Reviewerly/exHarmony huggingface dataset:
Some New Approaches to Comparative Evaluation of Algorithms for Calculating Distances Between Genomic Sequences

We continue to describe a new approach to the comparative evaluation of algorithms for calculating distances between DNA sequences. We compare the algorithms of calculation of distances, and we give our estimates of the comparative quality of these algorithms. We compare the total badness values for all triangles formed in the matrix and consider more acceptable the one of the initial algorithms of formation of the distance between two DNA sequences, for which the lower value of badness is obtained. In this paper, we apply a new approach. In it, we additionally assume that a “good” algorithm for counting the distances between a pair of given DNA sequences must yield a value close to some optimal number; of course, we do not know this number in advance. Therefore, we believe that considering all the resulting triangles (recall that they are formed quite a lot, a few thousand for the original matrices with a polarity of about 30), we have them in order of increasing the value of badness; and therefore we believe that for two “good” distance calculation algorithms should get a relatively large value of the pair correlation. In almost all computational experiments, our estimates of the comparative quality of algorithms for calculating distances between strings obtained by the two methods described are close to each other #COMESYSO1120.
On the Use of Differential Correction Clustering for Facing Spoofing Attacks to GNSS Augmentation Networks

Nowadays, Global Navigation Satellite Systems are the main source for high accuracy positioning and timing. For this reason, they are essential both for everyday activities and services, and for the industrial and critical infrastructure sectors. Moreover, the spread of increasingly autonomous vehicles results in strict accuracy and integrity requirements. This leads to the need for additional infrastructure to send corrections to the end users and mitigate the measurement errors, the Augmentation Networks. However, due to the increasing exploitation of localization functionalities, the Augmentation Networks could become a primary target for attackers resulting in a high financial and safety cost. Among the possible attacks, spoofing, that is the generation of a fake satellite signal which is seen as genuine by the receiver, is one of the most powerful and tricky. In this contribution, a detection and mitigation strategy for Augmentation Network spoofing attacks is proposed. We introduce two attack models and present a technique based on K-means clustering to counteract them. More in details, our approach is based on the computation of the number of clusters formed by the Augmentation Network corrections. Starting from the hypothesis that under nominal conditions only one cluster is present, the effects of the attacks on the clustering procedure are analyzed, and several attack simulations are performed to evaluate the algorithm performances. The proposed method has been compared both to an Augmentation Network attack detection technique, and to a receiver-level spoofing mitigation approach, showing comparable or better performances. Moreover, to the best of our knowledge, this is the first work addressing mitigation for spoofing attacks which target an Augmentation Network.
INFLUENZA: A Board Game Design Experiment on Vaccination

Introduction. Experts on vaccine hesitancy recommend tailoring interventions to local contexts, which presents an opportunity for game-based interventions to reflect local demographics and make them central to the experience of the game. Experimental game design is a research method that has already been used in educational games. Board games are relevant to the topic of vaccination, and present possibilities for game design of openness and flexibility. INFLUENZA was an experimental game design with the objectives of designing a vaccination-themed educational board game where: an aspect of local context was highly-relevant but also easily modifiable, means of emotional engagement were explored, and openness and flexibility in board game design were explored. Methods. The experimental game design study consisted of finding design solutions for achieving the study objectives in a single game, by analyzing comparable games and using iteration and two stages of live testing with players. Results. The game reflects national census data in a highly-relevant but also easily modifiable aspect of play (first objective). The game features aspects of personification to foster emotional engagement (second objective). The game is well-suited to changing the number of players, or allowing mediated play (third objective). Discussion. INFLUENZA has achieved each of its study objectives from an experimental game design perspective. Relevant features of educational games are theme, mechanics, and their integration, and the design experimentation in INFLUENZA follows this trend. Future work is running comparative trials of features of INFLUENZA, as well as measuring the impact of different local adaptations of INFLUENZA.
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as epsilon-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-optimal behavior with enough training. On the other hand, model-based RL aims to be sample efficient, and studies show that it requires far less training in the real environment for learning a good policy. A significant challenge with RL is that it relies on a well-defined reward function to work well for complex environments and such a reward function is challenging to define. Goal-Directed RL is an alternative method that learns an intrinsic reward function with emphasis on a few explored trajectories that reveals the path to the goal state. This paper introduces a novel reinforcement learning algorithm for predicting the distance between two states in a Markov Decision Process. The learned distance function works as an intrinsic reward that fuels the agent's learning. Using the distance-metric as a reward, we show that the algorithm performs comparably to model-free RL while having significantly better sample-efficiently in several test environments.
Digital Technologies and Elaboration of Mechanisms for Expert Examination of Scientific and Technical Potential of Universities

Within the framework of using digital technologies, this article substantiates mechanisms for examining scientific and technical potential of universities with introduction of methods of the active system management. Rigorous mathematical proofs are provided of the equilibrium state existence in expert examination procedures reflecting the experts’ subjectivity. In particular, experts’ interest in the examination results, as well as in their own personal ratings are demonstrated. It is concluded that digitalization of scientific and educational activities in modern universities possesses an important consequence in the form of elaborating programs for optimal management of their intellectual activity results. In addition, considered mechanisms of expert examination procedures make it possible with a high degree of correctness to evaluate complex projects connected to digitalization of scientific and technical products and optimization of resource management in executing programs at the universities.
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification

Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph structure in the GNN provide over the CNN (that ignores this graph structure). To address this question, we introduce edge entropy and evaluate how good an indicator it is for possible performance improvement of GNNs over CNNs. Our results on node classification with synthetic and real datasets show that lower values of edge entropy predict larger expected performance gains of GNNs over CNNs, and, conversely, higher edge entropy leads to expected smaller improvement gains.
Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction

The echo state network introduces a large and sparse reservoir to replace the hidden layer of the traditional recurrent neural network, which can solve the gradient-based problem of most recurrent neural networks in the training process. However, there may be an ill-posed problem when the least square method is used to calculate the output weight. In this paper, we proposed an echo state network based on L0 norm regularization. The main idea is to limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model.
Deductive systems with unified multiple-conclusion rules

Our goal is to develop a syntactical apparatus for propositional logics in which the accepted and rejected propositions have the same status and are being treated in the same way. The suggested approach is based on the ideas of Ƚukasiewicz used for the classical logic and in addition, it includes the use of multiple conclusion rules. A special attention is paid to the logics in which each proposition is either accepted or rejected.
Development of a Scheme of a Hardware Accelerator of Quantum Computing for Correction Quantum Types of Errors

This work is a study of the influence of the medium on the quantum system of qubits. The article assumes a description of the fundamentals of the quantum theory of information, as well as a place in it of the concept of quantum entanglement. A technique has been developed for correcting two basic types of quantum errors, based on the implementation and implementation of certain quantum schemes. The main difficulties in ensuring the protection of the quantum channel from various types of errors are analyzed and considered. The dependences of data distortion on noise and measures of decoherence on noise in one qubit are demonstrated. This article is devoted to solving the problem of research and development of corrective codes for correcting several types of quantum errors that appear during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types and types of quantum errors and to create a 3-qubit corrective code for quantum error correction. The work touches upon the tasks of research and development of the functioning methods of quantum circuits and models of quantum computing devices. The relevance of these studies lies in the mathematical and software modeling and implementation of corrective codes for correcting several types of quantum errors as part of the development and implementation of quantum algorithms for solving classes of classical problems. The scientific novelty of this area is expressed in the exclusion of one of the shortcomings of the quantum computing process. Currently, in many advanced countries of the world, intensive research is being carried out to develop and create quantum computers and their software, and there has been a rapid increase in interest in quantum computers. A large number of articles and monographs are published. The paper presents the main theoretical and practical results in the field of quantum computing.
OIDC Authentication for Educational Purposes and Solving Problems for Localization of Faults in Combinational Circuits

Web system for detecting malfunctions in combinational logic circuits using OIDC authentication is designed. This paper discusses some problems which can occur in combinational logic circuits, user authentication, and how we can cope with them. Online system using OIDC for solving such problems using the method “truth table” is described. Creating and using multiple login accounts for different educational systems can be problematic and hard to track for students. The web application for educational purposes will track student’s performance and will help teachers in evaluating their work and efforts.
Message from the ISMAR 2020 Science and Technology Program Chairs

We are delighted to welcome you to the 19th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2020), which had been originally planned to hold in Recife/Porto de Galinhas, Brazil.In order to preserve the safety and well-being of all participants under the global pandemic of COVID-19, ISMAR 2020 will be held as a virtual conference between November 9 and 13, 2020.ISMAR continues the over twenty year long tradition of IWAR, ISMR, and ISAR, and is undoubtedly the premier conference for Mixed and Augmented Reality in the world.We are convinced that the Science and Technology program of ISMAR 2020 will showcase cutting edge research in our field, will inspire and provoke discussions, and will help to establish new connections and reinforce established ones.We as program chairs have been mandated to select the best papers to be presented at this prestigious conferencethis was not an easy task!With the help of our international Program Committee and over 444 reviewers, from the 302 submissions we eventually selected 69 papers to appear in the proceedings in front of you in addition to the 18 papers which have been selected for a special issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG), which gives us acceptance rates of 22.8% and 5.96%, respectively.As program chairs we were aiming for the highest possible reviewing standards and conducted a decision process that aimed for final decisions in a fair, rigorous, and transparent way.We continued with the successful changes made in 2017 and the additional modifications from 2019 in order to improve the reviewing process.All papers were reviewed by the Science and Technology Program Committee (PC), which comprised 40 internationally renowned experts from the Asia-Pacific region, the Americas, and Europe.The program chairs also voted on paper decisions, so the PC had 44 voting members in total.There was a single paper submission category, papers from 4 to 10 pages in length, plus as many pages of references as needed.All submissions underwent a review process that encompassed two reviewing cycles, overseen by a coordinator from the PC.After PC members had declared
Constant Ciphertext Size Multi-Authority Attribute-based Scheme without Key Escrow

With the development of cloud computing application, attribute based encryption (ABE) with flexibly fine-grained data access control is adopted widely. However, the honest but curious authorities often peep at the user data. How to eliminate the key escrow is also an important and challenging problem in ABE schemes. In this paper, a constant ciphertext size multi-authority ciphertext-policy ABE scheme (RKE-MA-ABE) which resists key escrow is presented. In the proposed scheme, a user credential issuer ( UCI ) is introduced to generate a credential for each user credibly. UCI decentralizes the managing privilege of attribute authorities ( AAs ) and helps to embed the user’s secret value into the decryption key issued by the corresponding AAs . Additionally, AAs in this scheme work independently without interacting with each other to generate the master public key of the system during the system initialization phase. Our scheme avoids the collusion attacks by vicious users or authorities and has constant ciphertext length. It is proven CPA-secure under the decisional q-Bilinear Diffie-Hellman Exponent (q-BDHE) assumption in random oracle model.
Problems of Software Developing for the Automation of Scientific Activities

The article discusses the problems of software development for the automation of scientific activities. The analysis of the complex problem of automation of scientific research. The history of the creation of such systems is briefly reviewed. A review of the main automation systems is made, the basic algorithm for the functioning of such systems is determined. A system of indicators of the effectiveness of automation of scientific activity is considered. The reasons for the complexity of creating software are described. The main business process of scientific activity is determined. This process is the study and analysis of scientific information in scientific databases, databases of patents and inventions, scientific libraries. The tasks that require automation to improve the effectiveness of the scientific activities of the organization are considered. Such tasks include tasks: experiment management, the activities of scientists and dissertation councils, the preparation of scientific events, editorial and publishing activities, the expert evaluation of scientific projects, the work of postgraduate and doctoral departments, and a number of other tasks. For each type of automated activity, an approximate composition of the functional is determined. The software structure is defined. It is concluded that the development of such a system is most effective and expedient at the level of not the organization, but the industry as a whole because of the complexity of such an automation system. The prospects for further research on the topic of software development for the automation of scientific activities are outlined. #COMESYSO1120.
The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.
An Empirical Study on the Elective Course of Public Physical Education in Universities Based on “Project-Based Learning” -- a Case Study of the Elective Course of Public Physical Education (Yoga) At Taizhou University

The teaching mode reform of public physical education in colleges and universities has a profound impact on the improvement of curriculum quality. Different from the conventional teaching mode, “project-based learning” (PBL) gets rid of the time and space boundaries that constrain traditional classroom, and highlights the subjectivity of students as well as the improvement of students’ application ability and innovation ability. Herein, an experimental intervention was carried out in college students that took the elective course of public physical education (yoga). Specifically, a comparative study was conducted between the “project-based teaching mode” and the traditional teaching mode. Moreover, the course objectives of two dimensions, physiological function (special physical quality) and skill level (motor skill learning) were comprehensively evaluated. The changes brought by the two teaching modes to the realization of students' curriculum objectives were discussed. The research results show that PBL is more effective in improving students' physical qualities and their motor skills, showing a significant difference from the traditional teaching mode.
MODEL OF FUNCTIONING OF INFORMATION-CONTROL SYSTEM OF UNMANNED CAR WITH DIFFERENT SENSORS CHANNELS OF INFORMATION INTERACTIO

The article proposes an approach to the formalization of the process of information interaction of the information and control system of an unmanned vehicle with the environment, taking into account external destabilizing effects against the background of intra-system interference. The result of simulation modeling of the process of inverse processing of information arrays under conditions of a priori uncertainty is presented.
Dynamic Gesture Controlled User Interface Expert HCI System using Adaptative Background Masking: An Aid to Prevent Cross Infections

Human-Computer Interaction (HCI) interfaces need unambiguous instructions in the form of mouse clicks or keyboard taps from the user and thus gets complex. To simplify this monotonous task, a real-time hand gesture recognition method using computer vision, image, and video processing techniques has been proposed. Controlling infections has turned out to be the major concern of the healthcare environment. Several input devices such as keyboards, mouse, touch screens can be considered as a breeding ground for various micro pathogens and bacteria. Direct use of hands as an input device is an innovative method for providing natural HCI ensuring minimal physical contact with the devices i.e., less transmission of bacteria and thus can prevent cross infections. Convolutional Neural Network (CNN) has been used for object detection and classification. CNN architecture for 3d object recognition has been proposed which consists of two models: 1) A detector, a CNN architecture for detection of gestures; and 2) A classifier, a CNN for classification of the detected gestures. By using dynamic hand gesture recognition to interact with the system, the interactions can be increased with the help of multidimensional use of hand gestures as compared to other input methods. The dynamic hand gesture recognition method focuses to replace the mouse for interaction with the virtual objects. This work centralises the efforts of implementing a method that employs computer vision algorithms and gesture recognition techniques for developing a low-cost interface device for interacting with objects in the virtual environment such as screens using hand gestures.
Design Suggestions for Reading Support Method Based on Action Analysis

The readability of a paper document is often due to the “easy to handle” of the paper. In this paper, we analyze the action in ten kinds of readings on paper documents that occur in business (such as paper moving, page-turning). As a result, it was confirmed that various actions were performed at various frequencies for each reading. Moreover, in order to examine the reading support method from the viewpoint of the reading actions performed, we used cluster analysis of ten kinds of readings and obtained five clusters. Considering the support method for each of these clusters, the refreshed speed of the screen must be necessary for reading that frequently flips pages or moves between pages. Moreover, the lightness and thinness of reading devices must be remarkable for reading to move documents or to compare multiple documents. From our suggestions, it was clarified that the appropriate reading devices depend on the types of reading and that the supported functions depend on the types of reading.