Reviewerly/exHarmony huggingface dataset:
A trusted construction mechanism for sever based on trusted hard disk

At present, with the continuous development of hard disk technology and the continuous improvement of the performance of its master chip, the hard disk has more resources to improve security protection. In addition to the security protection of the hard disk itself, it can also provide more trusted security protection for the host, especially the server. The traditional server needs to modify the main board sequential circuit when building the trust chain transmission. Its implementation cost is high and it is difficult to promote. This paper aims at this problem by using the physical characteristics of the secure hard disk, modifying the control program of the secure hard disk to realize the security protection function of the trusted root, and connecting with the server with general PCIe and m.2 interfaces, which can improve the security protection ability of the server, Reducing the implementation cost of trusted security has a good reference value for the application system to implement trusted transformation and data trusted protection.
KLASIFIKASI TINGKAT KESEJAHTERAAN KELUARGA DI KECAMATAN SIDEMEN MENGGUNAKAN BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL

This research was conducted to determine the variables that have a significant impact on the stages of a well-off family in Sidemen Sub-district based on indicators obtained from the BKKBN and to classify the stages of a well-off family. This study used secondary data obtained from the stage of well-being data, Sidemen Sub-district, Karangasem Regency from BKKBN, totaling 1796 families. The method used is ordinal logistic regression and bagging ordinal logistic regression. Based on the logit regression model of ordinal logistic regression and ordinal logistic regression bagging, there are fourteen variables that have a significant effect on the dependent variable, namely marital status, type of insurance, age of head of household, occupation of head of household, having a source of income, eating a variety of food, having savings, accessing information from online media, families have ever recreated together, families have ever participated in social/community activities, the largest type of floor, main source of drinking water, ownership of a house/building, and children are still in school. The classification accuracy level in testing data using the ordinal logistic regression method was 79.4%, while the classification accuracy level using the bagging ordinal logistic regression method with 50,000 replications was 82.78%, so bagging showed an increase in classification by 3.38%.
Kernel-Free Quadratic Surface Regression for Multi-Class Classification

For multi-class classification problems, a new kernel-free nonlinear classifier is presented, called the hard quadratic surface least squares regression (HQSLSR). It combines the benefits of the least squares loss function and quadratic kernel-free trick. The optimization problem of HQSLSR is convex and unconstrained, making it easy to solve. Further, to improve the generalization ability of HQSLSR, a softened version (SQSLSR) is proposed by introducing an ε-dragging technique, which can enlarge the between-class distance. The optimization problem of SQSLSR is solved by designing an alteration iteration algorithm. The convergence, interpretability and computational complexity of our methods are addressed in a theoretical analysis. The visualization results on five artificial datasets demonstrate that the obtained regression function in each category has geometric diversity and the advantage of the ε-dragging technique. Furthermore, experimental results on benchmark datasets show that our methods perform comparably to some state-of-the-art classifiers.
Using natural language processing and machine learning algorithm for book categorization

This research analyzed the relationship between multiple elements of a book's classifica-tion through natural language processing and machine learning. This paper used SVM and KNN to classify books according to the titles and author respectively. Also, books are categorized by summary through Decision Tree, Nave Bayes and BERT. In the end, this work compared effects of these methods. Our findings show that 1) books have different levels of categorical characteristics in various parts of the book 2) through combining the title, author, and summary of the book, more accurate classification results were obtained 3) BERT achieved more accurate recognition compared to a variety of other algorithms used.
APLIKASI PREDIKSI NILAI DAN REKOMENDASI MATAKULIAH MENGGUNAKAN METODE COLLABORATIVE FILTERING DAN ALGORITMA C4.5 PADA PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS X

A common problem with lectures and colleges is that students have difficulty choosing a course due to the large number of choices, or the student credits are limited due to poor course results. We hope that by designing this value prediction application and course recommendations, we will help students get the best course recommendations according to their abilities and grades. The program uses the C45 algorithm to display the selection of courses with the lowest score, and the scores generated by collaborative filtering calculations are advanced by determining the value of similarity between students and predicting grades. The system will identify each course with the highest average score / highest pass rate and it will be used as a student course recommendation. Test results show that the accuracy in determining a student's recommended course using the C45 algorithm is 70%, and the calculation error in determining a student's value prediction is 40-56%.
SISTEM INFORMASI UNIT KEGIATAN MAHASISWA (UKM) UNIVERSITAS TARUMANAGARA BERBASIS WEB

The Information System of activity unit or UKM at Tarumanagara University on the Web-Based is an application designed to make easier for student to get all information about UKM so that more people are know and register their self to UKM’s new members and also help UKM to disseminate information and introduce their activities to all students at Untar. The Website uses the Waterfall System Development Life Cycle (SDLC) development methodology. The database used in this application is MySQL. The programming languages used are HTML, PHP and Boostrap Framework. The results of user testing of the application show that the application can be understood and can be used easily
Midyear Applications, Interviews, and Receptions

After the preparation of key applicationApplications materials has begun and the desired programs are chosen, the focus then shifts towards Midyear applications, interviews, and receptions. This part of the fellowship journey involves a significant amount of coordination and prioritization due to the nature of applying to multiple companies/programs across the country as well as the variability in application processes. This chapter aims to educate readers about the end-to-end interview process, from registration at Midyear and personal placement service (PPS) to on-site interviews.
Persistent Human–Machine Interfaces for Robotic Arm Control Via Gaze and Eye Direction Tracking

Human–Machine Interfaces In article number 2200408, Seunghyeb Ban, Yoon Jae Lee, Woon-Hong Yeo, and colleagues demonstrate a persistent human–machine interface (e.g., playing chess) by controlling a robotic arm via gaze and eye direction tracking.
Research on vulnerability assessment of physical protection system based on combined weighting-two-dimensional cloud model

In response to security threats to physical protection systems (PPS), monitoring their risk profile and assessing their vulnerability is an effective response. Aiming at the ambiguity and randomness in the assessment process, this paper proposes a vulnerability assessment method based on combined weights and cloud models. First, construct the security risk assessment index system, use the optimized mathematical model to find the optimal combination weight of the assessment index, obtain the cloud model digital characteristics of the assessment index and the comprehensive assessment cloud of the physical protection system with the help of the cloud generator, and construct the standard assessment cloud, At the same time, the final evaluation results are obtained by combining the improved cloud similarity calculation method, and finally the validity and feasibility of the model are verified by simulation experiments. The research results show that the model can obtain accurate evaluation results. Compared with methods such as fuzzy comprehensive evaluation, the evaluation method has higher reliability and better evaluation effect.
KLASIFIKASI BUAH SEGAR DAN BUSUK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS ANDROID

Fruit is a food and a good source of vitamins for the body's metabolic processes, but fruit is quickly damaged by the effects of physics, chemistry and microbiology if not given special treatment. Fresh fruit is one of the main needs in the health of the human body because the fruit contains nutrients and vitamins. Therefore, it is proposed to design an application that can classify fresh and rotten fruit. The method that will be used in this design is Convolutional Neural Network (CNN). The architecture that will be used in this design is AlexNet. The fruits that will be classified are apple, banana, grape, guava, jujube, orange, pomegranate, strawberry, mango and tamarillo. The test results on the training data produce an accuracy of 99% and the test on the test data or validation is 98% with the use of the adam optimizer. The confusion matrix shows that the trained model has an accuracy value of 98%, precision of 98%, recall of 98%, and F1-score of 98%. The output of the application is the introduction of fruit names and classification in the form of fresh or rotten.
PENENTUAN REKOMENDASI MATAKULIAH YANG BERPENGARUH DALAM DUNIA KERJA DENGAN C4.5

Education is closely related to the world of work, so we need a system that helps provide an overview of the courses needed by the company. It is hoped that the application of course recommendations that have an effect on the world of work will attract many students who can work in accordance with the desired company and are ready to face competition in the world of work. This application is designed to aim to provide recommendations for courses that are influential in the world of work so that students who are running lectures can use this application to make it easier to get information about the courses needed by the desired company so that students have no difficulty in choosing courses that are influential in the world work. In providing course recommendations, a method is needed, namely using the C4.5 Algorithm to make decisions and this application is made using the Javascript programming language and uses the MongoDB database system. In testing the method, accuracy is needed using a confusion matrix with several test schemes and the highest level of accuracy is using 50% data training and 50% data testing with 80% accuracy. Based on the tests carried out, this application can assist students in making decisions to choose the required courses and see the minimum achievements that must be passed to be able to work on the desired job.
impunity: Enforcing Physical Unit Consistency at Definition Time in Python

We introduce impunity, a Python library that enables static analysis of code annotations to ensure the consistency of physical units. It provides a framework for developers to annotate their Python code with physical units and automatically verifies if the units are compatible and adhere to predefined coherence rules. impunity comes as a decorator to apply on functions: it analyses the source code to check for consistency of physical dimensions, and applies minimal code rewriting if conversions are necessary. Overall, this approach takes the best of type-checking based methods and dynamic methods and provides a robust approach with no overhead at runtime.
ANALISA TOPIK TERHADAP KOMENTAR MENGENAI METAVERSE MENGGUNAKAN METODE CLUSTERING K-MEANS

The development of the internet doesn't stop there, but continues to develop and evolve, even in a game, humans can interact with each other, make transactions with each other and maybe it becomes an opportunity to earn income, one that combines all of these things is known as the Metaverse. Metaverse is a layer that connects two worlds, namely: the real world and the virtual world. Metaverse offers a 3-dimensional experience that can be shared between users and interact within this technology where every activity of its users can be carried out with the help of Augmented and Virtual Reality technology services. In the metaverse, people want to see what topics are contained in the discussion. So a website was created to determine the topic of metaverse comments from social media. The method used on this website is Clustering K-means. Use this method to divide comments into groups that have something in common. The group of comments will be determined by the topic of the highest frequency of words. Evaluation uses the Elbow Method to determine the optimal k value in Clustering K-means.
Pendeteksian Masker dan Klasifikasi Masker Menggunakan Metode Region-Based Neural Network

At this time the world is experiencing a pandemic, the virus is COVID-19 and to prevent a very fast spread, there are many ways to spread the virus, starting from touching, one of which is through saliva when sneezing or talking, therefore all people around the world The world is given rules for washing hands, social distancing and wearing mas.&#x0D; However, it is very unfortunate that there are still many who do not comply with the rules made. Due to this, the mask detection system exists to facilitate community monitoring to be more obedient to the regulations that have been made.&#x0D; In the proposed system the Region-based Convolutional Neural Network (RCNN) is used to classify images which consist of three classes including medical masks, non-medical masks and not using masks. Later the system will detect people in one image. With the Region-based Convolutional Neural Network (RCNN) method, 2 experiments were carried out on 30 epochs with 2 different layers and the first layer got 86% accuracy and 74% accuracy validation and the second layer got 80% accuracy and validation by 79%. With the level of accuracy obtained, it is hoped that it can help the government in slowing down the rate of increase in the number of COVID-19 and also that the community can be more obedient to the rules that have been applied.
Multi-Granularity Interaction for Multi-Person 3D Motion Prediction

Multi-person 3D motion prediction is an emerging task that involves predicting the future 3D motion of multiple individuals based on current observations. In contrast to motion prediction for a single person, this task requires a strong emphasis on learning the interacting dynamics among multiple individuals. Broadly speaking, current methods can be categorized into two groups: The first group involves the straight-forward adaptation of models originally developed for single-person scenarios to multi-person scenarios, which is evidently suboptimal. The second group focuses on utilizing off-the-shelf tools like graph convolutional networks to model interactions. While this approach has shown improved results, the interactions primarily consider entire human identities rather than finer details. This motivates the introduction of our novel solution to address this limitation and enhance the task’s performance. In this work, we strive to craft a novel framework that can effectively address two key issues ignored in previous works, namely the multi-granularity interaction and time-varying inter-person dynamics. In implementation in accord with above aims, the proposed model has mainly comprised two modules: a person-level interaction module and a part-level interaction module. The former is designed to learn the holistic and dynamic interaction among multiple persons in a coarse-grained sense. Critically, we would emphasize that a unique trait of the former module is learning temporal dynamics. For example, it recognizes that two individuals exhibit a strong correlation during handshaking but less correlation after parting ways. The latter part-level interaction module learns the interaction between the body joints of different persons. This module operates at a more fine-grained level, distinguishing it from existing approaches. By aggregating information from both granularities, our model enables accurate motion prediction. To validate the effectiveness of the proposed model, we conducted comprehensive experiments on three benchmark datasets: 3DPW, CMU-Mocap, and MuPoTS-3D. The results of these evaluations unequivocally demonstrate the empirical superiority of our model compared to previous state-of-the-art methods.
UPAYA MENINGKATKAN HASIL BELAJAR IPS DENGAN METODE PREDICTION GUIDE

This study aims to find out how the activity steps in the implementation of the Prediction Guide learning method are able to increase social studies learning outcomes in Class IX-12 SMP Negeri 2 Padangsidimpuan. This research is a descriptive qualitative classroom action research (CAR), which was conducted collaboratively between researchers and teachers involving 27 students. As a collaborative research, the researcher acts as a participant implementing the action, and a fellow teacher is invited as a collaborator who acts as an observer. Data was collected through observation and analysis of learning outcomes, the findings were then analyzed descriptively qualitatively according to the relevant theories. This study further proves that the implementation of the Prediction Guide learning model is able to improve social studies learning outcomes in Class IX-12 at SMP Negeri 2 Padangsidimpuan. This can be seen from the increased ability of students to understand the subject matter, increased participation and learning activities, and increased achievement of test scores. However, the implementation of learning activities with this method requires the maximum ability to guide and direct the teacher. This research still suggests other relevant research to obtain much more satisfactory results.
Privacy-aware crowd counting methods for real-world environment

Crowd counting with CCTV images is a challenging task that has gained attention due to its potential applications in safety management, urban planning, traffic congestion management, and advertising. However, privacy concerns and novelty problems make developing accurate and effective models for open-world scenarios difficult. This paper reviews and evaluates the five best-performing models for indirect and direct methods in privacy-aware crowd counting. They categorize CNN-based crowd counting methods and describe their advantages and disadvantages. The results show that Restormer, followed by CANet, produces the best results in the indirect method, while IIM performs best in the direct method. The paper concludes by discussing the key findings and their implications and highlights the need for further research to develop more effective and efficient models that can handle the unique and challenging aspects of the data and scenarios encountered in crowd counting with CCTV images.
Is Cybersecurity Liability a Liability?

One element of the Biden administration’s cybersecurity policy is liability for security failures. I’ve been saying for many years that this is an idea very much worth serious study—if implemented correctly, the market could work its magic and create incentives for software vendors to clean up their act. That said, there are a number of serious questions that have to be answered first—and the wrong answers could have a disastrous effect, both on innovation and on whether or not liability would actually work.
Disentangled Graph Social Recommendation

Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.
IMAGERY IN JULIANNE MACLEAN’S THE COLOR OF HEAVEN

Imagery is a mental picture imagined by a reader. This research discusses imagery that existed in Julianne MacLean's novel The Color of Heaven. The Color of Heaven is a novel that tells about Sophie Duncan's life and there is a lot of imagery used in it. The objectives of this research are to find imageries in the novel, to identify and classify the types of imagery, and to reveal the purposes of the imagery. The researcher uses Laurence Perrine's theory through Johnson &amp; Arp (2016) about seven types of imagery which are visual imagery, auditory imagery, olfactory imagery, tactile imagery, organic imagery, and kinesthetic imagery. The researcher uses qualitative research design since the analysis explains the types and the purpose of the imagery. The study uses a formalism approach because the analysis is based on the researcher's interpretation. The data are collected straight from the book by reading it several times and taking notes of the sentence that reflects imagery, then categorizing the data into its imagery types in the form of a list. The result of the analysis shows that there are found six types of imagery except one is gustatory imagery. There are 32 visual imagery, 34 auditory imagery, 12 olfactory imagery, 11 tactile imagery, 16 organic imagery, and 20 kinesthetic imagery found in the novel.
PREDICTING INAUTHENTIC ACCOUNTS ON SOCIAL MEDIA USING SVM

Detecting fake accounts on social media platforms is a challenging task due to the evolution of sophisticated creation methods.Existing systems using the Random Forest algorithm have limitations in handling categorical variables and scalability.Overfitting, imbalanced data, and feature correlation also impact performance.This project proposes a solution using the Support Vector Machine (SVM) algorithm for inauthentic user detection.The system preprocesses data, extracts relevant features, and trains the SVM model to classify users as genuine or fake.Techniques for handling imbalanced data are employed, and evaluation metrics assess performance.Efficiency and scalability are optimized for real-time detection on large-scale platforms.The proposed system enhances user safety by effectively identifying and mitigating fake profiles.By addressing the limitations of existing methods, this project contributes to improving the accuracy and reliability of fake account detection systems, creating a safer social media environment.