Reviewerly/exHarmony huggingface dataset:
Review Paper on Block-Chain Technology

Blockchain technology has gained immense popularity in recent years due to its ability to provide secure, decentralized, and transparent transactions. This paper presents a comprehensive review of blockchain technology, including its underlying concepts, applications, and challenges. The paper begins by discussing the basics of blockchain technology, including the structure of the blockchain, consensus mechanisms, and cryptographic techniques. It then explores the various applications of blockchain technology in areas such as finance, supply chain management, healthcare, and voting systems. The paper also examines the challenges associated with blockchain technology, such as scalability, interoperability, and regulatory concerns. Finally, the paper concludes with a discussion of future research directions and potential applications of blockchain technology.
Exploring Remote Power Attacks Targeting Parallel Data Encryption On Multi-Tenant FPGAs

Cloud service providers (CSPs) are increasingly incorporating Field Programmable Gate Arrays (FPGAs) into their cloud data centers due to the benefits of their flexibility and high performance in heterogeneous designs. However, the optimization of hardware resource utilization through multi-tenancy presents new security concerns. Prior research has demonstrated that remote side-channel attacks represent a significant security threat in the case of a single Advanced Encryption Standard (AES) module. However, it remains an open question whether parallel encryption can offer natural protection against Correlation Power Analysis (CPA). Our research focuses on side-channel attacks on parallel data encryption modules. We implemented delay-line based power sensors to collect mixed power traces and conducted CPA to steal the cipher key. Our results show that clocking methodology would have a significant influence on data protection. If parallel modules work at the same frequency without difference in clocking phase, the mixed voltage drops would contain sufficient information for attackers to decrypt the cipher key. Nevertheless, once the victim applies unique clocking phase to each module, he would convert voltage fluctuations from other modules into noises that offer a natural protection mechanism for parallel data encryption.
Metagenomics Data Visualization Using R

Communicating key finds is a crucial part of the research process. Data visualization is the field of graphically representing data to help communicate key findings. Building on previous chapters around data manipulating using the R programming language this, chapter will explore how to use R to plot data and generate high-quality graphics. It will cover plotting using the base R plotting functionality and introduce the famous ggplot2 package [2] that is widely used for data visualization in R. After this general introduction to data visualization tools, the chapter will explore more specific data visualization techniques for metagenomics data and their use cases using these basic packages.
A cost deviation warning of system based on historical data

The thesis proposes a new methodology of cost dynamic management to realize the whole process of power transmission and transformation project cost dynamic management. Based on historical cost data to modify expected cost control, building cost goal deviation classified warning system, using BP artificial neural network method for the cost of the deviation of the dynamic monitoring, power transmission and transformation project cost target deviation classified warning model is established. In the case of a substation project, to make a validation method. The results show that the proposed method is feasible and can achieve dynamic monitoring and early warning of power transmission and transformation project cost target deviation.
Impact of Strategic Analysis (SWOT) on the Performance of Jordanian Public Shareholding Industrial Companies: The Mediating Role of Scenario Planning

This study aims to examine the impact of strategic analysis (SWOT) on the organizational performance of Jordanian public shareholding industrial companies, taking into consideration the mediating role of scenario planning.The study's sample includes 38 companies out of the 54 operating at the Jordanian financial market.From each company, the functional managers are selected to represent the sampling unit of the study.The questionnaire is employed as the fundamental instrument for collecting the required information and data.A total of 165 questionnaires were distributed, and 151 were statistically significant.Smart PLS (3) program was utilized for statistical analysis.The results reveal that the surveyed companies are engaged in both SWOT analysis and scenario planning, and they are aware of the critical role of the two variables in enhancing organizational performance, particularly after the COVID-19 pandemic.Furthermore, the findings of the study show that scenario planning has a partial impact in the effect of SWOT analysis on companies' performance.Besides, the study sheds light on the significance of strategic analysis and recommends relying on more than one type of analysis and developing human resources with strategic thinking skills to be able to conduct scenario planning and build convenient strategies.
ON SOME ACTUAL PROBLEMS OF REFORMING RUSSIAN HIGHER EDUCATION AT THE PRESENT STAGE

The object of the research is the peculiarities of the latter-day reforms of Russian higher school that over the past two decades has gone through a serious transformation. The purpose of the paper is to analyze some issues of the performed changes, public experts’ proposals, and after reviewing theoretical and methodological studies of Russian and foreign scientists, some other countries’ experience of higher education reforming, the officially offered variant of modernization to substantiate the hypothesis that the maintenance of multi-level higher education in Russia will require to rethink profoundly the role of baccalaureate, specialty and magistracy in the higher educational process and to set up a unique modern based on the unified competence structure training system of both specialists with higher education and research staff. The paper examines the necessity of taking into account both the character of traditions and obvious advantages of Russian, including Soviet, education, and foreign positive operational experience. During the work the comparative method of historical analysis and system contingency approach were used. It was concluded that setting up an independent system of Russian higher education is primary at present.
PENGEMBANGAN MODUL BERBASIS CONTEXTUAL TEACHING AND LEARNING (CTL) DISERTAI ALAT PERAGA PADA MATERI PERTIDAKSAMAAN LINEAR

The teaching materials used at MTs Muhammadiyah Pubian are only in the form of books and blackboards which make students less understand of the material. The aim of this research is to produce a product in the form of a module based on CTL along with valid and practical teaching aids. This study used the ADDIE model, namely Analysis, Design, Development, Implementation, and Evaluation. The subjects of this study were class VIII students of SMP Negeri 3 Metro. The research instruments used included material validation questionnaires, design and language validation questionnaires, and student response questionnaires. Based on the results of the study it was concluded that the product met the very valid and practical criteria. The average percentage of the validity test is 84% ​​fulfilling very valid criteria. Furthermore, the results of the product practicality test obtained an average percentage of 79% and met the practical criteria. These results indicate that the developed module can be used in learning.
Fake Review Prediction Using Machine Learning

Fake Review prediction is one of the Complex areas to be scrutinized to know the original Commands. In Recent years have seen a change in movie reviews based on the expectations and frame of mind of the audience, which is crucial to a film's success or failure. After reading the review, individuals began to watch the film, which fixed their perception of the plot. The research we conducted to create a machine learning model that can determine if user reviews on the IMBD Movie Dataset are authentic or fake is summarized in this publication. To determine which machine learning categorization approach would produce the best results, we specifically applied and contrasted them. To make it easier to understand why some approaches are preferable to others in specific situations, comprehensive explanations are provided for each of the categorization strategies. The Support Vector Machine (SVM) classifier, which had an accuracy of 89.49%, produced the best results.
Modelling and Evaluating Restricted ESNs

We investigate various methods of combining Echo State Networks (ESNs), including a method that we dub Restricted ESNs. We provide a notation for describing Restricted ESNs, and use it to benchmark a standard ESN against restricted ones. We investigate two methods to keep the weight matrix density consistent when comparing a Restricted ESN to a standard one, which we call “overall consistency” and “patch consistency”. We benchmark restricted ESNs on NARMA10 and the sunspot prediction benchmark, and find that restricted ESNs perform similarly to standard ones. We present some application scenarios in which restricted ESNs may offer advantages over standard ESNs.
Marketing Research for Regional Development

The purpose of the article is to consider marketing methods of analysis for development of one of the regions in Russia for assessment the current state of automotive industry, which is the flagship for the economy of the tested region, and the impact of this assessment on the content for training the specialists and the graduates for the considered industry. The analysis of the organizational and marketing research enabled the authors to make a conclusion about the lack of marketing information about the automotive industry peculiarities. A forecast scenario for the development of the machine-building cluster until 2025, taking into account the specifics of functioning at the present time and the marketing data obtained in the study, is proposed. The obtained results can be used to optimize the production processes at businesses as well as the staff training for the car production and mechanical engineering.
Age and Gender Detection Using Deep Learning

Age and gender detection using deep learning with Convolutional Neural Networks (CNNs) is a popular computer vision task that involves predicting the age and gender of a person from their image. CNNs are powerful deep learning algorithms that are well-suited for image classification tasks. In this task, a CNN model is trained on a large dataset of labeled images, where each image is annotated with the corresponding age and gender of the person. The CNN model learns to extract features from the input image and uses these features to predict the age and gender. Age and gender detection using deep learning with CNNs has various applications in different domains, including surveillance, security, healthcare, and entertainment.
CAG-YOLO: A Lightweight Tennis Detection Method

To realize the high-precision and real-time tennis detection of intelligent tennis recycling robot, a lightweight tennis detection algorithm CAG-YOLO is proposed. Firstly, Coordinate Attention GhostBottleneck(CAG) is proposed and a lightweight backbone network CAG-Backbone is constructed. Bidirectional feature pyramid network is used for feature fusion. SCYLLA-IoU is used to calculate the coordinate regression loss, and an improved post-processing method of Non-Maximum Suppression is proposed to solve the problem of tennis overlap. The experiment on Wtennis dataset shows that the accuracy of CAG-YOLO is improved by 8.6% and the model volume is reduced by 31.7% compared with baseline method. The detection speed is 21ms, outperforming the competitive algorithms. It proves that CAG-YOLO can improve the detection accuracy with small-scale parameters and is easy to be transplanted to mobile intelligent device hardware.
Improved Spherical Evolution Algorithm with Intelligent Tuning of Search Space

The spherical evolution (SE) search algorithm has a more novel search style than the common meta-heuristic algorithm. In contrast to the traditional hypercube search model, SE uses a spherical search approach and achieves very good results. Although the SE algorithm is very effective, it suffers from the problem of sometimes slow convergence due to the large search space and the imbalance between the development of the algorithm and the exploration capability. To address this, we innovatively proposed a strategy to intelligently adjust the search space based on the population structure information and improved the SE algorithm (ISE). Review of the effectiveness of our strategy, we compare ISE with SE algorithm and several other well-known meta-heuristic algorithms. Our problems with 30 different solution spaces on the IEEE CEC2017 benchmark serve as the test set for the experiments. The experimental results show that ISE has a significant advantage over other algorithms.
Natural Language Processing based Recommendation System for Courses *

The goal of Course Recommendation System based on Natural Language Processing, is to suggest courses to users based on their various searches. Users now have an easier time locating the appropriate courses based on their searches thanks to this system. To determine whether a course is available, the system employs the TF-IDF algorithm and cosine similarity. The suggested method made use of natural language processing. The dataset must first be correctly prepared. The first module involves the dataset's preparation. Stopwords are removed, and the number of repeated rows has been reduced sufficiently. The dataset is thus ready. The first two fields of the dataset are combined to form a new field. The CourseTitle field is made by combining the Title and Description fields from the prepared dataset. Utilizing TF -IDF, the new dataset has been vectorized. The cosine similarity of the search to the TF-IDF vector matrix is considered when a user searches for a particular word. The cosine similarity determines the score. Now, the scores are given in decreasing order. Basedon the score values, the users preferred course is recommended. A methodology for recommendation systems is proposed that is more efficient and can overcome the difficulties posed by the cold start problem andthe data sparsity problem.
Detection of Metastasis in Breast Cancer Patients using Deep Neural Networks by Ensemble Stacking

Because of metastasis, which spreads breast cancer from one region of the body to another through the lymphatic system and bloodstream, breast cancer has become a severe issue for women, and early detection of metastasis in breast cancer patients is essential to lowering the death rate of female patients. The challenges faced by transfer learning are Choosing the Right Pre-Trained Model, Overfitting, and bias. Subsequently, utilizing histopathological images, a stacking ensemble approach is used by considering the pre-trained deep convolutional neural networks VGG-19, InceptionResNetV2, and the newly designed architecture called the Baseline model. These models were trained separately on the dataset before being integrated using the stacking ensemble technique. Among the pre-trained models, the baseline model got better accuracy. When these three models are combined using a stacking ensemble the ensembled model obtained better results than individual pre-trained models.
The Optimal Timing of Teaching Korean Learners ‘-는다’

본고의 목적은 대학 부설 한국어교육기관 학습자를 위한 ‘-는다’의 교육 시기를 제안하는 것이다. 교재 5종을 분석하여 ‘-습니다’, ‘-아요’의 교육 시기와 ‘-는다’의 교육 시기 차이가 크고 초급의 읽기 지문이 글의 장르와 상관없이 모두 구어체 종결어미로 작성되었음을 알 수 있었다. 학습자 작문 분석을 통해 고급 학습자의 작문에서도 문어체 종결어미 오류가 나타나는 것을 확인하였다. 이러한 문제점을 파악한 후 ‘-는다’의 제시 시기에 대한 교사들의 의견을 수렴해 보았다. 6명의 교사들은 구어체 종결어미와 비교하여 인식할 수 있도록 ‘-는다’를 현재보다 이른 시기에 교육하는 것이 효과적이라고 하였다. 이를 통해 현재보다 이른 시기에 ‘-는다’를 제시할 것을 제안한다. ‘-습니다’, ‘-아요’, ‘-는다’를 유사한 시기에 학습하면 학습자가 그 차별성을 확실하게 인지할 수 있다. 또한 초급 읽기 지문의 실제성을 확보할 수 있으며 문어체 형태를 갖춘 쓰기를 수행하는 데 보다 효과적일 것이다.The purpose of this study is to propose a new teaching approach for the '-는다' ending in Korean language education institutions affiliated with universities. We analyzed five textbooks to compare the current teaching time of '-는다', '-습니다', '-아요'. Most textbooks present '-는다' at the end of the beginner level, resulting in beginner-level learners being exposed to spoken language ending forms in all texts, regardless of the genre of the text. This error persists in advanced-level texts as well. In addition to identifying these problems, opinions from teachers were gathered on the timing of presenting '-는다'. Six teachers agreed that teaching '-는다' at an earlier stage than the current timing is more effective and suggested modifying the teaching time to present '-는다'. Therefore, this study proposes presenting '-는다' at an earlier stage than the current timing. If there is not much difference in the teaching time of '-습니다', '-아요', '-는다', learners can clearly recognize the differences among the three endings. In addition, securing the realism of reading texts from the beginner level can increase input to written texts, making it more effective for learners to perform writing in the written language.
Poster: Lightweight Features Sharing for Real-Time Object Detection in Cooperative Driving

In model partitioning for real-time object detection, part of the model is deployed on a vehicle, and the remaining layers are processed in the cloud. Model partitioning requires transmitting intermediate features to the cloud, which can be problematic, given that the latency requirements are strict. This paper addresses this issue by demonstrating a lightweight feature-sharing strategy while investigating a trade-off between detection quality and latency. We report details on layer partitioning, such as which layers to split in order to achieve the desired accuracy.
Shortest Path Finding Visualizer

Abstract: Visualization is an effective way to learn concepts faster than traditional methods. Modern technologies make it possible to create e-learning tools, which also greatly contribute to improving the quality of computer science teaching. The goal of this project is to create a Pathfinding Visualizer, a web-based e-learning tool that can be used to visualize the shortest path algorithm. Conceptual applications of the project are illustrated through implementations of algorithms such as Dijkstra's and DFS. This project aims to perform all these tasks with some knowledge of HTML, CSS, JavaScript and React framework. Since the final product is a web application, users can easily see and learn how the algorithm works. Ease of use of the project provides users with a simple instruction manual. The first results using the app promise the benefits of this learning tool for students who have a good understanding of the shortest path algorithm.
Fusion depthwise separable convolution inception lightweight encrypted traffic classification

In this paper, the Inception of lightweight encrypted traffic classification with depthwise separable convolution is proposed. By fusing the depthwise separable convolution on the basis of Inception model, the model reduces the parameters and computation of the model to a large extent without reducing the accuracy. The main motivation of this paper is to reduce the consumption of the network as much as possible, and to develop a model with small computation and high precision. The effectiveness of the proposed method has been verified in the internationally open ISCX VPN-non VPN dataset test experiment. Firstly, the original data were cleaned and converted to transform the encrypted traffic samples into gray maps. Secondly, 1D-CNN and Inception model integrated with depthwise classifiable convolution were used for classification. The experimental results show that the classification accuracy rate, precision rate, recall rate and F1-score are 96.27%, 95.49%, 97.39% and 96.43% respectively for encrypted traffic classified by 12 categories.
Extending Tempcyclegan for Virtual Augmentation of Gastrointestinal Endoscopy Training Simulators

Simulator training is a core part of gastrointestinal (GI) endoscopy training. Based on rubber or silicon dummies, training lacks a realistic visual appearance. Aiming at a hyperrealistic training environment, we propose a CycleGAN-based framework to translate the training videos into realistically appearing GI endoscopy videos. We build on the concept of tempCycleGAN and (i) extend it to a generic framework to simultaneously work on n subsequent video frames in order to increase temporal consistency of generated videos and (ii) formulate a conditional variant of it to selectively incorporate pathologies into the generated videos. Extension (i) will be shown to increase temporal consistency and realism of the generated GI endoscopy videos. Feasibility and potential of (ii) is illustrated with superficial and deep duodenal ulcer as conditional classes.
Construction and Simulation of Defect Detection Model of Automobile Coating Process Based on Improved YOLOv3

In view of the low accuracy of traditional machine vision in automobile coating defect detection, an automobile coating defect detection method based on improved YOLOv3 is proposed. On the traditional YOLOv3 network model, the anchor box computation, loss function optimization and candidate box generation are improved. The simulation results show that the average accuracy of the improved YOLOv3 network in the detection accuracy of automobile coating can reach 88.3%. In terms of the convergence speed, it is stabilized after 4000 iterations. This shows that the improvement of YOLOv3 network is feasible and has certain reference value.
Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over the last few years, almost all existing SGG models follow the same training paradigm: they treat both object and predicate classification in SGG as a single-label classification problem, and the ground-truths are one-hot target labels. However, this prevalent training paradigm has overlooked two characteristics of current SGG datasets: 1) For positive samples, some specific subject-object instances may have multiple reasonable predicates. 2) For negative samples, there are numerous missing annotations. Regardless of the two characteristics, SGG models are easy to be confused and make wrong predictions. To this end, we propose a novel model-agnostic Label Semantic Knowledge Distillation (LS-KD) for unbiased SGG. Specifically, LS-KD dynamically generates a “soft" label for each subject-object instance by fusing a predicted Label Semantic Distribution (LSD) with its original one-hot target label. LSD reflects the correlations between this instance and multiple predicate categories. Meanwhile, we propose two different strategies to predict LSD: iterative self-KD and synchronous self-KD. Extensive ablations and results on three SGG tasks have attested to the superiority and generality of our proposed LS-KD, which can consistently achieve decent trade-off performance between different predicate categories.