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检索条件"主题词=Partitioning Algorithms"

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms partitioning algorithms Convolutional neural networks Ovarian cancer

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An Effective Optimization Algorithm for Synthesizing Wideband Thinned Array Antenna

An Effective Optimization Algorithm for Synthesizing Wideban...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Shuyi Chen Hao Wen Pengfei Gu Zi He Dazhi Ding Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This paper proposes an optimization algorithm for a sparse wideband planar antenna array based on the covariance matrix adaptive evolutionary strategy algorithm. By dividing the planar antenna array into multiple regions, the goal of achieving faster optimization results is achieved. When directly applying the covariance matrix adaptive evolutionary strategy algorithm to optimize a large number of antenna elements in an antenna array, the computer may run slowly or even fail to produce results. For a given antenna array aperture, before using the covariance matrix adaptive evolutionary strategy algorithm to optimize the antenna array, the array can be divided into several sub-arrays symmetrically, so that the running efficiency of the optimization algorithm will be greatly improved.

关键词： Adaptive arrays Planar arrays Aperture antennas partitioning algorithms Broadband antennas Covariance matrices Electromagnetics

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Lion: Minimizing Distributed Transactions Through Adaptive Replica Provision

Lion: Minimizing Distributed Transactions Through Adaptive R...

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International Conference on Data Engineering

作者： Qiushi Zheng Zhanhao Zhao Wei Lu Chang Yao Yuxing Chen Anqun Pan Xiaoyong Du Renmin University of China Zhejiang University Tencent Inc.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Distributed transaction processing often involves multiple rounds of cross-node communications, and therefore, tends to be slow. To improve performance, existing approaches convert distributed transactions into single-node transactions by either migrating co-accessed partitions onto the same nodes or establishing a super node housing replicas of the entire database. However, migration-based methods might cause transactions to be blocked due to waiting for data migration, while the super node can become a bottleneck. In this paper, we present Lion, a novel transaction processing protocol that utilizes partition-based replication to reduce the occurrence of distributed transactions. Inspired by the fact that modern distributed databases horizontally partition data, with each partition having multiple replicas, Lion aims to assign a node with one replica from each partition involved in a given transaction's read or write operations. To ensure such a node is available, we propose an adaptive replica provision mechanism, enhanced with an LSTM-based workload prediction algorithm, to determine the appropriate node for locating replicas of co-accessed partitions. The adaptation of replica placement is conducted preemptively and asynchronously, thereby minimizing its impact on performance. By employing this adaptive replica placement strategy, we ensure that the majority of transactions can be efficiently processed on a single node without additional overhead. Only a small fraction of transactions will need to be treated as regular distributed transactions when such a node is unavailable. Consequently, Lion effectively minimizes distributed transactions, while avoiding any disruption caused by data migration or the creation of a super node. We conduct extensive experiments to compare Lion against various transaction processing protocols. The results show that Lion achieves up to 2.7x higher throughput and 76.4% better scalability against these state-of-the-art approaches.

关键词： Protocols Scalability Distributed databases Throughput Prediction algorithms Data engineering partitioning algorithms

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DcChain: A Novel Blockchain Sharding Method Based on Dual-constraint Label Propagating

DcChain: A Novel Blockchain Sharding Method Based on Dual-co...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Hao Zhou Pengcheng Xia Yiyang Ni Jun Li School of Cyberspace Security Nanjing University of Science and Technology Nanjing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Computer Engineering Jiangsu Second Normal University Nanjing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Blockchain technology has shown great application potential in many fields such as finance, supply chain, and the Internet of Things. As blockchain technology keeps evolving, the issue of insufficient scalability has progressively turned into a crucial bottleneck impeding its further progress. The application of sharding technology to enhance the scalability of blockchain systems has attracted considerable interest from the research community. However, the majority of sharding solutions suffer from a number of problems, including high ratio of cross-shard transactions, prolonged transaction confirmation latency, and unbalanced load among shards. To solve these problems, we propose a blockchain sharding method based on dual-constraint label propagating. This sharding method mainly depends on two constraints in the label propagation process to determine whether to perform partition transfers on blockchain accounts. Constraint 1 is to consider the correlation among accounts when splitting accounts. This constraint serves to minimize the cross-shard transactions ratio. Constraint 2 is that the load factor of the blockchain sharding system after the tag update cannot exceed the load factor before the update. This can bring the load of each shard to an almost consistent state, thereby reducing transaction confirmation latency. To verify the effectiveness of our proposed method, we compare it with the Metis and Monoxide algorithms in three aspects: cross-shard transaction ratio, transaction throughput, and transaction confirmation latency. Furthermore, we examine the influence of the load distribution across individual shards within the blockchain network on both throughput and transaction confirmation latency. The experimental results show that our proposed method makes the load among shards more balanced, thereby reducing the ratio of cross-shard transactions, decreasing the transaction confirmation latency, and increasing the transaction throughput.

关键词： Sharding Privacy Scalability Heuristic algorithms Simulation Supply chains Throughput Blockchains partitioning algorithms Security

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Distribution Systems Clustering Through Voltage Unbalance Sensitivity to Photovoltaic Presence

Distribution Systems Clustering Through Voltage Unbalance Se...

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Annual Conference of Industrial Electronics Society

作者： Rafael M Leite Mário Oleskovicz Daniel Barbosa Flavio B. Costa Department of Electrical and Computer Engineering University of São Paulo Brazil Department of Electrical and Computer Engineering Federal University of Bahia Brazil Electrical and Computer Engineering Department Michigan Technological Univesity Houghton U.S.A

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Distributed photovoltaic micro and mini-generators are gaining prominence as they become more accessible to end consumers. These systems promise to reduce expenses related to electrical energy and offer potential financial returns. One way to enhance the application and planning of the distribution network and its hosting capacity is by partitioning these systems based on the impact of distributed generation on power quality. This study aims to develop a generic network partitioning method using the Spectral Clustering algorithm, enhanced with a balancing algorithm to ensure similar cluster sizes. The input data for this method come from modeling actual standardized electrical systems and simulating the individual impact of photovoltaic generation on each low-voltage feeder connected to the primary grid. The unbalance sensitivity coefficient, which quantifies the change in the voltage unbalance factor at one node due to the variation of active power at another, is used as a metric. Considering all simulation states, the ratios of these coefficients per node form the elements of the Sensitivity matrix. By applying Balanced Spectral Clustering to this Sensitivity matrix, the method can identify continuous electrical regions that highlight the optimal partitioning of the tested distribution system. This proposed method may serve as an initial framework for defining the hosting capacity of smaller regions, taking into account the characteristics of the entire electrical system while managing its restrictive factors locally.

关键词： Photovoltaic systems Low voltage Sensitivity Limiting Power quality Clustering algorithms Voltage Planning partitioning algorithms Capacity planning

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MOVE: Matching Game for Partial Offloading in Vehicular Edge Computing

MOVE: Matching Game for Partial Offloading in Vehicular Edge...

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IEEE International Conference on Communications (ICC)

作者： Mahmuda Akter Debjyoti Sengupta Anurag Satpathy Sajal Das Department of Computer Science Missouri University of Science and Technology Rolla USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Autonomous Vehicles (AV s) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU's resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient partition strategy must consider the energy expended and adhere to the real-time operations' deadline needs. This paper proposes a partial offloading scheme, MOVE, catering to the above-mentioned challenges. A deferred acceptance algorithm (DAA) with preferences is proposed to address the first two challenges, whereas a novel energy-aware partitioning strategy resolves the final challenge. The performance of the proposed scheme is evaluated against baseline algorithms, and we observed a 54.04 % and 52.17 % reduction in offloading latency and energy.

关键词： Navigation Roads Energy resolution Games Real-time systems partitioning algorithms Servers

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Two-Stage Transpotation Problem with Unknown Consumer Demands: Consistency Aspects

Two-Stage Transpotation Problem with Unknown Consumer Demand...

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Problems of Logistics, Management and Operation in the East-West Transport Corridor (PLMO), International Conference on

作者： Viktor Stovba Olha Khomiak Department of Nonsmooth Optimization Methods V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine Department of Mathematical Methods of Reliability Theory of Complex Systems V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine

ISBN: (数字)9798350353099

ISBN: (纸本)9798350353105

A mathematical model of a two-stage transportation problem with unknown consumer demands and their lower and upper bounds is studied. The necessary and sufficient conditions for consistency of the constraints system of the linear programming problem, which corresponds to this model, are formulated, and justified. The classical two-stage transportation problem is given, to which the above problem is transformed if consumer demands are known, as well as the consistency conditions for constraints system of the problem. Modification of the initial model that permits product transportation only to the fixed number of consumers, which is less than their total number, is given.

关键词： Sufficient conditions Upper bound Software algorithms Transportation Programming Linear programming Mathematical models Software partitioning algorithms Logistics

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Research on Improved Adaptive Spectral Clustering HRM-ISC Algorithm

Research on Improved Adaptive Spectral Clustering HRM-ISC Al...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Hongxia Wang Weijian Tong School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

As a crucial component in the realm of data mining, In the past few years, the focus of researchers in the field of data mining has significantly shifted towards the exploration of clustering analysis, garnering widespread interest and recognition, and a considerable multitude of theories and methods have been produced. Based on the conventional clustering algorithm, spectral clustering converts the partition of the data set into the partition of the graph, and can identify the clusters composed of arbitrary shape sample data. The traditional spectral clustering algorithm NJW has problems such as difficult parameter selection in the clustering process and poor clustering effect on non-convex or non-uniform density data sets. In this paper, a hot region mining algorithm built upon improved spectral clustering (HRM-ISC) is suggested. In the first place, The enhancement of the local scale parameter in the self-tuning algorithm is achieved by incorporating the standard deviation of the sample neighborhood, which avoids the interference of the local scale parameter of the sample by noise points and the parameter selection depends on the empirical value to the maximum extent, so that the similarity matrix of the sample reflects the distribution information of the sample more accurately. Secondly, The conventional spectral clustering approach can not automatically estimate the optimal number of clusters and the eigenvector selection is difficult. According to the matrix perturbation theory, an adaptive spectral clustering algorithm utilizing eigengap is used to solve the problem. Finally, to tackle challenge of sensitivity to the initial clustering center encountered in the K-Means algorithm, the K-Medoids algorithm with variance maximization of initial clustering center was employed as a substitute.

关键词： Sensitivity Shape Perturbation methods Noise Clustering algorithms Signal processing algorithms Matrices partitioning algorithms Data mining Standards

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HeNCoG: A Heterogeneous Near-memory Computing Architecture for Energy Efficient GCN Acceleration

HeNCoG: A Heterogeneous Near-memory Computing Architecture f...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Seung-Eon Hwang Duyeong Song Jongsun Park School of Electrical Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Graph convolutional network (GCN), which first applies convolutional operations to process graph data, has gained attention in various tasks involving relational data. Previous GCN accelerators have been designed with heterogeneous cores, considering two stages of inference (aggregation and combination), or with a unified core based on the inference of multi-layer as an iterative sparse-dense matrix multiplication. However, those prior works have suffered from an unnecessary large number of multiply-accumulate (MAC) operations and/or main memory accesses. In this paper, we propose HeNCoG, a GCN accelerator that utilizes a heterogeneous MAC array core for the combination stage and a near-memory computing core for the aggregation stage. In HeNCoG, considering that the number of MAC operations is significantly reduced when changing the stage execution order, the combination stage is executed first with a row-stationary dataflow. In the aggregation stage, magneto-resistive random-access memory (MRAM)-based near-memory computing is employed to reduce the number of main memory accesses needed to access the adjacency matrix in the graph dataset. Graph partitioning and double buffering techniques are also applied to further improve hardware efficiencies. Simulation results show that the HeNCoG architecture reduces execution cycles by 97% and memory accesses by 42% compared to previous works.

关键词： Circuits and systems Magnetic cores Simulation Memory architecture Energy efficiency Hardware partitioning algorithms

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Resource Allocation with Service Affinity in Large-Scale Cloud Environments

Resource Allocation with Service Affinity in Large-Scale Clo...

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International Conference on Data Engineering

作者： Zuzhi Chen Fuxin Jiang Binbin Chen Yu Li Yunkai Zhang Chao Huang Rui Yang Fan Jiang Jianjun Chen Wu Xiang Guozhu Cheng Rui Shi Ning Ma Wei Zhang Tieying Zhang ByteDance Inc. University of California Berkeley Xi’ an Jiaotong University South China University of Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Containerization has garnered substantial favor among cloud service providers. Nevertheless, the notable network overhead incurred between containers has prompted concerns within the community. In cloud resource scheduling, collocating service containers that frequently communicate to the same machine - termed “service affinity” - is instrumental in enhancing application performance. In response to this concern, we present a solution that harnesses service affinity and collocates containers to enhance the overall system performance and stability. To maximize the benefits of collocating containers, it is necessary to calculate a new schedule that optimally and efficiently maximizes service affinity, especially within the expansive domain of industry-scale cloud environments. In pursuit of this, we leverage the skewness property of affinity and machine learning to fuse solver-based algorithms, thereby assuring both quality and efficiency for problems at scale. Our methodology encompasses the partitioning of a given task into discrete subproblems, with a keen focus on resolving the most critical ones. Via a graph neural network classifier, we assign each subproblem to be solved independently using methods based on off-the-shelf solvers in our algorithm pool - namely, MIP-based, or column generation. This strategic approach enables the efficient computation of a schedule for a cloud cluster that fully optimizes the overall service affinity. We further propose a heuristic algorithm to compute executable container migration plans for practical use, facilitating the transition to the new placement where service affinity is well optimized. Our solution has been deployed in our large-scale production environment, covering over a million cores within ByteDance. Through the successful real-world production deployment, our approach exhibits an average improvement in end-to-end latency by 23.75% and a reduction in request error rates by 24.09% compared to the original system.

关键词： Schedules System performance Heuristic algorithms Production Containers partitioning algorithms Classification algorithms

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