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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Singh, Yugpratap Kuruvila, Abraham Peedikayil Basu, Kanad Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9783981926354

In the age of Internet-of-Things (IoT), automobiles have become heavily integrated and reliant on computerized components for system functionality. Modern vehicles have many Electronic Control Units (ECUs) that control ignition timing, suspension control, and transmission shifting. The Engine Control Module (ECM) is generally recognized as one of the most essential components owing to its functionality of regulating air and fuel input to the engine. Consequently, automotive security is an emerging problem that will only escalate as vehicles integrate more computerized components in conjunction with wireless system connectivity. Attackers that successfully gain access to important vehicular components and compromise existing functionality can induce a plethora of malevolent activities. With the evolution and exponential proliferation of Malware, identifying malicious entities is critical for maintaining proper system performance. Traditional anti-virus software is inadequate against complex Malware, which has engendered a push towards Hardware-assisted Malware Detection (HMDs) using Hardware Performance Counters (hpcs). hpcs are special purpose registers that track low-level micro-architectural events. In this paper, we propose using machine learning models trained on hpc data to identify malicious entities in the ECM. Our experimental results determine that the proposed ML-based models can successfully identify malicious actions in an automotive system with a classification accuracy of up to 96.7%.

关键词： Automotive Security Hardware Performance Counters machine learning Cyber-physical systems

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Distributed and Adaptive Traffic Engineering with Deep Reinforcement learning 29

Distributed and Adaptive Traffic Engineering with Deep Reinf...

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29th ieee/acm International Symposium on Quality of Service (IWQOS)

作者： Geng, Nan Xu, Mingwei Yang, Yuan Liu, Chenyi Yang, Jiahai Li, Qi Zhang, Shize Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Peng Cheng Lab PCL Shenzhen Peoples R China

ISBN: (纸本)9781665414944

Lots of studies focus on distributed traffic engineering (TE) where routers make routing decisions independently. Existing approaches usually tackle distributed TE problems through traditional optimization methods. However, due to the intrinsic complexity of the distributed TE problems, routing decisions cannot be obtained efficiently, which leads to significant performance degradation, especially for highly dynamic traffic. Emerging machine learning technologies like deep reinforcement learning (DRL) provide a new choice to address TE problems in an experience-driven method. In this paper, we propose DATE, a distributed and adaptive TE framework with DRL. DATE distributes well-trained agents to the routers in the located network. Each agent makes local routing decisions independently based on link utilization ratios flooded by each router periodically. To coordinate the distributed agents to achieve the global optimization in different traffic conditions, we construct candidate paths, develop the agents carefully, and realize a virtual environment to train the agents with a DRL algorithm. We do extensive simulations and experiments using real-world network topologies with both real and synthetic traffic traces. The results show that DATE outperforms some existing approaches and yields near-optimal performance with superior robustness.

关键词： Degradation Adaptation models Network topology Virtual environments Optimization methods Reinforcement learning Quality of service

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Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training 5

Understanding Scalability and Fine-Grain Parallelism of Sync...

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5th ieee/acm Workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Li, Jiali Nicolae, Bogdan Wozniak, Justin Bosilca, George Univ Tennessee Knoxville TN 37996 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781728159850

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. With increasing complexity of learning models and amounts of training data, data-parallel approaches based on frequent all-reduce synchronization steps are increasingly popular. Despite the fact that high performance computing (hpc) technologies have been designed to address such patterns efficiently, the behavior of data-parallel approaches on hpc platforms is not well understood. To address this issue, in this paper we study the behavior of Horovod, a popular data parallel approach that relies on MPI, on Theta, a pre-Exascale machine at Argonne National Laboratory. Using two representative applications, we explore two aspects: (1) how performance and scalability is affected by important parameters such as number of nodes, number of workers, threads per node, batch size;(2) how computational phases are interleaved with all-reduce communication phases at fine granularity and what consequences this interleaving has in terms of potential bottle-necks. Our findings show that pipelining of back-propagation, gradient reduction and weight updates mitigate the effects of stragglers during all-reduce only partially. Furthermore, there can be significant delays between weight updates, which can be leveraged to mask the overhead of additional background operations that are coupled with the training.

关键词： deep learning data-parallel training behavior analysis

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Eye-Tracking Feature Extraction for Biometric machine learning

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FRONTIERS IN NEUROROBOTICS 2022年 15卷 796895页

作者： Lim, Jia Zheng Mountstephens, James Teo, Jason Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Sabah Malaysia Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu Sabah Malaysia

ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task. MethodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the ieee Xplore, the acm Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles. ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology. ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

关键词： classification eye-tracking fixation biometric machine learning feature extraction

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Scheduling optimization of parallel linear algebra algorithms using Supervised learning 5

Scheduling optimization of parallel linear algebra algorithm...

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5th ieee/acm Workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Laberge, Gabriel Shirzad, Shahrzad Diehl, Patrick Kaiser, Hartmut Prudhomme, Serge Lemoine, Adrian S. Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9781728159850

Linear algebra algorithms are used widely in a variety of domains, e.g machine learning, numerical physics and video games graphics. For all these applications, loop-level parallelism is required to achieve high performance. However, finding the optimal way to schedule the workload between threads is a non-trivial problem because it depends on the structure of the algorithm being parallelized and the hardware the executable is run on. In the realm of Asynchronous Many Task runtime systems, a key aspect of the scheduling problem is predicting the proper chunk-size, where the chunk-size is defined as the number of iterations of a for-loop assigned to a thread as one task. In this paper, we study the applications of supervised learning models to predict the chunk-size which yields maximum performance on multiple parallel linear algebra operations using the HPX backend of Blaze's linear algebra library. More precisely, we generate our training and tests sets by measuring performance of the application with different chunk-sizes for multiple linear algebra operations;vector-addition, matrix-vector-multiplication, matrix-matrix addition and matrix-matrix-multiplication. We compare the use of logistic regression, neural networks and decision trees with a newly developed decision tree based model in order to predict the optimal value for chunk-size. Our results show that classical decision trees and our custom decision tree model are able to forecast a chunk-size which results in good performance for the linear algebra operations.

关键词： hpc linear algebra loop-level parallelism supervised learning HPX Blaze AMT

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Social Recommendation for Social Networks Using Deep learning Approach: A Systematic Review 13th

Social Recommendation for Social Networks Using Deep Learnin...

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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Alrashidi, Muhammad Selamat, Ali Ibrahim, Roliana Krejcar, Ondrej Univ Teknol Malaysia UTM Fac Engn Sch Comp Skudai 81310 Malaysia Univ Teknol Malaysia UTM Media & Games Ctr Excellence MagicX Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Kuala Lumpur Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur 54100 Malaysia Univ Hradec Kralove Ctr Basic & Appl Res Fac Informat & Management Rokitanskeho 62 Hradec Kralove 50003 Czech Republic

ISBN: (纸本)9783030881139;9783030881122

The increasing popularity of social networks indicates that the vast amounts of data contained within them could be useful in various implementations, including recommendation systems. Interests and research publications on deep learning-based recommendation systems have largely increased. This study aimed to identify, summarize, and assess studies related to the application of deep learning-based recommendation systems on social media platforms to provide a systematic review of recent studies and provide a way for further research to improve the development of deep learning-based recommendation systems in social environments. A total of 32 papers were selected from previous studies in five of the major digital libraries, including Springer, ieee, ScienceDirect, acm, Scopus, and Web of Science, published between 2016 and 2020. Results revealed that even though RS has received high coverage in recent years, several obstacles and opportunities will shape the future of RS for researchers. In addition, social recommendation systems achieving high accuracy can be built by using a combination of techniques that incorporate a range of features in SRS. Therefore, the adoption of deep learning techniques in developing social recommendation systems is undiscovered.

关键词： machine learning Deep learning Recommendation Social media

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Proceedings of mlhpc 2019: 5th Workshop on machine learning in hpc environments - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of MLHPC 2019: 5th Workshop on Machine Learning ...

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5th ieee/acm Workshop on machine learning in hpc environments, mlhpc 2019

ISBN: (纸本)9781728159850

The proceedings contain 8 papers. The topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel training;DisCo: physics-based unsupervised discovery of coherent structures in spatiotemporal systems;GradVis: visualization and second order analysis of optimization surfaces during the training of deep neural networks;metaoptimization on a distributed system for deep reinforcement learning;scheduling optimization of parallel linear algebra algorithms using supervised learning;parallel data-local training for optimizing Word2Vec embeddings for word and graph embeddings;and fine-grained exploitation of mixed precision for faster CNN training.

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Accelerating GPU-based machine learning in Python using MPI Library: A Case Study with MVAPICH2-GDR

Accelerating GPU-based Machine Learning in Python using MPI ...

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ieee/acm Workshop on machine learning in High Performance Computing environments (mlhpc)

作者： S. Mahdieh Ghazimirsaeed Quentin Anthony Aamir Shafi Hari Subramoni Dhabaleswar K. DK Panda The Ohio State University

The growth of big data applications during the last decade has led to a surge in the deployment and popularity of machine learning (ML) libraries. On the other hand, the high performance offered by GPUs makes them well suited for ML problems. To take advantage of GPU performance for ML, NVIDIA has recently developed the cuML library. cuML is the GPU counterpart of Scikit-learn, and provides similar Pythonic interfaces to Scikit-learn while hiding the complexities of writing GPU compute kernels directly using CUDA. To support execution of ML workloads on Multi-Node Multi- GPU (MNMG) systems, the cuML library exploits the NVIDIA Collective Communications Library (NCCL) as a backend for collective communications between processes. On the other hand, MPI is a de facto standard for communication in hpc systems. Among various MPI libraries, MVAPICH2-GDR is the pioneer in optimizing GPU *** paper explores various aspects and challenges of providing MPI-based communication support for GPU-accelerated cuML applications. More specifically, it proposes a Python API to take advantage of MPI-based communications for cuML applications. It also gives an in-depth analysis, characterization, and benchmarking of the cuML algorithms such as K-Means, Nearest Neighbors, Random Forest, and tSVD. Moreover, it provides a comprehensive performance evaluation and profiling study for MPI-based versus NCCL-based communication for these algorithms. The evaluation results show that the proposed MPI-based communication approach achieves up to 1.6x, 1.25x, 1.25x, and 1.36x speedup for K-Means, Nearest Neighbors, Linear Regression, and tSVD, respectively on up to 32 GPUs.

关键词： Performance evaluation machine learning algorithms Conferences Linear regression Graphics processing units Writing Libraries

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Improving Code Recommendations by Combining Neural and Classical machine learning Approaches 42

Improving Code Recommendations by Combining Neural and Class...

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42nd ieee/acm International Conference on Software Engineering Workshops, ICSEW 2020

作者： Schumacher, Max Eric Henry Le, Kim Tuyen Andrzejak, Artur Potsdam University Potsdam Germany Heidelberg University Heidelberg Germany

ISBN: (纸本)9781450379632

Code recommendation systems for software engineering are designed to accelerate the development of large software projects. A classical example is code completion or next token prediction offered by modern integrated development environments. A particular challenging case for such systems are dynamic languages like Python due to limited type information at editing time. Recently, researchers proposed machine learning approaches to address this challenge. In particular, the Probabilistic Higher Order Grammar technique (Bielik et al., ICML 2016) uses a grammar-based approach with a classical machine learning schema to exploit local context. A method by Li et al., (IJCAI 2018) uses deep learning methods, in detail a Recurrent Neural Network coupled with a Pointer Network. We compare these two approaches quantitatively on a large corpus of Python files from GitHub. We also propose a combination of both approaches, where a neural network decides which schema to use for each prediction. The proposed method achieves a slightly better accuracy than either of the systems alone. This demonstrates the potential of ensemble-like methods for code completion and recommendation tasks in dynamically typed languages. © 2020 acm.

关键词： Python

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Scaling Deep learning for Cancer with Advanced Workflow Storage Integration

Scaling Deep Learning for Cancer with Advanced Workflow Stor...

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2018 ieee/acm machine learning in hpc environments, mlhpc 2018

作者： Wozniak, Justin M. Davis, Philip E. Shu, Tong Ozik, Jonathan Collier, Nicholson Parashar, Manish Foster, Ian Brettin, Thomas Stevens, Rick Argonne National Laboratory and University of Chicago Data Science Learning United States Rutgers University United States Argonne National Laboratory Mathematics Computer Science United Kingdom Argonne National Laboratory and University of Chicago Decision and Infrastructure Sciences United States Computing Environment and Life Sciences Argonne National Laboratory United States Computing Environment and Life Sciences Argonne National Laboratory University of Chicago United States

ISBN: (纸本)9781728101804

Cancer Deep learning Environment (CANDLE) benchmarks and workflows will combine the power of exascale computing with neural network-based machine learning to address a range of loosely connected problems in cancer research. This application area poses unique challenges to the exascale computing environment. Here, we identify one challenge in CANDLE workflows, namely, saving neural network model representations to persistent storage. In this paper, we provide background on this problem, describe our solution, the Model Cache, and present performance results from running the system on a test cluster, ANL/LCRC Blues, and the petascale supercomputer NERSC Cori. We also sketch next steps for this promising workflow storage solution. © 2018 ieee.

关键词： Neural network models

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