, and higher correlation coefficient.Conclusion: k-gaSDLMS can automatically select filtering parameters and efficiently remove MA.Significance: The proposed method can be successfully applied to ambulatory ECG signals and thus improve the reliability of diagnosis.
Fault Tolerance in Cloud: A Brief Survey

The Cloud computing environment has seen tremendous growth in terms of enterprises migrating to the cloud. The Cloud computing environment has been characterized by its support to multi-tenancy as well as rapid elasticity. A cloud service provider needs to adhere to service level agreements agreed with its tenants. As cloud infrastructure is composed of heterogeneous, it ought to have faults when different components interact with each other. Fault tolerance is one critical task in order to overcome faults in a timely manner and smooth operations. Through this paper, we present a brief survey of fault tolerance approaches available in the literature for the cloud computing environment. The fault tolerance techniques in literature have been broadly classified into three categories viz. Reactive approaches, proactive approaches, and resilient approaches. Reactive methods try to recover the system after faults. Proactive approaches try to prevent the system from entering in failure state by predicting faults beforehand. Resilient approaches are relatively new in the area of cloud fault tolerance predict faults for dynamically changing environments to prevent failures. Resilient methods are have used machine learning and artificial intelligence as a key factors for predicting faults.
Artificial Neural Network-Based Shared Control for Smart Wheelchairs: A Fully-Manual Driving for the User

Wheelchairs play an important role in regaining lost mobility and in the social reintegration of its users. However, users with quadriplegia yet have a few low cost solutions that meet their needs. The present work proposes a shared control strategy designed to operate together with discrete human-machine interfaces, where the user has few commands, and capable to integrate different types of sensors that can be attached to a commercial powered wheelchairs, without the necessity of localization. Simulations show that the proposed strategy provides a robust and safe navigation through daily environments and does not take away the navigation autonomy from the user, who performs a fully-manual driving.