Reviewerly/exHarmony huggingface dataset:
E-Learning: Succeeding Amid the Pandemic Period, Forgotten in the Post-Pandemic Era

As the coronavirus emerged, a necessity in the educational sector, instructors or lecturers have been required to establish teaching methodologies. On the other hand, as the epidemic winds down, the transmission of knowledge is gradually shifting back to being carried out offline or through a mixed approach. Online education is beginning to lose popularity as a result. This study aimed to examine the practical implications of knowledge gained through e-learning. Employing qualitative research by literature study, keywords used as data are mapped based on Google Scholar and Garuda sites in the form of books and articles are [“educational institutional”] and [“e-learning”] and [“pandemic”], by having ten inclusive data of literature associated with the intended subject matter. As a result, students felt more at ease and satisfied when using online learning as a platform for voicing their opinions and concerns. In addition, using various online learning media can facilitate an increase in the learner's level of autonomy. During the COVID-19 epidemic and the implementation phase, e-learning was particularly beneficial in online learning. Then, after it ends, learning with e-learning media is still very much needed because almost all educational institutions have optimally developed e-learning media along with improvements in implementation, always up to date with technological developments, and very effective and efficient by minimizing costs.
Re-thinking High-dimensional Mathematical Statistics

The workshop highlighted recent theoretical advances on inference in high-dimensional statistical models based on the interplay of techniques from mathematical statistics, machine learning, theoretical computer science and related areas. The workshop brought together about 50 researchers in order to present new results, exchange ideas and explore open problems.
Design and implementation of pedestrian detection system based on video image

Facing the problems of low working efficiency, poor accuracy and insufficient stability of traditional motion detection algorithms and machine learning algorithms in pedestrian detection, this paper will take artificial intelligence technology as the core, adopt R-CNN series deep learning models, combine Support Vector Machine (SVM) classifier, and rely on TensorFlow deep learning framework to realize pedestrian detection and recognition in video and picture files with the support of OpengCV Open Source Visual Library. Combined with ASP.NET framework, C# language is used to complete the design and development of each functional module, improve the deployment of the corresponding API interface, and build a front-end interactive interface to form a pedestrian detection system based on Web. The overall design of the system chooses B/S architecture, which allows users to access the Web Server through simple request operation to complete the detection of video image data in the database and return the detection results to the front page for display. The system will greatly improve the precision and accuracy of pedestrian detection and make a positive attempt to further expand the application scenarios of pedestrian detection in video images.
Multi-branch offset architecture for unaligned cross-view geo localization

Cross-view geo-localization usually requires matching a ground-view image with the corresponding aerial-view image. This task has attracted researchers' attention in recent years due to the huge differences in perspectives and the occlusion of information between the two views. Previous work has focused more on alignment conditions, where aerial and ground views are aligned north. The explicit alignment information is beneficial to model learning. However, in this work, we extend this task to unaligned conditions and expect the model to perform well even with random offsets in the ground-view image. To solve this problem, we propose a new multi-branch offset (MBO) architecture. Specifically, we divide the feature extracted by the CNN into N parts and shift the feature along the horizontal direction. Features of different offset sizes are then fed into subsequent multi-branch Transformer blocks, and the relationships between channels are further expressed by using channel attention. Finally, the enhanced features are fused with the output of the original CNN. We validated our results on popular dataset, where 78.13% of the Top-1 recalls and 33.78 FPS were made on CVUSA, which is higher than the existing model and achieves efficient inference speed.
An improved class incremental learning method based on multi-level distillation and continual normalization

Deep neural networks usually suffer from significant catastrophic forgetting when faced with class incremental tasks. Existing methods mostly use generative models to synthesize pseudo samples or save old exemplars to overcome the forgetting, but they are difficult to deal with the memory-constrained scenarios. In this paper we propose a Multi-Level Distillation and Continual Normalization (MLDCN) method which applies the framework of the exemplar-free method PASS. We first analyze the two bottlenecks in PASS: prototype mismatching problem and normalization preference for statistical properties of the current task. Hence, we propose MLDCN which contains a multi-level distillation framework to improve the model's ability to retain old knowledge. In addition, we introduce the continual normalization layer in the backbone to further enhance the stability of the model. Experimental results on CIFAR-100, ImageNet-sub show that our method can effectively alleviate the problem of catastrophic forgetting without saving old exemplars, and better preserve the knowledge of old categories in the incremental process. The performance of the proposed method outperforms many exemplar-free methods and several exemplar-replay methods.
Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm

An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved.
PEMANFAATAN PERPUSTAKAAN MADRASAH SEBAGAI SUMBER BELAJAR DALAM MENINGKATKAN PRESTASI BELAJAR SISWA DI MIN 10 HULU SUNGAI UTARA

The existence of a madrasa library is not limited to a place to store textbooks, but must be able to present alternative sources of knowledge needed by students, because the existence of a library so far is still difficult for students to access. Moreover, there is a tendency for teaching patterns in Madrasahs to generally rely on package books determined by madrasa. Ironically, often the package book is from only one publisher and is selected based on the proximity of the government or madrasa managers to a particular publisher. This condition triggers the student's desire to search for reading sources or information outside the package book. The function of the madrasa library has received less attention, but the existence of the madrasa library depends on the commitment of school residents since teachers, students and the support of the head of the madrasa how to respond to it.
Deploying hybrid quantum-secured infrastructure for applications: When quantum and post-quantum can work together

Most currently used cryptographic tools for protecting data are based on certain computational assumptions, which makes them vulnerable with respect to technological and algorithmic developments, such as quantum computing. One existing option to counter this potential threat is quantum key distribution, whose security is based on the laws of quantum physics. Quantum key distribution is secure against unforeseen technological developments. A second approach is post-quantum cryptography, which is a set of cryptographic primitives that are believed to be secure even against attacks with both classical and quantum computing technologies. From this perspective, this study reviews recent progress in the deployment of the quantum-secured infrastructure based on quantum key distribution, post-quantum cryptography, and their combinations. Various directions in the further development of the full-stack quantum-secured infrastructure are also indicated. Distributed applications, such as blockchains and distributed ledgers, are also discussed.
The Existe ntialism of “New Poetry”and Kim Sooyoung

The poets of “New Poetry” published the first volume of “New Poetry” in April 1948, and in October of the same year they participated in the special edition of Sartre's existentialism in “Shincheonji”. However, there is no trace of Kim Sooyoung's official activities as a member of the same poetry association until “New City and Citizens' Chorus” was published in 1949. In the early days of the New Poetry Theory, Kim Sooyoung seems to have concentrated on studying poetry theory rather than on external activities. Because he had no basis in poetry theory, he had no personal relationship with the modernist poets, and his pride was hurt. However, Kim Sooyoung did not show such an expression until the mid and late 1960s. However, about 10 years after Park Inhwan's death, he criticized Park Inhwan's criticism and at the same time expressed his critical attitude toward the past modernist activities to the outside world. This seems to be connected with the period when Kim Sooyoung first realized the “dimension of criticism”.&#x0D; Kim Sooyoung was deeply concerned with the concept of conscience, one of the central themes of Heidegger's existentialism. Park Inhwan and the New Poetry poets, on the other hand, focused on Sartre's existentialism. In particular, Heidegger's existentialism had a great influence on Kim Sooyoung's poetic worldview and became the basis for his original poetic theory called “Whole Body Poetics. Ten years after Park In-hwan's death, Kim Soo-young acquired the “level of criticism,” and on this basis he criticized the “difficult poetry” of Park In-hwan and New Poetry. Park Inhwan and the New Poetry Theory insisted on the social responsibility of literature, but Kim Sooyoung criticized this position of the New Poetry Theory. However, since Park Inhwan died in 1956, the question remains whether it is justified for Kim Sooyoung to blame Park Inhwan for the literary trend of the 1960s ten years later. Therefore, this paper will reveal new facts about Park Inhwan's cultural and artistic activities after his death. In addition, this paper points out that it is not possible to evaluate Park Inhwan properly due to the lack of data.
Criticism of the consideration of locations of Sejong Yukjin in Korean history textbook for high school

Border history is territorial history. The central theme of border history in the early Joseon Dynasty was the pioneering of Sagun-Yukjin during the reign of King Sejong. Among them, Yukjin is the core of the border history in the northeastern part of the early Joseon Dynasty. The writer investigated the problem by comparing the location of Yukjin which is considered in the current high school Korean history textbook, with the four-way boundary records recorded in “Sejong Silok Geography”. The current high school Korean history textbook describes the Yukjin area as the Hamgyeongbuk-do area along the Tumen River in North Korea. However, in “Sejong Silok Geography”, which is a record at the time of attack of Yukjin, the four-way the boundaries of the six Dohobu corresponding to Yukjin are recorded. In particular, the routes to Gongheomjin and Seonchunryeong, where you can know the northern boundary, are accurately recorded. According to this, it was confirmed that Yukjin area is considered (northern region of Hamgyeongbuk-do) in the Korean history textbook was about five times smaller than Yukjin recorded in “Sejong Silok Geography”
Distance learning gains via digital platforms during the Covid-19 pandemic: a qualitative study of the experience of faculty members

The Covid-19 pandemic has taken its toll on educational institutions, and learning has become fully dependent on digital platforms that have helped in the immediate transition from face-to-face to distance education. This study focused on the experience of faculty members regarding the gains involved in using digital platforms during the pandemic. The qualitative phenomenological method was used. The sample included (10) faculty members. The data was collected through the semi-structured interview tool. The data were analyzed using the objective analysis method. The results showed that there are a number of gains for distance education through digital platforms in light of the Corona pandemic, which can be developed in the future through continued learning through those systems, and the application of the integration between the electronic system and the traditional system, and these gains were represented in (4) main themes: the structure of E-learning, social interactions, outputs, trends towards the use of e-learning, under each main topic a set of sub-themes.
The Interactive of E-Learning Unit to employ The Students Skills in Cyber Security According to Determinates of Digital Citizenship

The study aimed to design an interactive of e-learning unit to employ students’ skills in cyber security according to determinants of digital citizenship then measuring its effectiveness. The study relies on the descriptive approach and the quasi-experimental approach. To achieve study objectives, measurement tools were developed, namely cognitive test and observation card to measure the cognitive and performance aspects of cyber security skills according of the nine determinants of digital citizenship, which were applied in the second semester of the academic year (1443 AH / 2022 AD); On a sample of (33) undergraduate students at Umm Al-Qura University. Study results showed that there are statistically significant differences at α ≤ 0.05 among average score of the study sample in the pre and post application of the cognitive test of cyber security skills as a total score, and as sub-domains: (Education Field, Respect Field, and Protection Field) and as sub-dimensions of digital citizenship. So, all differences were in favor of post-application. The study also showed that there are statistically significant differences at α ≤ 0.05 among average score of the study sample in the pre and post application of the observation card of the performance aspect of cyber security skills as a total score, and as sub-domains: (Education Field, Respect Field, &amp; Protection Field), and as sub-dimensions of digital citizenship. So, all differences were in favor of the post- application. The results also showed that the interactive e-learning unit has an impact on the development of the cognitive and performance aspects. and it was found that there is a positive correlation with statistical significance at α ≤ 0.05 between the cognitive side and the performance side of cyber security skills, after applying the program based on the interactive e-learning unit.
Sentimental Analysis on Product Reviews Using Support Vector Machine and Nave Bayes

People nowadays use internet platforms to exchange ideas, share opinions, and learn online. Huge amounts of data are being poured into social media in the form of tweets, blogs, and updates on articles and items, among other things. The data is all unorganized and unprocessed. It is necessary to arrange and examine it. It takes a long time to analyze and process information using traditional methods and is impossible to analyze each and every sentence. So, there is a need to have a better approach. It can be done through sentimental analysis which extracts the opinion of a user in a piece of text data. This sentimental analysis will predict the polarity of the sentence, whether the given sentence is positive or a negative one. The sentimental analysis can be achieved through three approaches namely lexicon based, machine learning based and hybrid approach. This sentimental analysis is a part of NLP. This project aims to perform sentimental analysis using machine learning techniques and few natural language processing techniques on a product reviews dataset.
Hybrid spam message detection using convolutional neural network and long short-term memory techniques

Short Message Service (SMS) is a feature of a mobile phone that enable convenient and instant way of sending electronic messages between users. As SMS usage increases fraudulent text messages, known as spam, are becoming more common. Spam SMS may result in leaking personal information, invasion of privacy or accessing unauthorized data from mobile devices. Users of mobile phones can mistakingly give away personal information with the assumption that they are sharing it with the right recipients. This work propose a SMS spam detection method that combines convolutional neural network (CNN) and long short term memory (LSTM) deep learning algorithms. The CNN is used for feature extraction while the LSTM classifies the message. The SMS spam dataset, collected from online repository, is used to train the model. Word embeddings is used to vectorize the words in the message to make it suitable for the model. The result obtained from the implementation outperforms other machine learning algorithms with an accuracy of 99.77%.
The Application of Machine Learning Algorithm and Causal Inference in Analyzing Student Performance

The causal inference is the process of determining the causality and correlation between different variables in a system. This paper mainly focuses on the introduction of theories of causal inference analysis and application of causal inference on student performance data. The adjustment formula, backdoor criterion and front-door criterion are three different methods to analyze different causal diagrams which can be used to quantify and analyze the causal effect of students' choices on performance and school support. Taking advantage of logistic regression model, counterfactual inference can utilize students' experience to predict the possibility of result under different choice conditions. The Natural Direct Effect in the path-specific effect is used to study the direct effect of sensitive attribute gender on the acquisition of school support, determining the fairness of decision-making of the school support
Recommender Systems: Collaborative Filtering and Content-based Recommender System

There are three algorithms of recommender systems proposed by this paper, which are item collaborative filtering(itemCF), user collaborative filtering(useCF) and content-based recommender system(CBRS). The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset. In accordance with the data we chose and ceaseless testing, we observe itemCF contains the most accurate rate. However, we theoretically and empirically conceive each algorithm owns different advantages and drawbacks, should be used in the specific circumstance.
ANALYSIS OF THE USE OF MULTIMEDIA TECHNOLOGIES DURING PROFESSIONAL TRAINING OF FUTURE IT TEACHERS

Currently, the world is experiencing a new stage of computerization of various activities related to the development of multimedia. Using multimedia tools makes it possible to increase the intensity and efficiency of the educational process. Learning multimedia is especially important for teachers of computer science because they are the ones who form knowledge, skills and competencies in information technology, in particular, in multimedia in students. In the process of training future teachers of computer science at the University of Pedagogy, students develop separate parts of multimedia software and integrated training courses for individual subjects, including video, sound, graphics, and animation.
Schema Theory in Consecutive Interpreting Note-taking

Essential to interpreting, note-taking skill training should be paid great attention to since it relates to the successful output of the target language. Especially for beginners, note-taking groundwork is indispensable, developing a good note-taking system of their own is a great start on the path to professional. This paper aims at assisting interpreters to formulate an efficient note-taking system or improving existing habits by applying schema theory which presents an efficient course to help intensify the note-taking ability.
Stress Analysis and Size Optimization of Suspension Beam Structure of Robot Manipulator

In this paper, the stress and deformation of a manipulator structure are analyzed, and the structural optimization design is carried out. The initial configuration is a cantilever beam structure with rectangular section, which is fixed at one end and bears a load of 1 ton at the other end. After stress and deformation analysis with ABAQUS software and SolidWorks software, three optimizations were carried out. Geometric configuration optimization, topology optimization and material optimization. After optimization, the overall quality of the structure is reduced by 80%, and there is no great loss of strength and safety.
Analysis of the Current Situation of Virtual Currency Based on Statistical Data and Prospects for Its Future Model

Bitcoin is known to the world from its birth to the present, and it only took 13 years. In the past 13 years, countless virtual currencies have flourished. In the rapid development of virtual currencies, many problems have been revealed. This article will mainly discuss (1) energy consumption, (2) miner consolidation, (3) encryption security and finally (4) miner income volatility through different types of virtual currency data. And use Android virtual currency as a case to look forward to virtual currency The direction of development. Readers can learn more about the main challenges faced by virtual currencies through this article, and explore the future development model of virtual currencies.
Ethical Issues of Automatic Driving in the Society

Autonomous driving is a very hot development direction in the future. With the application of electricity and the development of technology, autonomous driving technology will also be widely adopted. This study discusses the ethical issues of autonomous driving in society from the perspectives of social exclusion and private issues. The results demonstrate that although automatic driving is an excellent technology, it still needs more time to improve the whole systems and hardware facility to provide the best experience for people.