Reviewerly/exHarmony huggingface dataset:
Method of Differential-Based Remote Upgrade for ECUs on Vehicles*

Abstract In view of the problem that the current update flashing of the traditional automotive electronic control unit (ECU) software highly relies on professional maintenance personnel and external equipment, a method for ECU remote data flashing is proposed to achieve remote and efficient upgrade of ECU software. Furthermore, through the introduction of backup partitions and differential upgrade methods, the problems of lack of fault-tolerant processing, high bandwidth requirements and low efficiency in traditional ECU software update flashing are also solved. In this article, a remote update system is built, including the server-side and the vehicle, and the interface and communication protocols are defined based on HTTPS. The above methods have been verified on NXP IMX 6 platform. Associated testing of the simulation of the ECU by the STM32 and ROCKCHIP demo board has been made based on the CAN and Ethernet simulating the vehicle network architecture. By the real data catching from Ethernet and the CAN, the details of the log file of the programs’ event tracking information are analyzed to obtain the experimental results. The results indicated that the incremental remote data updating system built in this article can remotely update the CAN software accurately and efficiently in all simulation tests, which proved the practicability, accuracy, and efficiency of this method.
Content-Based Video Retrieval Based on Security Using Enhanced Video Retrieval System with Region-Based Neural Network (EVRS-RNN) and K-Means Classification

The recent development in video data requires more prominent and capable techniques in retrieval of video which might improve the searching quality of video as well as can understand the content of video in higher standard, whereas to search relevant secured video from a document is a major setback for the digital research community. However, the major problem is the external information brought from digital resources. Therefore, this research proposed a secure CBVR framework directly select features from the ciphertext domain on the cloud server side. Therefore, high secure data has been stored in cloud using enhanced video retrieval system with region-based neural network (EVRS-RNN). From the resulted retrieval data, accuracy had been improved using ensemble classifier linear discriminant analysis (LDA) and relevant video frames retrieval using K-means classification technique. The simulation performance shows efficiency in security with obtaining parameters and improves the accuracy.
Bringing Privacy, Security and Performance to the Internet of Things Through Usage Control and Blockchains

The Internet of Things (IoT) is bringing new ways to collect and analyse data to develop applications answering or anticipating users’ needs. These data may be privacy-sensitive, requiring efficient privacy-preserving mechanisms. The IoT is a distributed system of unprecedented scale, creating challenges for performance and security. Classic blockchains could be a solution by providing decentralisation and strong security guarantees. However they are not efficient and scalable enough for large scale IoT systems, and available tools designed for preserving privacy in blockchains, e.g. coin mixing, have a limited effect due to transaction cost and rate. This article provides a framework based on several technologies to address the requirements of privacy, security and performance of the Internet of Things. The basis of the framework is the IOTA technology, a derivative of blockchains relying on a directed acyclic graph to create transactions instead of a linear chain. IOTA unlocks distributed ledgers performance by increasing throughput as more users join the network, making the network scalable. IOTA being not designed for privacy protection, we complement it by privacy-preserving mechanisms: merge avoidance and decentralised mixing. Finally, privacy is reinforced by introducing usage control mechanisms for users to monitor the use and the dissemination of their data.
Denotation and Connotation in Beauty Advertisement: Implication for the teaching of Semantics

Advertising language sets an important role in conveying the purpose of influencing the consumers. To advertise a beauty product, the choice of words representing the concept of beauty becomes the first concern. This study aims to examine the types of meaning of the words used in a beauty product advertisement. To interpret the lexical meaning in beauty product, the method used in this study is descriptive qualitative analysis on six beauty products, particularly the hand body lotion product by Nivea company. The data were analyzed through Leech theory. The result showed two types of meaning in the body lotion products, namely the denotative and connotative meanings. Denotation occurs more in all of the product compared to the connotative meaning. The implication of the finding contributes to the teaching of semantics to enrich the understanding of the perspective of Leech theory in lexical meaning.
Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body.More and more people are suffering from lung diseases due to air pollution.These diseases are usually highly infectious.Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung.Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient.For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend.In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research.To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes.We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm.And alleviate the problem of unbalanced training caused by only a small number of positive samples.Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards.
Formation of Creativity Skills in Engineering Education Through the Disciplines of the Humanitarian and Legal Cycle

The article substantiates the role of creativity in the formation of meta-competencies of students, future engineers.The experience of the departments of transport law and personnel management of the Samara State Transport University (Samara, Russia) in teaching disciplines of socio-economic and humanitarian cycles is analyzed.Examples of successful pedagogical practice of using various forms of interaction between teachers and students in the learning process are given: discussion, business role-playing game, essay.The position on the relationship between the formation of students' creative abilities in the process of conducting such classes and their subsequent transformation in engineering education is argued.A parallel is drawn between academic education in higher education and the creative forms of work of students and teachers.The level of students' creative potential is made dependent on the professional level of the teaching staff; at the same time, the most important role of the latter is emphasized.The article discusses in detail the universal competencies formed in the process of training in engineering specialties, and their relationship with the disciplines of the socioeconomic and humanitarian cycle.
Smart DIPSS for Dynamic Stability Enchancement on Multi-Machine Power System

Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method. Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method.
Managing Print Serials: Jumping in Mid-Stream as a Newly Appointed Serials Librarian and Carrying Out an Inventory

Serials librarians are well aware of the fact that print serials require unique methods for proper management. From first check-in to shelving, maintaining, and potential weeding, there are a variety of tasks to engage in along the way to ensure essential management. This paper outlines two combined presentations on print serials management. The first section showcased how a newly appointed serials librarian “discovered” and managed her print collection over the course of a year and highlights her plans for future developments. The second part of the session provided a method for carrying out a multipart approach to a print serials inventory project that includes training, inventory assignments, quality control measures, and progress tracking. The inventory process aimed to enhance the library’s catalog and OCLC local holdings records; check the health of the print collection; and record an accurate total number of bound serial units.
Adaptive Threshold and Directional Weighted Median Filter-Based Impulse Noise Removal Method for Images

Elimination of impulse noise in image snap shots with side renovation is one of the complex duties in digital image processing. In this paper, the removal of random impulse noise is done in two important levels. In first level, the detection of the impulse noise is done on the premise of a double threshold selecting strategy after which in the another level, elimination of impulse noise is done by the usage of median filter and directional weighted median filter relying upon the noise map (Nmap) construction of corrupted pixels detected within the first level. The proposed method makes use of the statistical characteristics of noisy image graphs and the brink obtained is adaptable to one of a kind of snap shots and noise conditions. Comparative evaluation with different widespread de-noising techniques shows that the proposed method outperforms in terms of PSNR, SSIM, NMSE and Computation Time (CT) of the distinct trying out test images, with exclusive noise levels.
Support Vector Machine: Analisis Sentimen Aplikasi Saham di Google Play Store

The best application predicate has been awarded to the application that has the highest downloads and high star rating on Google Play Store. In rating an application, user comments need to be considered because many stock investment apps have almost the same downloads and star ratings, so the title of best app is a problem. Based on this condition, this research aims to analyze user feedback of stock investment applications as a variable to determine which stock investment application is the best on the Google Play Store. This study using the Support Vector Machine (SVM) classification method with the support of Rapid Miner to carry out the calculation process. From this research, it produces positive sentiment values for each application, namely HSB Investment about 1,134 positive sentiments with an accuracy value of 88.70%, Ajaib about 936 positive sentiments with an accuracy value of 61.89%, Pluang about 703 positive sentiments with an accuracy value of 68.25%, Bibit about 322 positive sentiments with an accuracy value of 64.89%, and Stockbit about 124 positive sentiments with an accuracy value of 66.95%. So it can be concluded that the HSB application as the best stock investment application based on user comments reviews where this application has the most positive sentiment reviews with a high accuracy value.
Innovative Technologies in Higher Education: Social Policy and Development of International Cooperation

The relevance of the study is conditioned by the possibilities of forming an international framework for the development of universities. The aim of the study was to identify all the factors that influence the quality of higher education. The novelty of the study is conditioned by the fact that the possibility of development for the university lies not only in the establishment of directed activities. The authors show that the possibility for the establishment of a macro-environment is achieved primarily by using tools for modelling the innovation environment in a university on a technological basis. In the course of a comparative analysis of the development of universities, the advantages and disadvantages of rating evaluation of universities were considered and the methods and results of leading rating agencies were analysed. It was found that ratings are an effective tool for managing the education system, a means of improving the competitiveness and quality of education, on the one hand, and an important source of information about the university for applicants.
Emotional Culture as a Context of Education: Why Emotional Culture is Important for Pedagogy?

In the article, I search for the connection between emotions culture and education by examining the affective reproduction of culture.Building on the tradition of Émile Durkheim, in the works by Arlie Russell Hochschild and Steven Gordon the concept of emotional culture is (re)constructed.Emotional culture is understood as the specific complex of emotion vocabularies, feeling rules, and beliefs about emotions.Emotions and their meaning provide a socio-psychological mechanism that controls/develops individuals and groups.In the text, it is argued that the concept of emotional culture adds a distinctive conceptual tool for studying different educational contexts and environments.To examine this argument, the article is divided into three parts.First, an overview of the concepts and theories that underlie the term of emotional culture is given.In the second part, the concept is analyzed in the light of modern cultural studies.The article closes by pointing out pedagogical implications, especially those connected with emotional education.
Detecting Slowly Accumulating Faults Using a Bank of Cumulative Innovations Monitors in Kalman Filters

<h3>Summary</h3> Extended Kalman filters (EKFs) that monitor innovations over time have been demonstrated to be effective at detecting slowly accumulating measurement faults (Quartararo &amp; Langel, 2020; Tanil et al., 2018). This paper first demonstrates that a single cumulative monitor becomes increasingly sensitive to measurement error model uncertainty as the accumulation interval increases, leading to false alarm and detection rates that can differ significantly from predefined design parameters. In response, a bank of finite-length cumulative innovations monitors is explored for fault detection in multisensor navigation systems. A novel extension to traditional covariance analysis (<i>Covariance Analysis Including Expected Values</i> or CAIEV) is developed to accommodate measurement faults and is used in addition to Monte Carlo simulations to present detection results for a variety of GPS fault profiles and inertial measurement unit (IMU) grades. Data for time-to-detect is presented alongside the position-domain bias induced by the fault at the time of detection. We show that the monitor bank can reliably detect the presence of faulty measurements after the position-domain bias has reached only tens of meters using tactical and aviation-grade IMUs for the cases considered, an improvement over other innovations-based techniques.
Examination in Conditions of Distance Learning

The existing forms, methods and tools for conducting the exam in a distance format, their potential and risks are analyzed in the article. Attention is drawn to the fact that the methods and tools used for remote monitoring have drawbacks, such as significant costs in organizing proctoring and difficulties in assessing one of the key competencies of a student, namely, the willingness to use systematized theoretical and practical knowledge to formulate and solve research problems. The authors see the solution to the problem in the application of the activity-based and competence-based approaches to the certification, offer methodological recommendations for conducting the exam remotely. Based on the developed methodological recommendations, tasks are formulated in accordance with the upper levels of Bloom's taxonomy, which makes it possible to increase the objectivity of the assessment, since the test will concern the ability to apply theoretical knowledge in practice. Possible options for practice-oriented examination questions for disciplines of natural science and social-humanitarian orientation for an online exam are presented. As a result of discussing the prospects for conducting a remote exam in accordance with the proposed methodological recommendations, it was revealed that the majority of the interviewed teachers and students are interested in the transition to a new format of the exam.
A Collaborative and Early Detection of Email Spam Using Multitask Learning

Email spam has become a huge nuisance since the last couple of years. It not only wastes valuable time but is also extremely dangerous as well. The various solutions that exist to detect spam do so by manual input of keywords or filtering particular domains. But no matter the amount of filtering, it is quite tedious and difficult to check for spam. This paper includes a unique solution that attempts to use deep neural network, a machine learning technique which detects any pattern of recurrent words which may have been classified as spam. Every other parameter of the email is examined as a feature and applied accordingly to the machine learning algorithm. Deep neural network is quite advanced and can easily differentiate between a proper and an improper output.
Review of insider and insider threat detection in the organizations

Aim:The insider threat is a severe issue in cyber security.Insider threats are largely overlooked by most companies.Workers, system administrators, and outside contractors all have access to confidential company data.It is critical for the organization's finances and reputation that such sensitive information is kept secret.If sensitive information were to escape the hands of even a tiny percentage of the authorized workforce, it could cause catastrophic financial losses.Protecting a company from the potentially disastrous actions of its own employees presents a formidable challenge, and identifying and eliminating the insider threat is a crucial part of that.This study aims to determine the types of insider threats that can exist within an organization and the best methods for countering them.Methodology: Research on the topic of insider danger is summarized in this paper.Insiders (representing types of insiders, motivation, insider access, methods used by insiders, insider profiling, and levels of insiders); Threat Detection Methods (describing methodology, techniques, datasets used to implement various insider threat detection techniques, and different analyzed user behavior); and Insider Threat Analysis (describing the various analyzed behavior of the user) are the three categories into which the research has been sorted.Findings: Within today's increasingly digitalized businesses, dishonest employees pose a significant risk.Since the global changes in the business environment, insider threats have become a problem for most companies.There has been an increase in the insider threat since 2019, and one primary reason is the widespread adoption of cloud computing and bring-your-own-device policies for remote work.Implications/Novel Contribution: Future studies are encouraged to improve threat detection methods, evaluate the efficacy of existing methods using a real-world dataset, and adopt a hybrid approach to developing effective models for detecting insider threats.
Research on Text Summarization Generator Method Based on Input Text Linguistic Features and Copy Mechanism

The Sequence-to-Sequence (Seq2Seq) of neural network (NN) method based on recurrent neural network (RNN) and attention mechanism plays an important role in information extraction and automatic summarization. However, this method can’t make full use of the text linguistic feature information, and there is a problem of unregistered words in the generated results, which affects the accuracy and readability of the text summarization. To solve this problem, the text linguistic feature is used to improve the input characteristics, and the copy mechanism is introduced to alleviate the problem of unregistered words in the summarization generation process. On this basis, a new method named Copy-Generator Model (CGM) based on Seq2Seq model is proposed to improve the effect of text summarization. The results of experiments using LCSTS (Large Scale Chinese Short Text Summarization) as the data source show that the proposed method in this paper can effectively improve the accuracy of summarization and can be applied to automatic text summarization.