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An efficient anonymous authentication scheme based on double authentication preventing signature for mobile healthcare crowd sensing 14th

An efficient anonymous authentication scheme based on double...

引用

14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Liu, Jinhui Yu, Yong Li, Yannan Zhao, Yanqi Du, Xiaojiang School of Computer Science Shaanxi Normal University Xi’an710119 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Department of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States

ISBN: (纸本)9783030142339

With the widespread growth of cloud computing and mobile healthcare crowd sensing (MHCS), an increasing number of individuals are outsourcing their masses of bio-information in the cloud server to achieve convenient and efficient. In this environment, Cloud Data Center (CDC) needs to authenticate masses of information without revealing owners’ sensitive information. However, tremendous communication cost, storage space cost and checking time cost lead to CDC that give rise to all kinds of privacy concerns as well. To mitigate these issues, To mitigate these issues, we propose a data anonymous batch verification scheme for MHCS based on a certificateless double authentication preventing aggregate signature. The proposed scheme can authenticate all sensing bio-information in a privacy preserving way. We then present that the proposed CL-DAPAS scheme is existentially unforgeable in the Random Oracle Model (ROM) assuming that Computational Diffie-Hellman problem is difficult to solve. Furthermore, we provide an implementation and evaluate performance of the proposed scheme and demonstrate that it achieves less efficient computational cost compared with some related schemes. © 2019, Springer Nature Switzerland AG.

关键词： Authentication

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Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation 38

Generated and Pseudo Content guided Prototype Refinement for...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Lili Lang, Congyan Chen, Ziyi Wang, Tao Li, Yidong Liu, Jun School of Computer Science & Technology Beijing Jiaotong University China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computing and Communications Lancaster University United Kingdom

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12.10% and 13.75% mIoU on S3DIS and ScanNet, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Multimedia and Ubiquitous Engineering 8

引用

FTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014

作者： Park, James J.[Jong Hyuk] Chen, Shu-Ching Gil, Joon-Min Yen, Neil Y. Department of Computer Science and Engineering Seoul National University of Science & Technology Seoul Korea Seoul Korea Republic of School of Computing and Information Sciences Florida International University MiamiFL United States School of Information Technology Engineering Catholic University of Daegu Kyongsang-bukto Korea Republic of School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan

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Deep knn for medical image classification 23rd

Deep knn for medical image classification

引用

23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhuang, Jiaxin Cai, Jiabin Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers. © Springer Nature Switzerland AG 2020.

关键词： Nearest neighbor search

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Generative AI for Advanced Malware Detection 4

Generative AI for Advanced Malware Detection

引用

4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Al Balawi, Mohammad Alnabhan, Mohammad Atoum, Mohammed S King Hussein School of Computing Sciences Princess Sumaya University for Technology Department of Cyber Security Amman Jordan King Abdullah II School University of Jordan Department of Computer Science Amman Jordan

ISBN: (纸本)9798350354775

Identifying and analyzing malicious software in high accuracy is critical requirement for mitigating security risks. This research paper presents a new AI-enabled malware detection model utilizing characteristics of Portable Executable (PE) files. Generative AI is used to analyze extracted strings, section names, digital signatures, and company information of PE files. In addition, the proposed model will detect patterns associated with known malicious behaviors of anti-debugging, code injection, and keylogging by analyzing functions imported from external libraries and the API calls. Hence, AI techniques will integrate with traditional parsing methods to process data. The results achieved prove that the proposed hybrid model is robust and develops the accuracy detection and depth of malware analysis. © 2024 IEEE.

关键词： Cyber attacks

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Multi-utility Agricultural IoT based Robot for Various Agricultural Activities 10

Multi-utility Agricultural IoT based Robot for Various Agric...

引用

10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Garg, Vishesh Aggarwal, Alok Aggarwal, Shalini Bhala, Tanishka Kumar, Adarsh Goyal, S.B. School of Computer Science University of Petroleum & Energy Studies UK Dehradun India School of Computing Graphic Era Hill University UK Dehradun India City University Faculty of Information Technology Malaysia

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

With the availability of various economical sensors and the implementation of Internet-of-Things (IoT), agriculture industry is moving towards more precise, data centric and smarter than ever. Almost every industrial domain has been resigned, including smart agriculture, with the rapid emergence of IoT based technologies with the aid of economical sensor technology. The use of agricultural robots in agriculture field is increasing to cover the necessity ever increasing population with static piece of land. In this work, a multi-utility agricultural IoT based robot has been proposed for performing various agricultural activities. It provides control of the agricultural activities remotely through VLAN and also through cloud network (be it Server based or Serverless), i.e. a hybrid control model. The proposed method requires minimal human labor to perform various farming activities. The end user can control the machine in the field and all agricultural activities can be performed remotely from anywhere with the help of Android application. Three modes of operation are supported, manual, semi-automatic and automated. The proposed system is to be controlled and trained for few days manually so that it gains accuracy in the path being followed to perform the task. Proposed system has solar powered system as the primary source of energy and Lithium-ion polymer battery for battery backup support. Anti-theft mechanism is provided with the help of GSM module. © 2022 IEEE.

关键词： Internet of things

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A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability 42

A Better Cardinality Estimator with Fewer Bits, Constant Upd...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Du, Yang Huang, He Sun, Yu-E Li, Kejian Zhang, Boyu Gao, Guoju Soochow University School of Computer Science and Technology Suzhou China Soochow University School of Rail Transportation Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing Shanghai China

ISBN: (纸本)9798350334142

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage. © 2023 IEEE.

关键词： Efficiency

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Comparison on Blockchain-based Intrusion Detection Systems for Internet of Things

Comparison on Blockchain-based Intrusion Detection Systems f...

引用

2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

作者： Mago Vistro, Daniel Kamran Abid, Muhammad Ahmad, Mughees Ali, Mubashir Hassan, Jawad Younas, Akhlaq School of Computing Asia Pacific University of Technology and Innovation Kuala lumpur Malaysia Nfc Institute of Engineering and Technology Department of Computer Science Multan Pakistan Department of Computer Science Zhob Pakistan Lahore Garrison University Department of Software Engineering Lahore Pakistan Air University Multan Campus Department of Computer Science and Engineering Multan Pakistan

ISBN: (纸本)9781665456371

The Internet of Things (IoT) is a cutting-edge concept that unites the Internet with actual physical objects from a variety of industries, such as home automation, manufacturing, human health, and environmental monitoring. Users are now able to use gadgets with Internet access for routine tasks. Along with many advantages, IoT also presents a number of security-related difficulties. The security of the IoT network has grown in importance as the popularity of the Internet of Things increases daily. An IoT network is more secure thanks to encryption and authentication, yet it can be challenging to defend IoT devices from online threats. Cyberattacks on the Internet of Things system not only result in data loss, but they also have the potential to bring the entire system to a standstill. The performance of the network can be negatively impacted by hostile activities, which are easily detected by intrusion detection systems (IDS). To safeguard IoT systems, an efficient intrusion detection system (IDS) is required. Another cutting-edge technology that helps security systems counter the most recent threats is blockchain. In this post, we analysed state-of-the-art blockchain-based intrusion detection methods for Internet of Things devices. New advancements to alleviate security threats are also listed in a table. Finally, we list the major questions brought on by the current restrictions. © 2022 IEEE.

关键词： Internet of things

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Implicit discourse relation recognition using neural tensor network with interactive attention and sparse learning 27

Implicit discourse relation recognition using neural tensor ...

引用

27th International Conference on Computational Linguistics, COLING 2018

作者： Guo, Fengyu He, Ruifang Jin, Di Dang, Jianwu Wang, Longbiao Li, Xiangang School of Computer Science and Technology Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Japan AI Labs Didi Chuxing Beijing China

ISBN: (纸本)9781948087506

Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separately, the ones considering pair specific clues ignore the bidirectional interactions between two arguments and the sparsity of pair patterns. In this paper, we propose a novel Neural Tensor Network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word pairs from two discourse arguments to model pair specific clues, and integrate them as interactive attention into argument representations produced by the bidirectional long short-term memory network. Meanwhile, (2) the neural tensor network with sparse constraint is proposed to explore the deeper and the more important pair patterns so as to fully recognize discourse relations. The experimental results on PDTB show that our proposed TIASL framework is effective. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Tensors

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SocialTransfer: Transferring social knowledge for cold-start crowdsourcing 23

SocialTransfer: Transferring social knowledge for cold-start...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Zhao, Zhou Cheng, James Wei, Furu Zhou, Ming Ng, Wilfred Wu, Yingjun Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Microsoft Research Asia Beijing China School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781450325981

An essential component of building a successful crowdsourcing market is effective task matching, which matches a given task to the right crowdworkers. In order to provide high-quality task matching, crowdsourcing systems rely on past task-solving activities of crowdworkers. However, the average number of past activities of crowdworkers in most crowd-sourcing systems is very small. We call the workers who have only solved a small number of tasks cold-start crowdworkers. We observe that most of the workers in crowdsourcing systems are cold-start crowdworkers, and crowdsourcing systems actually enjoy great benefits from cold-start crowd-workers. However, the problem of task matching with the presence of many cold-start crowdworkers has not been well studied. We propose a new approach to address this issue. Our main idea, motivated by the prevalence of online social networks, is to transfer the knowledge about crowdworkers in their social networks to crowdsourcing systems for task matching. We propose a SocialTransfer model for cold-start crowdsourcing, which not only infers the expertise of warmstart crowdworkers from their past activities, but also transfers the expertise knowledge to cold-start crowdworkers via social connections. We evaluate the SocialTransfer model on the well-known crowdsourcing system Quora, using knowledge from the popular social network Twitter. Experimental results show that, by transferring social knowledge, our method achieves significant improvements over the state-of-the-art methods. Copyright 2014 ACM.

关键词： Crowdsourcing

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