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Towards Greening MapReduce Clusters Considering Both Computation Energy and Cooling Energy

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第4期32卷 931-942页

作者： Toha, Tarik Reza Rizvi, A. S. M. Noor, Jannatun Adnan, Muhammad Abdullah Al Islam, A. B. M. Alim Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka 1000 Bangladesh Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this article, we propose a machine learning-based approach that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. Our approach predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of our approach through both real test-bed experimentation and simulation. Our evaluation reveals that our approach achieves substantial reduction in total energy consumption compared to other state-of-the-art alternatives while experiencing marginal performance degradation in a few cases.

关键词： Cooling Energy consumption Time factors Task analysis machine learning algorithms Cloud computing Temperature distribution Green parallel computing MapReduce hadoop machine learning

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Gather and Conquer: Region-Based Strategies to Accelerate Safe Screening Tests

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第12期67卷 3300-3315页

作者： Herzet, Cedric Dorffer, Clement Dremeau, Angelique INRIA Ctr Rennes Bretagne Atlant F-35042 Rennes France CNRS Lab STICC UMR 6235 ENSTA Bretagne F-29200 Brest France

In this paper, we propose new methodologies to decrease the computational cost of safe screening tests for LASSO. We first introduce a new screening strategy, dubbed "joint screening test," which allows the rejection of a set of atoms by performing one single test. Our approach enables to find good compromises between complexity of implementation and effectiveness of screening. Second, we propose two new methods to decrease the computational cost inherent to the construction of the (so-called) "saferegion." Our numerical experiments show that the proposed procedures lead to significant computational gains as compared to standard methodologies.

关键词： Optimization methods machine learning algorithms computational efficiency

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An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

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IEEE ACCESS 2022年 10卷 98860-98871页

作者： Zhou, Shaoze Du, Yongkang Chen, Bingzhi Li, Yonghua Luan, Xingsen Dalian Jiaotong Univ Sch Rolling Stock Engn Dalian 116028 Peoples R China

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny machine learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edge-aware applications of equipment such as automobiles and ships.

关键词： machine learning Rail transportation Real-time systems Monitoring Classification algorithms Training data machine learning algorithms Artificial neural networks Smart transportation TinyML IoT running state smart rail vehicle artificial neural network

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Traffic Anomaly Detection: Exploiting Temporal Positioning of Flow-Density Samples

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第5期25卷 4166-4180页

作者： Sarteshnizi, Iman Taheri Bagloee, Saeed Asadi Sarvi, Majid Nassir, Neema Univ Melbourne Dept Infrastruct Engn Melbourne Vic 3052 Australia

It is of paramount importance to detect traffic data anomalies in a real-time manner as it helps efficient traffic control and management. Several unsupervised anomaly detection algorithms are proposed previously in the literature;however, lack of proper ground truth labels for traffic data has been always a substantial barrier to deploy and evaluate them. In this paper, we introduce a concept named Temporal Positioning of Flow-Density Samples (TP-FDS) that can be used by domain experts for fast and reliable traffic data labeling. We mathematically show that deviations in two-dimensional TP-FDS completely reflect point and subsequence anomalies previously defined in the literature of time series data. Furthermore, benefiting from this concept, we propose a novel anomaly detection framework with the help of Fast Angle Based Outlier Detection (Fast-ABOD) to be used for traffic data. Extensive data labeling experiments are conducted with the opinions of 20 different experts. Implementation of several machine learning algorithms, like KNN, OC-SVM, iForest, and LOF, is also adapted with two different setups of hyper-parameters to be used in the proposed framework. Results indicate that our framework integrated with Fast-ABOD is able to detect anomalies in traffic data better than other machine learning and state-of-the-art deep learning algorithms with more than 72% and 96% of F1 score and AUC.

关键词： Anomaly detection Accidents Data models Real-time systems machine learning algorithms Roads Labeling flow-density samples macroscopic traffic data angle-based outlier detection

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Kernel Path for ν-Support Vector Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第1期34卷 490-501页

作者： Gu, Bin Xiong, Ziran Li, Xiang Zhai, Zhou Zheng, Guansheng Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Machine Learning Abu Dhabi U Arab Emirates Nanjing Univ Informat Sci & Technol Nanjing 210044 Peoples R China Adv Inst Big Data Beijing 100000 Peoples R China

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to nu-support vector classification (nu-SVC) with more than one equality constraint. Compared with plain SVM, nu-SVC has the advantage of using a regularization parameter nu for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP nu SVC) to trace the solutions of nu-SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of nu-SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of nu-SVC. Based on this equivalent formulation of nu-SVC, we propose the KP nu SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP nu SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP nu SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP nu SVC but also show the advantage of applying KEP to select the optimal kernel parameter.

关键词： Kernel Static VAr compensators Support vector machines Computational modeling Training Analytical models machine learning algorithms Kernel path model selection piecewise non-linear support vector machine (SVM)

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Coarsening Algorithm Based on Multi-Label Propagation for Knowledge Discovery in Bipartite Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1799-1809页

作者： Valejo, Alan Demetrius Baria Althoff, Paulo Eduardo Faleiros, Thiago de Paulo Filho, Geraldo Pereira Rocha Zhu, Yu-tao Yan, Jianglong Liu, Weiguang Zhao, Liang Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos Brazil Univ Brasilia Dept Comp Sci BR- 70910900 Brasilia RJ Brazil State Univ Southwest Bahia Dept Exact & Technol Sci BR-45083900 Vitoria Da Conquista Brazil Brics Inst Future Networks China Branch Shenzhen 518100 Peoples R China Univ Sao Paulo Inst Math & Comp Sci BR-05508220 Sao Carlos Brazil Zhongyuan Univ Technol Sch Comp Sci Zhengzhou 451191 Peoples R China Univ Sao Paulo Dept Comp & Math FF CLRP BR-14040900 Ribeirao Preto Brazil

Complex machine learning tasks for knowledge discovery in networked data, such as community detection, node categorization, network visualization, and dimension reduction, have been successfully addressed by coarsening algorithms. It iteratively reduces the original network into a hierarchy of smaller networks, resulting in informative simplifications of the original network at various degrees of detail. Few of these algorithms, however, have been specially built to cope with bipartite networks. Besides of this, current coarsening algorithms present the following theoretical limitations that should be addressed: 1) A high-cost search strategy in dense networks;2) current coarsening algorithms are usually based on label propagation, which is limited to propagate single-labels;and 3) the synchronous label propagation scheme yields the cyclic oscillation problem. To overcome such limitations, we propose a coarsening algorithm based on multi-label propagation, which is more suitable for large-scale bipartite networks and allows a time-effective implementation. Furthermore, our proposal improves the standard semi-synchronous strategy and simultaneously propagates multiple labels to create the coarsened network representation. The empirical analysis of synthetic and real-world networks provides evidence that our coarsening strategy leads to significant gains regarding accuracy and runtime against standard techniques.

关键词： Oscillators Task analysis machine learning algorithms Convergence Computer science Standards Search problems Bipartite networks coarsening label propagation multilevel approach multi-label propagation

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Towards a data-driven characterization of behavioral changes induced by the seasonal flu

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PLOS COMPUTATIONAL BIOLOGY 2020年第5期16卷 e1007879-e1007879页

作者： Gozzi, Nicolo Perrotta, Daniela Paolotti, Daniela Perra, Nicola Univ Greenwich Networks & Urban Syst Ctr London England Max Planck Inst Demog Res Rostock Germany ISI Fdn Turin Italy

Author summary Human behavior and infectious diseases are linked by a feedback loop. While individuals might change their behavior as a response to an epidemic, such changes might influence the spreading itself. So far, our understanding and characterization of behavioral changes induced by diseases has been strongly limited by the lack of empirical data. As result, the vast majority of research has been focused on theoretical, what if, scenarios. In this work, we collected a unique dataset comprised of 599 surveys submitted by 434 users to the participatory surveillance platform Influweb over the 2017 - 18 and 2018 - 19 flu seasons. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of self-initiated behavioral changes implemented by each participant. Our analysis, conducted adopting machine learning algorithms, show that both past experience of illness and personal beliefs about the disease are fundamental drivers of behavioral change. These findings are in good agreement with the constructs of the Health Belief Model and provide, to the best of our knowledge, the first data driven characterization of behavioral changes during the seasonal flu. In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we

关键词： Influenza Behavior Social distancing COVID 19 Pandemics machine learning machine learning algorithms Behavioral and social aspects of health

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A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With Bayesian Optimization Algorithm

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IEEE ACCESS 2021年 9卷 1219-1232页

作者： Shang, Qiang Feng, Linlin Gao, Song Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Shandong Univ Technol Sch Marxism Zibo 255000 Peoples R China

Automatic Incident Detection (AID) is an important part of Intelligent Transportation Systems (ITS). A hybrid AID method using Random Forest-Recursive Feature Elimination (RF-RFE) algorithm and Long-Short Term Memory (LSTM) network optimized by Bayesian Optimization Algorithm (BOA) is proposed in this article. Firstly, a relatively comprehensive set of initial variables is constructed using basic traffic variables and their combinations. Secondly, feature variables are selected from the initial variables using the RF-RFE algorithm. Then, the feature variables are used for training the LSTM network, and the hyper-parameters of the LSTM network are optimized by BOA. In addition, Synthetic Minority Over-Sampling Technique (SMOTE) is employed to solve the problem of imbalance between incident sample size and non-incident sample size. Finally, experiments are conducted using real-world data to test performance of the proposed method and compare with several state-of-the-art AID methods on multiple evaluation criteria. The experimental results illustrate that the proposed method achieved superior performance with respect to almost all the evaluation criteria. It also shows that the proposed method is promising for dealing with the problems of imbalance and small sample size of traffic incident data.

关键词： Detectors Prediction algorithms Volume measurement Support vector machines Roads machine learning algorithms Feature extraction Traffic incident detection feature selection long short-term memory network Bayesian optimization algorithm

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A NOVEL METHOD OF MAIZE LEAF DISEASE IMAGE IDENTIFICATION BASED ON A MULTICHANNEL CONVOLUTIONAL NEURAL NETWORK

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TRANSACTIONS OF THE ASABE 2018年第5期61卷 1461-1474页

作者： Lin, Z. Mu, S. Shi, A. Pang, C. Sun, X. Shandong Agr Univ Coll Informat Sci & Engn Tai An Shandong Peoples R China Shandong Agr Univ Sch Chem & Mat Sci Tai An Shandong Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance.

关键词： Artificial intelligence Convolutional neural network Deep learning Image classification machine learning algorithms Maize leaf disease

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Load Disaggregation Based on Time Window for HEMS Application

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IEEE ACCESS 2021年 9卷 70746-70757页

作者： Lemes, Dimas Augusto Mendes Cabral, Thales Wulfert Fraidenraich, Gustavo Meloni, Luis Geraldo Pedroso De Lima, Eduardo Rodrigues Neto, Fernando Bauer State Univ Campinas UNICAMP Dept Commun DECOM BR-13083872 Campinas Brazil Inst Pesquisas Eldorado Dept Hardware Design BR-275 Alan Turing Brazil Companhia Paranaense Energia COPEL BR-80420170 Curitiba Parana Brazil

This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.

关键词： Hidden Markov models Principal component analysis Monitoring Performance evaluation Home appliances Aggregates Power measurement Artificial intelligence in power systems load disaggregation PCA time window machine learning algorithms HEMS NILM RAE dataset REDD dataset

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