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arXiv 2019年

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Zhang, Lei National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology China National Key Laboratory of Integrated Automation of Process Industry Northeastern University China

Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the RGB-D camera maintains a limited range for working and is hard to accurately measure the depth information in a far distance. Besides, the RGB-D camera will easily be influenced by strong lighting and other external factors, which will lead to a poor accuracy on the acquired environmental depth information. Recently, deep learning technologies have achieved great success in the visual SLAM area, which can directly learn high-level features from the visual inputs and improve the estimation accuracy of the depth information. Therefore, deep learning technologies maintain the potential to extend the source of the depth information and improve the performance of the SLAM system. However, the existing deep learning-based methods are mainly supervised and require a large amount of ground-truth depth data, which is hard to acquire because of the realistic constraints. In this paper, we first present an unsupervised learning framework, which not only uses image reconstruction for supervising but also exploits the pose estimation method to enhance the supervised signal and add training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task comparably to the supervised methods and outperforms the previous state-of-the-art approach by 13.5% on KITTI dataset. Besides, our unsupervised learning framework could significantly accelerate the initialization process of ORB-SLAM system and effectively improve the accuracy on environme

关键词： Unsupervised learning

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Implementation of encrypted data for outsourced database

Implementation of encrypted data for outsourced database

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International Conference on Computational Intelligence and Natural computing, CINC

作者： Zheng-Fei Wang Ai-Guo Tang National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Computer Hunan Business College Changsha China

ISBN: (纸本)9781424477050

Encryption technology has become an important mechanism of securing data stored in the outsourced database. However, it is a difficulty to query efficiently the encrypted data and many researchers take it into consideration. To solve the problem, an encrypted schema, based on the Postgresql DBMS, is proposed. Through the security dictionary and the extended SQL, the approach implements the encrypted storage and efficiently query over the encrypted data in the outsourced databases. Results of experiments validate the efficiency and feasibility of our approach.

关键词： Indexes Dictionaries Random access memory Catalogs Encryption

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Hybrid Routing: Towards Resilient Routing in Anonymous Communication Networks

Hybrid Routing: Towards Resilient Routing in Anonymous Commu...

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IEEE International Conference on Communications (ICC)

作者： Yusheng Xia Rongmao Chen Jinshu Su Chen Pan Han Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.

关键词： Routing Resilience Public key Peer-to-peer computing Encryption

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An Online/Offline HIBS Scheme for Privacy Protection of People-centric Sensing

An Online/Offline HIBS Scheme for Privacy Protection of Peop...

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International Conference on Industrial Informatics, computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)

作者： Peixin Chen Xiaofeng Wang Jinshu Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467383134

People-Centric Sensing (PCS), which collects information closely related to human activity and interactions in societies, is stepping into a flourishing time. Along with its great benefits, PCS poses new security challenges such as data integrity, participant privacy. Hierarchical Identity-Based Signature (HIBS) scheme can efficiently provide high integrity messaging, secure communication and privacy protection to PCS. However, the low computation efficiency primarily hinders the PCS adoption of HIBS scheme. In this paper, we propose an online/offline HIBS (HIBOOS) scheme for securing PCS. By splitting the signing phase into online and offline procedures, our scheme achieves high signing efficiency.

关键词： Games Sensors Privacy Public key Generators Wireless sensor networks

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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Tendency-based caching in Content-Centric Networking

Tendency-based caching in Content-Centric Networking

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： YuSheng Xia YaPing Liu JinShu Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467399920

In content-centric networking, the schemes of innetwork caching can affect the performance of the whole network. Existing schemes lack of the global view, which results in inefficient caches. In this paper, we aim to analyze the real-time distribution of contents among caches from multiple perspectives. This paper proposes TCBRP, a scheme that analyzes caching tendency of various contents in reverse path, based on centrality of nodes, popularity of contents and replacement rate of nodes, to cache in-network contents. This scheme also has decent scalability and can be expended conveniently. The experimental results reflect that TCBRP report savings in average hops and balance cache hit rate, compared with BetwRep and LCE.

关键词： Probability Internet Network architecture Mathematical model Statistical analysis Correlation Fading channels

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RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering

arXiv

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arXiv 2024年

作者： Chen, Zhongwu Xu, Chengjin Wang, Dingmin Huang, Zhen Dou, Yong Jiang, Xuhui Guo, Jian National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology China IDEA Research International Digital Economy Academy Department of Computer Science University of Oxford United Kingdom

Retrieval-augmented generation (RAG) has shown promising potential in knowledge intensive question answering (QA). However, existing approaches only consider the query itself, neither specifying the retrieval preferences for the retrievers nor informing the generators of how to refer to the retrieved documents for the answers, which poses a significant challenge to the QA performance. To address these issues, we propose Rule-guided Retrieval-Augmented Generation with LMs, which explicitly introduces rules for in-context learning (RuleRAG-ICL) to guide retrievers to recall related documents in the directions of rules and uniformly guide generators to reason attributed by the same rules. The combination of queries and rules can be used as fine-tuning data to update retrievers and generators, achieving better rule-based instruction-following ability (RuleRAG-FT). Moreover, most existing RAG datasets were constructed without considering rules and Knowledge Graphs (KGs) are recognized as providing high-quality rules. Therefore, we construct five rule-aware RAG benchmarks for QA, RuleQA, based on KGs to stress the significance of retrieval and reasoning with rules. Experiments on RuleQA demonstrate RuleRAG-ICL improves the retrieval quality of +89.2% in Recall@10 and answer accuracy of +103.1% in Exact Match, and RuleRAG-FT yields more enhancement. In addition, experiments on four existing RAG datasets show RuleRAG is also effective by offering rules in RuleQA to them, further proving the generalization of rule guidance in RuleRAG. Code and RuleQA are at https://***/r/RuleRAG. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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Towards a multi-array architecture for accelerating large-scale matrix multiplication on FPGAs

arXiv

引用

arXiv 2018年

作者： Shen, Junzhong Qiao, Yuran Huang, You Wen, Mei Zhang, Chunyuan College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linear systolic array. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we propose a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal design parameters. Experiments on a real-life convolutional neural network (CNN) show that we can obtain the optimal extension of the linear array architecture. Copyright © 2018, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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