Reviewerly/exHarmony huggingface dataset:
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World

Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive research attention in recent years. However, without exploiting the intrinsic characteristics such as model-agnostic and human-specific patterns, existing works generate weak adversarial perturbations in the physical world, which fall short of attacking across different models and show visually suspicious appearance. Motivated by the viewpoint that attention reflects the intrinsic characteristics of the recognition process, this paper proposes the Dual Attention Suppression (DAS) attack to generate visually-natural physical adversarial camouflages with strong transferability by suppressing both model and human attention. As for attacking, we generate transferable adversarial camouflages by distracting the model-shared similar attention patterns from the target to non-target regions. Meanwhile, based on the fact that human visual attention always focuses on salient items (e.g., suspicious distortions), we evade the human-specific bottom-up attention to generate visually-natural camouflages which are correlated to the scenario context. We conduct extensive experiments in both the digital and physical world for classification and detection tasks on up-to-date models (e.g., Yolo-V5) and significantly demonstrate that our method outperforms state-of-the-art methods.
The career guidance organization concept of Institute of the System Analysis and Management and the formation of schoolchildren ICT-competencies

From year to year, the problem of the motivated choice of the direction of further education becomes more and more relevant for school graduates and their parents. The article is devoted to the concept of career guidance activities of Institute of the system analysis and management, as well as the system of additional education for schoolchildren to form the IT- competencies necessary for the successful completion of the exam in computer science and IT.
Reliability And Chaotic Risk Modeling For Real Time Data Driven Smart Systems

Reliability and chaotic risk modeling for a real time data driven smart system is an important problem of systems engineering. A real time data driven smart system is a multicomponent chaotic mechatronic system, such as driveless car with thousands of components. It uses physically sensors for its components and real time data for functioning of components. Its components having increasing, constant or decreasing risk functions. Reliability and chaotic risk modeling in a multicomponent chaotic system uses the reliability block diagrams. Mixture distribution models with weight functions are proposed for modeling reliability and chaotic risk of reliability block diagrams for real time data driven smart systems. Non-linear reliability and hazard functions for real time data driven smart systems are transformed to mixture reliability and hazard functions respectively. Mixture reliability and hazard functions are expressed as linear functions of component functions with non-linear weight functions. Mixture reliability and hazard functions for real time data driven smart systems are obtained as finite sum of products of component's non-linear weight functions with component pure reliability and hazard functions respectively. It is shown that the number of terms in mixture reliability and hazard functions for real time data driven smart systems are equal to the number of components in multicomponent chaotic systems. The effect of the structure of reliability block diyagram of a multicomponent chaotic system is reflected to the component's non-linear weight functions. Mixture distribution models with non-linear weight functions and pure component functions are used both for prediction of reliability or life times and risk of multicomponent chaotic system and its components concurrently. The working principle and computational steps of the proposed mixture model reliability and chaotic risk analysis of reliability block diagram of a multicomponent chaotic system were explained on an application for complex event processing.
PINDIT: an Online Digital Signage at Department of Electrical Engineering, University of Riau

Digital information boards are widely used for displaying one-way information and announcements in a fast, precise, and up-to-date manner. In the Department of Electrical Engineering University of Riau, the media used to convey information and announcements is still conventional, which is using wall magazines and banners. This study aims to build a digital information board or Papan Informasi Digital (PINDIT), a web-based information system that will be displayed on a monitor screen or LED TV and placed in front of the Electrical Engineering office. The academic community or visitors can easily access information or announcements related to campus activities through these digital information boards. Information on PINDIT can be updated by administrators from anywhere via the internet and displayed in real-time. PINDIT uses Rasberry Pi as a device that connects the information system with the LED TV screen, it also equipped with an RFID device which is used as a tool to record lecturers' attendance status in the room. We conducted Usability Testing for PINDIT on 33 respondents using the USE Questionnaire with aspects of learnability, efficiency, memorability, errors, and satisfaction. From the overall test results, PINDIT can be categorized as "Very Satisfactory" with an average percentage of each aspect of 86.23%.
Implementation of information and communication technology in educational system of Kazakhstan: challenges and opportunities

Information and Communication technologies (ICT) play a significant role in the educational process becoming a powerful tool of interaction between teachers and students; computer applications are used to present useful teaching resources and to improve knowledge of students. This article aims to examine the educational policy of the Republic of Kazakhstan within the framework of Information and Communication technology implementation. In this regard, we analyze the level of computerization of schools and universities in Kazakhstan, the teachers’ competencies, and computer literacy of population. Based on data and information from official documents, reports, and official sources of education, we concluded that the Government of Kazakhstan tries to create the “digital society” and to increase the quality of education using ICT. The results of the analysis reveal that Kazakhstan make greater effort on using ICT in the education process and the policy is aimed at increasing the number of computers in schools, opening new specialties at the universities related to information and communication technologies, as well as enhancing computer literacy of the population. However, due to the lack of funding and the comprehensive work on the training and retraining of teachers, the low speed of the Internet in some regions of the country, this process needs to be improved.
Crime of insurance fraud in criminal legislation of the Republic of Serbia and neighbour countries

The paper deals with the criminal offense of insurance fraud by analyzing the current provisions of the criminal legislation of the Republic of Serbia and countries in the region. From the neighbour countries, the author analyzes the provisions of the criminal legislation of Croatia and Bosnia and Herzegovina regarding the prescribing of the criminal offense of insurance fraud. The aim of the paper is to point out different models of incrimination of this specific form of fraudulent behavior. In addition to comparative legal analysis, the author points out the types and forms of this criminal act, offering at the end of the paper suggestions for improving the activity in the field, counteracting this socially negative phenomenon.
Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networks

The surge in the internet of things (IoT) devices seriously threatens the current IoT security landscape, which requires a robust network intrusion detection system (NIDS). Despite superior detection accuracy, existing machine learning or deep learning based NIDS are vulnerable to adversarial examples. Recently, generative adversarial networks (GANs) have become a prevailing method in adversarial examples crafting. However, the nature of discrete network traffic at the packet level makes it hard for GAN to craft adversarial traffic as GAN is efficient in generating continuous data like image synthesis. Unlike previous methods that convert discrete network traffic into a grayscale image, this paper gains inspiration from SeqGAN in sequence generation with policy gradient. Based on the structure of SeqGAN, we propose Attack-GAN to generate adversarial network traffic at packet level that complies with domain constraints. Specifically, the adversarial packet generation is formulated into a sequential decision making process. In this case, each byte in a packet is regarded as a token in a sequence. The objective of the generator is to select a token to maximize its expected end reward. To bypass the detection of NIDS, the generated network traffic and benign traffic are classified by a black-box NIDS. The prediction results returned by the NIDS are fed into the discriminator to guide the update of the generator. We generate malicious adversarial traffic based on a real public available dataset with attack functionality unchanged. The experimental results validate that the generated adversarial samples are able to deceive many existing black-box NIDS.
Profanity detection in social media text using a hybrid approach of NLP and machine learning

Profanity is socially offensive language, which may also be called cursing, cussing, swearing, or expletives. Nowadays where everything is digitally managed, there are lots of online platforms and forums which people use. If we take an example of any social media platform like Twitter, their privacy policy suggests that users cannot share or write any obscene/vulgar language on a public platform. Several corporate and research organizations discuss how such content is found and controlled, such as computer vision research has developed to detect illegal practices in public spaces, NLP has progressed to detect profanity in social media texts. However, existing profanity detection systems still remain flawed because of various factors. In this paper, we define and analyze the system which will use NLP and Machine learning approach to solve this. It is usually framed as a supervised learning problem. Generic features such as Bag-Of-Words or embeddings systematically deliver fair success in classification. Lexical resources in combination with models such as Linear Support Vector Machine (SVM); feature modeling specific linguistic constructs making it more effective in classification.
Reading by Syllables and Graphico-Orthographic Features of Old Russian Birchbark Letters

A significant part of birchbark letters use the so-called vernacular graphic system, the main distinguishing feature of which is the mixing of the letters ъ and o, ь and e. Researchers have suggested that the development and main features of this system are related to the skills that the authors of birchbark letters acquired in the process of mastering literacy. The article provides arguments supporting this hypothesis. In the final part of the article, the vernacular graphic system is considered in the context of the world history of writing.
Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. The trained low-level reinforcement learning controller would manipulate quadrotor toward the perching point in simulation environment. Once the simulated quadrotor has successfully perched, the relative trajectory information in simulation will be sent to tracking controller on real quadrotor and start the actual perching task. Generating feasible trajectories via the trained reinforcement learning controller requires less time, and the traditional trajectory tracking controller could easily be modified to control the quadrotor and mathematically analysis its stability and robustness. We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with high precision.
Federated Edge Learning with Misaligned Over-The-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning. OAC, however, hinges on accurate channel-gain precoding and strict synchronization among the edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for federated edge learning and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent, samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our sum-product ML estimator is linear in the packet length and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is non-severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.
Survey and Analysis of Research on WeChat Public Platform Use in China’s Libraries Based on CiteSpace

Recent years have witnessed a growing trend of more and more attention attached to the application of WeChat public platforms in China's libraries, and some achievements have also been made in the relevant research in this field. This paper attempts to summarize the research status of WeChat use in libraries in China by analyzing the literature collected by the CSSCI database from 2010 to 2016 with the aid of CiteSpace5.1, the quantitative visualization and analysis software, and the future development trend are proposed.
What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels

Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artistic texts that are difficult to generate synthetically and for languages other than English for which we do not always have synthetic data. However, there has been implicit common knowledge that training STR models on real data is nearly impossible because real data is insufficient. We consider that this common knowledge has obstructed the study of STR with fewer labels. In this work, we would like to reactivate STR with fewer labels by disproving the common knowledge. We consolidate recently accumulated public real data and show that we can train STR models satisfactorily only with real labeled data. Subsequently, we find simple data augmentation to fully exploit real data. Furthermore, we improve the models by collecting unlabeled data and introducing semi- and self-supervised methods. As a result, we obtain a competitive model to state-of-the-art methods. To the best of our knowledge, this is the first study that 1) shows sufficient performance by only using real labels and 2) introduces semi- and self-supervised methods into STR with fewer labels. Our code and data are available: https://github.com/ku21fan/STR-Fewer-Labels
Risk Prevention and Control of Automobile Enterprise Purchasing Parts Supply Management Based on Block Chain Technology

In recent years, with the development of science and technology, in order to further explore the world, we explore and study the supply management of automobile enterprise procurement parts based on block chain technology. Based on the reality of China's automobile industry supply chain, combined with the advanced supply chain management theory at home and abroad, this paper makes a comprehensive and systematic analysis of the automobile industry supply chain by combining theoretical research with enterprise cases, and tries to explain in theory, refine the method and structure of the supply chain, and construct the precise supply chain system and fine supply chain of the automobile industry management system and efficiency evaluation system. Based on the analysis of the subjective and objective elements and their interaction in the supply chain, an accurate comprehensive cost model of the supply chain is established. In order to achieve the balance between customer satisfaction and supply chain operation cost, the supply chain structure and operation process are designed in turn. The basic structure of the Bit coin system is designed and realized with the help of block chain technology. It can be said that the basic system of Bit coin is constituted by the block chain. Block chain technology has been considered by professionals to be a very promising technology since its discovery. This article introduces its key technology, composition, basic principles, and limitations, and conducts research on some related issues.
Advanced Technology by Using Geographic Information System (GIS) to Measure the Level of Natural Disaster Vulnerability of Archaeological Heritage in South Sulawesi, Indonesia

This study discusses the level of natural disasters vulnerability to archaeological objects in South Sulawesi. The data used was the result of an inventory from Cultural Preservation Office as well as from the Geo spatial Information Agency. Overlay analysis with the Geographic Information System (GIS) approach as the method used. There are two disaster variables tested on archaeological objects; floods and landslides. This research succeeded in identifying 346 archaeological objects in South Sulawesi that are prone to disasters with 88 prone to landslides and 258 flooding. There are 215 archaeological objects with moderate and 91 with high flood and landslide hazard. As the result, this analysis will become a reference in determining the priority of archaeological preservation in South Sulawesi in the future.
Feature Matching with Similarity Domains Network

Feature matching is an essential step in many computer vision applications. Similarity Domains Network (SDN) is a type of Neural Networks and has been recently proposed. SDN computes multiple kernel parameters to define the decision function. In this paper, we describe how to utilize the computed kernel parameters of SDN for feature matching. Normally, using only the distance value between the closest features does not yield good matches. However, in our experiments, we demonstrate that we can match features by using the distance value between the closest Gaussian kernel centers (also known as similarity domain centers) that are computed by SDN.
ROC Analyses Based on Measuring Evidence

ROC analyses are considered under a variety of assumptions concerning the distributions of a measurement $X$ in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The methodology is based on a characterization of statistical evidence which is dependent on the specification of prior distributions for the unknown population distributions as well as for the relevant prevalence $w$ of the disease in a given population. In all cases, elicitation algorithms are provided to guide the selection of the priors. Inferences are derived for the AUC as well as the cutoff $c$ used for classification and the associated error characteristics.
Online Teaching Experiences of Language Teachers in Higher Education in the Confinement Period

The inclusion of information and communication technology has been a challenge for higher education institutions in the confinement period caused by the COVID-19 pandemic. Mexican educational institutions seem not to be prepared to face that demand due to the lack of technological resources and lack of internet connection (INEGI, 2019). In the confinement period, in Mexico, face-to-face institutions turned into eLearning to continue students’ education. The use of ICT demands teachers to develop digital competencies to make learning a successful process. This descriptive-exploratory qualitative study aimed to identify the online teaching experiences of language teachers in the confinement period. The results showed that some teachers became social tutors, while others were cognitive tutors. Besides, they had positive online teaching experiences, which led them to grow academically, be in touch with their students, develop, and select materials. However, the increase of time and overwork, lack of internet and computer essential tools, and students’ low commitment affected teachers physically and emotionally, causing them to lived negative online teaching experiences. Teachers were having a difficult time trying to fulfill their responsibilities, and it seems that they were not considering students’ learning styles in their online teaching practice.
Pengaruh Pendekatan Contextual Teaching And Learning Berbantuan Media Tiga Dimensi Terhadap Motivasi Dan Hasil Belajar Matematika

The purpose of this study was to analyze the effect of the three-dimensional media-assisted Contextual Teaching and Learning (CTL) approach on learning motivation and student learning outcomes of SDN 112233 Sidorejo. The research method used is a quasi-experimental method. The research hypothesis was tested by using two-way analysis of variance (ANOVA) with a significance level of 0.05. Furthermore, the homogeneity and normality test were carried out, then the two-way analysis of variance (ANOVA) test was carried out. Based on the calculation of the ANOVA test shows the value of Fcount (5.609)&gt; Ftable (4.06) then H₀ is rejected Hₐ is accepted with the conclusion that the CTL learning approach is proven to have an effect on mathematics learning outcomes, based on the value of Fcount (33.630)&gt; Ftable (4.06) then H₀ is rejected Hₐ is accepted with the conclusion that students who have learning motivation are proven to have an effect on student learning outcomes and the value of Fcount (1.020) &lt;Ftable (4.06) then H₀ is accepted Hₐ is rejected and the significance value is 0.319&gt; 0.05, with concluded that there is an interaction between CTL approach to student learning motivation and student learning outcomes.
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