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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Zhang, Xu Shao, Zengzhen Li, Zhuangzhuang Han, Shuai Sun, Yajing Shandong Normal University Department Of Information Science And Engineering Jinan China Shandong Women's College Department Of Data Science And Computer Science Jinan China

ISBN: (纸本)9798350361445

At the doctoral level in the field of computer science, the domain of literature plagiarism detection encompasses numerous plagiarism detection algorithms, but relatively fewer have focused on methods specifically designed for topic-based plagiarism detection. To address this research gap, this paper introduces a text semantic matching algorithm based on contrastive learning and adversarial training, which we have named 'Comparative Adversarial Training (CAT).' Specifically, during the fine-tuning process, we generate affirmative and negative sentences using paper titles and keywords and optimize the model's performance through contrastive learning. Simultaneously, we generate adversarial samples by perturbing the word embedding matrix of the model and then employ these adversarial samples for adversarial training. Through experiments conducted on our proprietary internal dataset, the fine-tuned BERT model exhibited an average performance improvement of 3.71% compared to the baseline BERT model. Similarly, the fine-tuned RoBERTa model demonstrated a performance enhancement of 3.18% relative to the RoBERTa baseline model. Based on these experimental results, our algorithm's effectiveness has been thoroughly validated. © 2024 IEEE.

关键词： Semantics

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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On the Independence of Adversarial Transferability to Topological Changes in the dataset 10

On the Independence of Adversarial Transferability to Topolo...

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10th IEEE International Conference on data science and Advanced Analytics, DSAA 2023

作者： Newen, Carina Muller, Emmanuel Tu Dortmund Research Center Trustworthy Data Science & Security Computer Science Dortmund Germany

ISBN: (纸本)9798350345032

One curious property of neural networks is the vulnerability to specific attacks, often called adversarial examples. One of the directions adversarial transferability research has taken is to focus on dataset features. The transferability of adversarials is often linked to those common global features being present or not. To validate this theory, we tested if the transferability of attacks occurs when the underlying global features of a dataset remain the same. This is because topology promises to preserve the properties of an object under continuous deformations. In this paper, we test the correlation between topological similarities using the mapper algorithm by Singh et al. to generate an approximation of the topology in a graphical manner and a distance notion provided by the NetLSD algorithm, which promises size, scale, and permutation invariance. These two algorithms allow us to show that adversarial transferability is, in fact, independent of the topological similarity of datasets. We implement our findings in https://***/KDD-OpenSource/Topological-Transf. This is an astounding new insight, as former theories have led us to expect that if the assumption is true that global features are relevant for transferability, those should be captured using algorithms that detect global features under only topological change- Unless, of course, the transferability and those global features are explicitly agnostic to topological change. This might point to current research regarding adversarial transferability in different directions. More specifically, we take an experimental approach using topological approximation methods to capture essential features of datasets. Past studies concerning adversarial examples show that attacks can transfer in unforeseen ways and between different neural network architectures and may produce severe vulnerabilities in sophisticated learners. However, when tackling the problem of vulnerabilities to adversarial attacks, only a few a

关键词： Topology

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Packet Scheduling in Rechargeable Wireless Sensor Networks under SINR Model

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China Communications 2023年第3期20卷 286-301页

作者： Baogui Huang Jiguo Yu Chunmei Ma Guangshun Li Anming Dong School of Computer Science Qufu Normal UniversityRizhao 276826China Big Data Institute Qilu University of TechnologyJinan 250353China School of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio *** allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is *** first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor *** packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled *** second algorithm mainly focuses on the energy efficiency of *** priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled *** sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is *** consider imperfect *** is,the battery capacity is limited,and battery energy leaks over *** energy harvesting rate,energy retention rate and transmission power are *** simulation results indicate that the battery capacity has little effect on the packet scheduling ***,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.

关键词： packet scheduling physical interference model rechargeable sensor networks SINR model

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Semantic-Based Detection of Ponzi Smart Contracts: Enhancing Bytecode Analysis with Source Code 4

Semantic-Based Detection of Ponzi Smart Contracts: Enhancing...

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4th International Conference on Electronic Information Engineering and computer Technology, EIECT 2024

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

ISBN: (纸本)9798331528850

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. © 2024 IEEE.

关键词： Blockchain

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Vision Intelligence Assisted Lung Function Estimation Based on Transformer Encoder-Decoder Network With Invertible Modeling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3336-3349页

作者： Chen, Liuyin Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Zhang, Zijun City University of HongKong School of Data Science Hong Kong Nanfang Hospital Southern Medical University Department of Thoracic Surgery Guangzhou510515 China University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China University of Science and Technology Beijing Shunde Graduate School Foshan China

Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible deep learning method for lung function estimation via using computed tomography (CT) images. First, the projection method is proposed to flatten the three-dimensional (3-D) image onto a two-dimensional (2-D) plane, with preserving location information in 3-D. Next, the MBConv transformer-based encoder-decoder structure is developed to extract latent features. Finally, we develop an invertible normalizing flow (NF) model to infer lung function based on the extracted features and design two loss functions for two directions. The method enables both estimating the lung function based on CT images and metadata as well as generating the corresponding simulated CT image according to the lung function. Computational studies show that the proposed regression model outperforms all state-of-the-art image regression models. A comprehensive comparative analysis also demonstrates the effectiveness of using generated images and confirms the superiority of the proposed method. To the best of our knowledge, this work is the first of its kind in combining encoder-decoder network with NFs to ensure the effectiveness of the fully invertible framework, especially in lung CT image analysis. © 2020 IEEE.

关键词： Metadata

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PAC PREDICTION SETS UNDER LABEL SHIFT 12

PAC PREDICTION SETS UNDER LABEL SHIFT

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12th International Conference on Learning Representations, ICLR 2024

作者： Si, Wenwen Park, Sangdon Lee, Insup Dobriban, Edgar Bastani, Osbert Department of Computer & Information Science University of Pennsylvania United States Graduate School of AI and Computer Science & Engineering POSTECH Korea Republic of Department of Statistics and Data Science University of Pennsylvania United States

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting, where the probabilities of labels can differ between the source and target distributions. Our algorithm relies on constructing confidence intervals for importance weights by propagating uncertainty through a Gaussian elimination algorithm. We evaluate our approach on four datasets: the CIFAR-10 and ChestX-Ray image datasets, the tabular CDC Heart dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller prediction set sizes compared to several baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Forecasting

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Deep Learning-Based Multi-Criteria Recommender System: Leveraging Autoencoders for Improved Personalization 1

Deep Learning-Based Multi-Criteria Recommender System: Lever...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Chouti, Nithish Kusale, Sakshi Mishra, Sanket Rajora, Shashank Prasad, Prabhu Sinha, Bam Bahadur Vanahalli, Manjunath IIIT Dharwad Computer Science Engineering Karnataka580009 India NIT Sikkim Computer Science Engineering Ravangla737139 India IIIT Dharwad Data Science & Artificial Intelligence Karnataka580009 India

ISBN: (纸本)9798350353648

Multi-Criteria Recommender Systems (MCRSs) are a promising method that takes into account several elements of the user's preferences in order to improve recommendation accuracy. However, most existing research has focused on single-criterion ratings, overlooking the potential of multi-criteria considerations in deep learning-based recommender systems. This paper introduces a novel deep learning-based algorithm for MCRSs, leveraging deep autoencoders to uncover complex user-item interactions based on multi-criteria preferences. The proposed algorithm, evaluated using the TripAdvisor multi-criteria dataset, demonstrates superior recommendation capabilities compared to traditional methods, achieving a significantly lower RMSE of 0.975. The results highlight the algorithm's effectiveness in providing accurate and personalized recommendations tailored to diverse user preferences, marking a significant advancement in MCRS performance. © 2024 IEEE.

关键词： Collaborative filtering

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