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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Changhu, Cheng Peng, Yang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9780738110837

Few-shot learning aims to build a classification model by training a small amount of labeled sample data, which can be well adapted to new domains. The key point of few-shot learning is that a small amount of sample data cannot reflect the true data distribution. Training on a small amount of sample data will lead to over-fitting of the deep neural network model. The differences between different categories are ignored when using similarity measures for classification. This paper proposes a novel few-shot learning method based on similarity-difference relation network, which uses shallow wide residual network to extract the features of the training dataset and fuses them into a category prototype. Meanwhile, SDRN pays attention to the characterization of similarities and differences between positive and negative samples. This paper verifies the effectiveness of the similarity-difference relational network on the Mini-ImageNet and Tiered-ImageNet datasets. The experimental results show that the similarity-difference two-way relational network further improves image classification accuracy in the few-shot learning task. © 2021 IEEE.

关键词： Deep neural networks

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Estimation packet loss ratios for the two segments of end-to-end path on the monitor

Estimation packet loss ratios for the two segments of end-to...

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2012 International Conference on computer Science and Service System, CSSS 2012

作者： Cheng, Guang Gao, Zhongyao School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9780769547190

Packet loss may cause the degradation of network and application performance. This paper proposes a passive measurement method called LTS to estimate end-to-end path packet loss ratio for the two segments of the path divided by the monitor node. This method can estimate the packet loss ration of each segment and infer loss ratios based on IP headers identification field, TCP headers sequence number and packets interval. The segment estimation packet loss method can be useful to evaluate the performance of core network or internet work if the monitor is put in the border of a network. The algorithm is evaluated to use a test bed simulation measurement, and is analyzed reordering ratios and loss ratios based on the traces collected from the CERNET backbone links. © 2012 IEEE.

关键词： Packet loss

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Intrusion Model for Intrusion Tolerance System by Using Capabilities of Attackers

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电子学报(英文版) 2006年第4期15卷 613-618页

作者： WANG Liangmin MA Jianfeng ZHANG Jiaming Key Laboratory of Computer Network and Information Security Ministry of Education Xidian University Xi＇an 710071 China Computer Security Laboratory Department of Computer Science Jiangsu University Zhenjiang 212013 China

To solve the problem of modeling the intrusion in Intrusion Tolerance System, an intrusion model of state transition and its constructing algorithm is presented in this paper, which places its emphasis on the influence of the intrusion upon the system and describes the intrusion as the state transitions of the attackers' capability. Firstly, we correlate the alerts into meta-attack in the constructing algorithm, and then define Cover as the reduction of meta-attack. Secondly, we transform the cover of meta-attack to intrusion model and give the proofs of the equivalences among intrusion model, meta-attack and its cover. Thirdly, we present an algorithm for describing the intrusion model without employing manual work, which makes it superior to the existing methods. Finally, we do some correlation experiments to evaluate and show the performances of both the intrusion model and the algorithms for constructing and describing this model.

关键词：入侵模型入侵容忍系统攻击能力网络安全

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Encrypted traffic identification based on N-gram entropy and cumulative sum test 2018

Encrypted traffic identification based on N-gram entropy and...

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13th International Conference on Future Internet Technologies, CFI 2018

作者： Cheng, Guang Hu, Ying School of Cyber Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9781450364669

Since existing methods using entropy are less effective in characterizing encrypted traffic, this paper proposes an encrypted traffic identification method based on n-gram entropy and cumulative sum. This method analyzes the entropy characteristics of n-gram entropy for text, picture, compressed file, and encrypted traffic in the network. Furthermore, an analysis of cumulative sum is performed to better distinguish compressed file traffic and encrypted traffic. The experiments show that our propsed method reaches high accuracy for encrypted traffic identification and performs well in distinguishing compressed file traffic and encrypted traffic. © 2018 Association for Computing Machinery.

关键词： Entropy

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Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning 30

Few-Shot Semantic Dependency Parsing via Graph Contrastive L...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Bin Fan, Yunlong Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9782493814104

Graph neural networks (GNNs) have achieved promising performance on semantic dependency parsing (SDP), owing to their powerful graph representation learning ability. However, training a high-performing GNN-based model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labeled data. To address this drawback, we propose a syntax-guided graph contrastive learning framework to pre-train GNNs with plenty of unlabeled data and fine-tune pre-trained GNNs with few-shot labeled SDP data. Through extensive experiments conducted on the SemEval-2015 Task 18 English dataset in three formalisms (DM, PAS, and PSD), we demonstrate that our framework achieves promising results when few-shot training samples are available. Furthermore, benefiting from the pre-training process, our framework exhibits notable advantages in the out-of-domain test sets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Graph neural networks

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Light Up Fireflies: Towards Understanding the Design of Interpersonal Bodily Intertwinement in Bodily Play 24

Light Up Fireflies: Towards Understanding the Design of Inte...

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11th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI-PLAY Companion 2024

作者： Lu, Yingtong Li, Zhuying Ding, Ding Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9798400706929

In this paper, we explore the design of interpersonal bodily intertwinement through a VR social game, "Light Up Fireflies". Inspired by works of virtual co-embodiment, our game lets two players embody a single avatar, where each player controls half of the body. The gameplay involves walking in a virtual forest and collaboratively interacting with floating fireflies. While moving, poor coordination can lead to unbalanced movements, causing the avatar to lose its center of mass and fall. Such a design might blur the body boundary between players, providing an intriguing bodily experience of interpersonal bodily intertwinement. We hope our work offers novel insights into bodily play where players experience interconnected physical presence within the digital space. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

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Ponzi Scheme Detection in Smart Contracts Using Heterogeneous Semantic Graph 6th

Ponzi Scheme Detection in Smart Contracts Using Heterogeneo...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Chen, Wei Jiang, Xinjun Lan, Tian Liu, Leyuan Li, Chengyu Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610000 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China

ISBN: (纸本)9789819614103

Behind the booming decentralized finance (DeFi) ecosystem driven by blockchain technology, various financial risks lurking, including money laundering, gambling, Ponzi schemes, and phishing. Due to the decentralization and anonymity of Ethereum, Ponzi schemes can be easily deployed, causing huge economic losses to investors. Existing detection methods based on transaction data are difficult to provide early risk warnings, while detection methods based on smart contract source code and opcodes have insufficient feature fusion at multiple levels. We propose an approach for constructing a multi-level Heterogeneous Semantic Graph (HSG) of smart contracts, and improve the HAN model to detect Ponzi scheme smart contracts based on the Heterogeneous Semantic Graph. The experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 97.21%, a precision of 94.29%, and an F1 score of 90.41%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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A genetic algorithm for multiple mobile data collectors' path planning in WSNs

A genetic algorithm for multiple mobile data collectors' pat...

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2012 International Conference on information Technology and Software Engineering, ITSE 2012

作者： Shen, Zhuowei Cao, Min Key Lab. of Computer Network and Information Integration School of Computer and Engineering Southeast University Nanjing China

ISBN: (纸本)9783642345210

In mobile data collector (MDC)-based wireless sensor networks, we need to design paths for MDCs for better data collection. In this paper, we use predictable mobility pattern and apply a heuristic algorithm to design paths of MDCs. The algorithm aims to make the time cost by MDCs covering the whole network as short as possible. The algorithm is taken by two steps. Firstly, choose the stop points, and then, construct the paths. Both steps are computed using genetic algorithm (GA). In choosing stop points algorithm, improved stochastic tournament selection is adopted to deal with restraints and special fitness is used to enhance differentiation of solutions. An asexual reproduction based GA with 2-opt optimization mechanism is used to construct MDCs' paths. As experiments show, the algorithm proposed in this paper performs better than greedy algorithm. © 2013 Springer-Verlag.

关键词： Wireless sensor networks

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Science China(information Sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN Advanced Institute of Photonics Technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial Key Laboratory of Photonics Information Technology Guangdong University of Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection （COD）,which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network（DBFN）with a parallel attention selection mechanism （PASM）.In detail,a schismatic receptive field block（SRF）combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism （HAM）,and a depth feature polymerization module （DFPM）is employed for high-level information to extract detection features in the other ***,both texture features and detection features are input into the PASM to acquire selective expression ***,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling （ASPP）and a residual channel attention block （RCAB）being applied *** results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error （MAE）,weighted F βmeasure （Fβω）,structural measure （Sα）,and E-measure （Eφ）

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary

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An Iterative Detection/Decoding Algorithm of Correlated Sources for the LDPC-Based Relay Systems

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China Communications 2017年第9期14卷 190-198页

作者： Haiqiang Chen Hang Cao Xiangcheng Li Youming Sun Haibin Wan Tuanfa Qin The School of Computer and Electronic Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi University

An iterative detection/decoding algorithm of correlated sources for the LDPC-based relay systems is presented. The signal from the source-destination(S-D) link is formulated as a highly correlated counterpart from the relay-destination(R-D) link. A special XOR vector is defined using the correlated hard decision information blocks from two decoders and the extrinsic information exchanged between the two decoders is derived by the log-likelihood ratio(LLR) associated with the XOR vector. Such the decoding scheme is different from the traditional turbo-like detection/decoding algorithm, where the extrinsic information is computed by the side information and the soft decoder outputs. Simulations show that the presented algorithm has a slightly better performance than the traditional turbo-like algorithm(Taking the(255,175) EG-LDPC code as an example, it achieves about 0.1 dB performance gains aroundBLER=10^(-4)). Furthermore, the presented algorithm requires fewer computing operations per iteration and has faster convergence rate. For example, the average iteration of the presented algorithm is 33 at SNR=1.8 dB, which is about twice faster than that of the turbo-like algorithm, when decoding the(961,721) QC-LDPC code. Therefore, the presented decoding algorithm of correlated sources provides an alternative decoding solution for the LDPC-based relay systems.

关键词： correlated sources iterative de-coding LDPC codes relay channel

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