Reviewerly/exHarmony huggingface dataset:
Multi Feature Modulation Signal Recognition based on Deep Learning

A new round of technological revolution, industrial revolution and information revolution are developing rapidly, and the development of communication technology is also facing challenges. Modulation signal recognition is a critical technology in the field of information and communication engineering. It is everywhere in both civil and military fields, such as online education, electronic intelligence support technology, etc. But as the electromagnetic environment becomes ever more complex, we also need to constantly take on new challenges. In this article, the author proposed a method for multi feature modulation recognition based on shallow convolutional neural network (CNN) to enhance the internal connection of various features extracted by features during modulation signal recognition, thereby improving the signal recognition effect. The simulation results in this paper show that the technology proposed in this paper improves the recognition rate of modulated signals under different signal-to-noise ratios(SNRs), which means that this method can effectively improve the recognition performance of modulated signals.
A Bayesian conditional autoregressive with INLA: A case study of tuberculosis in Java, Indonesia

Most disease risk mapping models are modeled by using the Bayesian approach. In this approach, the posterior distribution is typically estimated by using Markov Chain Monte Carlo (MCMC) method. However, it is known that the MCMC method suffers from convergence issues and requires a high demand for computational resources. This study aims to identify potential covariates in tuberculosis reduction and to map the relative risk of tuberculosis disease in the Java region by using Bayesian Conditional Autoregressive (CAR) with Integrated Nested Laplace Approximation (INLA). The posterior distribution of Bayes was estimated analytically using INLA that resulting in a more efficient, fast, and accurate inference. The results showed that Besag York Mollié (BYM) has the smallest Deviance Information Criterion (DIC) and Mean Absolute Deviance (MAD), so this model gives the best prediction. The significant covariates in reducing the number of TB cases in Java are percentages of healthy homes, household clean and healthy behavior, non-smokers, and complete tuberculosis treatment. In addition, 52.1% of districts/cities in Java were found to have a relative risk greater than one, and most of them were located in West Java.
3D Dynamic Target Detection Algorithm Based on Voxel Point Cloud Fusion

Current methods used in the field of 3D target detection generally have the problem that target context features are not rich enough to achieve accurate dynamic multi-target detection in feature extraction. A 3D dynamic object detection algorithm based on voxel point cloud fusion is proposed. The algorithm uses a two-stage detection architecture of multiple and fusion of multi-scale feature. In the first stage, point cloud is directly processed to extract key point features and the voxel space is divided to extract multi-scale voxel features, and two features are firstly fused to generate preselection frame. In the second stage, a reference point is set in each voxel and surrounding key points are absorbed for the second feature fusion, and the final feature is input to a detection module to achieve optimization of the preselection box. Additionally, for the problem of inconsistency between classification and location reliability, a mandatory consistency loss function is proposed to further improve accuracy of detection. Compared with other algorithms in Kitti, Waymo and nuScene datasets, the accuracy of 3D dynamic target detection is 92.10%, the results on the real vehicle platform show that the algorithm has strong robustness, portability and generalization ability.
Spot diagram borders are always caustic curves and/or marginal rays: Proof applied by means of the Weierstrass theorem

Weierstrass theorem and different Zernike polynomials are used to prove that for any optical system with general exit pupil affected by any aberrations, the borders of all leaving rays are curves caustic and/or marginal rays.
Modeling Rates of Change and Aggregations in Runtime Goal Models

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We explore a goal-oriented approach to managing this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements in real-time. We offer an extended vocabulary of goal types, specifically by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a DDoS attack.
INNOVATIONS IN FOREIGN SCHOOL EDUCATION

The article presents an overview of innovations in the school system of certain foreign countries. The purpose of the article is to describe the changes in the modern foreign school and to gain useful experience in the formation of schoolchildren’s competencies necessary for life in modern conditions. Currently in school didactics there are constant discussions about the introduction of new school subjects that have more in common with our modern life. School subjects that are studied at school today were already in the school curriculum in the past, and even the century before. However the school must correspond to current realities. In today’s world students need to learn “soft skills” to be competitive and successful in their profession. Today the society needs initiative, independent thinking, creative people who are able to perceive reality critically, who know the basics of digital literacy. New school subjects to be introduced into the curriculum should better prepare graduates for their future lives in the context of globalization, digitalization and climate change. The goal of modern education is to “teach how to learn”. Therefore it is necessary to strengthen at all levels of school education the tendency to develop in students independent thinking, creativity, the ability to project new own research. The article describes and analyzes individual school subjects introduced into the school curriculum in a number of countries in recent decades. In some schools in Germany the school subjects “Research”, “Care”, “Nutrition and Health”, “Happiness” are studied. Schools in Italy, New Zealand, Finland have introduced the subject “Climate Change and Sustainable Development”, etc. Examples of the main forms of organizing lessons and the main approaches (project-based learning, cross-subject learning) are given. The role of the teacher is defined, they play the role of a consultant, whose main task is to motivate the student to research, regardless of the upcoming assessment, to teach them to think. The main principles in the implementation of student projects are the integration of individual school subjects, practical orientation, connection with modernity.
The Exponential Dynamic Analysis of Network Attention Based on Big Data

Based on Baidu Index and Internet Big Data, this paper analyzes the overall relationship of information flow spatial network by using social network method, and finds that index-time has dual structural characteristics and keeps changing trend. The research shows that the scale, correlation degree and control efficiency level of information flow show obvious structural characteristics of time dissimilation. Secondly, we study the behavior characteristics of index, dynamically analyze the temporal and spatial changes of big data of network attention and index dynamics, observe the inflow of index data in July and August, and find that the maximum value of a single index reaches 250,457 times. The inflow was the highest in the third quarter, with the maximum index reaching 659,329 times, showing the peak state of the whole period. Through information flow, the correlation between data is revealed, and the time distribution characteristics of monthly peak and quarterly peak are analyzed. Finally, it is concluded that the attention of information flow network has a clear direction in the two dimensions of "index-time", showing its correlation.
Learning difficulties in the school performance

This paper is a study on the concept of learning disabilities and reading comprehension. Specifically, it studies the learning difficulties and the reading ability in terms of the school performance of the students of the first grades of primary school.
A Lightweight Encrypted Network Traffic Classification Method Based on Protocol Field and K-Nearest Neighbor

Encrypted traffic classification is a crucial issue to be addressed with popularization and application of encryption protocols in the network. How to identify and classify encrypted traffic with high efficiency and accuracy has attracted increasing attention for reasons of network management and security. Although many deep learning methods have been reported, high complexity cannot satisfy the real-time classification requirement because of hardware and training costs. In this paper, we propose a lightweight traffic classification method for Transport Layer Security (TLS) protocol based on the Relative Distinguished Name (RDN) field information and k-nearest neighbor (KNN). A specific application is firstly identified by RDN field of TLS handshake messages. Secondly, KNN algorithm is used to classify flows of the same application into different service categories based on carefully selected spatial-temporal features. The effectiveness of the proposed method is well supported by detailed analysis. The experimental results demonstrate the good performance with high speed and precisions of 98.68%, 96.25%, 98.87%, 95.93% for VoIP, Chat, Streaming, File, respectively.
Building Protocols for Scalable Decentralized Applications

Blockchain protocols are a promising technology in the abstract, but, in reality, fall short of the promise of supporting arbitrary decentralized applications. For example, Bitcoin supports <10 transactions per second and Ethereum’s gas limit prevents computationally expensive applications to execute on its chain. This chapter provides an overview of mechanisms that have been proposed to overcome these limitations. In particular, we describe novel consensus protocols, sharding mechanisms, state and payment channels, subchains, and federated protocols. Additionally, we give insight into the tradeoffs and benefits of the different approaches.
Factor Analysis Results

The results of typologizing Asian societies through factor-analyzing human daily life satisfactions in 28 Asian societies are shown in alphabetical order, one by one: Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, China, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Laos, Malaysia, the Maldives, Mongolia, Myanmar, Nepal, Pakistan, the Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Tajikistan, Thailand, Uzbekistan, and Vietnam. Turkmenistan was not included in this analysis because the “don't know” responses numbered too many on the key question of “Please tell me how satisfied or dissatisfied you are with the following aspects of your life.”
Development of Substation Foundation Settlement Monitoring System based on BeiDou Positioning Technology and 5G Transmission

The substation is easy to cause electrical equipment accidents due to foundation settlement. The existing method of substation foundation settlement is mainly level monitoring method. This method can not judge the foundation settlement timely, accurately and remotely. Therefore, this method proposes a system scheme that can be monitored online and accurately based on 5G transmission network and BeiDou navigation and positioning technology. In this paper, 5G transmission and BeiDou Positioning Technology are briefly introduced, and then a substation settlement monitoring system is built based on their advantages. Through experimental research, it is found that the foundation settlement of substation can be monitored in real time and accurately. Finally, this paper briefly analyzes the causes of settlement. The results of this study can alert relevant maintenance departments to take corresponding equipment consolidation measures in advance. And it can reduce or avoid the occurrence of electrical equipment accidents caused by foundation settlement.
Performance of ACO-based Decision Tree Algorithm with Imbalanced Class Data Sets - A Heuristic Approach

The prediction of minority class can be like finding a needle in a haystack. Bio-inspired classifier such as Ant Colony Optimization (ACO) decision tree experienced ineffective decision boundaries since its entropy-based heuristic is affected by the strong presence of the dominant class. Consequently, the developed trees were dominated by the likelihood of the majority class where the rare class is under-represented. The proposed algorithm with class skew-insensitive heuristic namely the Hellinger-Ant-Tree-Miner (HATM) was compared to the Ant-Tree-Miner (ATM), via a simulation study and application to 15 imbalanced data. Simulation results revealed the advantage of HATM over the ATM under skewed class distributions as the number of covariates and sample sizes increase. Experiments with real data indicate a potential improvement of the ATM measured by balanced accuracy (BACC), F-Measure and minority class prediction (MCP). The Friedman tests justify that HATM performed better than ATM while being competitive with other well-known tree-based classifiers.
Research on the Academic Chinese Needs of International Graduate Students Studying in China

Research on the needs of international graduate students studying in China for learning and improving academic Chinese is the starting point and foundation of constructing a teaching and research system on this discipline. This paper adopts an in-depth interview methodology to gain in-depth insight into the needs of 20 international graduate students studying in China for learning and improving academic Chinese. The interviewees included eleven post-graduate students and nine doctoral students, majoring in a variety of disciplines including economics, law, education, literature and management. The results of the interviews show that international graduate students studying in China generally possess limited academic Chinese language skills that prevent competency in Chinese academic research. Hence, they are in great need of improving their academic Chinese skills and attending relevant academic Chinese courses, especially in academic Chinese writing. In view of the needs of and barriers for international students, this paper suggests improvement to the existing Chinese Proficiency Test (HSK) system by setting up an academic Chinese Proficiency Test and encouraging colleges and universities to conduct diagnostic assessments on academic Chinese based on majors, as well as offering courses related to academic Chinese.
YENİ MEDYADA KULLANICILAR TARAFINDAN ÜRETİLEN KÖTÜLÜĞÜN BOYUTLARI: TWİTTER ÖRNEĞİ

Depending on the rapid development of communication technologies with the Internet, the traditional communication process is also changing. The user is no longer limited to the passive receiver location, but can also be a sender. So users now have the opportunity to write, speak, share, and even set up their own channel and broadcast. This study focuses on the transformation of the active user in new media and the resulting evil events. The Başaran Holding jet crash, which took place in 2018, and included different discourse themes, was chosen as an example case for the situation in question. The jet crash has been on the agenda in traditional and new media; It has been discussed extensively on the Twitter platform. For this reason, the case was examined through tweets collected from Twitter. Norman Fairclough's critical discourse analysis, which is more suitable for the scope of the study, was chosen as the data analysis method. As a result, it has become clear how easily one can move away from certain traditions and customs and common moral norms that were settled in a society when conditions change. The conditions in question are switching from face-to-face communication to interface communication and hiding real identities with fake accounts. In addition, it has been found that platform providers, as profit-making companies, allow evil to breed with the limits they draw and the opportunities they offer to the user. In addition, it has been revealed again that those who produce the news, which Van Dijk describes as the symbolic elite, act with commercial concerns, cause the unequal social structure to continue, feed the existing power structures, and have a manipulative power on the society. In addition, the types of evil committed by users in the new media were defined and the type of evil committed in the sample case was determined as cyber-vilification, one of the types of cyberbullying
SELF-ASSESSMENT AND PEER ASSESSMENT ‒ DETERMINANT OF IMPROVING THE QUALITY OF THE PROCESS AND LEARNING OUTCOMES

The subject of the research is the activity of students in the assessment process - in self-assessment and peer assessment. The aim is to demonstrate the importance of such activities for improving the quality of the process and learning outcomes of students. The values of self-assessment and peer assessment were analyzed, and their theoretical foundations were characterized. The results of empirical research confirm the advantages of increasing student activity in the assessment process, but also indicate challenges related to the implementation of such forms of assessment into practice. The use of self-assessment and peer assessment by teachers in the process of teaching undoubtedly requires increased effort and time, but as a result it increases the quality of the desired learning outcomes of students.
REDUCING THE NEGATIVE IMPACT OF THE «INFORMATION COCOON» FROM THE POINT OF VIEW OF INCREASING STUDENTS’ DIGITAL COMPETENCES

The continuous development of digital technologies creates a new way of disseminating information - personalized information and recommendation services based on the interests of users. Although this to a certain extent meets the needs of the individual development of students, it will also lead to the accumulation of homogeneous information and form an “information cocoon” in the digital age. Therefore, the question of how students can effectively break this “information cocoon”, effectively receive and use information, has become one of the urgent issues that need to be addressed at the present time. Based on this, this article discusses the problem of the «information cocoon» in the digital age by summarizing the current state of research on the «information cocoon» phenomenon, analyzing the reasons for its formation and the impact on students, and in the future put forward appropriate proposals to mitigate the negative impact of the «information cocoon» in terms of increasing the digital competencies of students.
The architecture of non-local semantics for artificial general intelligence

This paper addresses the issue of providing artificial intelligence (AI) models by the non-local semantics that reflects non-formalisable and weak formalisable aspects of human consciousness and unconsciousness. Emotions, feelings, thoughts, insights, and transcendental state of mind cannot be formalised. But these aspects have to be taken into account during decision-making and analytical conversations with AI support. Trying to represent the aspects using traditional cognitive architectures, logical ontology, knowledge base, and so on are finishing in a formalised way. The decision of the problem, in our opinion, can be carried out in an indirect way or taking into consideration the peculiarities of the structures of non-local effects in outside spaces such as cosmic spaces and quantum fields. For this, it requires to introduce the concepts of non-local cognitive semantics. The paper suggests the approach by which this idea can be implemented during modelling in advanced AI, or artificial general intelligence (AGI).
A Review on the IOT System

The advancement of computer science and communication technologies is being enabled by something called the Internet of Things (IoT). The IoT is being used in many fields, so maintaining its Security is becoming an increasingly important concern. The IoT industry as a whole is going to be significantly impacted as a result of this. This study examines the IoT security problem at the IoT system's perception, network, and application levels suggests a secure way to build IoT and explains how to make it safe based on the problems that already exist in the IoT framework. The study begins with the concept of IoT, its characteristics, and how it functions as a system. Finally, the theoretical foundations necessary to construct a trustworthy IoT security system will be presented. The IoT will enable inanimate things to participate in human activities. IoT security is notoriously difficult to handle because items are often integrated into the control procedures of more complicated systems. In practice, the IoT is a complicated paradigm in which people interact with a technological ecosystem based on intelligent devices via various complicated procedures. Interactions between a person, an intelligent object, a technical environment, and a process show IoT security's cognitive and systemic components.
A Systematic Procedure for Comparing Template-Based Gesture Recognizers

To consistently compare gesture recognizers under identical conditions, a systematic procedure for comparative testing should investigate how the number of templates, the number of sampling points, the number of fingers, and their configuration with other hand parameters such as hand joints, palm, and fingertips impact performance. This paper defines a systematic procedure for comparing recognizers using a series of test definitions, i.e. an ordered list of test cases with controlled variables common to all test cases. For each test case, its accuracy is measured by the recognition rate and its responsiveness by the execution time. This procedure is applied on six state-of-the-art template-based gesture recognizers on SHREC2019, a gesture dataset that contains simple and complex hand gestures tested and is largely used in the literature for competition in a user-independent scenario, and on Jackknife-lm, another challenging dataset. The results of the procedure identify the configurations in which each recognizer is the most accurate or the fastest.
Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik

The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.