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2019 IEEE Conference on computer Communications, INFOCOM 2019

作者： Tang, Lu Huang, Qun Lee, Patrick P. C. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728105154

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch. Trace-driven evaluation shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to 3.38 × throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. © 2019 IEEE.

关键词： Data structures

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Exploring the Use of WebAssembly in HPC 23

Exploring the Use of WebAssembly in HPC

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian and Institute of Information Technology Kottayam Kerala India

ISBN: (纸本)9798400700156

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. © 2023 ACM.

关键词： Containers

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Designing a power-aware replication strategy for storage clusters

Designing a power-aware replication strategy for storage clu...

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2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

作者： Zhang, Lingwei Deng, Yuhui Department of Computer Science Jinan University Guangzhou 510632 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769550466

The exponential data growth is presenting challenges to traditional storage systems. Component-based cluster storage systems, due to their high scalability, are becoming the architecture of next generation storage systems. Cluster storage systems often use data replication to ensure high availability, fault tolerance, and load balance. However, this kind of data replication not only consumes a large amount of storage resources, but also generates more energy consumption. This paper presents a power-aware data replication strategy by leveraging data access behavior. This strategy uses 80/20 rule (80% of the data accesses often go to 20% of the storage space) to replicate only a small amount of frequently accessed data. Furthermore, the storage nodes are divided into a hot node set and a cold node set. Hot nodes, which store a small amount of hot data copies, are always in an active state to guarantee the QoS of the system. The cold nodes which store a large number of infrequently accessed cold data, are placed in a low power state, thus reducing the energy consumption of the cluster storage system. The experimental results show that the proposed strategy can effectively reduce the resource and energy consumption of the system, while ensuring the system performance. © 2013 IEEE.

关键词： Energy utilization

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Yogapriya, G. Kumari, R. Shalini Suganthi, S. Agalya, K. Elangovan, T. Pandian, D. Selva School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai India Department of Architecture Periyar Maniammai Institute of Science and Technology Tamilnadu Thanjavuri India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India

ISBN: (纸本)9798350334357

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction. © 2023 IEEE.

关键词： Convolutional neural networks

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An experimental investigation of the integration of smart building components with building information model (BIM) 36

An experimental investigation of the integration of smart bu...

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36th International Symposium on Automation and Robotics in Construction, ISARC 2019

作者： Afsari, K. Florez, L. Maneke, E. Afkhamiaghda, M. School of Construction Management Technology Purdue University United States Department of Architecture and Built Environment Northumbria University United Kingdom Department of Computer Graphics Technology Purdue University United States

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed. © 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved.

关键词： Visualization

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Decomposition of the unit test generation task for parallel computations 16

Decomposition of the unit test generation task for parallel ...

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16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Bazylevych, Roman Czarnul, Pawel Franko, Andrii Lviv Polytechnic National University Department of Software Lviv Ukraine Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Computer Architecture Gdansk Poland

ISBN: (纸本)9781665442572

A decomposition of unit test generation task for symbolic execution has been considered. The process has been divided into the subtasks: code parsing, paths generation, path analysis, constraints analysis and test generation. The code parsing and path generation time have been investigated. The dependency between time and input size is close to be linear on code parsing and test path generation. It has been shown that path generation takes almost three times larger time than code parsing. The results will be used for load-balancing of unit test generators based on symbolic execution. © 2021 IEEE.

关键词： Regression analysis

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A high performance SSVEP-BCI without gazing

A high performance SSVEP-BCI without gazing

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International Joint Conference on Neural Networks (IJCNN)

作者： M. A. Lopez-Gordo F. Pelayo A. Prieto Department of Computer Architecture and Technology University of Granada Granada Spain Department of Computer Architecture and Technology University of Granada Spain

Brain-computer interfaces based on steady-state visual evoked potential (SSVEP-BCIs) achieve a high performance due to their multiclass nature in paradigms where gazing is needed. Studies of binary SSVEP-BCIs have been presented without the need of gazing at the expense of low performance. This study presents a high performance binary SSVEP-BCI that allows an efficient communication without the need of gazing. The information transmission rate (ITR) obtained was 0.64±0.27 bits per seconds with peaks of 1.16 bits per second and an accuracy of 90±7%, which is an excellent performance for a binary SSVEP-BCI. To achieve this performance two main factors were involved: on the one hand, the use of both amplitude and phase of the SSVEP in the classification and, on the other, the use of the absence of gaze (“thousand-yard stare”) as a way of helping to ignore the stimulus.

关键词： Visualization Brain computer interfaces Correlation Discrete Fourier transforms Accuracy Steady-state Detectors

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The artificial neural networks (Ann) for batik detection based on textural features 7

The artificial neural networks (Ann) for batik detection bas...

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7th Mathematics, Science, and computer Science Education International Seminar, MSCEIS 2019

作者： Kasim, Anita Ahmad Bakri, Muhammad Septiarini, Anindita Departemen of Information Technology Tadulako University Palu Indonesia Department of Architecture Tadulako University Palu Indonesia Department of Computer Science Mulawarman University Samarinda Indonesia

ISBN: (纸本)9781631902567

This study aims to utilize artificial neural networks to distinguish batik motifs and non-batik fabric motifs. Several important steps are needed, namely the process of acquiring batik and non-batik images, pre-transforming batik and non-batik images to gray scale forms, texture feature extraction in gray scale images and detection of motifs using networks artificial nerve. Image acquisition is done by collecting batik and not batik images from several different motifs. Processing data sets is divided into 70% as training data and 30% as testing data. Artificial neural network models used in this research use the Backpropagation learning algorithm by comparing the Scaled conjugate gradient algorithm (trainscg) training method and the Levenberg-Marquardt algorithm (trainlm) training method. The results obtained for the accuracy using the Scaled conjugate gradient algorithm (trainscg) training method were higher with an accuracy value of 84.12%, compared to the Levenberg-Marquardt algorithm (trainlm) method by 86.11%. © 2020 EAI.

关键词： Neural networks

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Intrusion Detection System Through Deep Learning in Routing MANET Networks

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Intelligent Automation & Soft Computing 2023年第7期37卷 269-281页

作者： Zainab Ali Abbood DoguÇagdaşAtilla Çagatay Aydin Department Electrical and Computer Engineering Altinbas UniversityIstanbulTurkey Department Computer Technology Engineering Al-Esraa UniversityBaghdadIraq Electric Autonomous and Unmanned Vehicles Application and Research CentreAltinbas UniversityIstanbulTurkey Faculty of Engineering and Architecture Altinbas UniversityIstanbulTurkey Department Electrical and Electronics Engineering Ege UniversityIzmirTurkey

Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in *** reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security *** protecting MANETs from attack,encryption and authentication schemes have their ***,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the *** are a secondary deﬁance system for mobile ad-hoc networks *** since they monitor network trafﬁc and report anything ***,many scientists have employed deep neural networks(DNNs)to address intrusion detection *** paper used MANET to recognize com-plex patterns by focusing on security standards through efﬁciency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent *** with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)*** results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the ti

关键词： ARP CBPNN CNN DNN DL E2E FFNN IDS ML MANET security

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Formalising ODRL semantics using web ontologies

Formalising ODRL semantics using web ontologies

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2nd International Workshop on Open Digital Rights Language, ODRL 2005

作者： García, Roberto Gil, Rosa Gallego, Isabel Delgado, Jaime Technology Department Universitat Pomeu Fabra Barcelona E-08003 Spain Computer Architecture Department Universitat Politecnica de Catalunya Barcelona E-08034 Spain

ISBN: (纸本)1740645014

In order to move Digital Rights Management to the Internet, a common rights expression language is needed. ODRL (Open Digital Rights Language) is one of the proposed solutions. It is based on a XML language and thus it just formalises the language syntax, while language semantics are specified informally. Actually, ODRL seems quite complete and generic enough to cope with such a complex domain. However, the problem is that it has such a rich structure that it is difficult to implement. In our opinion, it lacks formal semantics that would help ODRL applications development. As the application context is the Web, our approach to formalise ODRL semantics is based on semantic web ontologies. Firstly, ORDL has been moved to the Semantic Web space using XML Schema to OWL and XML to RDF tools. This provides some simple semantics. In order to refine them, the resulting ODRL ontologies have been connected to IPROnto, a result of previous research. IPROnto, Intellectual Property Rights Ontology, models the IPR core concepts for creation, intellectual property rights and the basic kinds of actions that operate on intellectual property. It enables semantics-aware IPR applications that benefit from semantic queries, in contrast to the difficulties that emerge from the use of syntactic queries when the information space is as complicated as in the IPR field. Moreover, specialised reasoners can be used for license checking and retrieval. All these advantages have been propagated to ODRL thanks to this mapping.

关键词： Ontology

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