Reviewerly/exHarmony huggingface dataset:
Network Security Technology Based on Cloud Computing

With the rapid development of computers and the Internet, cloud computing network technology is widely used in all aspects of the world with its powerful data processing capabilities.While it promotes people's convenient life, it also faces many security issues.Aiming at the network security risks and threats under cloud computing, briefly describe cloud computing technology, analyze the types and characteristics of network security under cloud computing, and clarify the importance of network security under cloud computing.Research and analyze the implementation strategy and development direction of network security technology under cloud computing, in order to promote the development of cloud computing network security technology in the future.
Multiplicative Data Perturbation Using Random Rotation Method

Today’s applications rely on large volumes of personal data being collected and processed regularly. Many unauthorized users try to access this private data. Data perturbation methods are one among many Privacy Preserving Data Mining (PPDM) techniques. They play a key role in perturbing confidential data. The research work focuses on developing an efficient data perturbation method using multivariate dataset which can preserve privacy in a centralized environment and allow publishing data. To carry out the data perturbation on a multivariate dataset, a Multiplicative Data Perturbation (MDP) using Random Rotation method is proposed. The results revealed an efficient multiplicative data perturbation using multivariate datasets which is resilient to attacks or threats and preserves the privacy in centralized environment.
Marker Mapping Techniques for Augmented Reality Visualization

The requirements for tracking in augmented reality environments are stringent because o f the need to register real and computer-generated virtual objects. Driven by the need to track real objects within these environments, we propose two algorithms to distribute markers on complex rigid objects. The proposed algorithms employ an optimization technique with a spherical or cylindrical intermediary surface. The validity and effectiveness of the algorithms are tested heuristically by simulation.
Towards 10-100 Gbps Cryptographic Architectures1

Support for secure transactions over insecure public communication networks, built atop high-speed optical infrastructure, requires cryptographic primitives with 10-1OOGbps throughput. In this paper we present various hardware architectures for high-speed block and stream ciphers and study the associated throughput and area trade-offs. We demonstrate high-speed encryption architectures for Advanced Encryption Standard (AES) based block and stream ciphers and SNOW stream cipher with 4.7Gbp, 4.6Gbps and 212Gbps throughput rates respectively. We then present our ongoing work on high-speed architectures for tree-based stream cipher Leviathan.
Study on Teaching Strategies of Continuation Task

The continuation task is a combination of reading and writing, where students are required to write a composition which is highly consistent with the original text in terms of language, structure, plot and so on.This type of exercise is a comprehensive carrier to test students' ability of comprehension , expression and innovative thinking.Based on the analysis of related theories of the continuation task, this paper takes the English test of the National College Entrance Examination in China in July 2020 as an example to explore how teachers guide students to finish the continuation task in high quality.The purpose of this paper is to provide an effective reference for future English teaching in continuation task.
Construction of training platform for teachers professional ability of normal students based on ThinkPHP

Improve teachers' professional ability training and teaching skills of normal students, cultivate innovative and research-oriented teachers in teaching practice, build a classroom professional ability training platform for normal students based on ThinkPHP kernel, and use CSS3, PHP, HTML5 and other network languages to write front and back office related functional modules, And build the server running environment of nginx and MySQL open source database to realize load balancing and support highly concurrent business systems.The platform makes use of big data analysis technology and rich resources to personalized recommend learning and improve the professional ability of normal students, strengthen the combination of professional knowledge and theory of normal students and teachers, and shorten the adaptation period.
Research on the Situation Teaching Mode of Primary School Physical Education under the Concept of "Consistency of Teaching, Learning and Evaluation"

Under the background of the continuous deepening of curriculum reform, in addition to attaching importance to students' knowledge learning, more and more attention is paid to students' physical and mental health development, which gradually improves the status of primary school physical education in the field of education.It is affirmed that students' subjectivity in the teaching process and student-centered teaching is the core of teaching activities [1] .Therefore, PE teachers are required to optimize teaching in a reasonable way, improve PE teaching efficiency, and promote students' physical and mental quality to be comprehensively improved.The mode of PE situational education focuses on the performance of students in teaching, arouses the enthusiasm of students by setting up scenarios, and makes them actively participate in PE teaching.
Research of a new security mechanism on energy internet

The sustainable exploitation and utilization of new energy is the basis of future development of mankind. As the core infrastructure for the efficient and secure utilization of renewable energy sources, the energy internet is an important strategic resource and an embodiment of the comprehensive national strength. This paper presents a new security mechanism based on Trusted Platform Module (TPM). Fistly, the analysis of the structure of the energy internet is discribed from two aspects of logic structure and network topological structure. Sencondly the existing research methods of energy internet security are divided into three classes: security demand-driven method, security model-driven method and system-theoretic method. The vulnerability of isolated research and lack of integrated solution is found out. Then the new security mechanism is propossed and the security model along with the consuming layer protocol are put forward. Finally the security analysis is processed. The results show that the new security mechanism can satisfy the security requirements of energy internet.
Decoupling Predictions in Distributed Learning for Multi-center Left Atrial MRI Segmentation

Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local centers can vary from each other due to different imaging vendors, and annotation protocols. Such variation degrades the performance of learning-based methods. To mitigate the influence, two groups of methods have been proposed for different aims, i.e., the global methods and the personalized methods. The former are aimed to improve the performance of a single global model for all test data from unseen centers (known as generic data); while the latter target multiple models for each center (denoted as local data). However, little has been researched to achieve both goals simultaneously. In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data. Specifically, our method decouples the predictions for generic data and local data, via distribution-conditioned adaptation matrices. Results on multi-center left atrial (LA) MRI segmentation showed that our method demonstrated superior performance over existing methods on both generic and local data. Our code is available at https://github.com/key1589745/decouple_predict .
The Construction of the Cultivation Mechanism of "Double-Qualified" Teachers of Teacher-oriented English Major under the Cooperation between Secondary Schools and Colleges

In order to meet the needs of the development of the times and effectively improve the teaching effect of English teachers in teacher training colleges it is urgent to cultivate the "double-qualified" teachers.The purpose of this paper is to explore how to better complete the training of "double-qualified" teachers in teacher training colleges and universities from the construction of a "double-qualified" teacher training mechanism.
Graduation planning analysis using WILCOXON rank sum test

In order to discuss the different factors that influence students' graduation planning, this paper use the Wilcoxon rank sum test to analyze the collected data.The rank sum test does not depend on the specific form of the overall distribution, and can be applied without regard to what kind of distribution the subject is ,and whether the distribution is known, thus making it more practical.Therefore, this paper mainly uses R language and SPSS software to program and analyze the data.Further,by comparing the analysis results of different methods, the results show that the most important factor influencing students to choose further education is the desire to have a decent job,Under this results,we provide reference for schools to offer related courses.
Online Teaching Quality Monitoring from the Perspective of Quality Assurance

In the special period of "non-stop teaching and non-stop school", colleges and universities generally adopt the form of online teaching.The great value of online teaching form in dealing with emergencies and the obvious advantages of the application of "Internet plus" technology have been or are becoming a normal form of teaching, forming a good complement with offline teaching (or traditional teaching), forming a new ecological environment of classroom teaching.However, online teaching also puts forward new great challenges to teachers and students, which makes both teaching and learning face all kinds of confusion, especially the effect and quality of this teaching form are widely questioned by the society.This paper analyzes the differences between online teaching and traditional teaching, lists the main challenges faced by online teaching, focuses on the teaching quality, summarizes the key elements affecting the teaching effect, and constructs the online course teaching quality monitoring index system on this basis, in order to provide a useful reference for ensuring the online teaching quality.
A Qualitative Study on the Quality of Life in the Early Adult Period through Art Education as a Lifelong Learning

본 연구에서는 평생학습과 성인 전기, 삶의 질, 성인 미술교육의 개념을 고찰하고 미술교육을 통한 성인 전기의 삶의 질에 어떠한 변화를 가져오는지 논의하였다. 따라서 평생학습으로서 미술교육 프로그램을 통한 성인 전기들이 경험하는 과정을 현상학적으로 살펴보기 위한 질적 사례연구를 진행하였다. 연구의 결과로 평생학습으로서 미술교육은 성인 전기에게 개인의 성장과 긍정적 대인관계 등 심리적 안녕감을 고취시키고, 지속적인 성장 욕구를 통한 삶의 의욕과 회복을 경험하며 궁극적으로 정서적이고 정신적인 측면에서 높은 삶의 질을 느끼는 것을 알 수 있었다. 본 연구를 통해 미술교육은 삶의 질 향상에 영향을 주는 심리적 안녕감의 요소들을 경험하게 하며 성인 전기에게 평생학습으로서 미술교육이 효과적임을 본 연구를 통해 알 수 있었다.In this study, the researcher examined the concepts of lifelong learning, early adulthood, quality of life, and adult art education, and discussed how art education can change the quality of life in early adulthood. Therefore, as a lifelong study, a qualitative case study was conducted to examine the process experienced by the early adulthood through an art education program. According to the research, art education stimulates psychological well-being such as individual growth and positive interpersonal relationships in the early adulthood, experiences motivation and recovery through continuous growth needs, and ultimately feels emotional and high quality of life. Through this study, the researcher learned that art education allows experiencing a sense of psychological well-being that affects the improvement of quality of life, and that art education is effective as a lifelong study in the early adulthood.
Kernel Fuzzy Clustering based on Quasi-Monte Carlo Feature Map with Neighbor Affinity Constraint

In recent years, kernel-based fuzzy clustering has attracted significant attention, primarily benefiting from the outstanding performance of capturing the potential non-linear structure in data clustering. However, many existing kernel clustering methods are not available for large datasets due to computational costs. To overcome this limitation, the low-rank random feature map is utilized to approximate the kernel space. Nevertheless, this kind of feature approximation method ignores the graph structure information hidden in the data and does not take the correlations between data samples in the clustering into account. Thus, we present a new kernel fuzzy clustering based on Quasi-Monte Carlo feature map with neighbor affinity constraint (Na_QMC_KFC). In this scheme, the Quasi-Monte Carlo method is adopted to approximate the Gaussian kernel function so as to reduce the computational costs. Meanwhile, the neighbor affinity constraint is designed to maintain the graph structure information of the data and further facilitate the consistency of the membership degrees and the raw data. What’s more, the Alternating Direction Method of Multipliers method is utilized to optimize the problem with respect to the neighbor affinity lasso. The experiments on several non-linear and real-world datasets exhibits the efficiency of the presented algorithm.
Research on the Application of Cloud Computing in Medical Treatment

Cloud computing is changing most industries in the world, and the medical field, as the foundation of people's livelihood, also benefits from it.Limited by the limitations of medical business model, cloud computing applications are gradually going deep into the medical field through the reform of single sector business.Among them, remote medical treatment based on cloud video conference is highly recognized by the market.In addition, electronic medical records based on cloud computing technology remote escort and other applications are also important directions in the future!Starting with the current situation of cloud computing development in China, this paper analyzes the three advantages of cloud computing in medical clinical application, and finally lists the application of cloud computing in medical treatment.
Research on Image Style Convolution Neural Network Migration Based on Deep Hybrid Generation Model

The main content of image style transfer transforms the image style from one region to another.This task puts forward new needs for the traditional convolutional neural network architecture.Therefore, a deep hybrid generation model is usually used in the study of processing image style transfer.Image style transfer aims to transform the image into a new idea by image generation.This paper proposes an image-style convolution neural network migration model based on the deep mixing generation model based on background.The image quality is improved through image processing.The deep hybrid generation model mainly relies on to combine confrontation network generation and selfencoder.In this paper, unsupervised and supervised image style migrations are designed according to the different basic tasks of image style migration.On this basis, unsupervised image style migration of combative neural networks based on cyclic consistency and supervised image style migration of adversarial networks based on cross-domain self-encoder are proposed.This paper further improves the quality of created images by introducing an unsupervised and supervised image style migration standard dataset.
Research on Community Fire Risk Monitoring Technology Based on GIS

In recent years, with the preliminary construction of a well-off society, a series of fire safety problems in urban communities have gradually attracted wide attention.Community fire accidents have the characteristics of noticeable series effects, numerous combustibles, and too complex electrical lines.Therefore, fire safety departments have also highly valued this problem with the continuous occurrence of accidents.However, the current measures can only start from the prevention, and there are not many countermeasures for emergency rescue and monitoring and early warning.Therefore, based on the above background, this paper summarizes a Community Fire Risk Assessment System after an in-depth study by analyzing the classic community fire accidents in China and abroad in recent years.On this basis, the Community Fire Risk Assessment Model is transformed into a computer programming language, and the risk assessment management of fixed-point positioning and orientation of community fire in cities is realized based on GIS global geographic information technology.On this basis, the Community Fire Emergency Rescue Decision System based on GIS is developed and designed, and the calculation of the alarm path is optimized.
1607P Evaluation of scientific reliability and popularity of YouTube videos on cancer and nutrition

YouTube used by approximately two billion people per month and videos are watched in high numbers in the medical field. There are many different perspectives on nutrition in cancer patients and it is known that patients have a great search for this issue. In this study, we aimed to evaluate the scientific reliability and the relationship between the quality and popularity of the most watched YouTube videos on cancer and nutrition. On March 1, 2022, videos were scanned on the YouTube sharing platform using the keywords "cancer and nutrition", "nutrition in cancer", "cancer food" and "cancer diet". The videos were watched blindly by two specialist oncologists and the video quality was evaluated according to the internationally valid medical video/document evaluation scores; DISCERN score, Modified Discern Score, JAMA Score and Global Quality Score. Forty-six (58%) of the videos were uploaded to the platform by doctors or dietitians. The average duration of the videos was 11.6 minutes, the average number of views was 360.725 and the most watched video was watched 3.495.565 times. The average number of likes was 7785 and the average number of comments was 585. While 29 videos (36%) recommended a uniform diet, 51 videos (64%) had food suggestions that could be added to the diet. The most recommended foods were cruciferous (n=16, 20%) (broccoli and cauliflower) and berrys (n=12%, 15%) (strawberry, blueberry, raspberry). When the video quality was evaluated according to the DISCERN score, only 17 (21%) videos were evaluated as good or very good (Table). There was a strong negative correlation between the DISCERN score and the number of video views and likes (r=-.426, p <0.001 and r= -.226 and p=0.017, respectively). Videos about cancer and nutrition were highly watched, but the overall quality and reliability were low. Although the source of the information presented and its deficiencies and sometimes misleading statements were found, it was determined that the videos with less reliability were watched more. There is a need to attach importance to the education given to patients by physicians and nutritionists specialized in cancer and nutrition and to produce quality content on YouTube or similar platforms.
MAKİNE ÖĞRENME ALGORİTMALARIYLA SAHTEKÂRLIK ALGILAMA: BİR MOBİL ÖDEME SİSTEMİ ÇALIŞMASI

With the developing technology, mobile payment systems have become increasingly popular. In the public transport industry, this system has convenient to the sector in terms of purchasing, using, carrying and storing tickets. One of the greatest challenges encountered in the mobile payment system in this sector is fraud. Fraud reduces customer satisfaction, reduces snow margins and causes severe costs for the company. Therefore, it is very important to detect and prevent fraudsters. This study is based on users using a real mobile ticketing application in USA/Kansas, a customer of Kentkart, which has a smart public transportation system. An automatic and intelligent detection system was developed using a machine learning algorithm to detect whether the users in question are fraudulent or not. For this system, the historical profiles of the variables that represent a user that the risky behavior are created. These profiles are classified using Random Forest, Support Vector Machines, Logistic Regression, K-Nearest Neighbor and Naive Bayes machine learning techniques and results are combined with simple ensemble learning methods. Users classified as frauds are automatically blacklisted in accordance with the company's management policy. Thus, the fraud costs that these users caused the company have been reduced.
Real-Time Information Technology Human Detection Using Cloud Services

The current work proposes a complex solution for real-time human detection based on data received from CCTV cameras. This approach is based on modern deep learning methods and the use of cloud services such as AWS Rekognition, Cisco Meraki, MQTT broker. The solution consists of three parts: processing the video stream, human detection in the frame, mapping human locations and calculating the distance between individuals. The technology offers a semi-automatic method for remote acquisition of video stream data using modern cloud services, video stream framing, object recognition in received frames, separating human figures from other image objects, as well as counting people in the room and alerting the system administrator about exceeding the permissible limits for the presence of people. This technology has been integrated into the gym CRM-system. Testing of the modified CRM-system demonstrated its practical value and expanded capabilities for tracking and occupancy control without human intervention. Introduction of cloud technologies and machine learning in the CRM-system not only simplifies tracking and occupancy monitoring and could reduce unwanted contacts between people during the pandemic.
On a Traveling Salesman Problem with Dynamic Obstacles and Integrated Motion Planning

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated.