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Institution of Engineering and Technology International Communication Conference on Wireless Mobile and Computing

作者： Shen, Qiang Fang, Xuming Kim, Sunmyeng He, Rong Provincial Key Lab. of Information Coding and Transmission Southwest Jiaotong University Chengdu 610031 China High Performance Network Lab. Institute of Acoustics Chinese Academy of Sciences Beijing 100190 China School of Computer and Software Engineering Kumoh National Institute of Technology Gyeongbuk 730701 Korea Republic of

ISBN: (纸本)9781849191388

Routing in wireless mesh networks presents a great challenge due to unreliable wireless links and collisions. Recently, a new routing method, opportunistic routing (OR), is proposed to deal with them by exploiting broadcast nature of wireless networks. OR relies on the global knowledge of networks to select relays and determine priorities. In this paper, we propose a new routing scheme, OxDSR, which combines OR and dynamic source routing (DSR) and performs opportunistic transmissions with the help of the local information and the traditional route obtained by DSR. By taking the duplicates and additional wireless resource consumed by multiple ACK frames into consideration, we propose a new metric, equivalent data rate, to select and prioritize relays. Extensive simulation study shows that the proposed OxDSR is more resilient and achieves higher throughput than DSR based on the expected transmission count routing metric.

关键词： Medium access control

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Exploiting Conjugate lab.l information for Multi-Instance Partial-lab.l Learning

arXiv

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arXiv 2024年

作者： Tang, Wei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Lab. of Computer Network and Information Integration Southeast University MoE China School of Information and Physical Sciences The University of Newcastle NSW2308 Australia

Multi-instance partial-lab.l learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate lab.l set containing one true lab.l and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate lab.l sets for disambiguation, disregarding the intrinsic properties of the lab.l space and the supervised information provided by non-candidate lab.l sets. In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate lab.l information for Multi-Instance Partial-lab.l learning, which exploits the conjugate lab.l information to improve the disambiguation performance. To achieve this, we extract the lab.l information embedded in both candidate and non-candidate lab.l sets, incorporating the intrinsic properties of the lab.l space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-lab.l learning algorithms. Copyright © 2024, The Authors. All rights reserved.

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Disentangled high quality salient object detection

arXiv

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arXiv 2021年

作者： Tang, Lv Li, Bo Ding, Shouhong Song, Mofei Youtu Lab Tencent Shanghai China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods. © 2021, CC BY.

关键词： Pixels

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An improved identity-based group signature scheme

An improved identity-based group signature scheme

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Tan, Zuowen School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 Jiangxi Province China Key Lab. of Network Security and Cryptology School of Mathematics and Computer Science Fujian Normal University Fuzhou 350007 Fujian Province China

ISBN: (纸本)9780769539317

In a group signature scheme, a group member signs messages anonymously on behalf of the group. However, in the case of a dispute, the actual group signer's identity can be revealed by a designated entity. In this paper, we propose a new identity-based group signature scheme using bilinear maps. The group signature scheme is well suited for large groups, i.e., the length of the groups public key and the length of signatures are independent on the size of the group. Our signature scheme is based on bilinear maps. Furthermore, a group member can sign many messages using the same key pair. Cryptanalysis shows that the proposed group signature scheme is secure under the computational Diffie Hellman problem called CDHP assumption. And it is also more efficient than previous ones satisfying these requirements. © 2009 IEEE.

关键词： Cryptography

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Multiple influence maximization in social networks 10

Multiple influence maximization in social networks

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10th International Conference on Ubiquitous information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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pService: Towards similarity search on peer-to-peer web services discovery

pService: Towards similarity search on peer-to-peer web serv...

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1st International Conference on Advances in P2P Systems, AP2PS 2009

作者： Zhou, Gang Yu, Jianjun National Digital Switching System Engineering and Technological Research Center 450002 France State Key Lab. of Software Development Environment Beihang University 100191 China Computer Network Information Center Chinese Academy of Sciences 100190 China

ISBN: (纸本)9780769538310

We present the design of pService system for P2P Web services discovery supporting similarity earch with its locality-preserving feature based on modified Skip Graph. In pService, the most important property (e.g. service name) of a service is viewed as the indexing key of a peer node. The other properties are extracted as multiattributes which would be mapped into one-dimensional indices using HSFC (Hilbert Space Filling Curve), and assigned to membership vector of the peer node. These two features help to support similarity search in two aspects: peer nodes with similar indexing keys would be distributed neighborly, peer nodes with similar indices will connect to each other through routing table. Our evaluation showed that the pService performs considerable Web services discovery efficiency. © 2009 IEEE.

关键词： Web services

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Study of secret sharing schemes based on quantum theory

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Tongxin Xuebao/Journal on Communications 2009年第11 A期30卷 127-132+139页

作者： Zhu, Zhen-Chao Zhang, Yu-Qing Key Lab. of Computer Networks and Information Security of Ministry of Education Xidian University Xi'an 710071 China National Computer Network Intrusion Protection Center GUCAS Beijing 100043 China

Quantum secret sharing (QSS) which is based on the laws of quantum mechanics, instead of mathematical assumptions can share the information unconditionally securely, according to the form of sharing information, quantum secret sharing can be divided into QSS of classical messages and QSS of quantum information, QSS of classical messages can be divided into QSS of classical messages based on entanglement and QSS of classical messages without entanglement, the representative protocols in three main branches of the quantum secret sharing schemes and its latest development were introduced, also prospect analysis of the schemes' future development was given.

关键词： Quantum entanglement

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Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation

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International Conference on computer Vision (ICCV)

作者： Lihe Yang Wei Zhuo Lei Qi Yinghuan Shi Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Tencent Key Lab of Computer Network and Information Integration (Ministry of Education) Southeast University

ISBN: (纸本)9781665428132

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and fore-ground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlab.led data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5 i and 7.0% mIOU on COCO-20 i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is availab.e at https://***/LiheYoung/MiningFSS.

关键词： Training computer vision Costs Codes Training data Prototypes Benchmark testing

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Efficient (t, n) secret sharing scheme against cheating

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Journal of Computational information Systems 2012年第9期8卷 3815-3821页

作者： Liu, Yanxiao Zhang, Yuqing Hu, Yupu Key Lab. of Computer Networks and Information Security of Ministry of Education Xidian University China National Computer Network Intrusion Protection Center GUCAS China

A (t, n) secret sharing (SS) scheme can be divided into two protocols: share generation protocol and secret reconstruction protocol. In share generation protocol, a mutually trusted dealer selects a secret and distributes shares to shareholders secretly;in secret reconstruction protocol, a group of t qualified shareholders pool their shares together to reconstruct the secret. However, in secret reconstruction protocol, dishonest shareholders (i.e., cheaters) can pool forged shares to fool honest shareholders. By this way, the cheaters can recover the legitimate secret exclusively, and the honest shareholders get nothing but a fake secret. In this paper, we propose a (t, n)SS that is capable of detecting such cheating fact efficiently. Our scheme is an extension of Shamir's (t, n)SS that uses a symmetric bivariate polynomial. In addition, our scheme adopts no security assumption, it is unconditional secure. © 2011 by Binary information Press.

关键词： Shareholders

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Funny words detection via Contrastive Representations and Pre-trained Language Model

Funny words detection via Contrastive Representations and Pr...

引用

Artificial Intelligence, networking and information Technology (AINIT), International Seminar on

作者： Yiming Du Zelin Tian Key Lab of Computer Network and Information Integration School of Computer Science and Engineering- Southeast University (SEU) Nanjing China University of British Columbia Vancouver Canada

ISBN: (纸本)9781665412971

Funniness detection of news headlines is a challenging task in computational linguistics. However, most existing works on funniness detection mainly tackle the scenario by simply judging whether a sentence is humorous, whose result is unstable due to factors such as sentence length. To solve this issue, in this paper, our idea is to fine-grained mine the detailed information of the words and the contextual relationship between different words in the sentence, which help to evaluate the correlation between keywords and the funniness of news headlines quantitatively. Specifically, we propose a funny words detection algorithm based on the contrastive representations learning and BERT model. To quantify the impact of different words on the degree of humor, we first subtract the funniness grades of the original news headlines and the funniness grades of the original news headlines with a single word replaced. Both funniness grades are predicted with a pre-trained model, which is supervised by a a threshold to limit the amount of data and ensure the validity of data. To ensure the accuracy of our prediction, we further introduce the contrastive learning to constrain the differences of news headlines before and after word replacement. Finally, according to the Root Mean Square Error (RMSE) matrix in our experiment, we develop a BERT model with mixed sequence embedding to generate a table about words and their corresponding funniness improvement about the news headlines.

关键词： Seminars Representation learning Correlation Bit error rate Predictive models Data models Task analysis

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