Reviewerly/exHarmony huggingface dataset:
Multiple Object Tracking Based on the Deep Neural Networks and Correlation Filter

Online multi-object tracking is a fundamental problem in video analysis based applications. A major challenge in the popular tracking-by-detection framework is how to make data association between detections and trajectories. To tackle this, we propose an appearance model and a motion model based on the deep neural networks. In particular, we adopt a deeply learned appearance representation, which is trained on large-scale person re-identification datasets using a convolutional neural network, to improve the identification ability of our tracker. Towards the motion model, we design a long-short term memory based network for velocity estimation and use two fully-connected layers to measure the motion affinity between detections and trajectories. To alleviate the problem introduced by missing detections, a kernelized correlation filter is leveraged to localize the missing objects. Extensive experiments show that the integration of these three components is effective, and our tracker is competitive compared with existing methods.
High-resolution industrial radiography using convolutional neural networks

For quality control of electronic products with high-density electronic packaging and corresponding multi-layer printed circuit boards, x-ray inspection is not optional anymore. For high-resolution industrial radiography, we develop a de-blur filter using serial cascades of convolutional neural networks. We increase the number of channels along the network depth to accept complex features even at deeper depths. Various ways to increase the channel numbers are described. The performance of the networks is quantified in comparisons with the state-of-the-art networks. The result shows a large correlation between the performance and the total number of weights built in a network. Including the detailed results, this paper discusses the limitation of the present study, and future study.
Graph Editor Based on Cytoscape Package

Many practical tasks naturally reduce to the concept of “graph”. Therefore, it is relevant to implement convenient means of constructing graph images. The article describes one of the possible ways to solve this problem in the form of implementing a web service based on one of the open source software packages.
Model Based on Ontological Engineering as Support for Stakeholder Management

The objective of this article is a way of working that is used as an instrument for managing the parts with semantic and conceptual elements, such as mastery of management projects, and the effects of ontological engineering techniques, which solves frequently asked questions with respect to information related to this resource. This is done in an agile, precise, and effective way, facilitating its management in the phases that make up the life cycle of the project. The research will contribute to the integration of ontological engineering and project management, enabling the construction of a stakeholder information structure with semantic components that enable it to be the input of a more complex knowledge management model.
Game of Bloxorz Solving Agent Using Informed and Uninformed Search Strategies

Bloxorz is a block sliding puzzle game that can be categorized as a pathfinding problem. Pathfinding problems are well known problems in Artificial Intelligence field. In this paper, we proposed a single agent implementation to solve level-1 of Bloxorz game using Informed and Uninformed searching algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), and A-star (A*) searching algorithms. The agent solves the problem using the three algorithms to compare their performance and conduct a conclusion that may help in improving the use of searching algorithms in this area. In this paper, A* and breadth-first search algorithms are founded to be more convenient to solve this problem.
Transforming scientific libraries within the concept of bibliography as surrogate knowledge .

The author examines the concept of the bibliography as surrogate knowledge. The concept was developed by Prof. Yury Zubov (1924–2006), Dr. Sc. (Pedagogy), a prominent Soviet and Russian bibliographer, researcher, culturologist, in the late 1970s – early 1980s. The fundamentals of the concept are revealed and characterized, namely: 1) formalizing functions of bibliography that enable to structurize and compress the knowledge and knowledge information which makes an imperative for humans’ thinking limited with the brain capacity, time and spatial extent; 2) the concept logical justification based on the study of scientific knowledge genesis, beginning in the antiquity (encyclopedism – differentiation – integration). The scholar proved that bibliography development was determined by the scientific knowledge genesis and its content structure. The essential qualities of bibliography (formalizing qualities and genetic affinity with science and scientific knowledge) determine the concept’s prognostic character. The modern bibliographic activities have been coopting the features of science and scientific knowledge development, namely transdisciplinarity and postnonclassic science rationality norms. The concept key role in the development of scientific libraries in the circumstances of their transforming activities is substantiated.
RBAC-GL: A Role-Based Access Control Gasless Architecture of Consortium Blockchain

Blockchain-based decentralized applications (DApps) have been used in various industrial areas. More are more companies are willing to participate in blockchain technology. Notheisen [18] proposed a framework based on Ethereum [3] to trade lemons on the blockchain platform. In this work, we discuss the application aims to digitize valued assets in the commercial area, such as real estate and watches. This article introduces a general DApp framework and some standard attack methods in the beginning. Our proposed novel role-based access control (RBAC) model improves the system permission control, and gasless mechanism enhances security and reliability. It allows users to publish their assets and use cryptocurrency to trade online. Meanwhile, this work can prevent several categories of attacks, such as gas-related attacks and malicious API invokes, according to our improvements. Besides, the efficiency performance of the system remains the same as before.
Research on Public Opinion of "Chuang Youth Entrepreneurship Competition" Based on Emotion Analysis

[purpose/significance] this paper takes weibo, a communication tool, as the carrier, and analyzes the emotional trend and emotional timing trend of netizens in the "chuang youth entrepreneurial competition" through weibo comments, so as to provide reference value for relevant departments in information communication and management and control of the event.[method/process] this paper takes weibo as the data source and "chuang youth entrepreneurship competition" as the theme keyword, collects the text information of netizens on chuang youth entrepreneurship competition, USES the emotion analysis method, constructs the public opinion time sequence chart, and analyzes the netizens' emotion situation on chuang youth entrepreneurship competition.[conclusion/result] the research results show that the public's attention and enthusiasm for entrepreneurship is not too high, only limited to specific groups of people to pay attention to this event.
Reputational Genetic Model for Regular Inference

The regular inference is one of the main problems of the formal language theory, which is to synthesize a finite-state automaton corresponding to some unknown regular language represented with a list of positive and negative examples. In this paper, we propose a new algorithm for regular inference along with special measures for evaluating quality of elements of automaton we call reputation. The algorithm belongs to genetic algorithms family and transforms candidate automatons based on the reputation of its elements. We prove effectiveness of our model by experiments on pregenerated datasets.
Standardisation, Data Interoperability, and GDPR

The General Data Protection Regulation (GDPR) has changed the ecosystem of services involving personal data and information. It emphasises several obligations and rights, amongst which the Right to Data Portability requires providing a copy of the given personal data in a commonly used, structured, and machine-readable format – for interoperability. The GDPR thus explicitly motivates the use and adoption of data interoperability concerning information. This chapter explores the entities and their interactions in the context of the GDPR to provide an information model for the development of interoperable services. The model categorises information and exchanges and explores existing standards and efforts towards use for interoperable interactions. The chapter concludes with an argument for the use and adoption of structured metadata to enable more expressive services through semantic interoperability.
Real Time Obstacle Avoidance and Navigation with Mobile Robot via Local Elevation Information

This paper considers the real-time navigation and obstacle avoidance of mobile robots in complex environments. A novel local environment perception and navigation framework for mobile robot navigation based on the elevation information is proposed. A local elevation map is introduced to perceive and map obstacles in the local environment. Calculating obstacles' heights and shapes by local elevation maps, the terrain traversability of the surrounding environment of a robot is determined, and a two-dimensional local occupancy grid map for robot path planning and navigation in local environment is generated. The effectiveness of our approach is validated experimentally through real-time navigation and obstacle avoidance in the artificial environment. The experimental results show that using the proposed framework, a mobile robot can safely reach its target position while avoiding the non-crossable area. For indoor mobile robots with limited computing power, in complex environments, it could provides more safe navigation and path-planning.
Any Angle Path Finding in Stochastic Obstacle Scenes

Path planning with stochastic obstacles is well known researching area. The Canadian traveler problem (CTP) is a challenging stochastic optimization problem of traversing in a given graph having blocked edges and the disambiguation status of these edges can be settled with predefined probabilities. Discretized version of stochastic obstacle scene problem (D-SOSP) is most commonly used variant of CTP. The objective is to design a travel plan that would guarantee the shortest path including the obstacle disambiguation cost. In this work, we present Any-Angle (ANYA) path finding in discretized stochastic obstacle scenes using the exact algorithm AO* with caching (CAO*). The admissible upper bounds in the CAO* are found by making use of Dijkstra's shortest path. However, ANYA algorithm, being recently proposed, is already shown to outperform shortest path algorithms by investigating the interval sets. Our methodology is exhibited distinctly via computational examples involving a data map of navy forces minefield.
A Noise Resistant Method by Setting Thresholds for Use With Face Recognition

Face recognition is one of the important research contents of biometric recognition, and it is widely used in various fields of life. The recognized face image is acquired by devices such as video capture, and noise is generated by device factors or external influences during the acquisition process, thereby reducing the accuracy of recognition. If the noise intensity is basically fixed, the smaller the pixel value is, the larger the proportion of noise is. This pixel has low reliability for image description. Therefore, a method of noise resistant is proposed from the angle of image preprocessing to reduce the impact of this problem. This anti-noise method first sets a certain threshold. If the pixel value in the image is lower than it, the pixel values below the set value will be directly set to 0. Then, using the classification algorithm of cosine similarity, the similarity calculation is performed on the face image, so that the image is classified, finally recognized, and the results are output. The experimental results show that after the appropriate threshold is selected, the proposed method can reduce the interference of noise on the classification and improve the accuracy of face recognition.
Principi costituzionali e conoscenze genetiche: verso un nuovo orizzonte di senso per la persona in medicina

The contribution, relying also on a narrative approach, offers some insights into the challenges that innovations in genetics and genomics daily present to the law. Recent progress in these areas has led to clear tensions over some of the categories and rights that traditionally populate the field of medicine. Although briefly, the work draws a renewed image of the person in the face of the changing contexts of research and medicine and suggests how this can be framed in the original declinations of constitutional principles, such as the centrality of the person, equality and solidarity.
Development of an Autonomous Underwater Vehicle for Water Tunnel Inspection

In order to complete water tunnel inspection work, the development of an Autonomous Underwater Vehicle (AUV), which is used for visual inspection of fully filled water tunnels interior is presented in this paper. The AUV is divided into intelligent control system, navigation and positioning system, energy management system, propulsion system, and optical inspection system. The experiment results showed that the developed control algorithms, navigation and positioning algorithms, and optical inspection units all worked well. Even with very limited number of sensors onboard, the AUV still positioned accurately in the tunnel and completed the inspection work efficiently.
Identity Based Reliable Data Sharing with Revoked System in Cloud

In cloud storage services, users can store their data remotely on the cloud and exchange data with others. The cloud files consist of sensitive information. At the time of file sharing, may realizing the sensitive information of files hide, but this shared file cannot be used by others. The problem is to how to perform data sharing with sensitive information. To solve this problem, propose a Revocation algorithm to avoid the use of sensitive files from unauthorized users. In the system first, The system sanitizes the data blocks and data blocks corresponding to the organization’s sensitive information, sanitizes the sanitized data blocks signatures, and finally, sanitizers the sanitized files and their corresponding signatures in the cloud. These signatures are used to verify the integrity of files sanitized during the integrity audit phase. TPA then checks the integrity of the disinfected file. In this way, we are provide security for sharing data.
Multivariate Regression CNN for ill-posed Inverse Reconstruction of Satellite Images

In this paper, we propose Convolutional Neural Network models of Multivariate Regression (MRCNN) applied for the reconstruction of satellite images. Image reconstruction is an ill-posed inverse problem of computer vision. Generally, distortion in satellite images modeled as their convolution with an Airy pattern Point Spread Function (PSF) of circular symmetry and additive white Gaussian noise. The MRCNN training needs a few satellite images, to yield a reconstructed image during testing, with the goals of generalized and optimized restoration. Thus, it must manage a tradeoff between two objectives of optimization and generalization. The image to be restored is not provided to the network during training. The CNN has already proven its effectiveness in image classification and recognition but not thoroughly evaluated on multivariate regression and inverse problems of image processing and computer vision. Therefore, we experimented with different architectures of CNN feasible on a GPU embedded PC for an effective reconstruction of satellite images. The proposed network 5 comprised on three CNN layers and a dense layer with a novel data engineering procedure has shown better reconstruction of satellite images evaluated by peak-signal-to-noise-ratio.
Automating TEST Case Design, Selection and Evaluation Report on 10 Editions of A-TESTWorkshop

Trends such as globalisation, standardisation and shorter life-cycles place great demands on the flexibility of the software industry. In order to compete and cooperate on an international scale, a constantly decreasing time to market and an increasing level of quality are essential. Testing is at the moment the most important and mostly used quality assurance technique applied in industry. However, the complexity of software and hence of their development amount is increasing. Modern systems get larger and more complex, as they connect large amounts of components that interact in many different ways and have constantly changing and different types of requirements (functionality, dependability, usability, performance etc.). Data processing that impacts all aspects of our life is increasingly distributed over clouds and devices. This leads to new concerns, such as availability, security, and privacy, which are aspects that also needs to be tested. Consequently, the development of cost-effective and high-quality systems opens new challenges that cannot be faced only with traditional testing approaches, and specifically manual testing is simply insufficient and unreliable to manage the speed needed, and ensure the coverage of ever-changing systems. New techniques for systematization and automation of testing throughout the software and system life-cycle are required.
A Cloud Assisted Hybrid Model Based Speaker Recognition and Resource Sharing

The technological world, with its current focus on ease and convenience, has undeniably shifted towards developing smart systems, smart buildings and cities. Machine learning and artificial intelligence have become vital tools used in the establishment of smart embedded systems. In this context, a cloud assisted hybrid model based speaker recognition system is proposed. It can be effectively integrated in smart spaces like buildings, offices, homes, etc. This paper proposes a multivariable hybrid model with speaker recognition as biometric verification for identity authentication. It is also connected to the cloud for further security and robustness. The system functionality is successfully tested. Preliminary results are presented. A higher subject dependent speaker recognition accuracy is achieved, for the studied case, with the devised solution.
Application of Siamese Neural Networks for the Type of Emergency Determination

For reliable operation of systems for early detection and prevention of emergencies, their algorithms should use machine learning methods. The use of machine learning methods is associated with the replacement of detectors, usually used in such systems, with sensors that transmit the measurement results to the computing unit of the system. Measurement of the main factors, along with their threshold processing, allowed the use of machine learning methods to quickly detect the fact of ignition, determine the type of ignition source and its localization. The study is devoted to the development of a neural network algorithm for determining the type of fire in the early stages of an emergency. The results of emergency detection refresh the information of human-machine interface immediately. We proposed to use a complex neural network consisting of five Siamese networks based on distance and a Bayesian network. The proposed neural networks have a simple architecture and a small number of layers. To train the neural network, a computer model has been developed. It simulates the ignition process and inertia of the system's sensors.
Information Encryption Mechanism Based on QR Code

With the rapid development of Internet of things technology, more and more attention has been paid to the security protection of personal information. This paper proposes an information encryption mechanism based on QR code to solve the problem of information leakage in data communication. The error correction mechanism of the QR code allows it to record information normally even if it is partially damaged. Taking advantage of this feature, this paper records secret information by changing limited data bits without destroying the original QR code. In addition, the secret sharing algorithm is introduced in this paper to divide the secret information into multiple parts for storage and transmission, and the original secret information can only be reproduced when all the information fragments are collected, further enhancing the security of information transmission. By combining the error correction mechanism of the QR code with the secret sharing algorithm, the secret information can be transmitted safely. The encryption mechanism can effectively reduce the suspicion of the attacker on the secret information, and has the advantages of large storage capacity and flexible storage.
Learning Local Part Motion Representation for Skeleton-based Action Recognition

Skeleton-based action human recognition has drawn increasing attentions due to its properties of robustness and conciseness, while studies in recently years mostly have focused on extracting global motion features of skeleton but ignored the correlation among joints of local parts of skeleton. In this paper, we proposed a multi-stream network model based on local part joints motion features, our model focus on features extraction of local part joint motion and effect of fusion method on action recognition, utilizing LSTM and CNN structure a new network unit to grasp spatio-temporal information of joints in skeleton sequences. In order to explore distinctive motion modality of skeletal part, multi-stream mode is adopted and conducting effective recognition with weighted-score fusion. We evaluated our method on the NTU-RGB+D dataset, our result demonstrate a comparable performance of the proposed model in human action recognition.
Contribution of EMIS Platforms to Education Management and Recent Applications

The purpose of this study is to investigate the effect of educational management information systems (EMIS), which has a great importance in educational organizations. By the development of information technologies, EMIS became very popular in educational management issues. EMIS systems allow students, teachers, administrators, parents, and other stakeholders to analyze, synthesize, and disseminate educational data. This chapter focuses on data process, analysis, synthesis, and design process of educational management information systems. EMIS systems help educators to handle information management. This chapter, in depth, argues data storage, accuracy, planning, scalability, and transparency issues of EMIS systems. The focus is on how using EMIS systems helps educational administrators and decision makers as well as educational systems and economy.
Systemic effects and problems of implementation of the target model for digital educational environment

The article deals with systemic effects and problems of implementation of the target model for digital educational environment in the Russian Federation carried out as part of the federal project «Digital Educational Environment» of the national project «Education». Analysis of the legal framework which regulates development and phased implementation of the target model for digital educational environment has been performed. Proposals on improving the management of implementation of digital educational environment in institutions of general and secondary professional education in Russian Federation taking into account the real organizational and technological circumstances in sub-federal units of the Russian Federation has been put forward.