Reviewerly/exHarmony huggingface dataset:
HTGAT: A Heterogeneous Temporal Graph Attention Network for Scholars’ Impact Prediction

The primary goal of scholarly impact prediction is to forecast the impact of a scholar and their potential scholarly achievements in future research careers, such as academic monitoring, awarding tenure to researchers, and identifying promising candidates for research grants. Thus, scholarly impact prediction plays a crucial role in investigating scientific impact. Current research has predominantly utilized citation metrics-based methods, which do not frequently utilize the extensive data available in the intricate nodes and connections of varied scholarly networks that develop over time. Nevertheless, this study introduces a fresh outlook by conducting research into extracting the implicit semantic and structural data in academic text content, found within the temporal and structural information of collaborative networks, to forecast the scientific impact of academics. In this paper, we present the HTGAT, a heterogeneous temporal graph attention model that merges the graph attention mechanism model with recurrent neural networks. The model's main objective is to enhance the accuracy of temporal predictions by effectively addressing the complex interaction between entities in multi-relational temporal graphs. Utilizing the nodes' representation in diverse academic networks, alongside the temporal information inferred from node relationships, to augment the modeling of scholarly influence, we conduct predictive and quantitative analyses on genuine academic data networks. Empirical results demonstrate that the method improves prediction performance in comparison to both benchmark and citation metrics-based techniques.
Pengembangan Modul Interaktif Berbasis Augmented Reality Pada Materi Bangun Ruang Kelas V Sekolah Dasar

This research aims to develop interactive modules based on augmented reality on grade 5 building materials as a teaching tool in delivering lesson materials and supporting the learning process carried out at SDN 2 Kelayu Selatan in the 2022/2023 school year. This research is a research and development (Research and Development) using the ADDIE development model which consists of 5 steps, namely (1) analysis, (2) design, (3) development, (4) implementation, (5) evaluation. This study was conducted on grade 5 students with a total of 11 students. This research and development instrument uses validation sheets and student response questionnaires. The results of the media expert validation test with a total score of 94 are in the range of X &gt; 79.68 scores in the "very good" category. The results of the material expert validation test with a total score of 92 are in the range of X &gt; 79.68 scores in the "very good" category. The results of the questionnaire of student responses to the validity and effectiveness of using the module developed by comparing "yes" and "no" answers, obtained a "very good" response with a percentage of "yes" answers as much as 96.6% overall.
The core of the potential of Siberian science

This article attempts at a historical and cultural analysis of the two-volume Russian Academy of Sciences. Siberian branch. Personal composition. An opinion is expressed about the relevance and prospects of publishing scientific and reference editions representing full members and corresponding members of the Russian Academy of Sciences. It is shown that the assignment of highest academic titles to Siberian scientists is based on the traditions of the Russian Academy of Sciences, the scientific and organizational principles of the RAS Siberian Branch, the personal qualities of scientific leaders, and the achievements of the research teams headed by them.
Design of an Assisted Learning System Based on ChatGPT

This paper describes a system that utilizes ChatGPT as an auxiliary learning tool to assist students in overcoming learning difficulties. By providing ChatGPT with relevant domain-specific knowledge in advance, the system enhances the accuracy of responses. Additionally, the interface design facilitates students in posing high-quality questions, thereby obtaining precise answers. This paper aims to use this system design to help students address learning challenges, increase motivation for learning, and reduce the cost of developing domain-specific chatbots, thus enabling its application in a broader range of educational fields.
Let's Hide from LLMs: An Adaptive Contextual Privacy Preservation Method for Time Series Data

The emergence of the Internet of Things (IoT) has evolved various application areas, such as healthcare, smart energy management, and autonomous vehicles. These devices continuously transmit time-series data that can be utilized by a variety of applications to provide personalized services. Recently, Large Language Models (LLMs) have been widely adopted in these application areas to input time-series data into prompts for in-context learning and to retrieve relevant responses accordingly. The time-series data contains sensitive information, and its processing can lead to privacy concerns. Several solutions have been proposed in the literature using differential privacy, which protects single data points or batch-wise privacy preservation through manual configuration of the privacy parameter (ɛ). In this paper, we propose an adaptive contextual privacy preservation method that analyzes the data attributes required for specific application services, acting as context. It then identifies sensitive attributes and adaptively selects the value of ɛ for each data attribute to maintain a balance between privacy and service requirements. The proposed approach was evaluated using power consumption and solar power generation datasets. The results show that the proposed approach dynamically selects the privacy parameter for each data attribute. Moreover, the original and anonymized data were fed into the prompt to assess the textual responses generated by LLM. The results show that our proposed approach achieved an average degree of semantic similarity score of 94.5% for power consumption data and 95.23% for solar power generation data.
A Novel Hierarchical Clustering Technique to Analyze Style and Content Factorization During Image Recognition

Advancement in various technologies such as 5G and IoT has led to massive data generation. Data proliferation has brought along enormous privacy threats. Often privacy threats are overlooked by users. In this chapter, we exhibit the hidden privacy threat of user identification from handwriting. We propose a novel unsupervised clustering technique by which the attacker learns to group users according to the writing style. Distinctively, we use the handwritten digits dataset to describe the potential threat. Experimental results on digit recognition are discussed to elaborate on the possibility of exposing hidden information without affecting the actual performance. The motivation for the proposed work is to emphasize on the effects of overlooking privacy. Through our experimental results, we showcase the possible threats underlying in digit recognition task that is a popular machine learning task.
Supplement to the Classical Result of A.N. Tikhonov on Electromagnetic Sensing for a Medium with Thin Layers

Tikhonov’s result uses values of a function on an interval; i.e., restoring the medium requires infinitely many values of the function. In this paper, the question is posed: what information about the environment can be obtained if only several values of this function are known? The answer turned out to be most favorable. If the data array contains k function values, then the environment can be characterized by the same number of parameters.
IDENTIFICATION OF SOURCES OF TERRIGENIC SEDIMENTARY ROCKS BASED ON GEOCHEMICAL DATA USING A LINEAR PROGRAMMING MODEL

The possibility of identifying sources of terrigenous rocks based on geochemical data using a linear programming model is considered on the example of metasedimentary rocks of the Sakukan Formation of the Kemen Group of the Udokan Complex, Aldan Shield. It is shown that tonalite-trondhjemite gneisses (56.9%) and gneiss-granites (17.3%) of the Olekma complex, Paleoproterozoic granitoids of the Kuanda complex (7.6%) of the Chara-Olekma geoblock, metavolcanic rocks of the Fedorov Formation (15.4%) and gneiss-granites developed within it (1.8%) of the West Aldan megablock could be the sources of metasandstones of the Sakukan Formation. The results obtained are in good agreement with the Nd isotopic and geochronological data available for the metasandstones of the Sakukan Formation, which indicates the wide possibilities of identifying and searching for sources of terrigenous deposits using a linear programming model.
Research on Data storage Security based on Blockchain technology and distributed storage technology

With the rapid increase of electricity demand of current users, a large amount of data information will be generated in the process of providing power supply services for users, including user information, transaction information and so on. Faced with a large amount of data information, electric power enterprises must take effective technical measures to manage the data, to ensure the safety of data storage, to avoid information leakage to power enterprises and users bring losses. Therefore, strengthening data storage security is the key work of power enterprises. This paper proposes a distributed off-chain storage framework based on blockchain. Block nodes and storage nodes are deployed in the blockchain, wherein the block nodes are used to implement the underlying blockchain operation mechanism, and the storage nodes are used to store data and files. Off-chain storage is realized by separating the block and storage functions, thus ensuring the operation efficiency of the blockchain. In addition, this paper also proposes a global interactive verification method based on the classical data possession mechanism to ensure the distributed, reliable and provable storage of data files. When users add blocks (storage files) to the blockchain, the audit mechanism of fairness challenge mechanism will be triggered, thus implicitly verifying whether all files stored off-chain are complete.
A Study on the GAN-Stacking Model Framework for Fraud Dataset

Considering the high dimensionality and imbalanced distribution of fraud datasets, this study employs the Spearman's rank correlation coefficient for feature selection and utilizes Generative Adversarial Networks (GANs) to address the data imbalance issue and achieve data augmentation. Through experimental comparisons with four other data processing algorithms, namely BorderlineSMOTE, ENN, SMOTE, and ADASYN, using F1 score and G-mean as comprehensive evaluation metrics, the results demonstrate that GANs exhibit superior performance and stronger stability in handling imbalanced data. Furthermore, a GAN-Stacking model is constructed for classification prediction, achieving an F1 score of 0.936 and a G-mean of 0.941, indicating better performance of the GAN-Stacking model.
Design of translation error correction model based on differential fusion of syntactic features

In order to improve the ability of error correction in English translation, this paper proposed a translation error correction model based on differential fusion of syntactic features. Using the syntactic encoder, the dependency and syntactic features of the text to be translated were obtained, and the syntactic feature vector was got by feature fusion. The difference fusion method was used to fuse the grammatical features and syntactic features in the text, and the collaborative attention mechanism was further used to further improve its error correction ability. Conll-2014 data set was used as the test set of the model. The accuracy of the model in this paper reached 70.4%, the recall rate reached 38.4%, and the f0.5 index reached 60.7%. Compared with the syntax error correction model of the standard copy augmented transformer, the F0.5 index of the model in this paper improved by 5.1%. Compared with other models, this model has obvious advantages and important application value.
BANJARMASIN YOUTH DEVELOPMENT CENTER

Homeless children are unique and creative individuals. Most of the time these homeless children are based in the city center, however, they are often marginalized from the limelight. The homeless children need to be given the opportunity and the choice to improve their potential. The street corners of Banjarmasin are not the ideal home for these children. The Youth Development Center is the place to develop the potential of homeless children through creative fields with a sense of home in the design. It will be the hub for these children to express themselves through their creations and receive the opportunity to learn more without the space restriction. The Youth Development Center can also be a place for society to appreciate the homeless children’s potential. The design strategy uses the Third Place method as a space for interaction and solution of problems using the concept of Flexible Architecture so that space is defined not only by its function but by the potential of the space itself.
AN ANALYSIS DIGITAL READING CHALLENGES AND STRATEGIES

The purpose of this study is to determine what challenges students face when reading digitally and what strategies students use when reading digitally in eighth grade one at SMPN 3 Pattallassang. This was a qualitative descriptive research method. Data for the study was gathered through direct observation and questionnaires administered in schools. The participants in this study were eighth-grade students at SMPN 3 Pattallassang. A questionnaire was used to collect samples. Based on the research, it was found that the challenges and strategies of students in answering the questionnaire were underlining reading that was not understood, lack of words, inadequate quality of internet access, poor learning environment, use of smartphones from both parents, finding out the description of the material to be read, becoming an active thinker, namely finding the intent or composition, trying to write important words or sentences in the reading, writing a summary of what was read, reading first after reading the reading material. digital and five strategies that students lack exemplary in digital reading.
Information and Cyber Security in Digitally Transformed E-Commerce Landscapes in 21st Century: An Overview

The current paper aims at reviewing information security and cybersecurity in digitally transformed e-commerce landscapes. COVID-19 pandemic has led to the disruption of technology and met several businesses unprepared to take off technological transformation consequently, jeopardized many clients into untold hardships and loss. The lockdown period has changed the way people transact their businesses from physical to online mood. More recently, India has been placed on 8th position in terms of e-commerce. This requires protection of network and Apps used in communicating and transacting businesses. Students of commerce and other related programs should be encouraged to take interest in this regard for the continued development and progress of the country in particular and world in general. Governments, organizations, and societies need to security budgetary allocations for tackling the issues of cybersecurity squarely. Several issues have been discussed to justify the claim raised in the paper.
Research on the Application of Engine Mechanism in Secure Communication of Internet of Vehicles

In Internet of Vehicles communication, encryption technology is commonly used to ensure the safety of data transmission. OpenSSL provides encryption libraries, but there are certain risks in practical applications. Therefore, using third-party encryption libraries or encryption machines is the best choice for commercial applications. The Engine mechanism is a way to use a third-party encryption library or encryption machine. This article analyzes the security mechanism of the Internet of Vehicles, introduces the principle and specific usage of the Engine mechanism in detail, and shows an application example in the national security encryption algorithm.
LDA Algorithm Design of Face Recognition Technology Based on “Internet+” in Face Recognition Attendance Management System

In recent years, due to the continuous influence of traditional methods such as counterfeiting, people have increasingly attached importance to the safety and reliability of facial recognition technology. With the advancement of technology, facial recognition technology, iris recognition technology, fingerprint recognition technology, and fingerprint recognition technology have gradually replaced conventional recognition methods such as ID cards and passwords. Through biometric technology, the reliability and security of user identity information can be enhanced. Among them, facial recognition is the fastest developing one. At the same time, the emergence of "Internet+" has spawned many new industries. With the gradual increase of emerging industries, the use of facial recognition algorithms is also increasing. Linear Discriminant Analysis (LDA) is a typical facial feature extraction method. This article mainly focuses on facial recognition technology based on the LDA algorithm, combining facial recognition technology with attendance management, and developing a facial recognition attendance management system on this basis. This article conducts experimental analysis on the recognition rate and time consumption of the LDA algorithm. In the training samples, the LDA recognition rates of Olivetti Research Laboratory (ORL), CMU PIE, and Yale facial datasets were 96.83%, 96.12%, and 93.67%, respectively, with time consumption of 982ms, 975ms, and 431ms. From a series of data, it can be seen that the LDA algorithm has excellent performance in face recognition. It is worth further promoting and applying. The innovation of this article is the use of LDA method, which combines facial recognition technology with attendance management. Based on this, a facial recognition attendance management system has been developed and tested. The conclusion is that facial recognition technology based on LDA algorithm has stable performance, high security, short response time, and short transmission time, and has broad application prospects.
Research on Chinese Patent Text Classification in the Field of New Energy Vehicles Based on the XGBoost Model

To improve the efficiency of automatic patent text classification and further elevate the level of patent management, this paper proposes a Chinese patent text classification model in the field of new energy vehicles based on the XGBoost model. The research employs jieba word segmentation and word vectors generated by word2vec to represent the patent text. It then combines five machine learning algorithm models to train on the patent text corpus, achieving automatic classification of the patent text. Comparative experimental results demonstrate that the XGBoost model outperforms other models in terms of precision, recall, and f1_score, with classification accuracy reaching up to 95% for some categories. The model has guiding significance for automatic patent text classification.
Design and Implementation of a Parallel Algorithm for Solving Linear Equations Using Gaussian Elimination Method

In parallel computing, the potential parallelism in the computing process is utilized to communicate relevant data between different nodes through data communication, which then facilitates the computing resources of each node to accelerate the computing process. Large scale phenomenon equation systems often appear in practical engineering applications. This article analyzes the potential parallelism of Gaussian elimination method in elimination calculation and designs a parallel algorithm for Gaussian elimination method. Finally, practical testing shows that this algorithm has obvious advantages in solving large-scale linear equation systems.
A segmentation method for examination paper questions

This paper proposes a method for segmenting examination paper questions from the perspective of intelligent image processing. In terms of image preprocessing, firstly, the black area in the examination paper is extracted using the OTSU and adaptive threshold segmentation methods, followed by the extraction of edge contours using the sobel operator. To obtain the text and illustrations in the paper more accurately and completely, the contours are further enhanced using morphological operation to determine the areas containing the question items. Regarding the segmentation of the examination paper questions, the question areas are selected based on their aspect ratios, thus excluding the parts of option or illustration. Finally, a complete examination paper questions is determined by analyzing the top, bottom, left and right edge of the two adjacent questions areas, each complete question is segmented and displayed using rectangular bounding boxes.
Agile Design and Implementation of a “Snake Game” Based on Android Platform

The research is based on rapid mobile game development, and taking a popular "snake game" for single player on Android platform as an example. This study is devoted to completing Agile development of the game system with the technology of Android Studio and Scrum model of Agile, and a scientific and feasible design process is described which consists of description of game functions, designing of game scenario, rapid user requirement acquisition, implementation of major visual game elements of "snake", "game wall" and "food". Key classes of "MainActivity" and "Choice" for functions of switching on game UI (User Interface), judgment method of "snake moving", "food eating", "snake body increase" and "snake death" are implemented effectively together in the first game version, which runs stability and positively for players. Application result shows that further work will be done in the future. Firstly, customized controls which are visual and vivid will be created for better presentation of game UI. Secondly, multiplayers networking functions should be added to make the game more exciting.
Study of Cloud Computing Technology for Critical Site Security System

This article firstly analyses the situation and technology trends of security system, then proposes the conception and structure of the Cloud-based Security System for Critical Site (CSSCS), finally provide an outlook of CSSCS building approach, which is top-down mapping and bottom-up founding. CSSCS virtualized multiple sensors, weapons, facilities and equipment, break the fixed relations between each other, combine them with network, and build up flexible information resources network. The information resources network can recompose dynamically according to the battle situation, which can be combined into an operational structure with a subordinate relationship at any time. The aim of CSSCS is to achieve command-flattening, disposal-quickening, evaluation-automation and perception-precision.