检索结果-内蒙古大学图书馆

IEEE/IFIP International Conference on Embedded and Ubiquitous computing, EUC

作者： Lei Zhang Qianhong Wu Josep Domingo-Ferrer Bo Qin UNESCO Chair in Data Privacy Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Spain School of Computer Key Laboratory of AeroSpace Information Security and Trusted Computing Wuhan University of Technology China School of Science Department of Maths Xi'an University of Technology China

Certificateless cryptography eliminates the key escrow problem in identity-based cryptography. Hierarchical cryptography exploits a practical security model to mirror the organizational hierarchy in the real world. In this paper, to incorporate the advantages of both types of cryptosystems, we instantiate hierarchical certificate less cryptography by formalizing the notion of hierarchical certificate less signatures. Furthermore, we propose an HCLS scheme which, under the hardness of the computational Diffie-Hellman (CDH) problem, is proven to be existentially unforgeable against adaptive chosen-message attacks in the random oracle model. As to efficiency, our scheme has constant complexity, regardless of the depth of the hierarchy. Hence, the proposal is secure and scalable for practical applications.

关键词： Public key Games Artificial intelligence Tin Protocols

来源：评论

学校读者我要写书评

暂无评论

Approximation algorithm for constructing data aggregation trees for wireless sensor networks

引用

中国高等学校学术文摘·计算机科学 2009年第4期3卷 524-534页

作者： Deying LI Jiannong CAO Qinghua ZHU Key Laboratory of Data Engineering and Knowledge Engineering School of InformationRenmin University of ChinaBeijing 100872China Internet and Mobile Computing Lab Department of ComputingHong Kong Polytechnic UniversityHong KongChina

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs)for a group of sensor nodes to send collected information to a single sink *** data aggregation tree contains the sink node,all the source nodes,and some other non-source *** goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save *** prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy *** also give a distributed version of the approximation *** simulations are performed to study the performance of the proposed *** results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.

关键词： WSNs data aggregation tree approximation algorithm distributed algorithm

来源：评论

学校读者我要写书评

暂无评论

Large cross-phase-modulation between two slow pulses by coupled double dark resonances

引用

Physical Review A 2009年第3期79卷 033813-033813页

作者： B. P. Hou L. F. Wei G. L. Long S. J. Wang School of Physics and Electronic Engineering Sichuan Normal University Chengdu 610068 China Sichuan Key Laboratory of Visual Computing and Virtual Reality Sichuan Normal University Chengdu 610068 China Laboratory of Quantum Opt-electronic Information Southwest Jiaotong University Chengdu 610031 China Department of Physics Tsinghua University Beijing 100080 China Department of Physics Sichuan University Chengdu 610064 China

We present a scheme to realize a large cross-phase-modulation between two slow optical pulses by using the atomic configurations of coupled double dark resonances. In this scheme slow group velocities for these optical pulses can be automatically equalized in terms of the inherent symmetric atomic configuration, and the various absorptions can be overcome by using three-photon hyper-Raman amplification. Additionally, the switching between cross-type nonlinear amplification and absorption could be realized by just changing the sign of the difference between two pump detunings. This is useful for the implementations of all-optical switches. The linearity and cross nonlinearity in the Doppler-broadened medium with different laser propagating configurations have also been investigated.

关键词： cross-phase-modulation coupled resonances Cross-Phase Modulation DOPPLER BROADENING Scheme DOUBLE nonlinearity slow pulse darkness

来源：评论

学校读者我要写书评

暂无评论

Study on Multilocus Interactions for Hypertension by an Information Theoretic Method

Study on Multilocus Interactions for Hypertension by an Info...

引用

第三届最优化与系统生物学国际研讨会

作者： Junhua Zhang Wentao Huang Zhiyuan Zhao Biao Li Yuelan Wang Dongfeng Gu Guoying Li Runsheng Chen Academy of Mathematics and Systems Science CAS Key Laboratory of Random Complex Structures and Data Science Academy of Mathematics and Systems ScienceCAS College of Electronics and Information Engineering South-Central University for Nationalities Actuarial Department China Life Insurance Company Limited School of Informatics and Computing Indiana UniversityBloomingtonIndianaUSA

Hypertension,among diabetes,obesity and others,is one of the common human diseases that is genetically expressed as complex traits to which genetic,environmental,and demographic factors contribute *** the underlying genes and examining their interactions, a crucial step in understanding the molecular pathogenesis of complex diseases,is both a statistical and a computational challenge,stressing the need for novel strategies to move this process *** this paper we propose a new method to study the association of multiple gene interactions for complex *** method is carried out by two ***,we sequentially select additionally associated SNP loci combinations by minimizing the p-value of a test based on an information measure,measure of information ***,this approach is called MID ***,the significance of the selected associated loci combinations is assessed by an x *** MID method is model-free and nonparametric,it is easy to compute and *** capability of the MID method is confirmed by applying it to investigate the multiple gene interactions on risk of hypertension in northern Han Chinese,where thirty-three SNP loci with three-genotype in eleven candidate genes are *** results are consistent with those of Gu et al（2006）.Additionally,we get some other new *** indicates that our idea is indeed feasible and useful in practice.

关键词： Hypertension SNP locus multilocus interactions measure of information discrepancy

来源：评论

学校读者我要写书评

暂无评论

Conceptual Modeling for Advanced Application Domains 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Shan Wang Katsumi Tanaka Shuigeng Zhou Tok-Wang Ling Jihong Guan Dong-qing Yang Fabio Grandi Eleni E. Mangina Il-Yeol Song Heinrich C. Mayr

来源：评论

学校读者我要写书评

暂无评论

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

来源：评论

学校读者我要写书评

暂无评论

A Relation-Constraint Link Prediction Model for Dynamic Knowledge Graphs with Entity Drift

引用

ACM Transactions on Knowledge Discovery from data 1000年

作者： Xiulin Zheng Peipei Li Zan Zhang Jia Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China and School of Computer Science and Information Engineering Hefei University of Technology China School of Computing Faculty of Science and Engineering Macquarie University Australia

Knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of Knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat

关键词： dynamic knowledge graph entity drift link prediction relation constraint

来源：评论

学校读者我要写书评

暂无评论

Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation

引用

ACM Transactions on Recommender Systems 1000年

作者： Qingbo Zhang Xiaochun Yang Hao Chen Bin Wang Xiangmin Zhou Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China School of Computing Technologies RMIT University Melbourne Australia

Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.

关键词： session-based recommender systems personalized next-item recommendation user intention intention-specific preference

来源：评论

学校读者我要写书评

暂无评论

Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge computing Environment

引用

ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

来源：评论

学校读者我要写书评

暂无评论

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

引用

ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：