Reviewerly/exHarmony huggingface dataset:
Technological complex as a factor for increasing the efficiency of professional training of future specialists in the construction

The construction industry plays a leading role in the Russian economy. Its innovative development is defined by highly qualified personnel, trained at educational institutions. The process of training such specialists requires integration of systemic, context-oriented, competence-oriented, personal and environmental educational approaches. The integration should act as a leading principle while designing all the components of the education process at technical universities. Within the context of the requirement defined by the Federal Educational Standards, we argue that the most promising solution should be the development of the technological complex, including its basic element i.e. the scientific and educational cluster aimed at every curriculum. This center must be equipped with up-to-date machinery that allows carrying out researches. The main goal of the research is to design the technological complex, allowing ensuring the professional and personal development of future graduates. The main result of the research lies in developing new basics aimed at creating and using the technological complex to train civil engineering students. The results can be used at technical universities while designing and creating scientific and educational centers.
A Novel Structural and Semantic Similarity in Social Recommender Systems

Recommender systems are used to suggest items to users based on their preferences. Recommender systems use a set of similarity measures as part of their mechanism that could help to identify interesting items. Even though several similarity measures have been presented in the literature, most of them consider only the rating of similar users and suffer from a range of drawbacks. In order to fix these problems, we propose a novel similarity measure based on the semantic and structural information in the network. On one hand, the preference of the target user is calculated using the similarity between similar users based on several factors such as user profile, ratings, and tags. On the other hand, a user is an element who has relations with other elements in the network. Therefore, we can use the network topology in the similarity measurement. We apply this idea in the social recommender system to improve the quality of recommendations. The experimental results show that our method achieves better precision and accuracy and handles the cold-start problem.
Mt-Gan: A Training Framework To Enhance Image Classification Task With Image Translation

The emerging e-commerce ecosystem has made image understanding, particularly fashion image attributes (e.g., pattern, color, material, category) recognition and classification, a prerequisite for many downstream applications such as visually similar product retrieval and recommendation. We propose a training framework named Multi-Task Generative Adversarial Network (MT-GAN) that trains an image classifier as its discriminator and proves to improve the multi-class multilabel image classification task performance by 11.54% on average. A key contributor to the performance improvement is the feeding of the conditional input image to the image classifier in addition to the image to be classified, which enhances the classification performance by 7.36% relatively compared to the same classifiers trained in the same MT-GAN setting without being fed the conditional input. The proposed MTGAN training framework is GAN-agnostic and can be applied to image classification tasks in a supervised learning manner with various state-of-the-art designs of the generator.
Research on SVM Parameter Optimization Mechanism Based on Particle Swarm Optimization

As an common and effective pattern recognition method, support vector machine is used in many scenarios. The parameter selection of support vector machine directly affects its performance, determines its classification accuracy and generalization ability. This paper optimizes the parameter optimization mechanism of support vector machine (SVM) based on particle swarm optimization (PSO), adjusts the parameters of particle swarm optimization, and makes it have stronger global search ability. In this paper, support vector machine is firstly introduced, then the parameter optimization mechanism of support vector machine is elaborated, and the improved parameter selection model based on particle swarm optimization algorithm is proposed, then carries on the simulation experiment to the improved model on the open data set. Experimental results show that the improved model has higher classification accuracy. At last, the shortcomings of the study and other possible acting contents and optimization directions are described, and the focus and direction of the future research work are given.
Off-Chain Micropayment Pool for High-Throughput Bandwidth Sharing Rewards

This paper presents a layer-2 micropayment pool design which supports high-throughput blockchain off-chain payment, designed specifically for the use case of rewarding users who share redundant bandwidth to assist video stream delivery. We analyze the validity and effectiveness of the proposed micro-payment pool. Our analysis results demonstrate that the proposed micropayment pool design is better suited for the bandwidth reward use case compared to existing off-chain payment channel solution.
Research on database controlled memory authorization application model based on ARM architecture

The traditional database authorization application model uses forward and generalized authorization rules to authorize the database, which requires all authorization items to be examined in turn when authorizing, and the authorization granularity is too coarse, leading to a large amount of data redundancy in the authorization library and the complexity of the authorization library is increased. To address the above problems, the application model of controlled memory authorization for database based on ARM architecture will be studied. After database semantic view reconstruction in ARM architecture virtualization platform, the distribution of data controllable memory authorization rules is determined. The database memory authorization information is encrypted and managed using the NTRU algorithm, and the database controlled memory authorization application model is constructed and implemented on the basis of the RBAC access control model. The simulation experimental results show that the authorization model studied has an authorization card redundancy ratio lower than 8%, which is much smaller than the traditional authorization model, and the authorization granularity of this authorization model is small, which can effectively ensure the database security.
Multilevel Threshold Image Segmentation Based on Modified Moth-Flame Optimization Algorithm

Among many image segmentation methods, multi-threshold segmentation is an effective method. However, the calculational complexity of multi-threshold segmentation is high, and the traditional calculation methods is difficult to obtain satisfactory results. In view of this situation, an Modified Moth-flame Optimization algorithm (MMFO) is proposed in this paper. Taking maximizing Kapur entropy as the objective function, MMFO algorithm is successfully applied to image multi-level threshold segmentation. The performance of the algorithm is evaluated by using several images which are widely used in the field of image segmentation. The experimental results show that, compared with other algorithms, MMFO algorithm can find a better solution more effectively.
Evaluation of digital watermarking on subjective speech quality

New methods of securing the distribution of audio content have been widely deployed in the last twenty years. Their impact on perceptive quality has, however, only been seldomly the subject of recent extensive research. We review digital speech watermarking state of the art and provide subjective testing of watermarked speech samples. Latest speech watermarking techniques are listed, with their specifics and potential for further development. Their current and possible applications are evaluated. Open-source software designed to embed watermarking patterns in audio files is used to produce a set of samples that satisfies the requirements of modern speech-quality subjective assessments. The patchwork algorithm that is coded in the application is mainly considered in this analysis. Different watermark robustness levels are used, which allow determining the threshold of detection to human listeners. The subjective listening tests are conducted following ITU-T P.800 Recommendation, which precisely defines the conditions and requirements for subjective testing. Further analysis tries to determine the effects of noise and various disturbances on watermarked speech's perceived quality. A threshold of intelligibility is estimated to allow further openings on speech compression techniques with watermarking. The impact of language or social background is evaluated through an additional experiment involving two groups of listeners. Results show significant robustness of the watermarking implementation, retaining both a reasonable net subjective audio quality and security attributes, despite mild levels of distortion and noise. Extended experiments with Chinese listeners open the door to formulate a hypothesis on perception variations with geographical and social backgrounds.
Artificial Intelligence Applications That Support: Business Organizations and E-Government in Administrative Decision

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Discrete Cosine Transform as Universal Sentence Encoder

Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs.These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification.Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols.While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging.However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English.To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian.The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets.
Research on obstacle avoidance method of robot manipulator based on binocular vision

Abstract Aiming at static obstacles in indoor environment, the six-axis robot manipulator can avoid obstacles successfully through binocular stereo vision system. According to the principle of camera imaging, the camera calibration is completed by using the camera calibration toolbox of MATLAB, and then the three-dimensional coordinates of the feature points of obstacles are obtained by using the Structure from Motion (SFM) algorithm, so as to determine the location of obstacles. In the aspect of path planning, combining the characteristics of binocular visual obstacle recognition, the artificial potential field (APF) method is used to realize the obstacle avoidance. Finally, the simulation and data analysis under the Matlab development environment verify the feasibility of the algorithm in the binocular vision obstacle avoidance system.
Intelligent Traffic Signal Monitoring System Using Image Processing

This project aims at making an intelligent traffic signal monitoring system that makes decisions based on real-time traffic situations. The choices will be such that the traditional red, green, or amber lighting scheme is focused on the actual number of cars on the road and the arrival of emergency services rather than using pure timing circuits to control car traffic by using what the traffic appears like via smart cameras to capture real-time traffic movement pictures of each direction. The control system will modify the traffic light control parameters dynamically in various directions due to changes in traffic flow, thus increasing the traffic intersection efficiency and ensuring improved traffic management. This work involves performing a traffic management study of the city.
P‐70: Research of Touch Screen Design with Laser‐Photo Complementary Technology

In this paper, a new method of touch screen design using laser‐photo complementary technology is proposed, which can avoid fabricating unnecessary bonding area in large size products due to the splicing exposure process [1]. And also, the material of fan‐out area is the same with that in active area in this paper, which can achieve the overall design comparing with Roll‐to‐Roll technology [2] and improve the reliability. Most important of all, this method can meet the demand of different mechanical structures for one product under the condition of no new mask, such as different bonding area, which is significant in saving development costs.
Auto-Text Completion Framework to Improve Online Conversation

There are many abilities that make human intelligent. One test which often separates machines and human is how well a machine is able to understand the text and its meaning. The task of sentence completion is still one of the hardest problems in natural language processing. Making a machine understand what is written is a tremendous task. Traditionally, machines were good at performing mathematical calculations like addition, subtraction, etc. only; but with time, new techniques to map language to some mathematical functions emerged. In this work, we discuss why and how to use long short-term memory (LSTM) to create a basic auto-text completion model on the Hindi written in English script (Hinglish) dataset. This study aims at showing that LSTM works very well with Hinglish dataset as it does with English dataset. A Hinglish dataset is prepared and cleaned before processing on LSTM. The obtained result proves the usefulness of LSTM networks for auto-text completion.
The Network University of the World-Class Research and Academic Center “Kuzbass” as a Regional Strategy on Capacity Building through Infrastructure and Human Capital

Introduction. The paper introduces the concept of the Network University on the basis of the World-Class Research and Academic Center “Kuzbass”. It is one of the largest projects aimed at improving the scientific and academic landscape of the Kemerovo region. The digital twin technology can increase the current efficiency of the Kemerovo State University and develop its resources. The research objective was to assess this potential. Study objects and methods. The Network University project presupposes establishing a number of interacting Kuzbass campuses that will prepare competitive post-industrial production specialists able to solve various administrative, analytical, and economic problems that the region is currently facing. Results and discussion. The research involved an urban planning analysis of the future Network University campuses. It resulted in several infrastructure solutions in the context of digital education and the Fourth Industrial Revolution (Industry 4.5). Conclusion. In case of network universities, higher education does not equal qualifications: education provides each student with a conscious choice and a development pathway in the world of digital economy. A successfully operating link between education and professional activity makes this pathway attractive for investments. The author gave an initial assessment and defined the infrastructure solutions for the future campuses. All these issues are part of research on the capacity of the Kuzbass Network University and integrate the project into Priority 2030, which is the former Strategic Academic Leadership Program.
Scientific Aspects of Structural and Parametric Simulation Modeling of Blockchain Systems

Over the past decade, in addition to multiservice networks, blockchain technology has undergone significant development due to the possibility of organizing a safe, integral, reliable exchange and storage of information. Due to the great demand for the technology, there is a problem of data transmission to the operators' networks. At the same time, a key task appears to consider the effect of this technology on network characteristics to predict traffic behavior on the network and ensure the required service quality indicators, as well as the stability of the state of the public communication network elements when the distributed ledger technology is operating. However, to consider and analyze the influence of technology in a full-scale experiment is a labor-intensive task that cannot always be performed, therefore, in this article, the authors propose to consider approaches to structural-parametric modeling of these systems.
Pivoting Image-based Profiles Toward Privacy: Inhibiting Malicious Profiling with Adversarial Additions

Users build up profiles online consisting of items that they have shared or interacted with. In this work, we look at profiles that consist of images. We address the issue of privacy-sensitive information being automatically inferred from these user profiles, against users' will and best interest. We introduce the concept of a privacy pivot, which is a strategic change that users can make in their sharing that will inhibit malicious profiling. Importantly, the pivot helps put privacy control into the hands of the users. Further, it does not require users to delete any of the existing images in their profiles, nor does it require a radical change in their sharing intentions, i.e., what they would like to communicate with their profile. Previous work has investigated adversarial images for privacy protection, but has focused on individual images. Here, we move further to study image sets comprising image profiles. We define a conceptual formulation of the challenge of the privacy pivot in the form of an "Anti-Profiling Model". Within this model, we propose a basic pivot solution that uses adversarial additions to effectively inhibit the predictions of profilers using set-based image classification.
Proposed Secure and Robust Voting System Using Blockchain Conceptual Framework

Governance for the people of any country is executed by the democratically elected party through the voting process. Earlier the paper ballot voting system was used manually, which was later substituted by Electronic Voting Machine (EVM) in India. This mechanism has been suspected by some people and the possibilities of tampering in process cannot be ruled out. Every citizen of the country gradually expects to introduce the user-friendly, flexible, cost-effective, transparent, accurate, reliable, robust, and secure voting system in the twenty-first century using the latest concepts of emerging Blockchain technology by building Next-Generation Electoral system. It can save time, reduce cost and electoral fraud, increase voter privacy, and remove a lot of hurdles in the voting process on a secured platform, thereby increasing voter involvement and participation. The paper comprises of general evaluation of the current voting mechanism and proposes an effective and viable alternative model using Blockchain technology. This paper also provides a comparison of various pre-qualified criteria comprising of costing, timing, security, transparency, audit of voting process, and risk factors involved.
Azimuth Calibration and Data Association for a Radar-Optical Multi-Sensor System

Radar sensors have high false alarm rate in high density clutter and low angular measurement accuracy due to low angle resolution. This paper proposes an azimuth calibration and track association algorithm by utilizing both radar and optical sensor simultaneously to improve detection and estimation performance. First, based on “azimuth-time” search algorithm, data calibration of radar and optical measurements is realized. Then, the nearest neighbor (NN) algorithm and the probability data association (PDA) algorithm are used to associate the calibrated data. Finally, the effectiveness of the algorithm is verified through processing and analysis of measured data.
Infrastructure as a Service (IaaS) for Smart Education

Ha Huy Cuong, Nguyen Trong Trung, Nguyen Ngoc Khanh, Le Van Loi, NguyenThe Fourth Industrial Revolution puts a great impact in technology in all aspects such as artificial intelligence, big data, robotics, and IoT. The prospect of the development of education in the context of the Industrial Revolution for the fourth time, Vietnam will certainly face many difficulties in education. Sometimes, transmission of knowledge is not sufficient without looking into the quality and capacity of the learners; quality teaching staff, and management staff. With the influx of new learning models and the development of science and technology, the methods of traditional education will certainly be challenging, and each student needs different learning abilities. Advances in technology allowed the school to design individual learning pathways which is suitable for each particular case. Within the scope of the study, this paper deals with the technology infrastructure solutions to solve the whole distribution of resources, avoid deadlock, and concurrent access to the system for the learners.
Design of data integration platform for OCN business support system

In order to meet the development of OCN business, it is key to establish a perfect business support system. The designer of OCN enterprise data warehouse platform is the core part of the whole support system. This paper studies the framework mode and integration structure of OCN customer data integration platform, analyzes the functions of each layer in the logic architecture of the data platform, and analyzes the function of each layer in the data warehouse architecture. This paper analyzes the data integration process in detail, and describes the data acquisition method, data extraction strategy and data loading in detail.
Analisis Penilaian Risiko Terhadap Penggunaan Sistem Informasi Akademik Pada Universitas Muhammadiyah Palembang Menggunakan Metode Octave Allegro

The development of technology in meeting information needs has been implemented at the Muhammadiyah University of Palembang where there are various information systems used in managing information as a basis for creating quality services and supporting optimization in the work process. However, apart from the perceived benefits, the increasing dependence on information systems is in line with the risks that can arise, one of which is the risk of information security where information is an important asset for an organization that needs to be protected and secured. As an effort to maintain and protect information security, it is necessary to carry out risk evaluation in order to identify and anticipate risks that can interfere with information security. This study aims to conduct a risk assessment analysis of the use of Academic Information Systems as a very crucial information system in a university. This risk assessment analysis uses the Octave Allegro method with the main focus on information assets which consists of 8 steps and is organized into 4 stages starting with determining drivers, developing information asset profiles, identifying threats, and identifying and mitigating risks. The results of this study are 10 (ten) areas of concern with a mitigation approach of 7 (seven) risks that must be reduced (mittigate), 2 (two) risks that can be deferred (defer), and 1 (one) risk. that can be accepted (accept). From the risk assessment carried out, recommendations are given to prevent / minimize the identified risks as an effort to maintain and improve information security.