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Electronic Information Engineering and computer Technology (EIECT), International Conference on

作者： Bingjie Weng Jianhui Ma Faculty of Artificial Intelligence and Data Science University of Science and Technology of China Hefei Anhui China Faculty of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

The rapid development of blockchain technology has led to many innovations, with the widespread use of smart contracts being particularly notable. However, this trend has spawned diverse blockchain scams, including the Ponzi scheme, which takes the form of Ponzi contracts. Due to blockchain users' misunderstanding of blockchain transparency, this scam can often result in significant financial losses. To complicate matters further, the vast majority of smart contracts deployed on blockchain networks are not open source, and users can only see the contract's bytecode. This situation makes Ponzi contracts even more challenging to identify, making it difficult for users to judge the security and legitimacy of a contract beforehand, thus increasing the risk of being defrauded. Most existing work uses only bytecode for detection and cannot fully utilize the corresponding source code information. In this paper, we propose a novel bimodal learning-fusion framework for enhancing Ponzi contract detection only in the presence of bytecode. Firstly, we employ the semantic-aware neural network to extract semantic feature from source code and bytecode. Then, we propose a modal transformation strategy to learn the relationship between source code and bytecode. Finally, a modal attention mechanism is introduced to construct a fusion representation to detect the contract even in the absence of source code modality. Experimental results show that the method performs better than existing methods that use machine learning for detection of Ponzi contracts.

关键词： Technological innovation Attention mechanisms Source coding Smart contracts Semantics Neural networks Feature extraction Market research Blockchains Security

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Enhance Transferability of Adversarial Examples with Model Architecture

Enhance Transferability of Adversarial Examples with Model A...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingyuan Fan Wenzhong Guo Zuobin Ying Ximeng Liu College of Computer and Data Science Fuzhou University China Faculty of Data Science City University of Macau Macau

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead.

关键词： Adaptation models Closed box Signal processing Feature extraction Acoustics Speech processing

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Crossfire: An Elastic Defense Framework for Graph Neural Networks Under Bit Flip Attacks 39

Crossfire: An Elastic Defense Framework for Graph Neural Net...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kummer, Lorenz Moustafa, Samir Gansterer, Wilfried Kriege, Nils Faculty of Computer Science University of Vienna Vienna Austria Doctoral School Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

ISBN: (纸本)157735897X

Bit Flip Attacks (BFAs) are a well-established class of adversarial attacks, originally developed for Convolutional Neural Networks within the computer vision domain. Most recently, these attacks have been extended to target Graph Neural Networks (GNNs), revealing significant vulnerabilities. This new development naturally raises questions about the best strategies to defend GNNs against BFAs, a challenge for which no solutions currently exist. Given the applications of GNNs in critical fields, any defense mechanism must not only maintain network performance, but also verifiably restore the network to its pre-attack state. Verifiably restoring the network to its pre-attack state also eliminates the need for costly evaluations on test data to ensure network quality. We offer first insights into the effectiveness of existing honeypot- and hashing-based defenses against BFAs adapted from the computer vision domain to GNNs, and characterize the shortcomings of these approaches. To overcome their limitations, we propose Crossfire, a hybrid approach that exploits weight sparsity and combines hashing and honeypots with bit-level correction of out-of-distribution weight elements to restore network integrity. Crossfire is retraining-free and does not require labeled data. Averaged over 2,160 experiments on six benchmark datasets, Crossfire offers a 21.8% higher probability than its competitors of reconstructing a GNN attacked by a BFA to its pre-attack state. These experiments cover up to 55 bit flips from various attacks. Moreover, it improves post-repair prediction quality by 10.85%. Computational and storage overheads are negligible compared to the inherent complexity of even the simplest GNNs. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Graph neural networks

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CeTrivium:A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

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computer Systems science & Engineering 2023年第12期47卷 2895-2920页

作者： Osama S.Younes Abdulmohsen Alharbi Ali Yasseen Faisal Alshareef Faisal Albalawi Umar A.Albalawi Faculty of Computer and Information Technology University of TabukTabuk71491Saudi Arabia Faculty of Computers and Information Menoufia UniversityMenoufia13829Egypt School of Computing&Data Science Wentworth Institute of TechnologyBoston02115USA

Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream *** have the ability to perform parallel transformations,resulting in high throughput ***,they exhibit a natural tendency to resist fault *** stream cipher schemes based on CA have been proposed in the ***,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia *** and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault *** work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on *** is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and *** is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)*** NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream *** NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a *** of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel ***,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise *** performance of CeTrivium is evaluated and compared with other state-of-

关键词： Stream ciphers cellular automata securing real-time streaming cryptography CeTrivium

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Iterative Search Attribution for Deep Neural Networks 41

Iterative Search Attribution for Deep Neural Networks

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41st International Conference on Machine Learning, ICML 2024

作者： Zhu, Zhiyu Chen, Huaming Wang, Xinyi Zhang, Jiayu Jin, Zhibo Xue, Jason Shen, Jun School of Electrical and Computer Engineering University of Sydney SydneyNSW Australia Faculty of Computer Science & Information Technology University of Malaya Malaysia Suzhou Yierqi Suzhou China Data61 CSIRO SydneyNSW Australia University of Wollongong Australia

Deep neural networks (DNNs) have achieved state-of-the-art performance across various ***, ensuring the reliability and trustworthiness of DNNs requires enhanced interpretability of model inputs and *** an effective means of Explainable Artificial Intelligence (XAI) research, the interpretability of existing attribution algorithms varies depending on the choice of reference point, the quality of adversarial samples, or the applicability of gradient constraints in specific *** thoroughly explore the attribution integration paths, in this paper, inspired by the iterative generation of high-quality samples in the diffusion model, we propose an Iterative Search Attribution (ISA) *** enhance attribution accuracy, ISA distinguishes the importance of samples during gradient ascent and descent, while clipping the relatively unimportant features in the ***, we introduce a scale parameter during the iterative process to ensure the features in next iteration are always more significant than those in current *** experimental results show that our method has superior interpretability in image recognition tasks compared with state-of-the-art *** code is available at: https://***/LMBTough/ISA. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Effectiveness of K-means Clustering-Based Outlier Handling Techniques in Colonic Condition Endoscopy Image Classification Using CNN

Effectiveness of K-means Clustering-Based Outlier Handling T...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Ummi Athiyah Supriadi Rustad Moch Arief Soeleman Muljono Muhamad Akrom Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia Department of Data Science Telkom University Purwokerto Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

Colorectal cancer is one of the leading causes of cancer death worldwide, so accurate early detection is needed. Endoscopic imaging technology plays a vital role in diagnosis, but the presence of outliers in endoscopic images can significantly affect the accuracy of the classification model. This study addresses this challenge by applying Histogram of Oriented Gradients (HOG) for feature extraction and K-means Clustering for outlier detection and removal. A dataset of colon endoscopic images was obtained from the public database Gastrovision, which was divided into two classes: normal and abnormal conditions. This study applied a two-step modeling process, first with the original dataset and then with the cleaned dataset after outlier removal. The results showcased a significant improvement in model performance after outlier handling, with accuracy increasing from $61 \%$ to $85 \%$. The Area Under Curve (AUC) of the ROC curve also increased from 0.84 to 0.92, indicating a better model ability to distinguish between normal and abnormal conditions. These findings emphasize the importance of data quality and effective outlier management in training a machine-learning model for colon cancer endoscopic image conditions.

关键词： Training Accuracy Satellites Endoscopes Neural networks Imaging Machine learning Colorectal cancer data models Image classification

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