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Research on Domain Knowledge Representation techniques 26

Research on Domain Knowledge Representation Techniques

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Winter 2023

作者： Ji, Wei Cao, Qinghong Shi, Jin Zhu, Enyao Xu, Tianyi He, Hao School of Information Management Nanjing University Nanjing China Shanghai Stock Exchange System Operation Department Shanghai China

ISBN: (纸本)9798350345865

Knowledge is the fruit of human's knowledge of the objective world in practice and the crystallization of wisdom. A knowledge base makes a knowledge-based system (or expert system) intelligent by structuring and sorting out knowledge in a specific field and storing, organizing, managing and using it in a computer using a scientific knowledge representation. Based on the research of knowledge base construction in securities industry, this paper first summarizes and explains several representative knowledge representation models. Then, it summarizes the application scenarios of common knowledge representation techniques in the fields of product design, robot control, and natural language processing. In addition, based on the investigation of the knowledge characteristics of the securities industry, a knowledge representation model of the securities industry based on 5W1lH is proposed to organize and manage the multimodal information resources and provide high-value information for user needs, while the knowledge representation technology of the mechanical industry based on hypergraph embedding is examined and the specific processes and application scenarios are summarized. © 2023 IEEE.

关键词： Product design

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Commercial applications for high-performance computing

Commercial applications for high-performance computing

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Commercial applications for High-Performance Computing

The proceedings contain 21 papers from conference on Commercial applications for High-Performance computing. Topics discussed include resource allocation framework for interactive television environments;resource management in peer-to-peer and grid computing;automatic parallelization of large-scale computational applications;Finite Difference Time Domain (FDTD) techniques on supercomputers;Monte Carlo simulation of multilattice thin film growth;data mining;search engines;industrial applications of high-performance computing for phylogeny reconstructing and parallel Fast Fourier Transform (FFT) approach for derivative pricing.

关键词： parallel processing systems

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE international Symposium on parallel and distributed processing with applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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A Fast Large-size Production Data Transformation Scheme for Supporting Smart Manufacturing in Semiconductor Industry 13

A Fast Large-size Production Data Transformation Scheme for ...

引用

13th IEEE conference on Automation Science and Engineering (IEEE CASE)

作者： Suryajaya, Benny Chen, Chao-Chun Hung, Min-Hsiung Liu, Yu-Yang Liu, Jia-Xuan Lin, Yu-Chuan Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan Taiwan Chinese Culture Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781509067817

In this paper, we propose a novel data transformation scheme over a big data platform, aiming at injecting production data from the local database in the factory side and transforming them into the workpiece-centric form that many manufacturing analytics systems need. The key idea is to blend big data processing techniques, including table composition with external distributed files, columnar storage, partition, massively parallel processing into the data transformation scheme for minimizing the data processing time. Our proposed scheme brings two main impacts to the smart manufacturing. First, our scheme plays the key component to develop data-driven manufacturing decision systems, since large-volume production data sources can be efficiently transformed into the workpiece-centric form that other smart manufacturing services require. Second, our proposed scheme provides a development exemplar to assist a manufacturing factory toward the Industry-4.0 realm, since big data techniques are ingeniously blended in building data-intensive manufacturing services. We finally implement the prototype of the proposed scheme on the Hadoop platform and apply the prototype to a semiconductor factory for conducting integrated tests. Testing results of a case study physically applying the proposed scheme to a semiconductor factory demonstrate the success of our work.

关键词： cloud manufacturing big data processing industry 4.0 Hadoop databases

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PARSIR: a Package for Effective parallel Discrete Event Simulation on Multi-processor Machines 28

PARSIR: a Package for Effective Parallel Discrete Event Simu...

引用

28th IEEE/ACM international Symposium on distributed Simulation and Real Time applications, DS-RT 2024

作者： Quaglia, Francesco DICII - University of Rome Tor Vergata Italy

ISBN: (纸本)9798331527211

In this article we present PARSIR (parallel SImulation Runner), a package that enables the effective exploitation of shared-memory multi-processor machines for running discrete event simulation models. PARSIR is a compile/run-time environment for discrete event simulation models developed with the C programming language. The architecture of PARSIR has been designed in order to keep low the amount of CPU-cycles required for running models. This is achieved via the combination of a set of techniques like: 1) causally consistent batch-processing of simulation events at an individual simulation object for caching effectiveness;2) high likelihood of disjoint access parallelism;3) the favoring of memory accesses on local NUMA (Non-Uniform-Memory-Access) nodes in the architecture, while still enabling well balanced workload distribution via work-stealing from remote nodes;4) the use of RMW (Read-Modify-Write) machine instructions for fast access to simulation engine data required by the worker threads for managing the concurrent simulation objects and distributing the workload. Furthermore, any architectural solution embedded in the PARSIR engine is fully transparent to the application level code implementing the simulation model. We also provide experimental results showing the effectiveness of PARSIR when running the reference PHOLD benchmark on a NUMA shared-memory multi-processor machine equipped with 40 CPUs. © 2024 IEEE.

关键词： Batch data processing

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Continuous k-nearest neighbor queries on multi-core CPUs

Continuous k-nearest neighbor queries on multi-core CPUs

引用

international conference on Electrical and Control Engineering, ICECE 2010

作者： He, Hui Ying Liao, Wei Wu, Xiao Ping College of Electrical and Information Engineering Naval University of Engineering Wuhan China College of Electronic Engineering Naval University of Engineering Wuhan China

ISBN: (纸本)9780769540313

Modern microprocessors employ multi cores to accelerate computations, and parallelizing multiple queries execution to exploit multi-core parallelism has become a challenge for moving objects database applications. To evaluate massive concurrent queries towards moving objects, parallel processing techniques and cache-conscious algorithms adapting to memory hierarchy and multi-core architecture should be developed to maximize the processor computation abilities and reduce the memory stalls. This paper first presents a distributed moving objects updating strategy to reduce the computing cost of server. Further, in-memory spatial network adjacent matrix, shortest path matrix and hash table structures are introduced to describe the road network topology and store the moving objects. A shortest path based network expansion (SPNE) algorithm is proposed to decrease the processing cost of k nearest neighbor (k-NN) queries by reducing the search network space. For massive k-NN queries evaluation, we use a multi-staged engine, which exploits pipeline strategy and departs the query processing into three simultaneous stages to improve the parallelism with multithreaded technology. And we present a multi-threaded network expansion (MSPNE) algorithm for massive k-NN queries processing. MSPNE algorithm uses threaded workload parallelism and cache-conscious execution reorganization strategies to improve the spatial and temporal locality. Experimental evaluation on a dual-core platform and analysis show that SPNE and MSPNE algorithms achieve a performance improvement over the existing traditional optimization counterparts. © 2010 IEEE.

关键词： Query processing

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Performance prediction of distributed RAID storage system

Performance prediction of distributed RAID storage system

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1st international conference on Information Science and Engineering, ICISE2009

作者： Liu, Jun Cui, Baojiang Dept. of Information Science and Technology Tianjin University of Finance and Economics Tianjin 300222 China Information Security Centre Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9780769538877

Since information is growing explosively, the requirements for high performance storage systems are critical in practical applications. The research on performance prediction has become increasingly important in the area of distributed storage systems. This paper presents the architecture of iSCSI-based distributed RAID storage system. At the basis of analyzing the system data flow, the stochastic Petri net model of the system is constructed. And then an analytical method for computing system I/O response time is proposed utilizing equivalent conversion of serial or parallel transitions. Theoretical analyses show that bandwidth is the bottleneck of distributed RAID storage system in low-speed network;cache hit rate and CPU processing power at central server are the main factors affecting the I/O response time in high-speed network. The performance of distributed RAID system can be improved through increasing the network bandwidth and the performance of the centre node. ©2009 IEEE.

关键词： HPSS

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

引用

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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Fortifying NoC-based Manycores: distributed Monitoring to Detect Security Threats 31

Fortifying NoC-based Manycores: Distributed Monitoring to De...

引用

31st conference on Electronics Circuits and Systems

作者： Faccenda, Rafael Follmann Comaru, Gustavo Calazans, Ney Caimi, Luciano Lores Moraes, Fernando Gehm Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre Brazil Fed Univ Santa Catarina UFSC Comp Dept DEC Ararangua Brazil Fed Univ Fronteira Sul UFFS Chapeco Chapeco Brazil

ISBN: (纸本)9798350377217;9798350377200

As computing technology advances, manycore systems have become increasingly used due to their performance and parallel processing capabilities. However, these systems also present significant security risks, including threats from hardware Trojans, malicious applications, and peripherals capable of executing various attacks, such as denial-of-service, spoofing, and eavesdropping. Researchers have proposed several security enhancements to address these challenges, including establishing secure zones, authentication protocols, and security-aware routing algorithms. This paper introduces a distributed monitoring mechanism that can detect suspicious activities within NoC links, peripheral interfaces, and packet reception protocols, explicitly focusing on detecting DoS, spoofing, and eavesdropping attacks. We conducted a comprehensive attack campaign to evaluate the effectiveness of our monitoring mechanisms and test the platform's resilience. The results were promising, with the system successfully detecting all attacks and continuing to execute applications correctly. Although there was an average execution time penalty of 8.83%, which included the time taken to generate attack warnings from the monitoring mechanisms and apply countermeasures, this mechanism significantly enhanced the security of manycore systems.

关键词： Security NoC-based Manycores Security Threat Monitoring Security Evaluation

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GISQF: An Efficient Spatial Query processing System 7

GISQF: An Efficient Spatial Query Processing System

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7th IEEE international conference on Cloud Computing (CLOUD)

作者： Al-Naami, Khaled Mohammed Seker, Sadi Khan, Latifur Univ Texas Dallas Dept Comp Sci Dallas TX 75083 USA Istanbul Medeniyet Univ Dept Business Istanbul Turkey

ISBN: (纸本)9781479950638

Collecting observations from all international news coverage and using TABARI software to code events, the Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with 250+ million observations covering all countries in the world from January 1, 1979 to the present with daily updates. The purpose of this widely used dataset is to help understand and uncover spatial, temporal and perceptual trends and behaviors of the social and international system. To query such big geospatial data, traditional RDBMS can no longer be used and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proved to be a scalable platform to process and analyze Big Data in the cloud. Hadoop as an implementation of MapReduce is an open source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and falls short as it doesnt perform efficiently in terms of running time. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Querying Framework (GISQF) to process Massive Spatial Data. This framework has been built on top of the open source SpatialHadoop system which exploits two-layer spatial indexing techniques to speed up query processing. We show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on GDELT dataset with a size of 60 GB. We show the results for three types of queries, Longitude Latitude Point queries, Circle-Area queries, and Aggregation queries.

关键词： Query processing

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