Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN DASHBOARD TRIVY BERBASIS WEBSITE MENGGUNAKAN REACT JS DAN GOLANG

Container images can help developers in application development. In creating a container image, it is necessary to perform security checks before it can be used by others. This makes it important for the team to find a solution by detecting the vulnerability of the container image used. Trivy aims to scan container image vulnerabilities and uses the Common Vulnerabilities and Exposures (CVE) database as information from the vulnerabilities found in the container image. This research tries to design a dashboard using React JS and Golang as programming languages to build the frontend and backend sides and use Trivy which is a command line interface-based tool to check the vulnerability of container images. The research was conducted using the Software Development Life Cycle (SDLC) method. This research produces a website-based dashboard that has a user-friendly interface and can be used without any operating system restrictions.
Nonlinear mathematical modeling in the model of village cultural industry

Abstract To solve the problem that the current forecasting methods cannot describe the long-term correlation of traffic, which leads to low prediction accuracy, the author proposes a mathematical modeling, forecasting and analysis method for village nonlinear traffic. The original nonlinear traffic data collected from the village comes from each base station, the information contained is uneven, pre-processing the collected data, eliminating abnormal values and duplicate data, and supplementing the missing data. Nonlinear traffic contains limited information, so a random forest algorithm is used to extract traffic characteristics and reduce data processing dimensions. The nonlinear traffic characteristics of the village are convolved, and the cross entropy function is used as the loss function, the feature vector of the input prediction model is deeply learned, and the communication traffic prediction results are obtained. Taking the traffic data of the communication operation enterprise’s base station as the test data, the experiment results show that, in the test with 1 million pieces of data, the decision coefficient of the mathematical modeling, prediction and analysis method of village nonlinear flow designed by the author is 0.9599, which is 0.1267 and 0.1431 higher than the prediction and analysis method based on genetic algorithm and fuzzy clustering algorithm respectively. In the modeling and prediction of nonlinear flow, the determination coefficient of the method proposed by the author is closer to 1, the fitting degree of this method is better than that of the contrast method, and it is adaptive in the real scene with a large amount of data. It is proved that the mathematical modeling and prediction analysis method designed in this design can reduce NRMSE and MAPE, improve the determination coefficient of prediction results, and provide the basis for village analysis.
Comprehensive Sex Education Model of College Students Based on Personalized Recommendation Algorithm

Abstract This paper introduces the student-centered new intelligent, comprehensive teaching model. This paper uses the accumulated user behavior data on the platform to build a personalized recommendation model. The system provides a personalized learning route for students to choose learning strategies and resources. This method converts the user’s learning process into the user’s evaluation of the system to solve the problem of the scoring matrix. Secondly, it proposes to improve users’ similarity based on their initial marking to overcome the personalized model concept of cold start questioning of new customers effectively. Examples show that the proposed method can improve the efficiency of personalized recommendations.
An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper.
SLED: Structure Learning based Denoising for Recommendation

In recommender systems, click behaviors play a fundamental role in mining users’ interests and training models (clicked items as positive samples). Such signals are implicit feedback and are arguably less representative of users’ inherent interests. Most existing works denoise implicit feedback by introducing external signals, such as gaze, dwell time, and “like” behaviors. However, such explicit feedback is not always routinely available, or might be problematic to collect on a large scale. In this paper, we identify that an interaction’s related structural patterns in its neighborhood graph are potentially correlated with some outcome of implicit feedback (i.e., users’ ratings after consuming items), analogous to findings in other domains such as social networks. Inspired by this finding, we propose a novel Structure LEarning based Denoising (SLED) framework for denoising recommendation without explicit signals, which consists of two phases: center-aware graph structure learning and denoised recommendation . Phase 1 pre-trains a structural encoder in a self-supervised manner and learns to capture an interaction’s related structural patterns in its neighborhood graph. Phase 2 transfers the structure encoder to downstream recommendation datasets, which helps to down-weight the effect of noisy interactions on user interest modeling and loss calculation. We collect a relatively noisy industrial dataset across several days during a period of product promotion festival. Extensive experiments on this dataset and multiple public datasets demonstrate that the proposed SLED framework can significantly improve the recommendation quality over various base recommendation models.
Forensica Application as Learning Media on Forensic

Background: Time of death estimation is crucial to help investigators to solve a case. In this study, Forensica application, version 4, an android-based mobile phone application was developed as a tool to educate and calculate the time of death estimation. This study aimed to compare the level of knowledge between medical students that studied using the application and textbook. Methods: Both groups had the same study duration and answered the same questions. Participants had then filled out a questionnaire about the Forensica. Results: Statistical analysis shows that gender differences do not result in significant differences. The mean of the post-test result shows significant improvement (P=0.00) in both groups when compared to the pre-test result. Conclusion: The result shows that generally, the respondents feel that learning with Forensica was easier, faster, more fun, more practical, and more exciting than learning compared with textbooks. Thus, Forensica application can be a better learning method alternative to textbooks that is easier, faster, more fun, more practical, and more exciting.
Enhancement of Information Carrying and Decoding for Visual Cryptography with Error Correction

Recently, three visual cryptography schemes with t -error-correcting capability (VCSs- t EC) were introduced for preventing the shadows carrying additional information from being corrupted by noise interference. However, the concerns on VCS- t EC, such as the average amount of carrying information, decoding of information from shadows, and the demonstration way of a secret, should be considered and improved. In this article, two schemes, namely the ( k, n ) probabilistic VCS- t EC (PVCS- t EC) and the (2, n ) deterministic VCS- t EC (DVCS- t EC), are proposed. The concept of probabilistic VCS is combined with the Bose-Chaudhuri-Hocquenghem code (BCH code) for designing the ( k, n ) PVCS- t EC. Furthermore, some constant-weight BCH codewords are adopted to build the (2, n )-DVCS- t EC. Comprehensive results and experiments are demonstrated to clarify the enhancement of information-carrying and decoding by the two proposed methods.
LIVENESS FACIAL RECOGNITION SYSTEM FOR EXAMINATION HALL

The conduct of the examination is vital to the survival and reputation of that institution.Students are becoming smarter in the act of examination misconduct, therefore there is a need to curtail the act even before it occurs.Preventing the act can save a lot of resources which can then be channeled in solving other problems.Impersonation is one the disturbing examination misconduct and has proven to be difficult to eradicate.This implies bringing someone in place of the candidate to write the exam on the candidate's behalf.A security system aims to verify an individual's identity and prevent impostors from accessing protected resources.This work provides a solution to prevent the act of impersonation in examination halls.The facial liveness detection system was developed to identify candidates of a particular hall before entrance so that only the right candidates are allowed into the hall.The system also differentiated between real face (a live face) and fake face (picture or id card).When tested with 30 students, it was found to be 100% accurate.
Implementation Of Forward Chaining Expert System To Identify Facial Skin Types In Determining Facial Beauty Treatments

Skin is part of the outer layer of the human body which is flexible, soft and sensitive and is the first line of defense from various germs, viruses, bacteria and what is very important and the main part is facial skin, therefore we must keep it healthy, fresh and clean by determining proper beauty facial treatment. This research was conducted to design and create an expert system capable of identifying facial skin types to determine facial beauty treatments. Making this expert system using forward chaining method. The result of this research is an application that can help make it easier for users to determine facial facial treatments without having to queue and wait for consultations with a beauty doctor, so that they can immediately determine the appropriate type of skin facial treatment through the Android application.
BIM Model Design of Deep Foundation Pit Engineering Based on BP Network

Abstract It is of great significance to predict the multistage deformation of the foundation pit. A new neural network method is proposed in this paper. The disadvantages of the BP neural network multistage forecast method are discussed. A multistage recursive neural network model for foundation deformation prediction is established. They are taking a deep foundation pit project in the soft soil area as an example. The multistage deformation prediction method is verified in this paper. This new detection technique is feasible. This method can also be used for multistage forecasting.
Dance Emotion Characteristic Parameters Based on Deep Learning Model

Abstract In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with important emotional states are extracted from the time, frequency, and time-frequency domains, respectively. It was isolated using a deep belief network enhanced by neuro colloidal chains. Finally, the finite Boltzmann criterion integrates the features of higher abstractions and predicts the emotional states. The results of DEAP data show that the correlation between EEG channels can be discovered and applied by glial chains. The fused deep learning model combines EEG emotional features with temporal, frequency, and expressive qualities.
Improved K-SVD Model in Higher Vocational Physics Teaching

Abstract In physics class, the residual data before and after noise elimination contains more image information. In the equivalent wave domain, the K-SVD method is used to eliminate noise in the noise image of each frequency band and the residual of each frequency band. The main objective is to improve the image quality of noise. The method is combined with the denoised sub-spectrum to obtain the denoised sub-spectrum with the residual signal. An inverse isomorphic wave is used for noise reduction. The simulation results show that the noise reduction algorithm using K-SVD is better than the equivalent waveform and K-SVD mode. This method has achieved good results in practical application.
Characteristic Analysis of Cheerleading Self-selected Action Arrangement Elements Based on BP Neural Network

Abstract BP neural network is a typical algorithm in artificial intelligence networks, and it has strong nonlinear mapping ability, solving some nonlinear problems and pattern recognition is its most prominent part. The network topology of the BP neural network is simple, it has high error precision, it is easy to program, and also has strong operability, it is one of the most important algorithms in the field of data mining. Cheerleading is one of the dance cheerleading events, because of its sunshine, fashion and vitality, it has become a new sports event on campus, although cheerleading has developed rapidly in China, its promotion on campus is still at the initial stage, with the increasing number of public contestants, the competition among the participating teams has become increasingly fierce, excellent choreography is the key to victory, by using the methods of literature, video analysis and mathematical statistics, the author made an in-depth analysis of the top eight teams, including 24 teams, who achieved the best in the collective flower ball optional complete set of actions in the three open national cheerleading championships from 2019 to 2021, grasp the current situation and trend of the arrangement of free flower ball movements in the open children’s group, and summarize its rules, it provides a theoretical basis for the arrangement of the set of collective flower ball optional movements of the open university group, improve the overall level of the flower ball cheerleading program of the open university group, and play a role in consolidating the basic learning of flower ball cheerleading.
PENGARUH KETERSEDIAAN KOLEKSI PERPUSTAKAAN FAKULTAS ILMU SOSIAL DAN POLITIK USU DALAM PEMENUHAN KEBUTUHAN INFORMASI

For library development, one of the most important aspects to consider is the collection of information. The purpose of this study focuses on the effect of the availability of online proquest journals on meeting user information needs in the Library of the Faculty of Social and Political Sciences USU. This type of research is descriptive research using qualitative approach methods. This research data collection technique is through observation, interviews, and documentation. The result of this study is the availability of online ProQuest journals in the Library of the Faculty of Social and Political Sciences, based on good perceptions of library users, and the affirmation of library users that they are looking for reference materials for lecture assignments from online ProQuest journals rather than libraries. It has been used effectively by library patrons as a resource to aid in the creation of coursework. This journal provides a source of current and accurate information that can be used as a reference when completing courses and research. Thus, Proquest Journal has a significant impact on users' information demands. Library of the Faculty of Social and Political Sciences, University of North Sumatra.
PENGEMBANGAN MEDIA MONOPOLI BERBASIS CERITA RAKYAT JAWA UNTUK MENINGKATKAN KETERAMPILAN MEMBACA DAN MENULIS AKSARA JAWA KELAS IV SEKOLAH DASAR

Javanese language as one of the local content will run effectively if supported by innovative learning media and can attract students' attention. Based on the results of pre-research data at SDN 01 Pucakwangi, it has been found that the problem of using learning media has not attracted students' attention and the media seems monotonous only using books and pictures. This problem results in many students not having the skills to read and write Javanese characters so that from student learning outcomes there are still many students who have not reached the KKM. This study aims to develop, test the feasibility, and effectiveness of Javanese folklore-based mnopoli learning media to improve reading and writing skills of Javanese script in Javanese learning content of Javanese script material in class IV SDN 01 Pucakwangi. This study uses the type of research and development with the Borg and Gall development model. The results of this study indicate that Javanese folklore-based monopoly media is very feasible with a percentage value from material experts of 100% and a percentage of media experts of 95%. This learning media is effective in reading and writing Javanese script skills based on the t-test. The t-test conducted on both skills obtained the same results, namely sig. (2-tailed) of 0.000 &lt;0.05. The results of student responses obtained a percentage of 93.5%. Then based on the results of the N-gain test on Javanese reading skills, namely 0.51 and writing skills of 0.54 in the medium category. The conclusion of this study is that Javanese folklore-based monopoly media is feasible and effective to use to improve reading and writing skills of Javanese characters.
PENGEMBANGAN BAHAN AJAR ELEKTRONIK BERBASIS FLIPBOOK UNTUK MENINGKATKAN HASIL BELAJAR IPA SEKOLAH DASAR

The present study investigates the efficacy of flipbook-based electronic teaching materials as an alternative to traditional government-provided materials in elementary schools, which have been found to yield unsatisfactory learning outcomes. The study draws inspiration from the work of Borg and Gall and was conducted at Srondol Wetan 06 Public Elementary School, Semarang City, during the 2022/2023 school year. The study population consisted of 34 students, with a small-scale trial involving eight students in class VA, and a large-scale trial involving 26 students in class VB. Data collection methods included pre-test post-test assessments, observations, questionnaires, interviews, and data documentation. The results indicate high feasibility in terms of media aspect (83) and material criteria (69), with linguistic analysis yielding a feasibility score of 45. Furthermore, the effectiveness analysis showed a moderate N-gain score of 0.658, indicating improved learning outcomes, and student response questionnaires yielded a very positive score of 256. The result of t-test sig 0.000 there is a difference between pretest and posttest. These findings lead to the conclusion that flipbook-based electronic teaching materials are both feasible and effective for enhancing student learning outcomes in class V at SDN Srondol Wetan 06.
PENGEMBANGAN MEDIA PEMBELAJARAN IPA BERBENTUK TEKA-TEKI BERBASIS MACROMEDIA FLASH UNTUK SISWA KELAS V SEKOLAH DASAR

This research aims to produce interactive media with puzzle media in collaboration with Macromedia Flash. This medium can increase students' enthusiasm for learning and help students in understanding the subject matter. Research is used using research and development methods (Research and Development). This research was conducted in the odd semester of the 2022-2023 school year. The subjects of this study were grade V students of SD Negeri Cacaban 6, Magelang Tengah District, Magelang City. The respondents of this study were 6 students of SD Negeri Cacaban 6. Data analysis in this study used validity tests and student response questionnaires. Macromedia Flash-Based Puzzle Media has been validated by 2 validators, namely expert lecturer validators and class V teacher validators. As a result of the validation that has been carried out, the media showed a score of 97.7% with a category very worthy of use. Student responses to this media showed a percentage of 97.5% obtained from the respondents' questionnaire. Thus, the Macromedia Flash-Based Puzzle media is declared suitable for use for the learning process.
Research on Building an ARM-based Container Cloud Platform

ARM chips with low energy consumption and low-cost investment have been rapidly applied to smart office and smart entertainment including cloud mobile phones and cloud games. This paper first summarizes key technologies and development status of the above scenarios including CPU, memory, IO hardware virtualization characteristics, ARM hypervisor and container, GPU virtualization, network virtualization, resource management and remote transmission technologies. Then, in view of the current lack of publicly referenced ARM cloud constructing solutions, this paper proposes and constructs an implementation framework for building an ARM cloud, and successively focuses on the formal definition of virtualization framework, Android container system and resource quota management methods, GPU virtualization based on API remoting and GPU pass-through, and the remote transmission technology. Finally, the experimental results show that the proposed model and corresponding component implementation methods are effective, especially, the pass-through mode for virtualizing GPU resources has higher performance and higher parallelism.
Graph Theoretical Analysis of local ultraluminous infrared galaxies and quasars

We present a methodological framework for studying galaxy evolution by utilizing Graph Theory and network analysis tools. We study the evolutionary processes of local ultraluminous infrared galaxies (ULIRGs) and quasars and the underlying physical processes, such as star formation and active galactic nucleus (AGN) activity, through the application of Graph Theoretical analysis tools. We extract, process and analyse mid-infrared spectra of local (z ¡ 0.4) ULIRGs and quasars between 5-38μm through internally developed Python routines, in order to generate similarity graphs, with the nodes representing ULIRGs being grouped together based on the similarity of their spectra. Additionally, we extract and compare physical features from the mid-IR spectra, such as the polycyclic aromatic hydrocarbons (PAHs) emission and silicate depth absorption features, as indicators of the presence of star-forming regions and obscuring dust, in order to understand the underlying physical mechanisms of each evolutionary stage of ULIRGs. Our analysis identifies five groups of local ULIRGs based on their mid-IR spectra, which is quite consistent with the well established fork classification diagram by providing a higher level classification. We demonstrate how graph clustering algorithms and network analysis tools can be utilized as unsupervised learning techniques for revealing direct or indirect relations between various galaxy properties and evolutionary stages, which provides an alternative methodology to previous works for classification in galaxy evolution. Additionally, our methodology compares the output of several graph clustering algorithms in order to demonstrate the best-performing Graph Theoretical tools for studying galaxy evolution.