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34th International Conference on Software engineering and Knowledge engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Workshop: Advancements in Metaverse and IoT

Workshop: Advancements in Metaverse and IoT

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Wei Zhao Yuanlong Cao Liang Zhao Zhi Liu School of Computer Science and Technology Anhui University of Technology Anhui China School of Software Jiangxi Normal University Jiangxi China School of Computer Science Shenyang Aerospace University Shenyang China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

The metaverse is a virtual reality world where users can create and tailor their avatars and interact with each other in a digital environment. On the other hand, the Internet of Things (IoT) is a network of Internet-connected physical devices, such as smart home appliances, wearables, and smartphones, that can connect with each other. By incorporating IoT devices into the metaverse, users can handle and regulate a range of smart devices within the virtual world, generating a fresh form of connection between the digital and physical realms. This paper provides an overview of the workshop discussing recent advancements in the Metaverse and IoT. It also highlights various solutions aimed at addressing the inherent technical challenges associated with these technologies.

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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Smart Computing and Application (ICSCA), International Conference on

作者： Shams Tabrez Siddiqui Mohammad Rafeek Khan Zeba Khan Nadim Rana Haneef Khan Md Imran Alam Department of Computer Science College of CS & IT Jazan University Jazan Saudi Arabia Department of Computer and Network Engineering College of CS & IT Jazan University Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the “edge” of your local network, rather than transferred away. Technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems.

关键词： Productivity Cloud computing Privacy Systematics Education Systems architecture Real-time systems

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Optimal Feature Manipulation Attacks against Linear Regression

Optimal Feature Manipulation Attacks against Linear Regressi...

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作者： Li, Fuwei Lai, Lifeng Cui, Shuguang Department of Electrical and Computer Engineering University of California DavisCA95616 United States School of Science and Engineering Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modifying the original data points. Given the energy budget, we first provide the closed-form solution of the optimal poisoning data point when our target is modifying one designated regression coefficient. We then extend the analysis to a more challenging scenario where the attacker aims to change one particular regression coefficient while making others to be changed as small as possible. For this scenario, we introduce a semidefinite relaxation method to design the best attack scheme. Finally, we study a more powerful adversary who can perform a rank-one modification on the feature matrix. We propose an alternating optimization method to find the optimal rank-one modification matrix. Numerical examples are provided to illustrate the analytical results obtained in this paper. © 1991-2012 IEEE.

关键词： Linear regression

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ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Zhang, Xiang Lu, Yan Yan, Huan Huang, Jinyang Gu, Yu Ji, Yusheng Liu, Zhi Liu, Bin University of Science and Technology of China School of Cyber Science and Technology Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China AI Lab Shanghai200030 China Guizhou Normal University School of Computer and Information Guiyang230601 China National Institute of Informatics Tokyo101-8430 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

Because of the ambiguous and subjective property of the facial expression, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current methods often directly predict whether the label is noised or not, aiming to reduce the contribution of the noised data. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning. In this paper, we propose a more reliable noise-label suppression method called ReSup. First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on popular datasets shows the effectiveness of ReSup. © 2010-2012 IEEE.

关键词： Affective computing facial expression label noise label noise modeling label noise suppression

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Evaluating Large Language Models Via Multi-Modal User Knowledge Graphs: A Comprehensive Assessment Framework

Evaluating Large Language Models Via Multi-Modal User Knowle...

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International Symposium on System and Software Reliability (ISSSR)

作者： Pan Liu Zizhao Chen Yihao Li W. Eric Wong Faculty of Business Information Shanghai Business School Shanghai Institute of Network Technology (Yantai) Yantai China Department of Computer Science The University of Texas at Dallas Richardson TX USA China School of Information and Electrical Engineering Ludong University Yantai China

ISBN: (数字)9798331501242

ISBN: (纸本)9798331501259

Large language models (LLMs) have been widely adopted across various industries, but issues such as hallucinations, biases, and erroneous outputs frequently arise, compromising their reliability and safety. Objectively assessing how well an LLM interprets user queries is crucial for selecting the right model for practical problem-solving. This paper proposes an evaluation method that leverages user knowledge graphs to measure an LLM's comprehension of user input. Specifically, we construct both text-based and graphical test cases derived from a user knowledge graph, thereby enabling multi-modal assessment of the LLM's understanding. To implement our approach, a knowledge graph is first built from the user's input and then mutated to produce diverse test cases. A search-based testing method is then applied to evaluate the model's comprehension. We provide a case study demonstrating the framework. Our findings indicate that multi-modal test cases outperform purely text-based test cases in revealing the true understanding capability of LLMs. Among the eight models tested, DeepSeek and Doubao exhibit stronger comprehension than the remaining six.

关键词： Industries Visualization Large language models Knowledge graphs Cognition Robustness Safety Complexity theory Security Testing

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DDoS Attack Detection Based on CNN and Federated Learning 9

DDoS Attack Detection Based on CNN and Federated Learning

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Lv, Dingyang Cheng, Xinyu Zhang, Jinghui Zhang, Wei Zhao, Wei Xu, He School of Computer Science and Engineering Southeast University Nanjing China Digital Department China United Network Communications Group Co. Ltd. China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Computer Science and Technology Anhui University of Technology Hefei China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665407458

Distributed Denial of Service (DDoS) attack, which seriously affects the availability of the Internet, is one of the most dangerous network attacks. Machine learning is widely used in DDoS attack detection. However, due to data privacy and transmission overhead problems, the traffic data distributed on different network devices will not be easily concentrated together, which leads to difficulties in model training. In this paper, we combine federated learning (FL) and neural network and propose a FLDDoS system to counter DDoS attacks. We designed a CNN model and a data preprocessing algorithm to fully mine the features of DDoS traffic data. The federated learning framework is used for the model training, which can make full use of the data and computing performance of the terminal devices, and protect the data privacy. In addition, we also consider non-IID cases of the data and use a model mixing approach to provide a more personalized model for each client. The experimental results show that the proposed FLDDoS has a good performance. The accuracy of DDoS attack detection and multiclass classification is 99% and 90%, respectively. And the personalized method can overcome the non-IID problem of data in FL effectively. The accuracy of the CNN model proposed in this paper is 20% higher than that of the traditional model. Under the condition of using the same model, the model accuracy obtained by FL is basically the same as that obtained by centralized training. © 2022 IEEE.

关键词： Denial-of-service attack

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Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends

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Journal of Advanced Transportation 2022年 2022卷

作者： Elmoiz Alatabani, Lina Sayed Ali, Elmustafa Mokhtar, Rania A. Saeed, Rashid A. Alhumyani, Hesham Kamrul Hasan, Mohammad Department of Data Communications and Network Engineering Faculty of Telecommunications The Future University Khartoum Sudan Department of Electrical and Electronics Engineering Faculty of Engineering Red Sea University Bosaso Somalia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Selangor Bangi43600 Malaysia

Recently, artificial intelligence (AI) technology has great attention in transportation systems, which led to the emergence of a new concept known as Internet of Vehicles (IoV). The IoV has been associated with the IoT revolution and has become an active field of research due to the great need, in addition to the increase in the various applications of vehicle communication. AI provides unique solutions to enhance the quality of services (QoS) and performance of IoV systems as well. In this paper, some concepts related to deep learning networks will be discussed as one of the uses of machine learning in IoV systems, in addition to studying the effect of neural networks (NNs) and their types, as well as deep learning mechanisms that help in processing large amounts of unclassified data. Moreover, this paper briefly discusses the classification and clustering approaches in predicative analysis and reviews their abilities to enhance the performance of IoV application systems. © 2022 Lina Elmoiz Alatabani et al.

关键词： Vehicles

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Google Map-based Password Authentication Systems Using Tolerant Distance and Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Zhili Yang, Ching-Nung Wang, Shaowei Nan, Guoshun Cimato, Stelvio Zheng, Yifeng Wang, Qian Guangzhou University Institute of Artificial Intel-ligence China Guangdong Key Labor-atory of Blockchain Security Guangdong510006 China National Dong Hwa University Department of Computer Science and Information Engineering Hualian97401 Taiwan national engineering research center for mobile network technologies Beijing100876 China Universitá degli studi di Milano Italy The Hong Kong Polytechnic University Department of Electrical and Electronic Engi-neering Hong Kong Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Passwords are widely used for authentication in Internet applications. Recently, users tend to adopt graphical passwords instead of traditional alphanumeric passwords, since it is much easier for humans to remember images than verbal representations. However, the existing graphical password authentication systems generally suffer from three main issues. 1) It is required to remember and perform complicated operations during the registration/login phases, which significantly limits the systems' usability;2) The users' passwords are simply stored as plaintexts in servers, and thus the security is compromised;3) The users need to register/login to each server separately when they are applied in multi-server environment. To address the above issues, we propose a user-friendly and secure Google map-based graphical password (FS-GMGP) system using tolerant distance and homomorphic encryption. By using a homomorphic encryption scheme, each user encrypts his password point and response point selected on Google map, while the servers compute and decrypt the distance between the two encrypted points and then compare the resulting value with a tolerant distance for authentication. Moreover, the FS-GMGP system is extended for multi-server environment. The evaluation results and security analysis show that the FS-GMGP and its extended version achieve desirable usability and security in single-server environment and multi-server environment, respectively © 2025 IEEE.

关键词： Authentication Protocol

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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