Reviewerly/exHarmony huggingface dataset:
DVIT—A Decentralized Virtual Items Trading Forum with Reputation System

The metaverse provides us with an attractive virtual space in which the value of the virtual property has been increasingly recognized. However, the lack of effective cross-metaverse trading tools and the reputation guarantee makes it difficult to trade items among different metaverses. To this end, a decentralized reputation system for virtual items trading forum named DVIT is devised. To the best of our knowledge, DVIT is the first decentralized cross-metaverse item trading prototype inspired by the online-game trading system. We designed the corresponding transaction function and realized the autonomous governance of the community by introducing the reputation mechanism. An improved election mechanism is proposed to improve efficiency based on Delegated Proof-of-Stake (DPoS). Through token rewards associated with activity levels, users’ motivation can be stimulated. The experiments indicate that our proposed scheme could dynamically measure the trustworthiness degree of the users through the dynamic reputation value and thereby exclude malicious users from the blockchain within 20 epochs.
Effective Factors of Online Teaching Foreign Languages in Russian Universities

This paper studies the effectiveness of online mastering the foreign language course by Russian university students specializing in humanities and economics. The article discusses different formats of online learning and types of educational technologies for learning a foreign language remotely. An important point is a detailed analysis of Russian and foreign literature which allowed to identify not only the advantages and disadvantages of distance learning at universities, but also the factors influencing the success of students’ mastering the program. These factors include the psychological state of the student, technical capabilities (stable Internet and sufficient computer equipment), lack of live contact with the teacher and groupmates, tight deadlines, difficult tasks, etc. To determine the factors that influence the students’ success when mastering the foreign language course, a survey was conducted among 290 students of 16 Russian leading universities. The results of the survey showed the strengths and weaknesses of online teaching foreign language from the students’ point of view. A regression model showed that the students’ psychological state (mood, comfortable environment, lack of distractions) has a direct impact on the effectiveness of learning. One more notable fact is that recording a lecture or a seminar negatively affects the students’ motivation. The main result of the study is the authors’ recommendations on adapting the system of online learning for the foreign language course. Worked out for Russian universities’ administration, these suggestions include a variety of tools for conducting the educational process, the idea of students of different specializations’ collaboration, the use of gamification tools, etc.
Higher Education as a Set of Elements for Building the Future of Modern Youth

The modern university is increasingly focused on student subjectivity, that is, the activity is aimed not at constructing the future of students, but at constructing a subject that can determine and shape its future. But do students connect their current studies with their future life and work? The article examines how modern students evaluate the set of conditions, opportunities and tools that the university gives them to design their future. The purpose of the article is to identify the factors that determine in the minds of young people the connection between learning in the present and what is expected after completing training in the future. The empirical basis of the work was the data of the VIII stage of monitoring the students of the Sverdlovsk region, which are the results of a questionnaire survey of 2000 students from 13 universities of the Sverdlovsk region. The study examined the attitude to study, satisfaction with the quality of training, the use of various forms of education, and the opportunities offered by the university. As a result of the analysis, two groups of factors were identified: objective (a set of opportunities provided by the university) and subjective (personal responsibility and awareness of students). Their combination determines whether students perceive their studies as relevant for their future and whether they note the connection of the acquired knowledge, skills, and competencies with future life and work. It is concluded that the university should not only provide new educational opportunities for students but also engage in the formation of awareness and responsibility of students in designing their future. The results and conclusions presented in the paper will be useful to university management in designing various practices, forms, and methods of working with students. The article may also be of interest to researchers and the general public.
Certificateless public auditing with data privacy preserving for cloud-based smart grid data

As the promising next generation power system, smart grid can collect and analyze the grid information in real time, which greatly improves the reliability and efficiency of the grid. However, as smart grid coverage expands, more and more data is being collected. To store and manage the massive amount of smart grid data, the data owners choose to upload the grid data to the cloud for storage and regularly check the integrity of their data. However, traditional public auditing schemes are mostly based on Public Key Infrastructure (PKI) or Identity Based Cryptography (IBC) system, which will lead to complicated certificate management and inherent key escrow problems. We propose a certificateless public auditing scheme for cloud-based smart grid data, which can avoid the above two problems. In order to prevent the disclosure of the private data collected by the smart grid during the phase of auditing, we use the random masking technology to protect data privacy. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.
Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

Purpose Scholarly communications, particularly, questions and answers (Q&amp;A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&amp;A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation. Design/methodology/approach To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis. Findings The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation. Practical implications Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars. Originality/value The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.
Clustering-Based Refinement for 3D Human Body Parts Segmentation

A common approach to address human body parts segmentation on 3D data involves the use of a 2D segmentation network and 3D projection. Following this approach, several errors could be introduced in the final 3D segmentation output, such as segmentation errors and reprojection errors. Such errors are even more significant when considering very small body parts such as hands. In this paper, we propose a new algorithm that aims to reduce such errors and improve 3D segmentation of human body parts. The algorithm detects noise points and wrong clusters using DBSCAN algorithm, and changes the labels of the points exploiting the shape and position of the clusters. We evaluated the proposed algorithm on the 3DPeople synthetic dataset and on a real dataset, highlighting how it can greatly improve the 3D segmentation of small body parts like hands. With our algorithm we achieved an improvement up to 4.68% of IoU on the synthetic dataset and up to 2.30% of IoU in the real scenario.
Towards Synthetic Data: Dealing with the Texture-Bias in Sim2real Learning

In this paper we test and ultimately confirm the texture-bias hypothesis of the state of the art method for semantic segmentation, DeepLabv3+. However, our results show that even though texture bias is obvious, shape still plays an important role in the overall system performance. To perform the analysis, we propose using a synthetic dataset with highly realistic textures for sim2real learning in a leaf segmentation task. The labeled synthetic plant datasets used in the evaluation are created using a 3D modeling software, Blender, utilizing a custom designed procedural generation pipeline. We validate the accuracy of the models on real images of sweet peppers and we introduce an improved method for 6DOF pepper pose estimation which utilizes the presented leaf segmentation model. Accuracy of the pepper pose estimation method is experimentally validated.
Autonomous Hierarchy Creation for Path Planning of Mobile Robots in Large Environments

The main task of mobile robots in large environments such as factories, warehouses, and open spaces is to transport goods and people. Planning the path in large environments using classical graph-based search is computationally too intensive. A representation by a hierarchical graph (H-graph) facilitates graph creation and reduces the complexity of path planning. In this paper, we present an algorithm for autonomously generating hierarchy of the environment from floor plans. The hierarchical abstraction depicts the environment in levels, from the most detailed to the most abstract representation of the environment, where pre-computed partial paths at the most detailed level are graph edges in a higher level. We use the E* algorithm to find partial paths in the most detailed abstraction level, and we propose the extraction of higher levels automatically from lower levels. We verified the proposed H-graph creation on our University premises resulting in five abstract levels.
Cyber-Physical Platform with Miniature Robotic Vehicles for Research and Development of Autonomous Mobile Systems

The paper presents a cyber-physical platform that enables development and evaluation of algorithms for autonomous driving of mobile robots. The platform consists of three main parts: a vision system for object tracking, a small-scale physical model of an environment with miniature mobile robots and a system for simulating soft sensors. The vision system consists of a single camera above the platform, that enables global tracking of all objects in real time. The small-scale models of the environment are presented as drawings on a flat surface on which the miniature mobile robots with wheels can drive. Two physical models developed are presented: a town with miniature wheeled mobile robots and an industrial hall with miniature automated guided vehicles. Since not all essential sensors are available in a suitable, small form, an approach that enables soft sensors is presented. Different modular systems are integrated together using the framework provided by the Robot Operating System. The proposed platform can be deployed quickly and is easy to use. It is also very affordable, customisable and expandable.
Benchmark of Sampling-Based Optimizing Planners for Outdoor Robot Navigation

This paper evaluates Sampling-Based Optimizing (SBO) planners from the Open Motion Planning Library (OMPL) in the context of mobile robot navigation in outdoor environments. Many SBO planners have been proposed, and determining performance differences among these planners for path planning problems can be time-consuming and ambiguous. The probabilistic nature of SBO planners can also complicate this procedure, as different results for the same planning problem can be obtained even in consecutive queries from the same planner. We compare all available SBO planners in OMPL with an automated planning problem generation method designed specifically for outdoor robot navigation scenarios. Several evaluation metrics are chosen, such as the length, smoothness, and success rate of the resulting path, and probability distributions for metrics are presented. With the experimental results obtained, clear recommendations on high-performing planners for mobile robot path planning problems are made, which will be useful to researchers and practitioners in mobile robot planning and navigation.
Automated retrieval and comparison of sheet metal parts

With the number of 3D CAD models increasing rapidly, retrieving models of similar parts has become an important activity as existing designs may contain vital information regarding design and manufacturing. This paper is targeted at retrieving and comparing sheet metal parts. In the method proposed herein, the general shape of a sheet metal part is represented by a simplified hierarchical tree wherein the nodes represent the planar walls of the part and feature-related information is attached to these nodes as textual attributes. A shape index is derived from this simplified tree and subsequently used to retrieve similar parts from the database. The retrieved parts are assessed for their similarity with the enquiry part using five criteria: the general shape of the part, bends and their direction, feature types, feature location and feature size. In addition to determining the degree of similarity, the main advantage of the proposed method is that the assessment provides useful feedback to design and manufacturing engineers based on these five criteria. A prototype system developed in C++ has been tested on a database containing a few hundred parts and the effectiveness of the proposed method in retrieving and comparing the parts is discussed.
Bilingual acoustic voice variation: the case of Sorani Kurdish-Persian speakers

Many individuals around the world speak two or more than two languages. This phenomenon adds a fascinating dimension of variability to speech, both in perception and production. But do bilinguals change their voice when they switch from one language to the other? It is typically assumed that while some aspects of the speech signal vary for linguistic reasons, some indexical features remain unchanged across languages. Yet little is known about the influence of language on within- and between-speaker vocal variability. The present study investigated how acoustic parameters of voice quality are structured in two languages of a bilingual speaker and to what extent such features may vary between bilingual speakers. For this purpose, speech samples of 10 simultaneous Sorani Kurdish-Persian bilingual speakers were acoustically analyzed. Following a psychoacoustic model proposed by Kreiman (2014) and using a series of principal component analyses, we found that Sorani Kurdish-Persian bilingual speakers followed a similar acoustic pattern in their two different languages, suggesting that each speaker has a unique voice but uses the same voice parameters when switching from one language to the other.
The impact of mismatched recordings on an automatic-speaker-recognition system and human listeners

The so-called ‘mismatch’ is a factor which experts in the forensic voice comparison field encounter regularly. Therefore, we decided to explore to what extent the automatic-speaker-recognition system’s and the earwitness’ ability to identify speakers is influenced when recordings are acquired in different languages and at different times. 100 voices in a database of 300 recordings (100 speakers recorded in three mutually mismatched sessions) were compared with an automatic-speaker-recognition software VOCALISE based on i-vectors and x-vectors, and by 39 respondents in simulated voice parades. Both the automatic and perceptual approach seem to have yielded similar results in that the less complex the mismatch type, the more successful the identification. The results point to the superiority of the x-vector approach, and also to varying identification abilities of listeners.
Fractional-Order Robust State Observers and Encryption

This chapter introduces a new encryption algorithm for color RGB images and text. The encryption is based on the synchronization of fractional chaotic systems in a topology of master-slave. The encryption algorithm provides security against common encryption techniques, including known and chosen plain text attacks.
GNOLL: Efficient Multi-Lingual Software for Real-World Dice Notation and Extensions

Dice Notation is a system for describing how to roll collections of dice.It is often used to assist in understanding the rules of games -particularly tabletop roleplaying games (TTRPGs).Existing research software in this space has been primarily designed for other researchers and statisticians despite the fact that a large population of those actually playing these games are young (Natalt Design, 2020) or not involved in statistical research.GNOLL is an open-source library for parsing commonly used dice notations in gaming system research and/or software development.GNOLL is performant, supports most popular and obscure notations, has permissive licensing, and is integratable into many other systems due to it being written in C. At present, the repository has working examples of integration into 13 different programming languages, such as Go, Java, Julia, Python, Perl and R.
Enhancing Visual Exploration through Augmented Gaze: High Acceptance of Immersive Virtual Biking by Oldest Olds

The diffusion of virtual reality applications dedicated to aging urges us to appraise its acceptance by target populations, especially the oldest olds. We investigated whether immersive virtual biking, and specifically a visuomotor manipulation aimed at improving visual exploration (augmented gaze), was well accepted by elders living in assisted residences. Twenty participants (mean age 89.8 years, five males) performed three 9 min virtual biking sessions pedalling on a cycle ergometer while wearing a Head-Mounted Display which immersed them inside a 360-degree pre-recorded biking video. In the second and third sessions, the relationship between horizontal head rotation and contingent visual shift was experimentally manipulated (augmented gaze), the visual shift being twice (gain = 2.0) or thrice (gain = 3.0) the amount of head rotation. User experience, motion sickness and visual exploration were measured. We found (i) very high user experience ratings, regardless of the gain; (ii) no effect of gain on motion sickness; and (iii) increased visual exploration (slope = +46%) and decreased head rotation (slope = -18%) with augmented gaze. The improvement in visual exploration capacity, coupled with the lack of intolerance signs, suggests that augmented gaze can be a valuable tool to improve the "visual usability" of certain virtual reality applications for elders, including the oldest olds.
Introduction

In this chapter we give an overview of cryptography and cryptanalysis where basic concepts and definitions are given, also the relation of cryptography to chaotic systems synchronization is addressed.
On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?

Since Bitcoin has frequently witnessed price fluctuations and high volatility, the factors influencing its returns and volatility is an important research subject. To accomplish this goal, we applied the Gets reduction method which has a good reputation compared to other competing approaches in terms of the statistical apparatus available for a repeated search to determine the final set of determinants and the consideration of location shifts. We found that the reduced set of explanatory variables that affects Bitcoin returns is composed of Twitter-based economic uncertainty, gold return, the return of the Euro/USD exchange rate, the return of the US Nasdaq stock exchange index, market capitalization, and Bitcoin mining difficulty. In contrast, the volatility of Bitcoin is affected by only lagged terms of the ARCH effect and the volume of this cryptocurrency.
Applications of Mathematical in Computer Science

Abstract: Mathematics (The QUEEN’s mother of all Sciences), is the foundation of Computer Science. Mathematics can be perceived in our garden or park from symmetry of leaves, flowers, fruits etc. and by so many examples of Geometry and symmetry can be seen in nature. Scientists and researchers cannot ideally accomplish their work without the inclusion of mathematics. Mathematics is sociable for analytical skills needed in Computer Disciplines like; Concepts of binary number system, Boolean algebra, Calculus, Discrete mathematics, linear algebra, number theory, and graph theory are the most applicable to the subject of computer science with the accessional emergence of new concepts like machine learning, artificial intelligence, virtual reality and augmented reality make the future of mathematics grow endless. Mathematics has been an important intellectual preoccupation of man for a long time. Computer Science as a formal discipline is about seven decades young. Is the almost spontaneous use of computing? In this article, this paper convey to the frontage the many close connections and parallels between the Mother and daughter sciences. The paper underscores the strong interplay and interactions by looking at some exciting contemporary results from number theory and combinatorial mathematics and algorithms of computer science.
Complexity and Monitoring of Economic Operations Using a Game-Theoretic Model for Cloud Computing

In this study, a model is presented for allocating cloud computing resources based on economic considerations using tools from game theory. The model, called the Non-Cooperative Game Resource Allocation Algorithm (NCGRAA), is designed to achieve the optimum stage in cloud computing. In addition, the Bargaining Game Resource Allocation Algorithm (BGRAA) is introduced to the existing system to develop the billing process within the constraints of availability and fairness. This system-based algorithm implements methods for converging on and improving the Nash Equilibrium and Nash Bargaining solutions. While the Nash equilibrium helps to develop decision-making concepts with game theory, one of its main goals is to achieve the desired outcome and avoid deviation from the working stage. Nash Bargaining is a unique solution that occurs between two parties and takes into account the process of bargaining to provide a fair solution that is scale invariant and independent. In recent years, cloud computing has become a popular way to manage computing services and enable producers and consumers to interact. This process allows users to obtain goods at an affordable cost from sellers according to their expectations. This research investigates the economic operation monitoring of cloud computing using the gaming theory model. A Static Negotiation Analysis Method with a Bargaining Process (SNAM-BP) for a dynamic conceptual framework is presented to display the weighted relationship between primary issues and keywords used to evaluate the potential partnership of each country.