Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Secure Data Sharing Approach with Proxy Re-Encryption

In today's digital economy, data sharing has become imperative for advancing economic growth and technology development, as multi-party collaboration requires secure data exchange. During data sharing, upholding data security and privacy is paramount. However, existing data sharing solutions mostly rely on cloud storage and transfer, which have risks of single-point failure and compromise privacy and security. To tackle these challenges, we propose a novel solution that leverages proxy re-encryption and digital watermarking technologies, and employs blockchain to achieve equivalent access control functions based on the cloud environment, thereby mitigating the single point of failure issue. Moreover, our solution can effectively prevent data requesters from illegally sharing data with others. If any requester leaks data without authorization, we can trace the source based on the watermark embedded in the leaked data. In summary, our proposed solution enables more secure and privacy-preserving data sharing, which can potentially benefit a wide range of data sharing applications.
Hierarchical Abstract Syntax Tree Representation Learning Based on Graph Coarsening for Program Classification

Abstract Syntax Tree (AST) is a graph structure obtained from code syntax analysis whose representation has recently played an increasingly important role in program classification. In our study, we innovatively propose that a better AST representation should be hierarchical, combined with both low-and-high level features of the program in AST. Low-level program features in AST are mainly affected by code and writing style. Higher-level program features, such as the structure and logic of the program, are constituted by code blocks as program codes in the same code block share similar program logic features. However, it is difficult to cluster codes into code blocks by general code analysis. To efficiently extract hierarchical AST representations, we apply a graph coarsening (pooling) module to AST for the first time, which can coarsen and cluster the original AST nodes into several node clusters based on node features and structures. After graph coarsening, these node clusters are considered code blocks, and the whole coarsened AST graph representation will represent higher-level program features. Our experimental results show that hierarchical AST representations yield an improvement of about 2% −3% and 12%-17% accuracy of cross-language program classification on Dataset JC and Dataset Leetcode, respectively, compared to the AST representations only based on low-level program features.
A1BERT: A Language-Agnostic Graph Neural Network Model for Vulnerability Detection

In today's IT landscape, software systems are indispensable, yet vulnerable to cyberattacks. Traditional manual vulnerability detection methods are inadequate due to resource limitations. Consequently, making automating vulnerability detection in source code to be a crucial task. This paper introduces AlBERT, a practical, language-agnostic Graph Neural Network(GNN) model. AlBERT transforms source code into a graph-based format, enabling analysis of code structure and semantics. Residual connections effectively propagate information and condense it into a fixed-size vector for vulnerability prediction. AlBERT achieves promising results: 64% accuracy on the benchmark dataset, 79% on ReVeal, and 94% on MSR datasets. This paper also compares AlBERT to contemporary baseline models. AlBERT serves as a foundation for automated vulnerability detection.
Analysis and Research of Neural Network Adversarial Samples for Power Grid Security

In the electric power information system security attack and defense in the industry, the use of big data adversarial samples to attack the neural network model can enhance the authenticity of the adversarial samples. This paper strengthens the research on the authenticity of the samples by analyzing and studying the generation of neural network adversarial samples and simulated at-tack tests. Through a variety of experimental methods, for the improvement of complex neural network adversarial samples, the generation method of semantic adversarial samples is improved and perfected, and adversarial disturbances are added. The experimental results show that after ComDefend and feature compression defense processing, the authenticity is improved by at least 30% compared with the traditional method.
Research on the Prospect of Knowledge Graph Completion Based on the Federated Setting

Knowledge graph (KG) is a semantic network with graph structure. Knowledge graphs are widely used in industry due to their ease of understanding and coding. Unfortunately, triples in knowledge graphs are usually missing and struggle to cope with complex tasks in existing scenarios. Multi-source knowledge graph is the knowledge graph from different devices, which contribute to the Knowledge Graph Completion (KGC). However, due to the privacy security of graph data from different devices, there are often security risks in sharing real data. Therefore, Knowledge Graph Embedding (KGE) based on federated setting is proposed to solve this problem successfully. In this paper, we conduct extensive experiments based on the federated embedding learning framework FedE, and show the impact of the number of clients and the training data on the accuracy of the graph embedding model, so as to analyze the possibility of using federated Settings in KGC in the future.
Cross-System Data Integration Based on Rule-Based NLP and Node2Vec

In the era of Big data, a growing number of enterprises are paying attention to data governance and data services. Large enterprises (e.g., state grid corporations), have numerous information systems, which have caused issues such as inconsistent data standards, and different data storage methods. This makes it very difficult to share data information among different enterprises and departments. In this situation, enterprises have begin to use various methods to integrate data across different systems. In this study, we select the power industry and design a cross-system data fusion framework. Specifically, there are many types of power grid equipment, and the topology data, equipment data, and operation data associated with them are stored in different ways. For cross-system data, we first use equipment name information and design a ruled-based Natural Language Processing (NLP) technique to match the same data between different systems. However, the equipment names in different systems do not follow a strict and unified naming standard, which leads to a set of equipment nodes that cannot be accurately matched. To solve this issue, we use network structure information about equipment and design a node representation technique based on Node2vec. Based on the node representation, we further optimize the results of equipment matching. For evaluation, we use a cross-system dataset from a state grid corporation in China. Through experiments, we show that the proposed framework performs very well in cross-system data integration.
SCOBERT: A Pre-Trained BERT for Smart Contract Vulnerability Detection

The introduction of smart contracts has brought new opportunities to the development of blockchain. However, due to the potential security vulnerabilities in smart contracts, accurately identifying these vulnerabilities has become one of the key challenges. Existing detection methods mostly focus on open-source smart contracts and suffer from issues such as low data utilization, insufficient contract information extraction, and high false positive rates. Since the proportion of open-source contracts is extremely low, accurately extracting feature information from non-open-source contracts and identifying vulnerabilities has become an urgent problem to be solved. Therefore, we propose SCOBERT, a pre-training model for blockchain smart contracts. SCOBERT supports downstream tasks related to smart contracts, such as smart contract functionality identification and smart contract vulnerability detection. We develop SCOBERT using Transformer neural architecture and train it through pre-training tasks like MLM(Masked Language Model) and NSP(Next Sentence Prediction). This allows us to make full use of unlabeled smart contract data and capture the semantic information of smart contract opcode sequences effectively. We conduct experiments on three types of contract vulnerabilities: integer overflow, multiplication after division, and reentrancy, and validate them on the validation set. The experimental results demonstrate that the smart contract vulnerability detection method based on the SCOBERT pre-training model performs excellently in terms of accuracy, precision, recall, and F1 score.
Blockchain Storage Method Based on Erasure Code

Blockchain, as an emerging distributed database, effectively addresses the issue of centralized storage in IoT data, where storage capacity cannot match the explosive growth in devices and data scale, as well as the contradictions arising from centralized data management concerning data privacy and security concerns. To alleviate the problem of excessive pressure on single-point storage and ensure data security, a blockchain data storage method based on erasure codes is proposed. This method involves constructing mathematical functions that describe the data to split the original block data into multiple fragments and add redundant slices. These fragments are then encoded and stored in different locations using a circular hash space with the addition of virtual nodes to ensure load balancing among nodes and reduce situations where a single node stores too many encoded data blocks, effectively enhancing the storage space utilization efficiency of the distributed storage database. The blockchain storage method stores encoded data digest information such as storage location, creation time, and hashes, allowing for the tracing of the origin of encoded data blocks. In case of accidental loss or malicious tampering, this enables effective recovery and ensures the integrity and availability of data in the network. Experimental results indicate that compared to traditional blockchain approaches, this method effectively reduces the storage pressure on nodes and exhibits a certain degree of disaster recovery capability.
The Art of Learning

The article examines the pedagogical situation, the main content of which is communication with students who have a weak motivation to study. The problem is caused by the discrepancy between natural giftedness and the amount of work invested, which is appropriate for the development of a young musician's creative potential. The article considers elements of psychotechnology that contribute to the growth of motivation for professionally oriented educational activities. Its effective character is proved, leading to self-actualization and overcoming doubts, which is due to many factors: the achievement of like-mindedness between the student and the mentor, the establishing of new meanings of life, the determination of the value of the art of learning in building an image of the desired future, understanding of one’s way of thinking and its possible transformation. The educational significance of the association “Our Creative Workshop” and the participation of students in musical and educational concert projects is determined.
A Simple Pipeline for Orthogonal Graph Drawing

Orthogonal graph drawing has many applications, e.g., for laying out UML diagrams or cableplans. In this paper, we present a new pipeline that draws multigraphs orthogonally, using few bends, few crossings, and small area. Our pipeline computes an initial graph layout, then removes overlaps between the rectangular nodes, routes the edges, orders the edges, and nudges them, that is, moves edge segments in order to balance the inter-edge distances. Our pipeline is flexible and integrates well with existing approaches. Our main contribution is (i) an effective edge-nudging algorithm that is based on linear programming, (ii) a selection of simple algorithms that together produce competitive results, and (iii) an extensive experimental comparison of our pipeline with existing approaches using standard benchmark sets and metrics.
Computing Hive Plots: A Combinatorial Framework

Hive plots are a graph visualization style placing vertices on a set of radial axes emanating from a common center and drawing edges as smooth curves connecting their respective endpoints. In previous work on hive plots, assignment to an axis and vertex positions on each axis were determined based on selected vertex attributes and the order of axes was prespecified. Here, we present a new framework focusing on combinatorial aspects of these drawings to extend the original hive plot idea and optimize visual properties such as the total edge length and the number of edge crossings in the resulting hive plots. Our framework comprises three steps: (1) partition the vertices into multiple groups, each corresponding to an axis of the hive plot; (2) optimize the cyclic axis order to bring more strongly connected groups near each other; (3) optimize the vertex ordering on each axis to minimize edge crossings. Each of the three steps is related to a well-studied, but NP-complete computational problem. We combine and adapt suitable algorithmic approaches, implement them as an instantiation of our framework and show in a case study how it can be applied in a practical setting. Furthermore, we conduct computational experiments to gain further insights regarding algorithmic choices of the framework. The code of the implementation and a prototype web application can be found on OSF .
Removing Popular Faces in Curve Arrangements

A face in a curve arrangement is called popular if it is bounded by the same curve multiple times. Motivated by the automatic generation of curved nonogram puzzles, we investigate possibilities to eliminate the popular faces in an arrangement by inserting a single additional curve. This turns out to be $$\textsf{NP}$$ -hard; however, it becomes tractable when the number of popular faces is small: We present a probabilistic $$\textsf{FPT}$$ -approach in the number of popular faces.
A Novel Miner Action Recognition Model Based on Contrastive Learning

To address the problems of high training resource consumption and lack of generality in current miner action recognition methods, in this paper, we design a new miner action recognition model based on the multimodal learning framework of contrast learning, which more effectively utilizes the semantic information in the labels and improves the scalability of the model. Then, we use a fixed pre-trained model as the backbone in the video encoder, which greatly reduces the parameters that need to be trained and the resources consumed in the training. At the same time, we design an auxiliary branch to extract temporal information from the video, which effectively improves the accuracy of miner action detection without significantly increasing the computational consumption. Finally, we validate the accuracy performance of the model on the miner action dataset, and its recognition accuracy outperforms other methods in the comparison.
SUPExplainer: Subgraph-Level Explanations for Graph Neural Networks via Reinforcement Learning Mechanism

Graph neural networks(GNNs) have attracted extensive attention due to their effective applications in various graph tasks[1] [2]. However, GNNs regarded as black-boxes generally meet the challenges of explanation and confidence. To address the above challenge, we propose SUPExplainer, a novel Subgraph-level explanation approach, which can provide a reliable explanation for GNN model. Given an original graph and a trained GNN, SUPExplainer efficiently select the significant subgraphs via Reinforcement Learning(RL) Mechanism to represent the critical structural information of the graph. The model is trained to maximize cumulative rewards through proximal policy optimization(PPO) and acts in an explanation-based environment. However, the system can only receive global rewards, which can not attribute cooperative contributions to different subgraphs. To solve such a problem, we further adopt approximate Shapley value which enhances the sample efficiency and reduces computations. Furthermore, experimental results indicate that SUPExplainer can identify important graph structures in explanation more intuitively, and is quantitatively superior to some baseline methods in reasonable calculation level.
Prediksi Dampak Pembelajaran Hybrid Learning Menggunakan Naive Bayes

This research use to predict the impact of hybrid learning on Medan State Polytechnic students. This algorithm was chosen because it has excellent performance in classification compared to other algorithms. Statistical and probabilistic methods are used in the operation of this algorithm to make predictions about what will happen in the future. Technology mastery, level of teacher-student interaction, and mastery of teaching materials are the variables used in this study. The sample data used came from students of the Software Engineering Technology Study Program of Medan State Polytechnic. The prediction results carried out manually with naïve bayes, with training data of 100 (one hundred) students and test data of 1 (one) student, produced a result of 0.012, which indicates an increase in student academic results. The test results were proven using the phyton programming language. The first test results, with 20% test data, resulted in an increase in academic results by 86% around 13 students with an accuracy value of 80%, and the second test, with 40% test data, resulted in an increase in academic results by 92% around 29 students with an accuracy value of 88%.
Law of Probability Distribution Function of The Module of Difference of Discrete Random Variables

The use of the absolute value symbol in theoretical studies often leads to difficulties in analytical operations. However, from a practical point of view, transformations based on absolute differences are the most visual and effective. The article discusses functional transformations, which are modules of the differences of discrete random variables. The concept of the first difference modulus is given. The laws of the probability distribution of random variables obtained as the modulus of the difference of two independent random variables that have the same discrete distribution (Bernoulli or geometric), as well as the distribution laws of new random variables that are the sums of the moduli of the first difference, have been found. The main numerical characteristics of new random variables are calculated.
The Procedure for Selecting a Curve from the Johnson System by Percentile Matching and Maximum Likelihood and Least Squares Approaches in R

It describes the implementation and application of a procedure in the R language designed to select a distribution curve from the Johnson system. In the proposed procedure, unlike the already implemented function from the SuppDists package, the selection of a family of curves and the estimation of parameters is carried out using the percentile method and a combination of maximum likelihood and least squares methods. The ability to set the initial values of the quantile vector of the standard normal distribution allows the user to select the curve more flexibly. In addition, the assessment of the quality of the selection can be performed visually, using graphs of density functions and distribution functions, as well as formalized with the application of Pearson's chi-squared agreement criterion.
The Power of Precision: Why Your Research Focus Should be SMART?

Having a SMART research focus is essential for researchers to achieve the desired results. By being specific, measurable, achievable, relevant, and time-bound, researchers can improve the effectiveness and efficiency of their research. This study aims to explore the importance of having a SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) research focus. This study used a descriptive-analytical approach by collecting data through literature review and analysis of existing research. The collected data is analyzed qualitatively by reading, understanding, and synthesizing relevant information. The results of this study show that a SMART research focus can increase research productivity, improve decision-making processes, and increase the chances of obtaining meaningful results. In conclusion, by adopting the SMART framework researchers can maximize the effectiveness and efficiency of research results significantly. Thus, this study provides a better understanding of the importance of having a SMART research focus and has implications for more effective and efficient research in the future.
Implementation of the Bomb Snakes and Ladders Game Based on the ECOLA Learning Model to Improve Reading Skills in Elementary Schools

In PISA in 2018, Indonesia was ranked 74th out of 80 countries. In the reading ability category, Indonesia obtained an average score of 371, which is below the OECD average score of 489. In fact, literacy is an initial ability that every individual must have to live life in the future. Schools are responsible for creating a reading culture for students. This is a challenge for teachers to help increase students' reading motivation. This classroom action research aims to improve the reading skills of grade 5 elementary school students by implementing the ECOLA learning model based on the bomb snakes and ladders game. This research is a type of classroom action research (PTK) using two cycles. The design used in the research is the Kemmis and Mc Spiral model. Taggart was developed by Stephen Kemmis and Robin Mc Taggart in 1988. In one cycle there are four components including planning, action, observation and reflection in a spiral that are interconnected with each other. The subjects of this research were 24 grade 5 elementary school students consisting of 15 boys and 9 girls. Based on research that has been conducted, the use of the ECOLA learning model based on the bomb snakes and ladders game media can improve students' ability in reading skills. This is proven by an increase in the percentage of completeness of student learning outcomes from cycle I and cycle II learning.
Personalized and dynamic rear-view mirror adjustment and profiling with voice signature

This paper introduces a novel automated system designed for adjusting automobile rear-view mirrors intelligently by utilizing head pose orientation. In today's increasingly personalized car interiors, ensuring the correct alignment of the rear-view mirror based on the driver's head orientation significantly enhances both safety and driving comfort. Manual adjustment of rear-view mirrors can result in issues such as improper angles and driver distractions. This study aims to automate the rear-view mirror adjustment process by accurately detecting the driver's head position and orientation, thereby mitigating these challenges. The system incorporates a camera within the vehicle's cockpit to track the driver. Raw data captured by the camera undergoes processing using the Perspective-n-Point (PnP) algorithm to determine the driver's head position and orientation. The computed positional information is then employed to precisely align the rear-view mirror, optimizing the field of view and eliminating blind spots. Within a brief timeframe, the system establishes the most suitable mirror settings for any driver. Moreover, it dynamically adapts to changes in the driver's posture during driving, ensuring consistently optimal visibility. Additionally, faster and more comfortable use of this in-car smart system is aimed with voice identification technology. Mirror angle values are kept in memory and can be applied instantly at the driver's request, with driver profiles created with voice identifications. Consequently, this research proposes an innovative application that contributes to the integration and personalization of smart technologies, potentially becoming part of forthcoming automotive cockpit designs.