Reviewerly/exHarmony huggingface dataset:
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation

In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs). Different from previous works, GTEA models the temporal dynamics of interaction sequences in the continuous-time space and simultaneously takes advantage of both rich node and edge/ interaction attributes in the graph. Concretely, we integrate a sequence model with a time encoder to learn pairwise interactional dynamics between two adjacent nodes. This helps capture complex temporal interactional patterns of a node pair along the history, which generates edge embeddings that can be fed into a GNN backbone. By aggregating features of neighboring nodes and the corresponding edge embeddings, GTEA jointly learns both topological and temporal dependencies of a TIG. In addition, a sparsity-inducing self-attention scheme is incorporated for neighbor aggregation, which highlights more important neighbors and suppresses trivial noises for GTEA. By jointly optimizing the sequence model and the GNN backbone, GTEA learns more comprehensive node representations capturing both temporal and graph structural characteristics. Extensive experiments on five large-scale real-world datasets demonstrate the superiority of GTEA over other inductive models.
Hyper contour cryptography with master <scp>Birch‐Brown Paruvala‐SLSTM</scp> model for enhanced healthcare security

Abstract Blockchain is a reliable ledger database that may be used to store various private healthcare data, but uploading and sharing data is difficult due to privacy concerns. Hence, to overcome these issues, a novel HCCLO with master Birch‐Brown Paruvala‐SLSTM model has been proposed to store various patients' medical data and shield it from threats. Hyper Contour Cryptography with Lepidoptera Optimization (HCCLO) has been proposed in this model, which encrypts data to reduce unwanted time stamp creation and prevent private key theft. Also, the Master Birch‐Brown‐Parulava NN design is introduced to prevent a false identity that leads to a data breach, and it allows for private exchange and enhances framework reconciliation, acting as an immune response to information breaks in the chain and preventing data breaches from spreading further. Then, the SLSTM layer implant RF Classifier is introduced to categorize data as user requests and attacks, safeguarding the blocks against changeability caused by malware attacks. The proposed approach offers minimal block mine time, maximum accuracy, and healthcare data security.
Investigation of the Effectiveness of Usage of Graph Databases for Big Data Analysis

The purpose of research . The purpose of this work is to study graph models of databases and develop a methodology for comparative analysis of database models. The theoretical and methodological basis of the study was the fundamental scientific works of domestic and foreign authors in the field of basic problems of database theory, algorithm theory, graph theory, data processing structures and methods. Methods . The paper uses methods of structural, comparative and content analysis, as well as statistical methods of information processing and methods of graph theory. As a result of the conducted research, the authors justified the features, advantages and disadvantages of using a graph data model. Results . The relevance of this study is due to the intensive development of information technologies intended for the economic development of the country, the pandemic and the geopolitical situation in the world. These prerequisites orient researchers to use new methods of data processing and analysis. However, it is possible to optimize big data processing processes not only with the help of powerful new algorithms, but also with the use of fundamentally different data structures and models other than relational. The paper presents applied examples of using the graph model of databases in various subject areas. A method of comparative analysis of data models in relation to big data analysis has been developed. The main points of data model design are highlighted: system scaling, compliance with requirements and standards, the ability to change data model structures, language complexity, performance and data processing speed. The proposed technique made it possible to numerically evaluate the effectiveness of graph models. Conclusion . The theoretical significance of the research consists in the development of methodological and technological approaches to the analysis of big data and the formation of structures and databases. The practical results of the study can be useful to large IT companies, as well as to the financial, logistics and commercial sectors, where the problem of big data analysis and research is most acute.
Compensation for the Error of Narrowing the Defuzzification Range by the Areas’ Ratio Method

The purpose of research is to examine the hypothesis that the area ratio method can be used to compensate for the defuzzification interval narrowing error inherent in traditional models, such as center of gravity sums, height models, first maxima, mean maxima, and last maxima. Methods . A fuzzy model consisting of two input variables and one output variable was used to analyze the properties of the area ratio method. The input variables had two triangular membership functions each, and the output variable had three triangular membership functions. The knowledge base consisted of four fuzzy rules. Zadeh's compositional rule was used as an implication model. Two models of classical center of gravity and a model based on the area ratio method were used in the defuzzification process. Results . In the course of experimental studies, it was found that the proposed defuzzifier based on the area ratio method compensates the error of narrowing the defuzzification interval. It was also found during the experimental studies that when using the center-of-gravity model, a resultant surface that only 50% overlaps with the caliper of the output variable is formed at the output, which forms the error of defuzzification interval narrowing. When the area ratio method is used, the resulting surface overlaps 100% with the output variable caliper, suggesting that the area ratio method eliminates the error associated with defuzzification interval narrowing. C onclusion . This article presents a fuzzy MISO model that is used to analyze the properties of the area ratio method. A distinctive feature of the proposed model is the use of the area ratio method in defuzzification. Analysis of its simulation process has shown that this method allows to compensate the error of defuzzification interval narrowing.
A novel classification method combining phase-field and DNN

This paper proposes a novel classification method. Firstly, we use the deep neural network (DNN) to classify the training set. After several iterations, we obtain the output vector Y. The component of the largest value in vector Y is represented as the label being classified, which we take as the output value. Because we chose the sigmoid function as our activation function, the output value is between 0 and 1. Therefore, the output value can represents the probability of the classified label by the DNN. Depending on the distribution of output values, we set tolerance values (Tol) that categorize similar output values as the same label in the DNN. If the output value is lower than Tol, we consider it categorically anomalous. Subsequently, we use the Phase-Field model to classify these anomalies and obtain better classification results. As this classification method combines Phase-Field model and DNN, we named it Phase-Field-DNN. In the numerical experiment using MNIST handwritten digit data set as experimental data, the classification accuracy of Phase-Field-DNN model is higher than that of Phase-Field model and DNN model through the analysis of the classification results of binary classification and multi-classification problems with this data. In addition, the model we proposed is used to classify the normal and abnormal brain MRIs, and the classification results are compared with those of others. After comparison, we find that our proposed model achieve the best classification results.
An improved method of concentric circle positioning in visual measurement

Target with concentric circles has been widely used in artificial target and camera calibration of visual measurement systems. The accuracy of concentric circle target directly affects the accuracy of vision measurement system. In this study, an improved method for accurate positioning of concentric circles projection center is proposed. Compared with similar methods, the proposed method has high accuracy and does not need to know the radius of the concentric circle in advance. The influence of inner and outer circle radius and rotation angle of concentric circles on the position error of circular projection center is analyzed by single camera experiment. The experimental results show that the position error of this method is lower than that of similar methods. The concentric circles calibration board is designed as an application to calibrate the binocular vision system. It is shown that the calibration results of the camera's intrinsic parameters are almost the same as those of PPAGC. However, compared with PPAGC, the total projection error of the left camera is reduced by 30.77% and that of the right camera by 28.29%.
Dialog summarization for software collaborative platform via tuning pre-trained models

Software collaborative platforms, e.g., Gitter live chat and GitHub Discussions, are essential in software maintenance. Summarizing the live chat logs is useful for extracting, retrieving, and sharing knowledge for software developers. Automatic text summarization has been studied in many areas such as code summarization, and title generation. However, the previous studies rely on rich collected labeled data for model training which are absent for the noisy interleaved dialogs, resulting in poor performance in the few-shot scenario. To tackle the issue, we propose a novel Automatic Dialog Summarization Approach based on pre-trained models, named ADSum. To alleviate the high-cost problem of the from-scratch manual annotation, ADSum finetunes the Text-To-Text Transfer Transformer (T5) model by exploiting the discussion posts on GitHub, and then recommends summaries for an annotator. To solve the poor performance in the few-shot scenario, we propose to employ the prompt tuning paradigm for tuning the T5 model by exploiting the disentangled dialog data on Gitter. Meanwhile, the soft prompt is used to avoid the manual effort of designing appropriate prompt templates. To verify the effectiveness of our approach, we extract 38,964 high-quality discussion posts from GitHub and manually annotate 3,039 dialog summarizations from Gitter. Experimental results show our approach achieves state-of-the-art performance in terms of three performance metrics. In particular, our proposed method outperformed the Transformer-based and other pre-training models by 39% and 14%, respectively, on the GitHub dataset regarding the Rouge-L metric. The experiments of handling data scarcity and a human evaluation also confirm the effectiveness of ADSum.
RENEW: Modularized Architecture and New Features

Renew is an extensible Petri Net IDE that supports the development and execution of high-level Petri Nets and other modeling techniques. Over the past seven years, Renew has undergone significant development and refinement. To this end, Renew ’s code base has been reworked extensively, and its tool collection has been expanded. The reworking was necessary due to technical debt caused by environmental changes: especially Java’s transition from version 9 to 17. Adapting to the latest Java versions enables the modularization of Renew ’s architecture through the Java Platform Module System (JPMS) which was introduced with Java 9. Additionally, some new features have been implemented, which were used to test our new architectural design. One of our main results gave Renew a cleaner code interface design and a more modern architecture. Examples of the extensions and improvements made are the new P/T-nets with synchronous channels (PTC) formalism and the Modular Model Checker (MoMoC). In addition to the aforementioned changes, the GUI has also been altered and now offers an all-in-one window.
Neural Network-Based Obstacle and Pothole Avoiding Robot

The main challenge of any mobile robot is to detect and avoid obstacles and potholes. This paper presents the development and implementation of a novel mobile robot. An Arduino Uno is used as the processing unit of the robot. A Sharp distance measurement sensor and Ultrasonic sensors are used for taking inputs from the environment. The robot trains a neural network based on a feedforward backpropagation algorithm to detect and avoid obstacles and potholes. For that purpose, we have used a truth table. Our experimental results show that our developed system can ideally detect and avoid obstacles and potholes and navigate environments.
QuantumCharge: Post-Quantum Cryptography for Electric Vehicle Charging

ISO 15118 enables charging and billing of Electric Vehicles (EVs) without user interaction by using locally installed cryptographic credentials that must be secure over the long lifetime of vehicles. In the dawn of quantum computers, Post-Quantum Cryptography (PQC) needs to be integrated into the EV charging infrastructure. In this paper, we propose QuantumCharge, a PQC extension for ISO 15118, which includes concepts for migration, crypto-agility, verifiable security, and the use of PQC-enabled hardware security modules. Our prototypical implementation and the practical evaluation demonstrate the feasibility, and our formal analysis shows the security of QuantumCharge, which thus paves the way for secure EV charging infrastructures of the future.
Low Memory Attacks on Small Key CSIDH

Despite recent breakthrough results in attacking SIDH, the CSIDH protocol remains a secure post-quantum key exchange protocol with appealing properties. However, for obtaining efficient CSIDH instantiations one has to resort to small secret keys. In this work, we provide novel methods to analyze small key CSIDH, thereby introducing the representation method —that has been successfully applied for attacking small secret keys in code- and lattice-based schemes— also to the isogeny-based world. We use the recently introduced Restricted Effective Group Actions ( $$\textsf{REGA}$$ ) to illustrate the analogy between CSIDH and Diffie-Hellman key exchange. This framework allows us to introduce a $$\textsf{REGA}\text {-}\textsf{DLOG}$$ problem as a level of abstraction to computing isogenies between elliptic curves, analogous to the classic discrete logarithm problem. This in turn allows us to study $$\textsf{REGA}\text {-}\textsf{DLOG}$$ with ternary key spaces such as $$\{-1, 0, 1\}^n, \{0,1,2\}^n$$ and $$\{-2,0,2\}^n$$ , which lead to especially efficient, recently proposed CSIDH instantiations. The best classic attack on these key spaces is a Meet-in-the-Middle algorithm that runs in time $$3^{0.5 n}$$ , using also $$3^{0.5 n}$$ memory. We first show that $$\textsf{REGA}\text {-}\textsf{DLOG}$$ with ternary key spaces $$\{0,1,2\}^n$$ or $$\{-2,0,2\}^n$$ can be reduced to the ternary key space $$\{-1,0,1\}^n$$ . We further provide a heuristic time-memory tradeoff for $$\textsf{REGA}\text {-}\textsf{DLOG}$$ with keyspace $$\{-1,0,1\}^n$$ based on Parallel Collision Search with memory requirement M that under standard heuristics runs in time $$3^{0.75 n}/M^{0.5}$$ for all $$M \le 3^{n/2}$$ . We then use the representation technique to heuristically improve to $$3^{0.675n}/M^{0.5}$$ for all $$M \le 3^{0.22 n}$$ , and further provide more efficient time-memory tradeoffs for all $$M \le 3^{n/2}$$ . Although we focus in this work on $$\textsf{REGA}\text {-}\textsf{DLOG}$$ with ternary key spaces for showing its efficacy in providing attractive time-memory tradeoffs, we also show how to use our framework to analyze larger key spaces $$\{-m, \ldots , m\}^n$$ with $$m = 2,3$$ .
Psychological and demographic predictors of investment in cryptocurrencies during a crisis in the MENA region: the case of Lebanon

Purpose The purpose of this study is to discover the motivating factors for cryptocurrency investment during an economic crisis in the MENA region, with reference to the economic crisis of 2019–2022, in Lebanon. Design/methodology/approach The authors used t -test, and logistic regressions on a sample of 254 Lebanese investors to differentiate between cryptocurrency investors, and non-investors. Linear regressions of a subsample of cryptocurrency investors determined the factors that explained increasing cash investment in cryptocurrencies. Data were collected from investors in Lebanon, which could limit the generalization of the research results across the MENA region. Findings Investors differed from non-investors in that they were male, owned investments in the stock, bond and commodity markets, had prior investment experience in cryptocurrencies, were risk-takers and had expectations of high returns. Investors increased the dollar investment in cryptocurrencies, if they were male, as they invested more funds in securities, had previously invested in cryptocurrencies and had stronger risk-taking propensity. Expectations of high returns drove investors to cryptocurrencies, but such expectations do not stimulate further cryptocurrency investment. Originality/value This study is an initial attempt to comprehend the reactions of investors in the MENA region to a currency crisis that triggered investment in cryptocurrencies following the collapse of fiat currencies, central bank default and restrictions on bank withdrawals.
TCAMixer: A lightweight Mixer based on a novel triple concepts attention mechanism for NLP

Large-scale model sizes and expensive computing costs cause the challenge of deploying and applying large pre-trained models. Hence, this paper presents a novel Triple Concepts Attention Mechanism and a lightweight TCAMixer model for edge devices to classify texts. Furthermore, the TCAMixer abstracts textual concepts in a human way, which is unmatched by other counterparts such as pNLP-Mixer (a projection-based MLP-Mixer model for Nature Language Processing) and HyperMixer (a hyper network using dynamic token-mixing layers). Experimental results on several public datasets demonstrate that the TCAMixer outperforms the counterparts by a significant margin, for example, achieving 3% higher accuracy with a smaller model size of 0.177M. Additionally, the TCAMixer achieves a performance of 85% to 98.7% compared to that of large pre-trained models but only occupies 1/3000 to 1/2000 of their size on most test datasets.
E-Voting System Using Blockchain

Abstract: The introduction of online voting has revolutionized the electoral process, providing a secure and convenient way for people to cast their votes without having to be physically present at polling stations. However, traditional online voting systems are still susceptible to security and integrity issues, such as hacking and tampering. Blockchain technology provides a solution to these problems by offering a decentralized and immutable ledger that can ensure the security and transparency of online voting. In this paper, we propose an online voting system using blockchain technology, which is designed to provide a secure and transparent voting process.
Context Sensitive Tamil Language Spellchecker Using RoBERTa

A spellchecker is a tool that helps to identify spelling errors in a piece of text and lists out the possible suggestions for that word. There are many spell-checkers available for languages such as English but a limited number of spell-checking tools are found for low-resource languages like Tamil. In this paper, we present an approach to develop a Tamil spell checker using the RoBERTa (xlm-roberta-base) model. We have also proposed an algorithm to generate the test dataset by introducing errors in a piece of text. The spellchecker finds out the mistake in a given text using a corpus of unique Tamil words collected from different sources such as Wikipedia and Tamil conversations, and lists out the suggestions that could be the potential contextual replacement of the misspelled word using the proposed model. On introducing a few errors in a piece of text collected from a Wikipedia article and testing it on our model, an accuracy of 91.14% was achieved for error detection. Contextually correct words were then suggested for these erroneous words detected. Our spellchecker performed better than some of the existing Tamil spellcheckers in terms of both higher accuracy and lower false positives.
Jigsaw Learning Model Improves Student Learning Outcomes In Mechanical Engineering Basic Knowledge

There are still many students who get scores below the average. Teachers still use conventional learning models that make students less motivated to learn. This study aims to analyze the effect of the jigsaw cooperative learning model on the learning outcomes of class X students in the subject of basic knowledge of mechanical engineering. This study used a posttest-only group design. The population of this research is class X Welding Engineering students. Sampling was done by using a random sampling technique. The first sample (class A), totaling 30 students, is an experimental class. The second sample (class B), totaling 30 students, is the control class. The methods used in data collection are the documentation and test methods. Data analysis of the two groups used descriptive statistical analysis and inferential statistical analysis through independent sample t-tests. The results showed differences in the learning outcomes of students' basic knowledge of mechanical engineering in the experimental class taught using the jigsaw learning model using conventional learning models. It was concluded that the jigsaw learning model could improve student learning outcomes in mechanical engineering subjects.
MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator

Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment and tested for its statistical strength. Our results show that the random bit sequence extracted from the detections successfully passed established randomness tests. The detections correspond to cosmic rays recorded using a common smartphone during our experiment. Despite the limited sample, our work provides valuable insights into the use of ultra-high energy cosmic rays as an entropy source.