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International Conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Networks Engineering School of Computer National University of Defense Technology Changsha China

In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource efficiently and fairly is an interesting and practical problem. In this paper we proposed a cooperative spectrum sharing strategy (RT-CSS) for the relay-trading mode from the fairness view. The cooperative SUs are gathered in a cooperative sharing group (CSG), and contribution metric (CM) is proposed to measure each CSG member's contribution to CSG as well as benefit from CSG. The adjustment of CM can guarantee the fairness and efficiency of spectrum sharing. The numerical simulation shows that RT-CSS can achieve better performance than the sense-uncooperative mode.

关键词： Relays Cognitive radio Measurement Qualifications Wireless sensor networks Radio networks

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Text line detection based on mutation analysis

Text line detection based on mutation analysis

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2013 5th International Conference on Computational and Information sciences, ICCIS 2013

作者： Hou, Dewen Wang, Xichang Liu, Jiang Chen, Xia Key Laboratory for Distributed Computer Software School of Information and Science Engineering Shandong Normal University Shandong China Shandong Oumasoft Co. Ltd Research Institute of Digital Image Processing Shandong China

ISBN: (纸本)9780769550046

A text line detection method based on wavelet transformation and mutation analysis is proposed in this paper. First the character density image is acquired from the input document image by the project function with strips, and then the density image is analyzed by wavelet transformation where the Gaussian or its derivative function is used as the wavelet basis. The local peak positions indicate the line texture which will be continuous and single pixel. Because of the statistical properties of project function and wavelet transformation of mutations signal detection, this algorithm is not sensitive to noise and warped distortions. Experiments show that this algorithm is accurate and robust. © 2013 IEEE.

关键词： Signal analysis

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Efficiently indexing sparse wide tablesin cloud computing

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Information Technology Journal 2013年第20期12卷 5415-5423页

作者： Huang, Bin Peng, Yuxing Department of Computer Huaihua University 418008 Huaihua China School of Computer Science Wuhan University 430072 Wuhan China National Laboratory of Parallel and Distributed Processing National University of Defense Technology 410073 Changsha China

For its perception of unlimited resources and infinite scalability, Cloud Computing has emerged as a pervasive paradigm for hosting? data-centric applications in large computing infrastructures. The data produced by these applications are essentially sparse and wide and may change schema frequently, traditional relational data model is inappropriate for their data management. A new data model, called Sparse Wide Table, was introduced for this task. Unfortunately, we have to face many challenges in building the secondary index for Sparse Wide Table in cloud, as the distributed and column-oriented storage which eliminates a number of NULLs. In this study, we present a three-level index scheme for efficient data processing in the Cloud. Our approach can be summarized as follows. First, we build an index for each column by which the records can be rebuilt easily. Second, we build a bitmap index for each storage node which only indexes the data residing on the node. Third, we organize the storage nodes as a structured overlay and each node maintains a portion of the global index for the all different data. The global index is a bitmap index to indicate the node each data resides in. Finally, based on the three-level index scheme, some query algorithms are implemented. We conduct extensive experiments on a LAN and the results demonstrate that our indexing scheme is dynamic, efficient and scalable. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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Two-Stage Convolutional Neural Network for Knee Osteoarthritis Diagnosis in X-Rays

Two-Stage Convolutional Neural Network for Knee Osteoarthrit...

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International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2021

作者： Wang, Kang Niu, Xin Dou, Yong Yang, Di Xie, Dongxing Yang, Tuo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China Department of Orthopaedics Xiangya Hospital Central South University Changsha410008 China Department of Health Management Center Xiangya Hospital Central South University Changsha410008 China

ISBN: (纸本)9789811638794

Knee osteoarthritis (OA) is a common musculoskeletal illness. To solve the problem that inaccurate knee joint localization and inadequate knee OA features extracted from plain radiographs affect the accuracy of knee OA diagnosis in X-rays, we propose a novel Two-Stage Convolutional Neural Network (TS-CNN) method, consisting of the KneeDetnet and the KLnet. The KneeDetnet with two small multi-task convolutional neural networks is proposed to locate knee joints, improving the accuracy of knee joint localization. Then KLnet is designed to assess knee OA, where a shared Siamese network via ResNet is used to extract more discriminative deep learning features that are fused with gender information for obtaining richer features. Our method is evaluated on public OAI and MOST datasets. The highest detection accuracy of knee joints can reach 99.93% and 99.02% on two datasets, respectively. The KLnet algorithm achieves 78.85% and 68.20% prediction accuracy on the OAI and MOST datasets, respectively. Experimental results show that our method outperforms the existing workhorse. The proposed approach may become a potentially useful tool for assisting physicians. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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A distributed algorithm for SI transactions serializability in cloud computing

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Information Technology Journal 2013年第24期12卷 8435-8445页

作者： Huang, Bin Peng, Yuxing Peng, Xiaoning School of Computer Science Wuhan University Wuhan 430072 China Department of Computer Huaihua University Huaihua 418008 China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

There are well known anomalies permitted by snapshot isolation that can lead to violations of data consistency by interleaving transactions that individually maintain consistency. Until now, there are some ways to prevent these anomalies only in single computer and there are not the corresponding solving methods in cloud computing. This paper describes our PDCC algorithm to detect cycles in a snapshot isolation dependency graph and abort transactions to break the cycle in cloud computing. The algorithm ensures serializable executions for SI transactions in cloud computing. Based on the transaction concurrency control of Percolator, we have implemented our algorithm in an open source cloud database system (HBase) and our performance study shows that PDCC throughput and scalability are good. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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A distributed approach to consistent order delivery of concurrent events in asynchronous DVEs

ICETC 2010 - 2010 2nd International Conference on Education ...

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ICETC 2010 - 2010 2nd International Conference on Education Technology and computer 2010年 4卷 V495-V498页

作者： Zhou, Hangjun Zhang, Wei Peng, Yuxing Li, Sikun Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Department of Information Management Hunan College of Finance and Economics Changsha Hunan 410205 China

ISBN: (纸本)9781424463688

In large-scale asynchronous distributed virtual environments(DVEs), one of the difficult problems is to deliver the concurrent events in a consistent order at each node. Generally, the previous consistency control approaches can be classified into two categories: causal order and time stamped order. However, causal order approaches can merely preserve the cause-effect relation of events and time stamped order approaches seem intrinsically complex to be used in serverless large-scale asynchronous DVEs. In this paper, we proposed a novel distributed algorithm to identify the concurrent events and preserve the consistent order delivery of them at different nodes. Simulation studies are also carried out to compare the performance of this algorithm with that of the previous ones. The results show that the new algorithm can effectively deliver the concurrent events in consistent order at each node and is more efficient than the previous algorithms in large-scale asynchronous DVEs. © 2010 IEEE.

关键词： Virtual reality

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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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The Efficient In-band Management for Interconnect Network in Tianhe-2 System

The Efficient In-band Management for Interconnect Network in...

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Jijun Cao Liquan Xiao Zhengbin Pang Kefei Wang Jiaqing Xu College of Computer National University of Defense Technology China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

Interconnect network plays an important role in high performance computing systems. And its manageability directly affects the RAS (i.e., Reliability, Availability, and Serviceability) of the whole system. The Tianhe-2 system located in NSCC-gz (i.e., national Supercomputing Center of China in Guangzhou) uses proprietary interconnect network, which includes 5,856 high-radix network router chips (i.e., NRC) and 18,304 network interface chips (i.e., NIC). For such a very large-scale interconnect network, it is a great challenge to manage (such as configure, monitor, and debug) the numerous network chips and its network ports in an efficient way. By implementing the in-band management with very few hardware resources, the interconnect network in Tianhe-2 system achieves a highly efficient network management. In this paper, we introduce the design and implementation of the in-band management for interconnect network in Tianhe-2 system, especially emphasizing on several key features, including the set of achieved management functionalities, the architecture of network management, the format of management packets, the data flow and processing of management packets, etc. In this paper, we also evaluate the performance of in-band management by mainly comparing with out-band management scheme. The preliminary results demonstrate the efficiency of the in-band management for interconnect network in Tianhe-2 system.

关键词： Routing Ports (computers) Network topology Optical switches Hardware Topology Registers

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