Reviewerly/exHarmony huggingface dataset:
STRATEGI MANAJEMEN DALAM MENINGKATKAN PRESTASI BELAJAR SISWA

This study aims to determine management strategies for improving student achievement. The research method used in this study was a descriptive analysis. The research approach used was qualitative. Data collection techniques were carried out the library research method. The results showed that the management strategy to improve student performance is to monitor all routine activities that occur every day. The principal carries out daily leadership activities that are environmentally friendly, familial, and democratic and always prioritizes cooperation rather than selfishness for the benefit of students and all staff. Principals are also involved to assist teachers, staff, and employees in monitoring student success. In addition, as a pioneer in improving student achievement, school leaders become a top priority in the teaching and learning process.
DYNAMICS OF PROFESSIONAL SELF-DETERMINATION OF COLLEGE STUDENTS IMPLEMENTING INCLUSIVE PRACTICE

The relevance of the article is due to the fact that in the modern world there is high competition among specialists in the labor market. In these conditions, proper professional self-determination of young people is important. Schoolchildren have not formed the skill of orientation among the specialties. They lack knowledge about the requirements for a particular profession, often they cannot competently assess their personal qualities and abilities, they are guided by the opinion of their parents or the prestige of their future profession. As a result, they go to study at those universities and technical schools that do not correspond to their orientation and interests. It is only during the course of training that students begin to understand that they do not have the necessary abilities that the profession they receive presents to them. However, both teachers and parents of these students insist on further education and obtaining a diploma, instead of helping them choose another specialty. An educational institution, as a rule, is not interested in students going to another educational institution, as this leads to a decrease in its financial security. Psychology, characterizing a mature personality, notes that one of the main criteria of maturity is professional competence in a particular field, not excluding changes in the trajectory of the choice of specialties throughout life. For this reason, researchers believe that professional activity is not a choice for the rest of life and may vary from the stages of personality formation. The research is devoted to the study of the dynamics of professional self-determination of college students implementing inclusive practice, as well as the study of differences in professional self-determination of conditionally healthy students and students with disabilities.
Digital Twin System for Propulsion Design of UAVs

With the widespread applications of unmanned aerial vehicles (UAVs) in various domains, the need to develop specialized UAVs to perform various operations is increasing, which presents challenges to the design of UAVs. On the other hand, the digital twin has demonstrated its great potential in smart manufacturing and other areas. This paper introduces a digital twin system for the propulsion design of UAVs, developed with Matlab and Unity. Our system is armed with a trajectory planning and visual analysis component to help evaluation of UAV propulsion design effectively so that users can explore how different propulsion configurations can affect the trajectory and vice versa. The performance and effectiveness of the system are verified by testing various aspects such as FPS, calculation analysis, and network transmission performance.
Student's reading interest and satisfaction of reading texts during studying at home

The Covid-19 pandemic brings a new design of teaching and learning process.Teaching and learning process is done at home.Students access their learning material online.Especially, in reading class, students get online reading text to be learned.This study aimed at investigating the level of students reading interest and the correlation with the reading satisfaction during in online learning.There were 100 senior high school students became the subject of the present study.The data were obtained through conducting survey technique using close ended questionnaire.The result of the study showed that the interest level of most students was high with 3.60 mean score and 1.21439 (St).In addition, the score of Sig.(2-tailed) was 0.037 in which 0.037 < 0.050 means that there is a significant correlation between reading interest and reading satisfaction.Thus, it can be said that reading interest influence reading satisfaction.All the educational stakeholders are expected to build a reading habit and facilitate learning source to the students.
Intensification Dimension of Prices Cryptocurrency with Hamiltonian Quantum Mechanics

Aims: Cryptocurrency (CC) is a digital currency innovation that has impacted the financial sector's distribution mechanism since December 2013. This research aims to discover a non-linear mathematical Quantum relationship between the escalation of the development of cryptocurrency price fluctuations and prices in CC predictors.&#x0D; Study Design: The research uses simulation techniques to operate numerical models that correspond to the process of dynamic observation behavior.&#x0D; Place and Duration of Study: Types of crypto chosen based on this research data are Bitcoin (BTC), Ethereum (ETH), USD Coin (USDC), and Binance (BNB). The data collection period coverage is required with a number per week from 2019 to 2021 or 52 weeks.&#x0D; Methodology: From the collection of crypto prices that have been selected, there are 157 data samples taken using a systematic sampling strategy with elements that are randomly selected and then followed by the next element from the column on the table after the first choice. The conceptual form of the research background is modeled by a multiple regression term equation which predicts a continuous variable unit as a non-linear mathematical function.&#x0D; Results: The results of the research study found that the most prominent altcoins traded in the market are not affected by the price of Bitcoin securities. Percentage-wise, there were 40.33% of factors that influenced the movement of crypto coin price progress, with 59.67% being the remaining limiting factors in the study. Partial results show Bitcoin and altcoin assets have arbitrage potential with significant positives on put option volatility with asset discounting producing negative results and martingale strategies arising from a Hamiltonian perspective.&#x0D; Conclusion: By time limitations, BTC and USDC have the most potential in martingale conditions, while the cryptocurrency ETH might be a solution in picking assets with growing values at medium risk. Meanwhile, crypto BNB is an asset that offers new data on many market indices.
What Next for Affective Computing?

If affective computing is to reach its full potential, it must be able to address a long list of ethical and legal hurdles.
Testing Convex Truncation

We study the basic statistical problem of testing whether normally distributed n-dimensional data has been truncated, i.e. altered by only retaining points that lie in some unknown truncation set S ⊆ ℝn. As our main algorithmic results1. We give a computationally efficient O(n)-sample algorithm that can distinguish the standard normal distribution N(0,In) from N(0,In) conditioned on an unknown and arbitrary convex set S.2. We give a different computationally efficient O(n)-sample algorithm that can distinguish N(0,In) from N(0,In) conditioned on an unknown and arbitrary mixture of symmetric convex sets.These results stand in sharp contrast with known results for learning or testing convex bodies with respect to the normal distribution or learning convex-truncated normal distributions, where state-of-the-art algorithms require essentially samples. An easy argument shows that no finite number of samples suffices to distinguish N(0,In) from an unknown and arbitrary mixture of general (not necessarily symmetric) convex sets, so no common generalization of results (1) and (2) above is possible.We also prove lower bounds on the sample complexity of distinguishing algorithms (computationally efficient or otherwise) for various classes of convex truncations; in some cases these lower bounds match our algorithms up to logarithmic or even constant factors.
The ℓ<sub><i>p</i></sub>-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines

In the ℓp-subspace sketch problem, we are given an n × d matrix A with n > d, and asked to build a small memory data structure Q(A,ε) so that, for any query vector x ∈ ℝd, we can output a number in given only Q(A,ε). This problem is known to require bits of memory for d = Ω(log (1/ε)). However, for d = o(log(l/ε)), no data structure lower bounds were known. Small constant values of d are particularly important for estimating point queries for support vector machines (SVMs) in a stream (Andoni et al. 2020), where only tight bounds for d = 1 were known.We resolve the memory required to solve the ℓp-subspace sketch problem for any constant d and integer p, showing that it is bits and words, where the Õ(·) notation hides poly(log(1/ε)) factors. This shows that one can beat the Ω(ε-2) lower bound, which holds for d = Ω(log(1/ε)), for any constant d. Further, we show how to implement the upper bound in a single pass stream, with an additional multiplicative poly(log log n) factor and an additive poly(log n) cost in the memory. Our bounds extend to loss functions other than the ℓp-norm, and notably they apply to point queries for SVMs with additive error, where we show an optimal bound of for every constant d. This is a near-quadratic improvement over the lower bound of Andoni et al. Further, previous upper bounds for SVM point query were noticeably lacking: for d =1 the bound was Õ(e-1/2) and for d = 2 the bound was Õ(ε-4//5), but all existing techniques failed to give any upper bound better than Õ(ε-2) for any other value of d. Our techniques, which rely on a novel connection to low dimensional techniques from geometric functional analysis, completely close this gap.
RIPOSTE: A Collaborative Cyber Attack Response Framework for Automotive Systems

The automotive domain has got its own share of advancements in information and communication technology, providing more services and leading to more connectivity. However, more connectivity and openness raise cyber security and safety concerns. Indeed, services that depend on online connectivity can serve as entry points for attacks on different assets of the vehicle. This study explores collaborative ways of selecting response techniques to counter real-time cyber attacks on automotive systems. The aim is to mitigate the attacks more quickly than a single vehicle would be able to do, and increase the survivability chances of the collaborating vehicles. To achieve that, the design science research methodology is employed. As a result, we present RIPOSTE, a framework for collaborative real-time evaluation and selection of suitable response techniques when an attack is in progress. We evaluate the framework from a safety perspective by conducting a qualitative study involving domain experts. The proposed framework is deemed slightly unsafe, and insights into how to improve the overall safety of the framework are provided.
Identification Of Pedestrian Movement And Classification Using Deep Learning For Advanced Driver Assistance System

Prediction of pedestrian behavior is the most challenging task in today's era. Especially, in India, when a pedestrian is crossing the road it is very important to avoid accidents and slow down the traffic unnecessarily at the same time. The direction of movement of a pedestrian finds its application in autonomous driver assistance systems, security surveillance, medical applications to detect human posture abnormality, pedestrian analysis, biometric systems, robotics, etc. In order to improve the safety of the pedestrians and to enable the cars to drive independently or assist the driver in real life situations, people flow analysis along roadside, at road crossings and junctions is of utmost importance. There have been many proposed algorithms to detect a pedestrian and identify its movement but automation industries still are in search of a robust, efficient, simple and more accurate systems to achieve the desired goal. In recent years, Convolutional Neural Networks (CNN) a technique in deep learning, which is a subset of machine learning, has proved its stance in the field of artificial intelligence. CNN has successfully shown better accuracy and reliability performance than any other techniques used for similar tasks in computer vision. The paper present a system that recognizes a pedestrian moving in a particular direction using Convolutional Neural Networks. By thoroughly studying, understanding and augmenting stage-wise analysis method this paper proposes an innovative thought process that helps achieve better precision as compared to legacy techniques that involve more computational time. In this paper, CNN is used to train and classify binary as well as categorical pedestrian data. Pedestrian walking from left to right and vice versa are the two categories that are considered for binary classification. The accuracy observed for binary classification was 98.10%. This binary class became multiclass by adding two more categories of pedestrian carrying a belonging and walking from left to right and viceversa. The accuracy observed for these four categories was 87.07% for four class data.
Deep Neural Network based Sign Language Detection

Deaf and dumb persons who are physically impaired use sign language to communicate. The main obstacles that have prevented much ASL study have been incorporated characteristics and local dialect variance in this work sets. To communicate with them, sign language should be learned. Peer groups are typically where learning happens. There aren't many study resources accessible for learning signs. The process of learning sign language is therefore a very challenging undertaking. Finger spelling is the first stage of sign learning, and it is also used when the signer is unfamiliar of the equivalent sign or when there isn't one. The majority of the currently available sign language learning systems rely on expensive external sensors. By gathering a dataset and using various feature extraction approaches to extract relevant data, this research discipline has been further advanced. The data is then entered into various supervised learning algorithms. The reason why the proposed results differ from existing research work is that in the developed fourfold cross validation, the validation set corresponds to the images of a person, which are different from the people present in the training set. Currently, the fourfold cross validated results are provided for various techniques.
AI in Entertainment – Movie Recommendation using cosine similarity

This age of information brings in a huge amount of data and combined with the number of options available on the internet, it becomes a huge problem in choosing a single thing. This is an example of the saying finding a needle in the haystack. The solution for that is using a magnet to attract the needle, the magnet in this case is a good recommendation system. A recommendation system is a tool that can help a user find the best option for the wide variety of options available. Nowadays every application we use is controlled by a recommendation system. Many platforms like Amazon Prime, Voot, ZEE5, which suggest movies, Zomato which suggests food, Spotify, Wynk, Hungama Music which suggests music, Airbnb, Trivago which suggests hotels and policy bazaar which recommends policy. In this project, we used Cosine similarity for a movie recommendation. Cosine similarity is a distance calculation metric that will tell us the distance between two items if the cosine of the angle between them is small it means that the items are close to each other and vice versa.
A Comparative Analysis of Consensus Algorithms for Decentralized Storage Systems

Blockchain has emerged as a revolutionary technology for various industrial applications of the next century. One of them is a decentralized storage system enabled by the corporation of Blockchain technology. In this context, Blockchain provides innovation to address traditional storage challenges by securely transmitting files through a fully decentralized network and eliminating the data failure risks that arise from a centralized structure. The decentralized storage system allows people to rent out unused hardware storage space and must prove that they store unaltered files. This study gives general readers an overview of decentralized storage technologies in various aspects, including background and technical details. Then, we explore the different consensus protocols used in the decentralized storage system. Finally, we discuss the various aspects of the Blockchain-based decentralized storage system.
Tiny Quantum Chip Developed By Researchers

Researchers Develop A ‘Tiny’ Quantum Communication Chip.
Creation of Interactive Models of Historical and Cultural Heritage Objects and Their Use in Pedagogical Activities

The issues of teaching university students to create interactive objects of cultural heritage and assemble virtual museum expositions on their basis using the example of virtual museums "Soldiers of Victory", "Heritage of the Tambov Land", "Desktop Museum of the History of the Cossacks: Augmented Reality Technologies", as well as their application in educational activities, are considered in the article. Work on these virtual museums is carried out by students and teachers of K.G. Razumovsky Moscow State University of Technologies and Management with the support of the Moscow Department of Education and Science, the Federal Agency for Youth Affairs and the Presidential Grants Fund. The principles of creating interactive objects, technologies for creating virtual museums, as well as the results of studying various types of content for the target audience of projects are described.
Scholarship Recipients Prediction Model using k-Nearest Neighbor Algorithm and Synthetic Minority Over-sampling Technique

This research aims to develop a prediction model for scholarship recipients using the k-Nearest Neighbor (KNN) and Synthetic Minority Over Sampling Technique (SMOTE) algorithms. The method in model development uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which consists of phases of business understanding, data understanding, data processing, modeling, evaluation, and deployment. The dataset used is the Indonesian Smart College Card (KIP-K) Scholarship data in the real world obtained at one of the universities in Indonesia. The evaluation of the model uses a confusion matrix to calculate the values of accuracy, recall, precision, F1-Score, and AUC. The research results show seven attributes that influence the selection process for prospective scholarship recipients, namely non-academic achievement scores, selection test scores, survey scores, interview scores, economic condition scores, academic achievement, and choice of study programs. While the results of model testing produce the best model performance at a value of k=3 with 90.12% accuracy, 85.57% precision, 96.51% recall, 90.71% F1-score, and 0.957 AUC, which are included in the Excellent Classification category. Based on the test results, the model can be applied to a prediction system for prospective scholarship recipients at higher education institutions to make it more targeted.
Investigation of Cross-Social Network User Identification

The development and popularization of Internet technology has stimulated the growth of users' network demands. A large number of users will choose many different social networks to provide users with rich content and services. Cross-social network user identification can help improve user information, provide personalized service recommendations and data mining. This article firstly introduces the cross-social network user identification technology that can identify accounts belonging to the same user on different networks through user attributes, user posted content, user behavior, and network topology relationship models. Secondly, it introduces similarity calculation method of user identification technology, various algorithm performance indicators, and some recent datasets. Finally, the article points out the future research directions of cross-social network user identification technology, which should focus on the weight distribution of user attribute information, multi-dimensional data identification, and large-scale user identification.
Texture feature dimensionality reduction-based mammography classification using Random Forest

Breast cancer is the most frequent cancer and the leading cause of death among females. Diagnosis mass from mammogram correctly can reduce the unnecessary biopsy to a large extent. In this paper, we present a novel mammogram classification method combining the Random Forest and the Locally Linear Embedding (LLE) dimensionality reduction algorithm for texture features. The proposed method consists of three stages. In the first stage, preprocessing is performed to enhance the contrast and suppress the noise of the ROI images. Then, the sixteen-dimensional texture features are extracted from Grey Level Co-occurrence Matrix (GLCM) as the input dataset of LLE and being mapped into a five-dimensional subspace. Finally, a Random Forest classifier is investigated for the mammogram classification and compared with the other four classifiers (SVM, KNN, Logistic Regression, MLPC). The experimental results show that the Random Forest classifier outperforms than the others, with an average accuracy of 92.87% and the AUC value of 0.99, that indicates that the combination of LLE algorithm and Random Forest classifier is a promising method for the mammogram classification.
UP-SSO: Enhancing the User Privacy of SSO by Integrating PPID and SGX

Single sign-on (SSO) services are widely deployed on the Internet as the identity management and authentication infrastructure. In an SSO system, after authenticated by the identity providers (IdPs), a user is allowed to log into relying parties (RPs) by submitting an identity proof. However, SSO introduces the potential leakage of user privacy, which is indicated by NIST. That is (a) a curious IdP could track a user's all visits to any RPs and (b) collusive RPs could link the user's identities across different RPs, to learn the user's activity profile. NIST suggests that the Pairwise Pseudonymous Identifier (PPID) should be adopted to prevent collusive RPs from linking the same user, as PPID mechanism enables an IdP to provide a user with multiple individual IDs for different RPs. However, PPID mechanism cannot protect users from IdP's tracking, as it still exposes RP identity to IdP. In this paper, we propose an SSO system, named UP-SSO, providing the enhanced PPID mechanism to protect a user's profile of RP visits from both the curious IdP and the collusive RPs by integrating PPID and SGX. It separates an IdP service into two parts, the server-side service and user-side service. The generation of PPID is shifted from IdP server to user client, so that IdP server no longer needs to learn RP ID. The integrity of user client can be verified by IdP through remote attestation. The detailed design of UP-SSO is described in this paper, and the systemic analysis is provided to guarantee its security. We implemented the prototype system of UP-SSO, and the evaluation of the prototype system shows the overhead is modest.