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EPAD: Ethereum phishing scam detection via graph contrastive learning

Expert Systems with Applications 2025年 288卷

作者： Hao Sui Jiale Zhang Bing Chen Di Wu Xiaobing Sun Shivakumara Palaiahnakote College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China School of Information Engineering Yangzhou University Yangzhou 225009 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000 China School of Mathematics Physics and Computing University of Southern Queensland Toowoomba 4350 Australia School of Science Engineering and Environment University of Salford UK

Ethereum has evolved into a widely-used public blockchain platform, enabling developers to build and deploy smart contracts and decentralized applications. However, a significant issue is that users can conduct trades directly through their Ethereum addresses, which allows phishing scammers can easily disguise themselves as legitimate accounts to defraud large amounts of money. Current phishing scam detection methods largely focus on enhancing detection accuracy by continuously extracting account features. A further challenge is data class imbalance. Since the publicly available URLs for labeling phishing scam data are no longer updated, it causes an incomplete understanding of the data by supervised learning methods. To address these challenges, we propose EPAD, a self-supervised phishing scam detection method based on graph contrastive learning, which effectively handles the issue of unavailable labeled data. EPAD can learn account features by constructing positive and negative samples without labeling. To further enhance the inconspicuous feature representation in contrastive learning, our approach leverages ”node versus local subgraph” as contrastive instances and learns feature information of neighboring nodes through the contrastive training between nodes and their subgraphs. Our extensive experiments on two real-world datasets demonstrate that EPAD can effectively detect phishing scams without labeled training data, achieving powerful performance with 97 % precision and 95 % F1 score, surpassing baseline methods.

关键词： Graph contrastive learning Local subgraph Phishing scams detection Transaction network

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On the development of a web extension for text authentication on Google Chrome 2

On the development of a web extension for text authenticatio...

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2nd International Conference on Electrical, computer and Communication engineering, ECCE 2019

作者： Kabir, Muhammad Nomani Tayan, Omar Alginahi, Yasser Hasan, Md Munirul Rahman, Md Arafatur Faculty of Computer Systems Software Engineering University Malaysia Pahang Kuantan Pahang Malaysia Dept. of Computer Engineering NOOR Research Center College of Computer Science and Engineering Taibah University Madinah Saudi Arabia Dept. of Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (纸本)9781538691113

Web extension is a software that can be installed on a web browser. A web-extension link is displayed as an icon on the toolbar of the browser. Based on browsing activity, the extension works automatically or by clicking the extension icon depending on the functionalities made in the extension software. In this work, we developed a web extension on Google Chrome browser to verify online texts by simply clicking on an extension button. Upon clicking the button, the underlying algorithm in the extension software retrieves the texts from the current web-page being displayed. Verification and authentication of texts are performed by comparing the retrieved texts with text database. According to the comparison, texts are highlighted in colors. We consider authentication of Arabic Hadith texts for a case study. The authentic Hadith texts are highlighted by green color;authentic texts with partial diacritics, by yellow;and unauthentic texts, by red. This technique can also be used to authenticate laws, constitutions and Government documents in any language. © 2019 IEEE.

关键词： Authentication

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Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning Visualization Semantics Neural networks Data visualization software Task analysis

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Improving Code Summarization Through Automated Quality Assurance

Improving Code Summarization Through Automated Quality Assur...

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International Symposium on software Reliability engineering (ISSRE)

作者： Yuxing Hu Meng Yan Zhongxin Liu Qiuyuan Chen Bei Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China Pengcheng Laboratory Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665425889

The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.

关键词： Codes Quality assurance Filtering Collaboration software reliability Task analysis

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Energy-Efficient Data-Collection with Rendezvous-Based Approaches in Large-Scale WSNs with Multi-UGV 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Jian Tan, Ling Zhuang, Wei College of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing210044 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Prolonging the lifetime of wireless sensor networks (WSNs) is a crucial issue referring to energy conservation or balancing of data collection. In this paper, we propose an optimal algorithm for data collection using UGVs based on clustering by definition of rendezvous nodes (RNs). Due to the considerable large difference of long-distance transmission, the rendezvous-based clustered model for load balancing with multiple UGVs is proposed. Furthermore, considering different delay-tolerant applications, we design a data transmission mode called Two-Orbit Back-Propagation algorithm (TOBPA). In these process, we utilized utility function on energy to realize the optimal energy consumption in the sense of energy transmission model and take energy balancing into account meanwhile. As a result, we analyze the cluster lifetimes in different situations and achieve balance to save significant. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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Usefulness of high-level parallel compositions in genomics 32

Usefulness of high-level parallel compositions in genomics

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32nd European Modeling and Simulation Symposium, EMSS 2020

作者： Rossainz-López, Mario Zúñiga-Herrera, Sarahí Pineda, Iván Olmos Pineda-Torres, Ivo Capel-Tuñón, Manuel Faculty of Computer Science Autonomous University of Puebla San Claudio Avenue and South 14th Street City San Manuel Puebla Puebla72000 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741454

This work shows the use of parallel objects to build High Level Parallel Compositions or HLPC and their usefulness in genomics through four case studies related to sequencing DNA chains. The first two case studies are combinatorial optimization problems: grouping fragments of DNA sequences and the parallel exhaustive search (PES) of RNA strings that help the sequence and assembly of DNAs in the construction of gnomes. The third case study shows the implementation of a Convolutional Neuronal Network as a Parallel Object Composition to solve the problem of the recognition of DNA sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6). The results of this classification were obtained in terms of percentages of training precision and validation precision. The fourth and final case study shows the problem of sequence typing (STP) as a form of DNA sequence classification. It is particularized in a proposal for a parallel solution to find conserved regions of sequences that help discriminate between different types of hepatitis C virus, through the creation of a decision tree using HLPC. We show the algorithms that solves these problems using modeling and parallel simulation, their design and implementation as HLPC and the performance metrics in their parallel execution using multicores, video accelerator card and CPU-SET or processors with shared-distributed memory. © 2020 The Authors.

关键词： DNA sequences

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Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next Point-of-Interest Recommendation

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Expert Systems with Applications 2025年 284卷

作者： Zhang, Qing Huang, Xiao Zhang, Qi Zhou, Wei Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe Chongqing400044 China College of Computer and Information Science Southwest University Chongqing400715 China

Some studies based on the joint modeling of users’ long- and short-term preferences have achieved significant performance improvements in the next POI prediction task. However, these methods fail to fully explore the potential behavioral commonalities within the long- and short-term visit sequences, thus failing to effectively mitigate the sparsity issue in check-in data. Meanwhile, due to the lack of labeled data that clearly distinguishes general tastes from short-term interests, existing joint modeling approaches for long- and short-term preferences struggle to provide clear supervision and differentiation for target preferences, leading to potential entanglement between these two preferences and consequently affecting the accuracy of preference modeling. Based on these, we propose a novel Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next POI Recommendation (DLSPRec) method to accurately distinguish the differences and commonalities between users’ general tastes and short-term preferences, thereby improving the accuracy of the next location recommendation. This is the first attempt to disentangle long- and short-term preferences in the field of next POI recommendation. Through tests on three real-world datasets, we validated the superiority and rationality of the DLSPRec method. Compared to the suboptimal baseline model, DLSPRec achieved an average performance improvement of 10.39%, 9.69% and 16.39% on the PHO, NYC, and SIN datasets, respectively. The code is available at https://***/IsZSS/DLSPRec. © 2025

关键词： Contrastive learning Next POI recommendation Preferences disentanglement

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Crowd-MECS: A novel crowdsourcing framework for mobile edge caching and sharing

arXiv

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arXiv 2020年

作者： Jiang, Changkun Gao, Lin Wang, Tong Jiang, Yufei Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518060 China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen Guangdong518055 China

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision-making of different stakeholders. In this paper, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the EDs can decide whether to cache and share contents for the CP, and the CP can decide to share a certain revenue with EDs as the incentive of caching and sharing contents. We formulate such an interaction as a two-stage Stackelberg game. In Stage I, the CP aims to maximize its own profit by deciding the ratio of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs by choosing to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs’ best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the non-atomic game theory. Then, we identify the piece-wise structure and the unimodal feature of the CP’s profit function, based on which we design a tailored low-complexity one-dimensional search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that both the CP’s profit and the EDs’ total welfare can be improved significantly (e.g., by 120% and 50%, respectively) by using the proposed Crowd-MECS, comparing with the Non-MEC system where the CP serves all EDs directly. Copyright © 2020, The Authors. All rights reserved.

关键词： Crowdsourcing

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The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

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China Communications 2018年第7期15卷 98-110页

作者： Lejun Zhang Tong Wang Zilong Jin Nan Su Chunhui Zhao Yongjun He College of Information Engineering Yangzhou University College of Information and Communication Engineering Harbin Engineering University School of Computer and software Nanjing University of Information Science and Technology School of Computer Science and Technology Harbin University of Science and Technology

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

关键词： social network public opinion propagation control influence network

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Object-based and multi-frame motion information predict human eye movement patterns during video viewing

Object-based and multi-frame motion information predict huma...

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2019 Human Vision and Electronic Imaging Conference, HVEI 2019

作者： Ma, Zheng Wu, Jiaxin Zhong, Sheng-Hua Heinen, Stephen J. Smith-Kettlewell Eye Research Institute San FranciscoCA United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Compared to low-level saliency, higher-level information better predicts human eye movement in static images. In the current study, we tested how both types of information predict eye movements while observers view videos. We generated multiple eye movement prediction maps based on low-level saliency features, as well as higher-level information that requires cognition, and therefore cannot be interpreted with only bottom-up processes. We investigated eye movement patterns to both static and dynamic features that contained either low- or higher-level information. We found that higher-level object-based and multi-frame motion information better predict human eye movement patterns than static saliency and two-frame motion information, and higher-level static and dynamic features provide equally good predictions. The results suggest that object-based processes and temporal integration of multiple video frames are essential to guide human eye movements during video viewing. © 2019, Society for Imaging science and Technology

关键词： Eye movements

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