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4th Asia Service sciences and software engineering Conference, ASSE 2023

作者： Wu, Jiajing Liu, Jieli Chen, Jinze Chen, Ting Li, Jingwei Song, Shuwei He, Jiahao School of Software Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Security University of Electronic Science and Technology of China China

ISBN: (纸本)9798400708534

In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection. © 2023 ACM.

关键词： Smart contract

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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： Automation

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RGA-Reid: Neural Architecture Search Based on Relation-Aware Global Attention for Person Re-Identification 11

RGA-Reid: Neural Architecture Search Based on Relation-Aware...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Chen, Shijie Chen, Shengbo Lei, Zhou School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350333664

With the continuous development of deep learning, reid has been continually improved. However, the network backbones of reid are mostly manually designed. This paper proposes RGA-Reid based on auto-reid. NASBR-Reid obtains the network backbone through neural architecture search. It replaces the part-base module in the auto-reid search algorithm with the RGA module, which effectively integrates global relationships to enhance attention training between different parts. Experimental results demonstrate that RGA-Reid can retrieve superior network backbones. © 2023 IEEE.

关键词： network backbone Neural Architecture Search Reid Relation-Aware Global Attention

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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computer Systems science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Dynamic Topology Management In Ad-Hoc Networks For Improved Performance 2

Dynamic Topology Management In Ad-Hoc Networks For Improved ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anakath, A.S. Kannadasan, R. Margarat, G. Simi Pandian, A. Pasumpon Antony Sibiya Varghese, V. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383867

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Decision making

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Slice-Level Label Attention with Global-Guided Attention Regularization for Multi-Label Classification in Knee MRI Sequences

Slice-Level Label Attention with Global-Guided Attention Reg...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Yang, Jingzhi Shen, Hua Liu, Ziyu Wu, Weilong Wu, Ji Ni, Ming Yuan, Huishu Fu, Xiangling Li, Miao National Pilot Software Engineering School Bupt School of Computer Science Beijing China Tsinghua University College of Ai Department of Electronic Engineering Beijing China Peking University Third Hospital Beijing China

ISBN: (纸本)9798350362480

Magnetic Resonance Imaging (MRI) is crucial for diagnosing various knee-related diseases, and developing automatic diagnostic models based on knee MRI data is highly valuable. However, this task presents significant challenges due to the need to manage MRI data with multiple sequences and numerous images, where different diseases are often associated with specific images within certain sequences. To address these challenges, we propose a multi-label classification framework designed to effectively process MRI data and handle a large-scale label space encompassing hundreds of disease categories. Our approach introduces a Slice-Level Label Attention mechanism, which enables the model to learn the alignment between labels and images within sequences, thereby enhancing both performance and interpretability. Additionally, we present a Global-Guided Attention Regularization mechanism that further improves the consistency and robustness of the Slice-Level Label Attention results. We validate our framework on a large-scale MRI dataset involving multi-label classification across hundreds of fine-grained disease categories. Experimental results demonstrate that our method not only achieves superior performance but also provides more robust and consistent interpretability. © 2024 IEEE.

关键词： Deep learning

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A Multi-Label Recommendation Algorithm Based on Graph Attention and Sentiment Correction 4

A Multi-Label Recommendation Algorithm Based on Graph Attent...

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4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

作者： Wu, Ziyi Dai, Xindi Wang, Xiaoguang Xiong, Yao Gao, Shang Liu, Dainan School of Software Engineering Jilin University Changchun China Jilin University Public Computer Education and Research Center Changchun China School of Computer Science Jilin University Changchun China School of Electronic Science and Engineering Jilin University Changchun China

ISBN: (纸本)9798350314526

Traditional recommendation algorithms overlook the abundant information present in user feedback and are limited to the binary relationship between items and users. In contrast, this paper proposes an algorithm that takes advantage of item labels and user feedback to generate faster and more precise recommendation lists. The algorithm employs graph attention link prediction and sentiment analysis to calibrate parameters and enhance traditional recommendation algorithms by utilizing user information comprehensively. Empirical results on publicly available datasets indicate that the proposed algorithm outperforms both traditional and sentiment-uncorrected recommendation algorithms in terms of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Shared experiences across multiple devices:a new digital interactive experience method for cultural heritage based on mixed reality

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 1-12,23页

作者： Lu Zhichao Zhao Haiying School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

In order to study the role of the new technological concept of shared experiences in the digital interactive experience of cultural heritage and apply it to the digital interactive experience of cultural heritage to solve the current problems in this field,starting from the mixed reality(MR) technology that the shared experiences rely on,proper software and hardware platforms were investigated and selected,a universal shared experiences solution was designed,and an experimental project based on the proposed solution was made to verify its *** the end,a proven and workable shared experiences solution was *** solution included a proposed MR spatial alignment method,and it integrated the existing MR content production process and standard network synchronization ***,it is concluded that the introduction and reasonable use of new technologies can help the development of the digital interactive experience of cultural *** shared experiences solution for the digital interactive experience of cultural heritage balances investment issues in the exhibition,display effect,and user *** can speed up the promotion of cultural heritage and bring the vitality of MR technology to relevant projects.

关键词： mixed reality(MR) augmented reality(AR) shared experiences cultural heritage digital interactive experience

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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Back in time:digital restoration techniques for the millennium Dunhuang murals

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 13-23页

作者： Tao Yifeng Song Yucheng Xu Mengqiu Zhang Chuang Wu Ming Bai Sule School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

In the long history of more than 1 500 years,Dunhuang murals suffered from various deteriorations causing irreversible damage such as falling off,fading,and so ***,the existing Dunhuang mural restoration methods are time-consuming and not feasible to facilitate cultural dissemination and permanent *** by cultural computing using artificial intelligence,gated-convolution-based dehaze net(GD-Net) was proposed for Dunhuang mural refurbishment and comprehensive ***,a neural network with gated convolution was applied to restore the falling off areas of the mural to ensure the integrity of the mural ***,a dehaze network was applied to enhance image quality to cope with the fading of the ***,a Dunhuang mural dataset was presented to meet the needs of deep learning approach,containing 1 180 images from the Cave 290 and Cave 112 of the Mogao *** experimental results demonstrate the effectiveness and superiority of GD-Net.

关键词： image inpainting image enhancement mural restoration

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