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第三十九届中国控制会议

作者： Fengze Miao Ying Tian School of Computer Science and Software Engineering University of Science and Technology Liaoning

The computer image technology has achieved impressive recognition rates in image classification tasks *** order to tackle the complex defect features in strip steel,we conduct a study of the effectiveness of principal component analysis and extreme learning machine(ELM) in the classification of strip surface in this *** the principal component analysis,it extracts the characteristics of dimension reduction,obtains the characteristic quantity,reduces the dimension used as the input sample for the Extreme Learning Machine and trains the support vector machine and realizes the *** with traditional extreme learning machine and support vector machine(SVM),PCA-ELM algorithm shows a higher accuracy and better generalization ability,which play a guiding sole on actual production.

关键词： Strip steel surface defect Principal component analysis Extreme learning machine Defect classification

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G-thinker: a general distributed framework for finding qualified subgraphs in a big graph with load balancing

G-thinker: a general distributed framework for finding quali...

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作者： Yan, Da Guo, Guimu Khalil, Jalal Özsu, M. Tamer Ku, Wei-Shinn Lui, John C. S. Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science and Engineering The Chinese University of Hong Kong N.T Shatin Hong Kong

Finding from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems that attempt to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker for subgraph finding algorithms, which adopts a task-based computation model, and which also provides a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph finding algorithms that can be easily adapted from existing serial algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the idle time of CPU cores. To further improve load balancing on graphs where the workloads of individual tasks can be drastically different due to biased graph density distribution, we propose to prioritize the scheduling of those tasks that tend to be long running for processing and decomposition, plus a timeout mechanism for task decomposition to prevent long-running straggler tasks. The idea has been integrated into a novelty algorithm for maximum clique finding (MCF) that adopts a hybrid task decomposition strategy, which significantly improves the running time of MCF on dense and large graphs: The algorithm finds a maximum clique of size 1,109 on a large and dense WikiLinks graph dataset in 70 minutes. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://***/gthinker with detailed documentation. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Spring

关键词： Large dataset

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An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments

An effective data locality aware task scheduling method for ...

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2011 International Conference on Cloud and Service Computing, CSC 2011

作者： Zhang, Xiaohong Feng, Yuhong Feng, Shengzhong Fan, Jianping Ming, Zhong School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454000 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China National High Performance Computing Center at Shenzhen Shenzhen 518055 China

ISBN: (纸本)9781457716362

Data locality has recently been extensively exploited in Cloud computing to improve system performance. However, when schedule Map tasks in Hadoop MapReduce framework working in a heterogeneous environment, existing methods either cannot reduce the occurrence of these Map tasks or injure fairness, thus degrading the system performance. In order to address this problem, this paper proposes a data locality aware scheduling method to improve the Hadoop MapReduce system performance in heterogeneous computing environments. After receiving a request from a requesting node, our method preferentially schedules the task whose input data is stored on the requesting node. If no such tasks exist, our method will select the task whose input data is nearest to the requesting node, and then make a decision on whether to reserve the task for the node storing the input data or schedule the task to the requesting node by transferring the input data to the requesting node on the fly. As a proof of concept, we implement the method in Hadoop-0.20.2. In order to evaluate the performance, we carry out an experimental comparison study on our proposed method against the default scheduling method used in Hadoop-0.20.2. The experiment results show that our proposed method improves the data locality and reduces the normalized execution time as well as the response time of jobs. © 2011 IEEE.

关键词： MapReduce

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Medicine and Disease Association Prediction via Attention-Based Medical Heterogeneous Information Network Representation Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 69-78页

作者： Zhang, Bin Yang, Dan Lin, Zhihuang Master of Computer Science and Software Engineering Department University of Science and Technology Liao Ning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liao Ning Anshan China

Knowledge of medication and disease has been rapidly accumulated. Also, an increasing number of researchers have paid more attention to predicting medicine-disease associations by machine learning methods. The associations of entities in the medical heterogeneous information network involve different association types. In this paper, we propose MHIN-MD, a representation learning of medical heterogeneous network model to solve the association between medicines and diseases in medical heterogeneous information network. Specifically, we first construct a medical heterogeneous information network contains diseases, medicines, diagnosis, etc. Next, we design a neural network model to learn the feature vector representation of medical nodes information. We try to find the association between medicines and diseases through the characteristics of medicines structure and the interaction between medicines. Using the Euclidean distance to calculate medicine similarity and disease similarity, generates a set of similar nodes between medicines and diseases to prediction association between medicines and diseases. Finally, our extensive experiments on MIMIC- III datasets and DrugBank datasets, the result demonstrates that MHIN-MD can outperform baselines in the association prediction. © 2022. All Rights Reserved.

关键词： Diagnosis

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Expansion microscopy allows quantitative characterisation of structural organisation of platelet aggregates

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Journal of Thrombosis and Haemostasis 2025年

作者： Emma L. Faulkner Jeremy A. Pike Evelyn Garlick Robert K. Neely Iain B. Styles Stephen P. Watson Natalie S. Poulter Steven G. Thomas Department of Cardiovascular Sciences School of Medical Sciences College of Medicine and Health University of Birmingham Birmingham UK COMPARE University of Birmingham and University of Nottingham Midlands UK Current address: Institute for Interdisciplinary Data Science and AI University of Birmingham Birmingham UK Research Software Group Advanced Research Computing University of Birmingham Birmingham UK Current address: Abberior Instruments GmbH Hans-Adolf-Krebs-Weg 637077 Göttingen Germany School of Chemistry University of Birmingham Birmingham UK School of Computer Science University of Birmingham Birmingham UK Current Address: School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

Background Current microscopy approaches applied to platelet aggregates from multiple settings indicate their structure has important implications in efficient haemostasis and in clinical treatment of thrombosis. However, current fluorescence microscopy approaches are not amenable to detailed volumetric imaging of platelet aggregate structures. This is due to the small size of individual platelets and tight packing of platelets within aggregates, resulting in optical opacity. Objectives To address this, we investigated methods for performing super-resolution microscopy and image analysis on aggregates of platelets to describe their morphology. Methods We applied expansion microscopy and custom analysis workflows to extract quantitative information on the structure of platelet aggregates. Results We demonstrate that expansion microscopy applied to platelet aggregates, reveals multi-scale information about their structure. We produced volumetric images at nanoscale resolution of >700 platelet aggregates stained for cytoskeletal and membrane components under normal conditions and following cytoskeletal disruption. We demonstrate our custom analysis workflow provides quantitative description of platelet numbers, volumes and morphology within entire platelet aggregates. Additionally, we quantitatively describe subcellular organisation of F-actin. By comparing these measurements following treatment with the actin inhibitors, cytochalasin D and latrunculin A, we can robustly detect structural disruptions in platelet aggregates. Conclusions Together these data provide a workflow to qualitatively and quantitatively describe the architecture of platelet aggregates at a range of scales (whole aggregates down to sub-cellular features within individual platelets). This provides an important tool for analysis of platelet aggregates and thrombi in conditions such as haemostasis, immunothrombosis and cardiovascular disease.

关键词： F-actin platelet aggregation microscopy

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String Processing and Information Retrieval 1

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丛书名： Lecture Notes in computer science

1000年

作者： Amihood Amir Andrew Turpin Alistair Moffat

ISBN: (数字)9783540890973

ISBN: (纸本)9783540890966

This book constitutes the refereed proceedings of the 15th International Symposium on String Processing and Information Retrieval, SPIRE 2008, held in Melbourne, Australia, in November 2008. The 25 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on compression and performance, information retrieval scoring and ranking, string matching techniques, self-indexing, string matching: space and practicality, information retrieval, non-standard matching, and bioinformatics.

关键词： Data Mining and Knowledge Discovery Programming Techniques Artificial Intelligence Database Management Data Structures Coding and Information Theory

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Adaptive Forwarding with Probabilistic Delay Guarantee in Low-Duty-Cycle WSNs

Adaptive Forwarding with Probabilistic Delay Guarantee in Lo...

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作者： Cheng, Long Kong, Linghe Song, Yongjia Niu, Jianwei Luo, Chengwen Gu, Yu Mumtaz, Shahid He, Tian Department of Industrial Engineering Clemson University ClemsonSC United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering Beihang University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Visa Inc. AustinTX United States Instituto de Telecomunicacoes Aveiro Portugal Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees (i.e., delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery. © 2002-2012 IEEE.

关键词： Wireless sensor networks

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Stable Initialization Scheme for K-Means Clustering

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Wuhan University Journal of Natural sciences 2009年第1期14卷 24-28页

作者： XU Junling XU Baowen ZHANG Weifeng ZHANG Wei HOU Jun School of Computer Science and Engineering Southeast University Nanjing 211189 Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 Hubei China Department of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 Jiangsu China

Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.

关键词： clustering unsupervised learning K-means initialization

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Improve the Response Diversity of Multi-turn Dialogue System by Combining Knowledge

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第3期49卷

作者： Li, Zhengpeng Wu, Jiansheng Miao, Jiawei Yu, Xinmiao School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Recently, neural network language models have been trained in an end-to-end and fully data-driven manner, generating more flexible responses. Traditional neuro-language models have some problems, such as generating meaningless security responses and containing less information, resulting in unattractive conversations. According to the above problems, we propose a knowledge-driven hierarchical recurrent attention network (Kd-HRAN) model. The Kd-HRAN model adds a knowledge entity generator to hierarchical recurrent attention network infrastructure and introduces a knowledge awareness gate in its decoder. The knowledge awareness gate can integrate the context-related external knowledge into the reply process, and determine whether the final output word is generated from the decoder or copied from the knowledge entity generator. The Kd-HRAN model ensures the consistency, richness, and sustainability of the dialogue system. After a large number of experimental verification, the Kd-HRAN model is superior to the baseline model and has diversified and robust responses. © 2022, IAENG International Journal of computer science. All Rights Reserved.

关键词： Decoding

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