Reviewerly/exHarmony huggingface dataset:
You Only Look at Interested Cells: Real-Time Object Detection Based on Cell-Wise Segmentation

In this paper, we study real-time object detection based on cell-wise segmentation. Existing object detection methods usually focus on detecting interesting object's positions and sizes and demand expensive computing resources. This process makes it difficult to achieve high-speed and high-precision detection with low-cost devices. We propose a method called You Only Look at Interested Cells or in-short YOLIC to solve the problem by focusing on predefined interested cells (i.e., subregions) in an image. A key challenge here is how to predict the object types contained in all interested cells efficiently, all at once. Instead of using multiple predictors for all interested cells, we use only one deep learner to classify all interested cells. In other words, YOLIC applies the concept of multi-label classification for object detection. YOLIC can use exiting classification models without any structural change. The main point is to define a proper loss function for training. Using on-road risk detection as a test case, we confirmed that YOLIC is significantly faster and accurate than YOLO-v3 in terms of FPS and F1-score.
Infinite Families of 2-Designs from a Class of Linear Codes Related to Dembowski-Ostrom Functions

Due to their important applications to coding theory, cryptography, communications and statistics, combinatorial [Formula: see text]-designs have attracted lots of research interest for decades. The interplay between coding theory and [Formula: see text]-designs started many years ago. It is generally known that [Formula: see text]-designs can be used to derive linear codes over any finite field, and that the supports of all codewords with a fixed weight in a code also may hold a [Formula: see text]-design. In this paper, we first construct a class of linear codes from cyclic codes related to Dembowski-Ostrom functions. By using exponential sums, we then determine the weight distribution of the linear codes. Finally, we obtain infinite families of [Formula: see text]-designs from the supports of all codewords with a fixed weight in these codes. Furthermore, the parameters of [Formula: see text]-designs are calculated explicitly.
Data Validation Meets Answer Set Programming

Data validation may save the day of computer programmers, whatever programming language they use. In fact, processing invalid data is a waste of resources at best, and a drama at worst if the problem remains unnoticed and wrong results are used for business. Answer Set Programming is not an exception, but the quest for better and better performance resulted in systems that essentially do not validate data in any way. Even under the simplistic assumption that input and output data are eventually validated by external tools, invalid data may appear in other portions of the program, and go undetected until some other module of the designed software suddenly breaks. This paper formalizes the problem of data validation for ASP programs, introduces a declarative language to specify data validation, and presents a tool to inject data validation in ordinary programs. The proposed approach promotes fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid. Additionally, the proposed approach opens the possibility to take advantage of ASP declarativity for validating complex data of imperative programming languages.
WITHDRAWN: Strip lines and blotches noise removal using dynamic artifacts removal algorithm for HD images

The proposed method demonstrates the removal of different degradations that occur along with impulse noise in images. It consists of two steps; first, it detects the noise variance. Second, it filters all the degradation along with impulse noise. The current pixel value is replaced by a processed value in the filter’s output when the point of pixel is centered at that time. A prime benefit to this adaptive approach is that it not only wears down away edges or other small structures in the image but also replaces a number of autonomous algorithms required for removal of various degradations with impulse noise. The performance of the algorithm is calculated in terms of different metrics like MSE, PSNR and MSSIM.
Initial Center Weight Self Organizing Map Using Genetic Algorithm

Missing values occur in data that have incomplete or missing attribute values. Missing value can concern using the imputation method, which as applied to an appropriate value for filling in the missing value. SOMI clustering is used for clustering-based imputation but has constraints in determining the best weight and parameters specified for the artificial neural network. The problem is that choosing the optimal weight value for network node compatibility is a big deal. Furthermore, the Genetic Algorithm (GA) has proposed determining each feature's optimal weight because the output nodes grow dynamically during the input pattern grouping process. The selection of SOM weights using a genetic algorithm is called GAWSOM can be improved by determining the smallest RMSE in the winning unit of the appropriate node so that result stable cluster. The experimental results show that GA is efficient in finding the optimal initial weight value from a large search space. The results show that using the GAWSOM algorithm can find a better solution with elite chromosomes in the real data when the missing value is 10%, with a shorter time than features with mixed data. The results of GAWSOM's performance with elitist search show that the time difference is 100 seconds faster because the searching is less chromosome than GAWSOM nonelitist search.
Teaching English for Specific Purposes to Future Programmers on the Moscow Area Digital College Platform

This paper describes the content of the English for Specific Purposes (ESP) electronic learning pack developed by the author for college students majoring in Information systems and programming and published on the interactive platform “Moscow Area Digital College”, the system of exercises for language and communicative competence development, and some issues on using the learning pack on the Moscow Area Digital College platform. The thematic content of the learning pack covers both the specializations the speciality “Information Systems and Programming” comprises and Worldskills competencies related to this speciality. The system of exercises includes lexis and grammar training, reading texts and performing the tasks for speech skills development. The learning pack shows college students different professions in the field of information technology and facilitates a certain flexibility in the labour market formation. The learning pack is student-friendly, and it proved its usefulness for college students.
Algorithmic Support for Rabin Three-Modular Cryptosystem Based on the Operation of Addition

Algorithmic support for three-modular asymmetric Rabin encryption algorithm using only the addition operation is developed. Squaring is proposed to perform using the vector-modular method of modular multiplication. The method of adding product modules is proposed to use for decimal number recovery from its residues during decryption. Conducted studies of time complexities of the proposed approach and the classical one showed the advantages of the developed method. Its use allows us to reduce time complexity from cubic to quadratic.
Cyclomatic Complexity-Based Encapsulation, Data Hiding, and Separation of Concerns

Three design principles are prominent in software development-encapsulation, data hiding, and separation of concerns.These principles are used as subjective quality criteria for both procedural and object-oriented applications.The purpose of research is to quantify encapsulation, data hiding, and separation of concerns is quantified using cyclomatic-based metrics.As a result of this research, the derived design metrics, coefficient of encapsulation, coefficient of data hiding, and coefficient of separation of concerns, are defined and applied to production software indicating whether the software has low or high encapsulation, data hiding, and separation of concerns.
Extraction and Summarization of Reviews using Lexicon based Approach

Abstract Online service providers have enabled users to freely comment in the form of review on an online items. These large scale of reviews contains valuable information and poses the need of automatic summarization. In this paper the extraction and summarization of reviews expressed by customers on Samsung mobiles has been carried out. The proposed approach extracts the Samsung reviews using custom build scraper from Flipkart and classify them as positive, negative and neutral using Lexicon based approach (SentiWordNet).
Review of the Research on Augmented Reality Maintenance Assistant System of Mechanical System

Abstract In order to ensure the normal operation of mechanical system and solve the problems of complex structure and insufficient cognition caused by mechanical system updating, the advantages of applying augmented reality technology in the field of fault diagnosis and maintenance are analyzed. Firstly, this paper summarizes the application of augmented reality technology for maintenance assistance cases at home and abroad, analyzes its industry characteristics and application advantages, then summarizes the key technologies of augmented reality, and finally prospects the development trend of the system application.
The Effect of Remote Classroom Attendance on Students’ Course-Satisfaction

Introduction of a totally remote classroom in Russian and foreign higher educational institutions became a methodological response to COVID-19 educational disruption. This new, unfamiliar format of teaching and learning was enforced by universities' authorities and had to be adopted quickly by both, teachers and students. This research investigates students' response to new educational environment in terms of two criteria: changes in overall course satisfaction, and the most preferable format of training. To get students' feedback we conducted an anonymous survey in LMS Moodle for 350 first year students at North-Western State Medical University named after I.I. Mechnikov. The survey measured 3 key parameters of the course satisfaction: course expectations fulfillment, learning environments satisfaction, and achieving personal goals. Analysis of the survey outcomes shows that educational process was not disrupted despite the abrupt and rapid character of the introduction of the totally remote classroom. Since students had been familiar with learning digital tools, their goals and needs in education did not change. Psychological aspect gives reasons for the biggest concern: students miss safety and comfort of a conventional classroom. In general, course satisfaction and students' motivation were not affected largely by the introduction of the totally remote classroom. At the same time, we conclude that students perceive it as a temporary measure and there is a need at higher educational institutions to return to conventional in-classroom learning with digital tools as an additional method to realize educational program.
Design and Analysis of Hand Gesture Controlled Quadcopter

Hand control quadcopter, which is control of the motion of the hand.The user needs to wear a glove, which incorporates the sensors.The sensors will record the development of the movement in a particular direction, which will result in the development of the quad copter in that individual direction.The quad copter and hand motion glove are associated wirelessly by means of RF waves.The wireless correspondence empowers the user to cooperate with the quad copter in a friendlier manner.This project structured a quadcopter framework utilizing an ardupiolt mega2.8chip with an RF transmitter with a few live control shapes accessible just as a mission organizer for autopilot control.
Performance Analysis Using a Deep Belief Network by a Self-Organizing Map

Since resource consumption is the main reason for software aging occurrences, many methods have been applied to accurately predict the resource consumption series. Among these methods, neural networks are powerfully applied to forecast the series data. For some existing problems of artificial neural networks such as the choice of initialization and local optimization, the improvements of neural networks are not only a hot research topic in the field of time series prediction but also a research hotspot in resource consumption prediction of software aging. In this paper, we propose a method for resource consumption prediction of software aging using deep belief nets (DBNs) with the restricted Boltzmann machine (RBM). This presented method contains the following steps. First, a pre-processing is introduced by two parts: smoothing data by a self-organizing map (SOM) and removing a linear trend by a difference method. Second, a method, DBN with two RBMs, is presented to capture the features and forecast future values. Third, a glowworm swarm optimization (GSO) method is used to learn the hyper-parameters of DBN with two RBMs. In the experiments, two types of resource consumption series are used to validate our proposed method compared with some state-of-the-art algorithms.
Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

The continuous evolution of modern software technologies combined with the deluge of available “ready-to-use” data has triggered revolutionary breakthroughs in several domains, preservation of cultural heritage included. This breakthrough is more than obvious just by considering the numerous multimedia tools and frameworks that actually serve as a means of providing enhanced cultural storytelling experiences (e.g., navigation in historical sites using VR, 3D modeling of artifacts, or even holograms), which are now readily available. In this context and inspired by the vital importance of sustainability as a concept that expresses the need to create the necessary conditions for future generations to use and evolve present artifacts, we target the software engineering domain and propose a systematic way towards measuring the extent to which a software artifact developed and applied in the cultural heritage domain is sustainable. To that end, we present a data-driven methodology that harnesses data residing in online software repositories and involves the analysis of various open-source multimedia tools and frameworks.
Simultaneous Visualization of Documents, Words and Topics by Tensor Self-Organizing Map and Non-negative Matrix Factorization

The purpose of this work is to develop a simultaneous visualization method of documents, words, and topics. The task of the proposed method is to map a set of documents to a pair of low-dimensional latent spaces corresponding to documents and words, by which the relations between them are visualized. In addition, the method also decomposes the mapping as the sum of topics, so that the topic distributions are visualized on the latent spaces. To achieve the task, we combined the tensor self-organizing map and the non-negative matrix factorization. We applied the method to NeurIPS data set, and the result shows that the method enables us to understand the tripartite relation between document, words and topics easily.
Mathematical model of power supply system for remotely operated underwater vehicle with dc power transmission line and load voltage feedback

Abstract The paper deals with a mathematical model of the closed loop power supply system for a remotely operated underwater vehicle with a DC transmission line via a rope-cable and the load voltage feedback. The state-space method is used to develop the mathematical model. Using differential equations in the form of Cauchy alleviates significantly the mathematical description. The results for both simulation and mathematical models of the closed loop system are compared, thus, confirming the adequacy of mathematical description and prospects for further application in the design of a versatile tool for calculating and adjusting the parameters of the control system for the power supply system under study.
Variable selection in finite mixture of regression models with an unknown number of components

A Bayesian framework for finite mixture models to deal with model selection and the selection of the number of mixture components simultaneously is presented. For that purpose, a feasible reversible jump Markov Chain Monte Carlo algorithm is proposed to model each component as a sparse regression model. This approach is made robust to outliers by using a prior that induces heavy tails and works well under multicollinearity and with high-dimensional data. Finally, the framework is applied to cross-sectional data investigating early warning indicators. The results reveal two distinct country groups for which estimated effects of vulnerability indicators vary considerably.
Study of convolutional neural network based on fusion residual mechanism

In recent years, due to the rapid development of computer technology, the calculate ability has been continuously improved. The depth of the deep convolutional neural network is getting larger and larger, and the performance of the model can also be improved. However, beyond a certain range, the training of the network model is prone to gradient explosion or vanishing. This article starts from the design of the residual mechanism, integrates with the traditional Vgg-16 network model, transfers image information from multiple aspects, obtains richer input features, “reduces” the depth of the network, and controls each network parameter operation effectively, suppress the occurrence of gradient explosion and vanishing, and improve the accuracy of picture classification. Experimental results show that the model proposed in this paper improves the accuracy of the network classification by 5.8% compared with that before the improvement.
Blockchain for the Authentication and Immutability of Academic Credentials Issued in Brunei Darussalam

Blockchain technology is a distributed ledger based on a peer-to-peer topology that is highly capable of storing data on a global scale over thousands of participating servers. The technology has evolved far beyond its initial development for cryptocurrencies. Today, the technology has been implemented by various sectors and industries due to its ability to provide security to its immutable database over its decentralized network. This paper provides a detailed study into providing a feasible solution based on blockchain technology for the real-time authentication and secure deployment of hashed academic credentials issued by higher institutions in Brunei Darussalam under the governing body of the sultanate's Ministry of Education. The feasibility of the designed system is tested through the execution of smart contracts onto the Kovan Ethereum test network.
Flight design and dynamics analysis of a new water-air UAV

Abstract In the field of UAV research, water-air amphibious is an important direction. If water-air amphibious is realized, the UAV will no longer be restricted by the space field, and can realize various functions in the three domains of water, land and air more flexibly and freely to complete various flight and exploration missions. Therefore, this paper designs the model and flight algorithm of a new type of water-air amphibious variable wing UAV, and verifies the dynamic analysis of the relevant flight components of the UAV.
Why are Ontologies Not Reused Across the Same Domain?

Even though one of the main characteristics of ontologies has always been claimed to be their reusability, throughout this paper it will be shown that ontology reuse across a given domain is not a consolidated practice. We have carried out a statistical study on ontology reuse in the ontologies collected in Linked Open Vocabularies (LOV), in addition to a particular analysis of a use case. The results of the present work show that, when building an ontology, the heterogeneity between the needed conceptualisation and that of available ontologies, as well as the deficiencies in some of such ontologies (concerning documentation, licensing, etc.) are important obstacles for reusing ontologies of the same domain of the ontology under development. A possible approach to lessen these problems could be the creation of communities similar to open software ones in charge of developing and maintaining ontologies.
Intellectual Networking in Digital Education – Improving Testing for Enhanced Transfer of Knowledge

Development of technical solutions and equipment cost reduction led to the expansion of digital tools to different fields of activities, computing has become ubiquitous due to formalization and rationalization of living processes. Cognitive activity is also transforming due to the introduction of computing, but it is necessary to measure the degree of digitalization of intelligent processes so that researchers and educators can better understand the practical results of digital learning. The effectiveness of digital learning tools has been evaluated over the past decade based on online courses. Testing is a learning element that easily turns knowledge assessment into routine operations. The study of knowledge creation demonstrates the complex role of networking and communication among students, which is built and structured in preparation for passing tests. Developing test content and design and improving test conditions can help improve learning by transferring and sharing knowledge between students. A survey conducted at the St. Petersburg Polytechnic University allowed researchers to formulate several technical, organizational and methodological recommendations for improving testing and draw conclusions about the social, cultural and intellectual impact of tests on learning outcomes.
Beyond the VPN: Practical Client Identity in an Internet with Widespread IP Address Sharing

To support remote employees, organizations often use virtual private networks (VPNs) to provide confidential and authenticated tunnels between the organization's networks and the employees' systems. With widespread end-to-end application-layer encryption and authentication, the cryptographic features of VPNs are often redundant. However, many organizations still rely upon VPNs. We examine the motivations and limitations associated with VPNs and find that VPNs are often used to simplify access control and filtering for enterprise services.To avoid limitations associated with VPNs, we propose an approach that allows straightforward filtering. Our approach provides evidence a remote user belongs in a network, despite the address sharing present in tools like Carrier-Grade Network Address Translation. We preserve simple access control and eliminate the need for VPN servers, redundant cryptography, and VPN packet headers overheads. The approach is incrementally deployable and provides a second factor for authenticating users and systems while minimizing performance overheads.
Predictability of AI Decisions

Artificial intelligence evolves rapidly and will have a great impact on the society in the future. One important question which still cannot be addressed with satisfaction is whether the decision of an intelligent agent can be predicted. As a consequence of this, the general question arises if such agents can be controllable and future robotic applications can be safe. This chapter shows that unpredictable systems are very common in mathematics and physics although the underlying mathematical structure can be very simple. It also shows that such unpredictability can also emerge for intelligent agents in reinforcement learning, especially for complex tasks with various input parameters. An observer would not be capable to distinguish this unpredictability from a free will of the agent. This raises ethical questions and safety issues which are briefly presented.