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Temporal Closing Path for PLM-based Temporal Knowledge Graph Completion

Temporal Closing Path for PLM-based Temporal Knowledge Graph...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xin Zhou Yongxue Shan Zixuan Dong Haijiao Liu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Denfense Technology Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Temporal Knowledge Graph Completion (TKGC) aims to predict missing parts of quadruples, which is crucial for real-life knowledge graphs. Compared with methods that only use graph neural networks, the emergence of pre-trained model has introduced a trend of simultaneously leveraging text and graph structure information. However, most current methods based on pre-trained models struggle to effectively utilize both text and multi-hop graph structure information concurrently, resulting in insufficient association mining of relations. To address the challenge, we propose a novel model: Temporal Closing Path for Pre-trained Language Model-based TKGC (TCP-PLM). We obtain the temporal closing relation path of the target relation through sampling, and use the relation path as a bridge to simultaneously utilize text and multi-hop graph structure information. Moreover, the relation path serves as a tool for mining associations between relations. At the same time, due to the design of entity-independent relation paths, our model can also handle the inductive setting. Our experiments on three benchmarks, along with extensive analysis, demonstrate that our model not only achieves substantial performance enhancements across four metrics compared to other models but also adeptly handles inductive settings.

关键词： Training Measurement Knowledge engineering Bridges Analytical models Knowledge graphs Transforms

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Understanding User Interests Acquisition in Personalized Online Course Recommendation

Understanding User Interests Acquisition in Personalized Onl...

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Xiao Wang, Ting Wang, Huaimin Tang, Jintao Information Center National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9783030012977

MOOCs have attracted a large number of learners with different education background all over the world. Despite its increasing popularity, MOOCs still suffer from the problem of high drop-out rate. One important reason may be due to the difficulty in understanding learning demand and user interests. To helper users find the most suitable courses, personalized course recommendation technology has become a hot research topic in e-learning and data mining community. One of the keys to the success of personalized course recommendation is a good user modeling method. Previous works in course recommendation often focus on developing user modeling methodology which learns latent user interests from historic learning data. Recently, interactive course recommendation has become more and more popular. In this paradigm, recommender systems can directly query user interests through survey tables or questionnaires and thus the learned interests may be more accurate. In this paper, we study the user interest acquisition problem based on the interactive course recommendation framework (ICRF). Under this framework, we systematically discuss different settings on querying user interests. To reduce performance-cost score, we propose the ICRF user interest acquisition algorithm that combines representative sampling and interest propagation algorithm to acquire user interests in a cost-effective way. With extensive experiments on real-world MOOC course enrollment datasets, we empirically demonstrate that our selective acquisition strategy is very effective and it can reduce the performance-cost score by 30.25% compared to the traditional aggressive acquisition strategies. © 2018, Springer Nature Switzerland AG.

关键词： Surveys

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Cooperative spectrum sharing in relay-trading mode: A fairness view

Cooperative spectrum sharing in relay-trading mode: A fairne...

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4th International Conference on Cyber-Enabled distributed computing and Knowledge Discovery, CyberC 2012

作者： Liu, Lixia Hu, Gang Xu, Ming Peng, Yuxing National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China Department of Networks Engineering School of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769548104

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. © 2012 IEEE.

关键词： Cognitive radio

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A novel hybrid architecture for high speed regular expression matching 1

引用

1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Xu, Chengcheng Zhao, Baokang Chen, Shuhui Su, Jinshu College of Computer National University of Defense Technology No. 137 Yanwachi Street ChangshaHunan410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

Mobile devices play an important role in our everyday lives, but they also bring great security threats. Deep packet inspection (DPI) is one of the most efficient methods to detect the malicious information hidden in the mobile traffic, and regular expression matching is widely used in DPI for its powerful expressive ability. However, with the increasing complexity of regular expressions, traditional solutions cannot meet the requirements of both storage and high performance. In this paper, we propose a novel hybrid matching architecture and two-stage memory architecture for the state of the art hybrid FA to solve this problem. Experiment results confirm that our architecture is scalable to complex rule sets, and the matching performance outperforms state of the art memory centric solution by up to 15x. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Memory architecture

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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OLM2: Automatic Optimal Strategy Generating for Large-Scale Model Training with Limited-Memory

OLM2: Automatic Optimal Strategy Generating for Large-Scale ...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Zhilin Yang Yu Tang Linbo Qiao Xi Yang Zhen Huang National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China

The scale of model parameters and the amount of training data is exponentially increasing. It requires more GPU memory with the exponential increasement of model parameters. Recomputation and swapping are two main memory optimization methods that have been extensively studied, and there are also optimization strategies that combine the two methods. However, most of them are based on heuristic search strategies, which do not explore the complete solution space and can’t guarantee the optimality of the solution results. An optimal search strategy with tensor-level recomputation and swapping is expected in large-scale model training. In this paper, we propose an optimal strategy searching algorithm combining tensor-based recomputation and swapping. Specifically, the memory swapping strategy is reformulated as an optimization problem, which converts the memory constraints into mixed integer programming, to find the optimal memory optimization strategy. By leveraging the advantages of both recomputation and swapping, this approach minimizes computation consumption without exceeding the available memory limitation. Experimental results show that our method exhibits about 60% reduction in memory requirements during the training process. Furthermore, our method can reduce the overall training time beyond the existing algorithms. Compared to Checkmate, our approach achieves about 0.3–0.9% reduction in computation cost per iteration.

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Two improved GPU acceleration strategies for force-directed graph layout

Two improved GPU acceleration strategies for force-directed ...

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International Conference on Computer Application and System Modeling (ICCASM)

作者： Yong-Xian Wang Zong-Zhe Li Lu Yao Wei Cao Zheng-Hua Wang National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

Force directed approach is one of the most widely used methods in graph drawing research. However, the running time is increased intolerablely along with the enlargement of the graph size, which restricts the algorithm's practicability. By the aid of GPU (graphics processing unit) computing platform, we can speed-up the graph layout with low cost, but the existing GPU implementation mainly employees an “one-by-one” style to update the vertex' coordination per iteration, which has a lower convergent rate than the “batch” style which is instead used commonly in traditional CPU implementation. As a result, the aesthetics of graph layout would be decreased if the total running time is restricted. It is hard to achieve both a high speedup factor of GPU over CPU and a high convergent rate in existing GPU computing implementation. In order to solve this problem partially, this paper presents two new strategies to implement the large-scale graph layout on CPU+GPU heteromerous platform to accelerate the force directed layout for graph drawing problem. The numerical computation results show that our GPU implementation can dramatically improve the performance of force-direct layout and is 20 times on a NVIDIA GeForce 9800 GT GPU at 1.44 GHz faster than the one on single-CPU core of Intel Pentium 4 PC at 3.0 GHz for the graph layout with moderate size (typically 1000 vertices).

关键词： Graphics processing unit Gallium nitride Heating

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A multi-objective emergency resource scheduling method based on MOEA/D 2

A multi-objective emergency resource scheduling method based...

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2nd International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2019

作者： Li, Yanrong Li, Xiaoyong Hou, Liyang Kong, Wenping Ma, Wenxue Key Laboratory of Trustworthy Distributed Computing and Servic Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Integrated Command and Dispatch Technology Beijing China

ISBN: (纸本)9781450372619

After unconventional events occur, rapid emergency response is often needed in order to reduce losses. When there are more than one disaster points and more than one supply points, how to coordinate the local dispatch of emergency resources among these points is a problem worth studying. In this paper, we design a multi-objective, multi-point to multi-point and multi-resource emergency scheduling model that consider resources arrive time and transportation cost. Then a new emergency resource scheduling algorithm based on multi objective evolutionary algorithm based on decomposition (MOEA/D) is proposed, a new mutation operator was also designed to solve this problem. The experimental results show that this method can get a feasible scheduling scheme, which is faster than NSGA-II and has better scheduling results. © 2019 ACM.

关键词： Multiobjective optimization

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OSSEAN: Mining Crowd Wisdom in Open Source Communities

OSSEAN: Mining Crowd Wisdom in Open Source Communities

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2015 IEEE Symposium on Service-Oriented System Engineering

作者： Gang Yin Tao Wang Huaimin Wang Qiang Fan Yang Zhang Yue Yu Cheng Yang National Laboratory of Parallel and Distributed Computing National University of Defense Technology ChangSha Hunan China

Nowadays open source software represents a successful crowd-based software production model and is becoming an ecosystem combining huge amounts of software producers (such as software developers) and consumers (such as software users and customers). Lots of research work has been conducted on analyzing software artifacts created by producers, but few of them reveal the power of feedback from consumers which we believe is very important for the evaluation and evolution of open source software. This paper introduces OSSEAN, a platform for Open Source Software Evaluating, Analyzing and Networking. OSSEAN divides the open source communities into two groups: software production communities and software consumption communities. The former contain structured software artifacts such as projects, source code and issues, while the latter are full of textual documents with rich semantics of user feedback. We show the power of OSSEAN with some interesting demos by analyzing more than 200 thousands of open source projects and 10 million documents.

关键词： Communities Production Data mining Crawlers Open source software Ecosystems

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Efficient semantic-aware coflow scheduling for data-parallel jobs

Efficient semantic-aware coflow scheduling for data-parallel...

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IEEE International Conference on Cluster computing

作者： Ziyang Li Yiming Zhang Yunxiang Zhao Dongsheng Li National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781509036547

This paper studies the communication pattern of data-parallel applications from the perspective of job execution, and discovers multiple inter-coflow dependencies. These inter-coflow dependencies, collectively named as semantic flow (seflow), can expose job-level semantics. It is observed that most distributed computing frameworks describe their job execution as directed acyclic graphs (DAG). So a seflow comprises not only all the coflows of a job but also the DAG-based relationship between them. Seflow, coflow and flow can be viewed as the top-down abstractions for communication of jobs.

关键词： Semantics distributed computing Sparks Scheduling algorithms Scheduling Schedules

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