Reviewerly/exHarmony huggingface dataset:
From the Perspective of Jurisprudence View the Application of Urban Image Monitoring Technology and the Application and Improvement of the Information Collection System in This Field

As times go by, social management faces new challenges. This article examines the application of urban image surveillance technology and the methods of information collection and processing from a legal perspective, and explains the necessity of creating image surveillance. This article introduces the application of the system to the construction of legal systems in countries where urban image surveillance has been applied earlier and with more advanced legal systems at home and abroad, from the construction of the image surveillance legal system, the protection of personal privacy rights, and the protection of communication data. Explain the legislative principles to be followed in the legislative process, and put forward the principles of human rights and freedom, the principle of public interest, the principle of rule of law, and the principle of information security. Finally, I put forward a point of view on how to formulate a legal and fair legal system. It is clear that in the field of legislation, it is necessary to seek constitutional support, use civil law to regulate, use personal information protection law to regulate, and use urban image monitoring system legislation to manage. This paper proposes a “peer-to-peer tree” architecture of a two-tier distributed indexing service system based on service types. Its joining and leaving algorithms create and maintain the framework, cascading organizations related to service interests into a tree structure. Learning the neighbor search algorithm can slowly evolve the peer layer composed of many cascaded trees into an overlay network with small-world characteristics, thereby ensuring a higher search efficiency. Research shows that through functional testing and performance testing, it is found that when the number of supernodes is 200, the success rate is the highest.
An Intelligent Anti-interference Communication Method Based on Game Learning

Wireless communication is vulnerable to malicious jamming due to its inherent broadcast characteristics. With the development of intelligent technology, the jammer can actively adjust the jamming strategy according to the feedback of the jamming effect, so as to achieve intelligent jamming with lower consumption and higher efficiency. The existence of intelligent jamming poses more serious challenges to the reliable transmission capacity of wireless communication system. In this paper, an intelligent anti-jamming communication method based on game learning is proposed. The communication confrontation between jammer and user is modeled as Stackelberg game, and intelligent anti-jamming decision-making in frequency domain is realized with the help of reinforcement learning algorithm, so as to improve the reliability of wireless transmission and realize intelligent anti-jamming communication in the jamming environment. The simulation results show that the proposed scheme can obtain stable policies, and the proposed anti-jamming communication method has higher anti-jamming performance than the conventional method.
High Accuracy Positioning for C-V2X

Abstract With the rapid development and popularization of 5G and C-V2X, services based on C-V2X are rapidly expanding. Specially, the positioning accuracy is the most basic requirement in V2X service. According to the different requirements for V2X use cases, the positioning accuracies are also different. So the positioning schemes are various according to the positioning requirements. This paper contains a study of requirements, architecture, key technologies and demonstrations in high accuracy positioning for V2X services. Combining the scene analysis and performance requirements, high accuracy positioning, as a key part of the whole C-V2X system, its architecture can be divided into device-centric positioning architecture and network-centric positioning architecture. In addition, we summarize the key technology for high accuracy positioning of vehicles and demonstrate the proposal through testing.
Philosophical Assumptions, Methodological Choices and Research Design: E-Learners Versus Non E- Learners

E-Learning has recently become the most predominant feature in higher education following the COVID-19 pandemic. This study aims at investigating learners' perceptions and observable behavior in the selection of research methodology pre and post COVID-19. It specifically addresses the increasing demands for information and knowledge and the accompanying changes in philosophical assumptions, methodological choices and research design used in conducting research over time. A sample of 250 Level-3 and Level-4 students, who are attending various accounting and business writing courses in which a research project is a prerequisite at the University of Bahrain, has been carefully observed during the period 2018-2020. Students are divided into two groups of non e-learners and e-learners depending on whether they are taught using conventional or electronic teaching methods pre and post COVID-19, respectively. Results show that there are remarkable and substantially significant differences among students in favor of e-learners. Hence, it can be concluded that recent transition to e-learning systems and platforms can be effectively utilized in improving students' learning and research skills in higher education. The study has important implications for learners and researchers, for instructors as well as for educational institutions. It contributes to the global challenge of promoting the quality of e-learning as the most appropriate learning alternative in higher education in such pandemic period and onwards.
Increasing Student Motivation by Using Dynamic Rating: Approach and Implementation as Part of the LMS Based on Open-Source Software

The issue of choosing a modern learning management system for working with part-time students has been investigated in detail. The possibilities of integrating the most popular modern learning management systems with the digital environment of the university are disclosed. The disadvantages of using ready-made solutions are shown. The influence of the choice of a student performance monitoring system and its integration into the modern learning management system on the motivation of a virtual group of students is studied. The disadvantages of the credit rating system are shown. An approach based on a dynamic rating is proposed. The methodology and calculation formulas for calculating the student rank are given. The modern technologies of building web services and mobile applications are investigated. The choice of technological tools for building a modern learning management system at NRNU “MEPhI” is justified. The idea of a three-level modern learning management system is revealed. Examples of ready-made system elements and a description of e-architecture are given.
Attacks on the Information System of Unmanned Vehicles in the Etiology of Road Accidents

Highly automated vehicles (HAVs) integrate numerous technologies that provide safe and efficient transportation without a driver. Connecting mechanisms ensure communication between vehicles and their infrastructure, and the exchange of data, such as the positioning of the vehicle, its speed, etc. The purpose of each of these functions is to support further automation, which turns a driver from a road user into an observer-controller by transferring to technologies those functions that were earlier performed by people. The automation of control is achieved through a constant analysis of the environment by sensors and the use of previously obtained information, such as maps or data on the road covering, for planning the performance of the vehicle. HAVs use multi-functional hardware and software units that include sensors: lidars, radars, cameras, GPS, odometers, hyrosystems, etc.; systems of data exchange with the road infrastructure using GPRS, 5G, Wi-Fi and other standards; software that controls transport systems, including the systems of machine vision, and neural networks for controlling the road situation. The authors present a classification of threats to HAVs’ security and analyze risks connected with the threats of compromising sensors, control and connection mechanisms, as well as the vulnerabilities of the used technologies. They suggest the wording of a number of new articles in Chapter 27 of the Criminal Code of the Russian Federation devoted to HAVs, which incorporate the following: persons who participated in the development of the corresponding software and guidelines for using the vehicle, operators; actions that could inflict damage, a complex of publically dangerous consequences. A detailed description of four groups of subjects (violators) of infringements on the information system of HAVs is presented: persons using the vulnerability of an information system; malware creators; creators of information systems; operators of information systems. These groups should be taken into account when developing the corresponding norms within Chapter 27 of the Criminal Code of the Russian Federation, the features of aggravating circumstances and the individualization of punishment.
TEACHING A FOREIGN LANGUAGE AT A UNIVERSITY: POINT-RATING SYSTEM INFLUENCE ON STUDENTS ' ACADEMIC MOTIVATION

The purpose of this study is to determine the impact of the point-rating system on the motivation of educational activities in the process of teaching a foreign language at a University. To achieve this goal, it was needed to solve the following tasks: 1) to evaluate student’s academic performance after three semesters of study, based on the point-rating system; 2) to evaluate the academic motivation of students studying on the basis of the point-rating system; 3) to analyze the data for significant differences; 4) to draw a conclusion about the impact of the point-rating system on the motivational sphere of educational activity in the process of teaching a foreign language at a University. The study involved students studying at the Institute of applied mathematics and mechanics of Peter the Great Saint Petersburg Polytechnic University (85 people). The data obtained during the pedagogical diagnostics of academic success, and the results of the survey "Motivation and application of strategies in learning" - Motivated Strategies for Learning Questionnaire MSLQ made it possible to conclude that the point-rating system has a positive effect on the formation of motivation to study a foreign language in higher education.
Particle-Filter-Based Intelligent Video Surveillance System

In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience.
Steganography Technique Inspired by Rook

A steganographic technique inspired by rook is presented in this paper to ensure privacy and secrecy. In this approach, the cover image is partitioned into n × 1 pixel blocks and converted equivalent n × 8 binary bit planes. Then the functional output of each block is calculated on the basis of the number of rook positions, which are attacked by opponent rooks. The rook is a chess piece that moves only forward and backward in a straight line. In binary bit plane, 0 and 1 are considered as a black and white opponent rook, respectively. The secret information is considered as stream of binary bits. The binary bits of secret information are compared with the functional output of the corresponding block. If it is equal to the functional output of the corresponding block, then nothing needs to be done. In case of inequality, the small number of bits needs to be flipped in such a way that the functional output of the corresponding block becomes equal to the corresponding secret binary bits and the distortion of the cover is minimized.
The Application of Corpora on the Lexical Approach

Abstract The linguistic scholars has used word frequency for research for a long time. With the rising of corpora study, researchers pay a lot attention to words and their frequency information, and even develop the teaching approach and teaching book. The Lexical Syllabus, The Lexical Approach and Data-Driven Leaning introduced by this paper are the representative of the development of corpora. From the background of this research, this paper gives the definition of some relative concepts, and discusses the arrangement of lexical syllabus and teaching content, and uses examples to explain the practice of The Lexical Approach.
Case Studies on Discrete Wavelet Denoising via Kernel Based Nonlinear Component Analysis

Complex real world problems are essentially nonlinear. Linear models are relatively simple but inaccurate to describe the nonlinear aspects of dynamic system behaviors. Denoising techniques have been broadly applied to numerous applications in the spatial domain, frequency domain, and time domain. To increase the adaptability of denoising techniques to signal processing of arbitrary nonlinear systems, kernel based nonlinear component analysis is proposed to enhance wavelet denoising. In the multilevel wavelet decomposition, the low frequency approximations and high frequency details are produced at each level. Discrete wavelet transform (DWT) will help to decompose low frequency approximations exclusively at all the succeeding levels, while wavelet packet transform decomposes both approximations and high frequency details at each level. DWT is selected for wavelet denoising in this study, where details at each level and the approximation at specified level are all subject to simplification using nonlinear component analysis. Case studies of typical nonlinear denoising problems in various domains are conducted. The results manifest strong feasibility and adaptability across diverse denoising problems of nonlinear systems.
Ultrasonic sensor for monitoring corn growth based on Raspberry Pi

Abstract For measuring plant height, an automated system as an indicator in the growing plant has developed in a modified micro propagation system. In the agricultural sector, there is some kind of work that needs monitoring, especially on corn. The monitoring will be used starting from the height measurement of plant manually or physical documentation condition from a plant using human resources. To capture and deliver the data by the farmer manually done and has to be informed quickly, then data from the farmer will be delivered to The Coordinator of Farmer. There is always occurred a problem with providing the data manually, more time is needed, and the accuracy of data not good enough. Because of this case, there is a required technology that can help this kind of work. The ultrasonic sensor as an electronic component is being used for the height of corn plant measurement automatically under the Raspberry Pi controller. The component work is being controlled by Raspberry Pi for how the system is working and deliver the data. The process of transmitting data will use Local Area Network (LAN) on the same IP Address. The monitoring results can be used as a reference regarding the health of the plant being monitored. This system can monitor the growth of corn, whether healthy or there is a problem, so that countermeasures can be taken more quickly and precisely.
Search for Compatible Source Code

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9–60.9[Formula: see text] in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4–11[Formula: see text] in Precision.
An Energy-Efficient Accelerator for Rain Removal Based on Convolutional Neural Network

Rain removal is an important operation in intelligent visual surveillance systems. Many rain removal algorithms based on convolution neural networks (CNNs) are rarely deployed on resource constrained devices. One of the limiting factors is that memory access leads to high energy consumption. To reduce memory access during computation, previous works usually use a fixed computation pattern for different layers in CNN. For different and massive input and output feature maps, fixed computation pattern would lower the power efficiency. Thus, we propose a reconfigurable architecture to support different convolution mapping method. We use hybrid data reuse pattern to reduce energy consumption by 2.4-5.9 times over fixed computation pattern. The hardware is synthesized in the area of 9.83 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at TSMC 65 nm technology and can restore a 1280 ×720 real world image in 0.57 s which achieves 56.1 × and 2.1 × speed-up comparing to CPU and GPU implementations. The simulation results show that the power efficiency is 287.7 GOPS/W running rain removal network.
Uncertainty Quantification in Deep Learning Context: Application to Insurance

Nowadays, Deep learning becomes the most powerful black box predictors, which has achieved a high performance in many fields such as insurance especially in fraud detection, claims management, pricing, etc. Despite these achievements, the main interest of these classic deep learning networks is to focus only on improving the accuracy of the model without assessing the quality of the outputs. In other words, classic deep learning networks do not incorporate uncertainty information but it consists only in returning a point prediction. Knowing how much confidence there is in a prediction is essential for gaining insurers' trust in technology. In this work, we propose a solution to detect automobile insurance fraud with quantified uncertainty, our model uses two methods to quantify uncertainty. The first one is called Monte Carlo Dropout method, which is considered as an approximate Bayesian inference in deep Gaussian processes. The second is named Deep Ensembles method. These two methods mitigate the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We found that our proposed method gives good results in comparison to the existing methods on the automobile insurance data set “carclaims.txt”.
Complexity of Error-Correcting Code Based on Nearest Neighbor Search Algorithm

The Nearest Neighbor search algorithm considered in this paper is well known (Elias algorithm). It uses error-correcting codes and constructs appropriate hash-coding schemas. These schemas preprocess the data in the form of lists. Each list is contained in some sphere, centered at a code-word. The algorithm is considered for the cases of perfect codes, so the spheres and, consequently, the lists do not intersect. As such codes exist for the limited set of parameters, the algorithm is considered for some other generalizations of perfect codes, and then the same data point may be contained in different lists. A formula of time complexity of the algorithm is obtained for these cases, using coset weight structures of the mentioned codes
The Fusion of Blockchain and IoT Technologies with Industry 4.0

In academic fields and industry, the internet of things (IoT), especially the industrial IoT (IIoT) or Industry 4.0, has grown rapidly and is receiving a lot of attention, but IoT privacy risks and security vulnerabilities are emerging from a lack of fundamental security technology. The blockchain technique, due to its decentralization and disclosure of knowledge, was proposed as a decentralized and distributed approach to guaranteeing security requirements and to inspire the growth of IoT and IIoT. In this paper, we first present the basic structure and key features of blockchain and summarize the security criteria for the growth of IoT and Industry 4.0. Then, we explore how blockchain can be applied to Industry 4.0 IoT using its security tools and technology. This chapter is an effort to break the ground for demonstrating and presenting the use of blockchain technology with IoT in the fourth industrial era. In this chapter, we explore some concepts of Industry 4.0 using blockchain technology.
<scp>EPA</scp>: The effective pipeline architecture for <scp>CNN</scp> accelerator with high performance and computing efficiency based on <scp>FPGA</scp>

Abstract Thanks to the great developments of the latest Field Programmable Gate Array (FPGA), the performance bottleneck of Deep Learning hardware accelerators has been converted to computing ability. In this paper, a novel FPGA‐based Convolutional Neural Network (CNN) Accelerator architecture, named the Effective Pipeline Architecture (EPA) is proposed to optimize the resource usage for the implementation of the CNN calculation. As the unique storage strategies, which contain many creative designing details, are adopted and optimized for different CNN models and layers, great DSP computing efficiency can be achieved in the fine‐grained pipeline. Moreover, compared with the traditional architectures, through the kernel combination and data scheduling, twice throughput for the general matrix multiplication is realized in a great many parallel DSP48E resources. As a result, the realization of Yolov2‐Tiny achieves 873 Giga Operations Per Second (GOPS) by 902 DSPs with 67 Frames Per Second (FPS), and the computing efficiency in most layers can even reach more than 90%, which improves the calculation performance and efficiency comparing with the previous designs, and is significant to meet the increasing computing requirement.