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ieee international workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Chunyan Yan Jianyu Yuan Zhiwei Ye Zhiyong Yang School of Computer Science Hubei University of Technology Wuhan China Science and Technology Information

ISBN: (纸本)9781665426060

Data have become an important factor of production in various fields. While there are many missing values due to some irresistible reasons, which may lead to incorrect results and conclusions. A set of imputation methods have been proposed for filling continuous data, while only a few works focus on discrete missing data. In this paper, we propose an improved multi-layer perceptron (IMLP) imputation method based on the momentum gradient descent (MGD) algorithm to impute the discrete missing data via the one-hot encoding technique. To verify the performance of this method, comparisons have been made of mode, K-nearest neighbor, and auto-encoder imputation methods on different missing patterns and missing rates. The simulation result shows that the IMLP has a good imputation performance on discrete data with a high missing rate.

关键词： Simulation Conferences Data acquisition Production Filling Encoding

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2020 2nd international Conference on Embedded and Distributed systems, EDiS 2020

2020 2nd International Conference on Embedded and Distribute...

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2nd international Conference on Embedded and Distributed systems, EDiS 2020

ISBN: (纸本)9781728175034

The proceedings contain 30 papers. The topics discussed include: versatile software framework for the monitoring and control of distributed computing systems;a new levy flight trajectory-based grasshopper optimization algorithm for multi-objective optimization problems;quality of context in the internet of things: parameters and models;the impact of products exchange in multi-levels multi-products distribution network;multiuser access via OFDMA technology in high density ieee 802.11ax WLANs: a survey;and dynamic clustering approach for run-time applications mapping on NoC-based multi/many-core systems.

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multi-Disease Prediction with Artificial Intelligence from core Health Parameters Measured through Non-invasive Technique

Multi-Disease Prediction with Artificial Intelligence from C...

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international Conference on Intelligent computing and Control systems (ICICCS)

作者： Vijayalaxmi, A. Sridevi, S. Sridhar, N. Ambesange, Sateesh CMR Inst Technol Dept ECE Bengaluru India Pragyan AI Sch Bengaluru India

ISBN: (纸本)9781728148762

Healthcare falls under the basic amenities to be provided to the society. In the current world, Non-invasive diagnostic tests are of prime requirement in the medical field to avoid the painful process of collecting samples. This work aims to develop a portable and affordable diagnostic system to measure basic health parameters such as Body mass index (BMI), temperature, pulse rate, blood oxygen saturation level, blood pressure, and blood glucose content and use the machine learning (ML) models to predict the future health complications that could be used to take preventive measures. In this work, we demonstrated that using only core health parameters many diseases can be predicted with good accuracy. Diabetes is taken as a case study and predicted using the Pima Indian Diabetes Dataset (PIDD) of UCI Machine learning Repository. Diabetes is predicted by extracting only core parameter data and compared with the prediction using the complete data set and resulted in comparatively similar accuracy. Decision tree, Naive Bayes, and K-neighbors classifiers (KNN) are used. KNN outperforms the other two algorithms in prediction in both data sets.

关键词： Artificial Intelligence Machine Learning Non-invasive Body mass index Blood oxygen saturation level cloud

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Modeling of multi-core CPU Power Consumption during Online Video Conference

Modeling of Multi-Core CPU Power Consumption during Online V...

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ieee international workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Iryna Zhuravska Kateryna Obukhova Petro Mohyla Black Sea National University Mykolaiv Ukraine

ISBN: (纸本)9781665426060

Recently, more and more services - industrial, educational, medical, etc. - are provided remotely, via the Internet. This has become especially relevant in the field of health care, for example, during the rehabilitation period for post-stroke and post-infarction patients. In this case, several video conferencing sessions are opened simultaneously on the doctor's gadget. This leads to overload of the gadget's processor (CPU), a significant increase in power consumption, overheating of the cores, and as a result to a violation of the stability of the connection. The proposed Colored Petri Net model and developed software for each cores' monitoring the processes in the CPU, built on the *** architecture, allow decision-making on CPU load management with increasing the share of use of LITTLE cores. This approach will increase the time of use of the user device without recharging to 6.8%.

关键词： Power demand multicore processing Petri nets Process control Medical services Task analysis Monitoring

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A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

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ieee COMMUNICATIONS SURVEYS AND TUTORIALS 2020年第1期22卷 196-248页

作者： Khan, Rabia Kumar, Pardeep Jayakody, Dushantha Nalin K. Liyanage, Madhusanka Natl Res Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk 634050 Russia Swansea Univ Dept Comp Sci Swansea SA2 8PP W Glam Wales Sri Lanka Technol Campus Fac Engn Padukka 10500 Sri Lanka Univ Coll Dublin Sch Comp Sci Dublin D04 V1W8 4 Ireland Univ Oulu Ctr Wireless Commun Oulu 90014 Finland

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model;network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.

关键词： multi-access edge computing (MEC) network function virtualization (NFV) network security network slicing physical layer security (PLS) privacy software-defined networking (SDN) and telecommunication 5G

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An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous multi-processor Embedded systems 2020

An OpenMP Parallel Genetic Algorithm for Design Space Explor...

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11th workshop on Parallel Programming and Run-Time Management Techniques for many-core Architectures / 9th workshop on Design Tools and Architectures for multicore Embedded computing Platforms, PARMA-DITAM 2020

作者： Muttillo, Vittoriano Giammatteo, Paolo Fiorilli, Giuseppe Pomante, Luigi University of L’Aquila L’Aquila Italy

ISBN: (纸本)9781450375450

Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. Therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. This approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about Parallel Genetic Algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. The descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. © 2020 Association for computing Machinery.

关键词： multiobjective optimization

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The collection Virtual Machine: An abstraction for multi-frontend multi-backend data analysis 16

The collection Virtual Machine: An abstraction for multi-fro...

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16th international workshop on Data Management on New Hardware, DaMoN 2020

作者： Müller, Ingo Marroquín, Renato Koutsoukos, Dimitrios Wawrzoniak, Mike Akhadov, Sabir Alonso, Gustavo ETH Zurich Systems Group Department of Computer Science Switzerland Oracle Labs Databricks United States

ISBN: (纸本)9781450380249

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of analytics has made this challenge even more difficult. In practice, system designers are overwhelmed by the number of combinations and typically implement a single analytics type on one platform, leading to repeated implementation effort-and a plethora of semi-compatible tools for data scientists. In this paper, we propose the "Collection Virtual Machine" (or CVM)-an extensible compiler framework designed to keep the specialization process of data analytics systems tractable. It can capture at the same time the essence of a large span of low-level, hardware-specific implementation techniques as well as high-level operations of different types of analyses. At its core lies a language for defining nested, collection-oriented intermediate representations (IRs). Frontends produce programs in their IR flavors defined in that language, which get optimized through a series of rewritings (possibly changing the IR flavor multiple times) until the program is finally expressed in an IR of platform-specific operators. While reducing the overall implementation effort, this also improves the interoperability of both analyses and hardware platforms. We have used CVM successfully to build specialized backends for platforms as diverse as multi-core CPUs, RDMA clusters, and serverless computing infrastructure in the cloud and expect similar results for many more frontends and hardware platforms in the near future. © 2020 ACM.

关键词： Program processors

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F-LEMMA: Fast Learning-based Energy Management for multi-/many-core Processors 20

F-LEMMA: Fast Learning-based Energy Management for Multi-/Ma...

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2nd ACM/ieee workshop on Machine Learning for CAD (MLCAD)

作者： Zou, An Garimella, Karthik Lee, Benjamin Gill, Christopher Zhang, Xuan Washington Univ St Louis MO 14263 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (数字)9781450375191

ISBN: (纸本)9781450375191

Over the last two decades, as microprocessors have evolved to achieve higher computational performance, their power density also has increased at an accelerated rate. Improving energy efficiency and reducing power consumption is therefore of critical importance to modern computing systems. One effective technique to improve energy efficiency is dynamic voltage and frequency scaling (DVFS). In this paper, we propose F-LEMMA: a fast learning-based power management framework consisting of a global power allocator in userspace, a reinforcement learning-based power management scheme at the architecture level, and a swift controller at the digital circuit level. This hierarchical approach leverages computation at the system and architecture levels, and the short response times of the swift controllers, to achieve effective and rapid ps-level power management. Our experimental results demonstrate that F-LEMMA can achieve significant energy savings (35.2% on average) across a broad range of workload benchmarks. Compared with existing state-of-the-art DVFS-based power management strategies that can only operate at millisecond timescales, F-LEMMA is able to provide notable (up to 11%) Energy-Delay Product improvements when evaluated across benchmarks.

关键词： hierarchical power management integrated voltage regulator learning-based fast power management manycore system multicore system power efficiency reinforcement learning

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D2Graph: An Efficient and Unified Out-of-core Graph computing Model

D2Graph: An Efficient and Unified Out-of-Core Graph Computin...

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ieee international Conference on Big Data and Cloud computing (BdCloud)

作者： Baoke Li Cong Cao Yuhai Lu Yanbing Liu Baohui Li Binxing Fang Jianhui Fu School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Chinese Information Technology Securiry Evaluation Center Beijing China Institue of Electronic and Information Engineering of UESTC in Guangdong Dongguan China Shandong Institutes of Industrial Technology Jinan China

Graph computing recently receives intensive interests due to a wide range of needs to analyse big graphs in real time. many single-machine out-of-core graph computing systems have been developed to process large-scale graphs by utilizing effective memory access techniques. The current systems either focus on graph compression or pay attention to disk scheduling strategies. In this paper, we propose an efficient and unified out-of-core graph computing model on a single machine named D 2 Graph which combines the differential storage strategy and the dynamic caching mechanism. D 2 Graph improves the spatial locality of graph data and the temporal locality of graph computing at the same time. The main contributions of our work lie in three aspects: firstly, we design a fine-grained differential storage(FGDS) strategy to reorganize graph data on disk; secondly, we propose a dynamic caching mechanism (DCM) to selectively load the necessary edge chunks; thirdly, we combine FGDS and DCM to construct D 2 Graph and evaluate it on five public graph data sets. What’s more, to optimize the performance of D 2 Graph, we also make use of the multi-threaded parallel technology. A series of results show that D 2 Graph outperforms state-of-the-art out-of-core graph computing systems by 1.3x-17.9x.

关键词： Processor scheduling Computational modeling Scalability Heuristic algorithms Loading Parallel processing Real-time systems

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Masa: Responsive multi-DNN Inference on the Edge

Masa: Responsive Multi-DNN Inference on the Edge

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ieee international Conference on Pervasive computing and Communications (PerCom)

作者： Bart Cox Jeroen Galjaard Amirmasoud Ghiassi Robert Birke Lydia Y. Chen TU Delft Delft Netherlands ABB Research Switzerland Switzerland

ISBN: (纸本)9781665447256

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted via executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. The response times of multi-DNN highly affect users' quality of experience and safety as well. Different DNNs exhibit diversified resource requirements and execution patterns across layers and networks, which may easily exceed the available device memory and riskily degrade the responsiveness. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution framework, an on-device middleware featuring on modeling inter- and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.

关键词： Pervasive computing Schedules Image analysis Processor scheduling Image edge detection Real-time systems Safety

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