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International Symposium on Parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms computer architecture Machine learning Matrix decomposition

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Analysis of vocational education dynamics evolution based on topic modeling

Analysis of vocational education dynamics evolution based on...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xinhua Wang Xiaomei Yu Yunmeng Jiang Xiangwei Zheng Wei Li School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Clarifying the focus of vocational education dynamics is crucial for understanding the current situation and future development trends. However, comprehensive exploration on vocational education dynamics is inadequate presently. In this paper, textual information on vocational education is analyzed with Latent Dirichlet Allocation, BERTopic, and the Dynamic Topic Model. Two key issues are thoroughly investigated as follows: (1) What are the main aspects of the current development of vocational education; (2) How to learn the embeddings of vocational education text and further to promote the formulation of policy. The experiments are conducted and the experimental results show that LDA and BERTopic model achieve effective performance in clustering the texts of vocational education dynamics, and the DTM captures the topics trend of vocational education, which facilitate the government to guide the formulation and implementation of policies.

关键词： Analytical models Education Government Market research Dynamic scheduling Resource management Information technology

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Mining Approximate Frequent Patterns from Noisy Databases

Mining Approximate Frequent Patterns from Noisy Databases

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International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA)

作者： Xiaomei Yu Yongqin Li Hong Wang School of Information Science and Engineering Shandong Normal University Jinan Shandong China Shandong Provincial Key Laboratory for Distributed Computer software Novel Technology School of Teacher's Education Shandong Normal University Jinan Shandong China

ISBN: (纸本)9781467383165

As an important branch in the field of frequent pattern mining, approximate frequent pattern (AFP) mining attracts much attention recently. Various algorithms have been proposed to discover long true AFPs in presence of random noise. This paper considers the key issues of AFP mining in noisy databases, and categorizes the previous approaches according to the ways they cope with missing items in the transactions. And then a study of different data models on AFP is presented, in which the merits and defects are analyzed. Finally, we draw a conclusion and propose some solutions to deal with the problems in the field of AFP mining.

关键词： Itemsets Algorithm design and analysis Approximation algorithms Data mining Noise measurement Data models

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Network Forensics Scenario Reconstruction Method Based on Hidden Markov Models

Network Forensics Scenario Reconstruction Method Based on Hi...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Weiping Gu Liancheng Xu Min Ren Xiaoyan Han School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Mathematics and Quantitative Economics Shandong University of Finance and Economics Jinan China

Reconstruction Method of Network Forensics Scenario has grown into a mature and rich technology that provides advanced skills to get the chain of evidence. Using statistical methods to analyze intrusion logs in order to present evidentiary values in court are often refuted as baseless and inadmissible evidences which is not considering the input spent. These spendings is to generate the reports no matter they are well-grounded evidences or not. Thus, this paper presents the Scenario Reconstruction Method combines the Viterbi algorithm, the most likely sequence of Meta evidence which replaces the Meta evidence was acquired. With suspected evidence, thus obtaining the chain of evidence. However, the Viterbi algorithm parameters is derived from the Baum-Welch (B-W) algorithm, and the B-W algorithm is easy to fall into local optima solution. While an Adaptive Genetic Algorithm (AGA) is used to estimate parameters of the Hidden Markov model (HMM), where Chromosome coding method and genetic operation mode are designed. The experimental results show that, this method can accurately reproduce the crime scene of network intrusion, compared with the network forensic evidence fusion method which is based on the HMM. The method has been applied to forensics system, and has obtained good result.

关键词： Hidden Markov models Sociology Statistics Maximum likelihood decoding Biological cells Genetics Genetic algorithms

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Instrumental variable identification for a class of non-uniformly sampled-data systems

Instrumental variable identification for a class of non-unif...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Shengtao Li Nan Jiang Dan Li College of Information Science and Engineering Shandong Normal University Jinan Shandong Province China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology College of Information Science and Engineering Northeastern University Shenyang Liaoning Province China

A class of non-uniformly sampling system is investigated, and the lifted state space model of non-uniformly sampled-data system is derived by using integration method. The corresponding transfer function model is further obtained. To solve the problem that there exist unknown mid-variables in information vectors of identification model, the auxiliary model technique is applied to replace the unknown system variables. The auxiliary models based instrumental variable identification algorithm of non-uniformly sampled-data system is presented. At last, a numerical example is given to illustrate the proposed method is effective.

关键词： Instruments Vectors Parameter estimation Transfer functions Mathematical model Equations Educational institutions

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DETECTING THE RUN TIME ATTACKS IN THE CLOUD WITH AN EVIDENCE COLLECTION BASED APPROACH

DETECTING THE RUN TIME ATTACKS IN THE CLOUD WITH AN EVIDENCE...

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2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems

作者： Jie lin Chuanyi Liu Zhichun Ning Binxing Fang School of Computer Science Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education School of Software Engineering Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications

As the impacts of security threats are amplified and spread quickly in the cloud,especially with the attribute of large-scale *** of researches focus on monitoring the network ***,these methods don't check how the attack happened and what damage the attack caused for the virtual *** paper presents a logging method to analyze the potential attacks out of VM and a prototype was *** experimental results show that this method can detect the real world attacks effectively and the performance overhead is acceptable.

关键词： Virtual machine Cloud Security Control

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An energy saving and balanced clustering routing protocol for wireless sensor networks

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ICIC Express Letters 2014年第9期8卷 2577-2584页

作者： Liu, Fangai Zhang, Chunhua Wang, Jiwei School of Information Science and Engineering Shandong Normal University No. 88 East Wenhua Road Lixia District Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology No. 88 East Wenhua Road Lixia District Jinan 250014 China

In order to prolong the lifetime of wireless sensor networks, we propose an energy saving and balanced clustering routing protocol (ESB). ESB employs energy and distance in probability model to generate candidate cluster-heads (CHs). Candidate CHs set timer based on residual energy to ensure the nodes with high residual energy become CHs. ESB employs residual energy and the distance to Sink in competing radius. When normal nodes select the optimal CH to join, they consider energy, its communication cost and CHs' communication cost. When CHs select relay-node, they consider optimal forwarding hops, energy, inter-cluster communication cost and intra-cluster communication cost. These measures can not only save energy, but also remit the imbalance problem of energy consumption. Through performance analysis, ESB has favorable expansibility and low algorithm complexity. The results of simulation experiments on the OMNet++ show that ESB can better balance energy consumption of all nodes in the network, save network's energy, and prolong network's lifetime compared with other cluster-based routing algorithms. © 2014 ISSN 1881-803X.

关键词： Energy utilization

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Research on Assistant Diagnostic Method of TCM Based on BERT

Research on Assistant Diagnostic Method of TCM Based on BERT

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chuanjie Xu Feng Yuan Shouqiang Chen Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China School of Information Engineering Shandong Management University Jinan China Center of Hear of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9781728194202

Traditional Chinese medicine (TCM) auxiliary diagnosis is a systematic diagnosis platform that uses computer modeling technology to assist TCM doctors in recording diseases, providing on time diagnoses, writing prescriptions, performing tele-medicine, and supporting medical teaching. This study proposes a Bidirectional Encoder Representations from Transformers TCM auxiliary diagnosis model using 20,000 items of TCM records. These records were collected from the outpatient clinic of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine. Specifically, our model aims to predict final diagnosis while taking TCM symptoms as inputs; for example, when we input relief of chest tightness but persistent tiredness and sluggishness, the model provides a diagnosis of chest paralysis. An experiment was conducted on these real-world Chinese medical data. Results show that our model achieves state-of-the-art performance. Hence, our proposed model can effectively use the information from the four diagnostic procedures in the TCM text.

关键词： Systematics Computational modeling Education Predictive models Writing Transformers Data models

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Towards Accurate and Efficient Super Spreader Detection with Sketching

Towards Accurate and Efficient Super Spreader Detection with...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Shouyou Song Pu Wang Yangchun Li Nanyang Wang Wei Jiang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT Beijing China Chinese Academy of Cyberspace Studies Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China School of Computer and Information Engineering Chuzhou University Anhui China

Super spreaders are the flow that have a large number of distinct connections (also called spread), which related with many threats to networks. Estimating flow spread is the crucial step in super spreader detection. However, existing methods cannot achieve flow spread estimation in terms of accurate, efficient, and reversible simultaneously. All these characteristics is highly required for high-speed network measurement. In this paper, we propose MorphSketch, a new data structure that estimates flow spread for super spreader detection with high accuracy, memory efficiency, high throughput and reversibility. MorphSketch combines hashing with sampling to process packets in order to improve throughput. It uses self-morph bitmap to record spread information, which can adaptively enlarge the upper bound of spread estimation under limited memory usage to ensure accuracy and memory efficiency. Moreover, MorphSketch can track candidate super spreader by comparing corresponding spread information, which realizes reversibility in super spreader detection. We perform a series of performance evaluations on real world traffic trace. Experiment results demonstrate that under same memory usage, the MorphSketch significantly outperforms existing work in terms of accuracy and efficiency.

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