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IEEE International Nanoelectronics Conference (INEC)

作者： Yaqing Chi Haiqin Zhong Chao Zhang Liang Fang National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Hunan China National University of Defense Technology Changsha Hunan CN

Multi-island single electron transistor (MISET) is a kind of single electron transistor (SET), which has advantages of the room temperature operating. A novel semi-empirical compact model for MISET is proposed. The new approach combines the orthodox theory of single electron tunneling for single Coulomb island and a novel empirical analysis for a chain of Coulomb islands. The model is verified by the Monte-Carlo method in SIMON simulator, and is much faster than the traditional multi-island SET simulator, which has the advantages for the large scale multi-island SET circuit simulation.

关键词： Single electron transistors Circuit simulation Tunneling Large-scale systems Voltage Temperature Character generation Electrodes distributed processing Concurrent computing

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Attention-based fault-tolerant approach for multi-agent reinforcement learning systems

arXiv

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arXiv 2019年

作者： Geng, Mingyang Xu, Kele Li, Yiying Liu, Shuqi Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Big Data Management and Analysis Northeastern University Shenyang110000 China

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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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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Controllable Template Generation for Document-level Event Extraction

Controllable Template Generation for Document-level Event Ex...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Quntian Fang Feng Liu Zhen Huang Zhenliang Guo Changjian Wang Dongsheng Li Minghao Hu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China Information Research Center Academy of Military Sciences Beijing China

Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed.

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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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Replication-Based Fault-Tolerance for Large-Scale Graph Processing

Replication-Based Fault-Tolerance for Large-Scale Graph Proc...

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International Conference on Dependable Systems and Networks (DSN)

作者： Peng Wang Kaiyuan Zhang Rong Chen Haibo Chen Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science Shanghai Jiao Tong University

The increasing algorithm complexity and dataset sizes necessitate the use of networked machines for many graph-parallel algorithms, which also makes fault tolerance a must due to the increasing scale of machines. Unfortunately, existing large-scale graph-parallel systems usually adopt a distributed checkpoint mechanism for fault tolerance, which incurs not only notable performance overhead but also lengthy recovery time. This paper observes that the vertex replicas created for distributed graph computation can be naturally extended for fast in-memory recovery of graph states. This paper proposes Imitator, a new fault tolerance mechanism, that supports cheaply maintenance of vertex states by replicating vertex states to their replicas during normal message exchanges, and provides fast in-memory reconstruction of failed vertices from replicas in other machines. Imitator has been implemented by extending Hama, a popular open-source clone of Pregel. Evaluation shows that Imitator incurs negligible performance overhead (less than 5% for all cases) and can recover from failures of more than one million of vertices with less than 3.4 seconds.

关键词： Fault tolerance Fault tolerant systems Computer crashes Checkpointing Synchronization Clustering algorithms Computational modeling

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

arXiv

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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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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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Mobile Robot Object Recognition in The Internet of Things based on Fog computing

Mobile Robot Object Recognition in The Internet of Things ba...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Meixia Fu Songlin Sun Kaili Ni Xiaoying Hou National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Mobile robot object recognition has attracted significant attention in the internet of things recently, in which there are many challenging tasks, such as the objects, the communication networks and the computer system. It still needs a large of computation, communication and storage capability for the whole system. In this paper, we propose a scheme of mobile robot object recognition in IOT and use edge nodes to process the data from robot vision instead of cloud computing. Besides, we adopt YOLOv3 as the main algorithm in the edge nodes to process the video data. The video from the camera on the robot is transmitted to the fog node by a Wi-Fi router. The advantages of using edge nodes for computation local robot clusters are reliability, real-time capability and flexibility compared with the cloud. In our experiment, we efficiently train the computer model using COCO database deployed to GPU. The robot using the proposed model could recognize the objects in real-time. We achieve mAP of 31.0% and response time of 52ms that illustrates the state-of-art performance of the proposed framework.

关键词： Object recognition Edge computing Cloud computing Real-time systems Servers Mobile robots

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A distributed paging RAM grid system for wide-area memory sharing 06

A distributed paging RAM grid system for wide-area memory sh...

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Rui Chu Nong Xiao Yongzhen Zhuang Yunhao Liu Xicheng Lu Distributed Processing National Key Laboratory for Parallel Changsha Hunan China Science and Technology Hong Kong University Kowloon Hong Kong China

ISBN: (纸本)9781424400546

Memory-intensive applications often suffer from the poor performance of disk swapping when memory is inadequate. Remote memory sharing schemes, which provide a remote memory that is faster than the local hard disk, are able to improve the performance of such applications. Due to the limitation of being applicable within single clusters only, however, most of the previous remote memory mechanisms, such as the network memory scheme, fail to be extendable into a large scale, distributed, heterogeneous, and dynamic environment. In this work, we propose a service-oriented grid memory sharing scheme, distributed paging RAM grid (DPRG). We study the properties and criteria of large scale memory sharing, and then design major operations and optimizations to fit the usage of grid systems. We collect trace from our grid environment, and evaluate DPRG through comprehensive trace-driven simulations. Results show that DPRG significantly outperforms existing remote memory sharing schemes and supports grid computing applications effectively

关键词： Read-write memory Random access memory Hard disks Meteorology Large-scale systems Grid computing Scientific computing Computer networks distributed computing Laboratories

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