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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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Superconducting processors for HTMT: issues and challenges

Superconducting processors for HTMT: issues and challenges

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Frontiers of Massively parallel Computation

作者： K.B. Theobald G.R. Gao T.L. Sterling Computer Architecture and Parallel Systems Laboratory Department of Electrical and Computer Engineering University of Delaware Newark DE USA NASA Jet Propulsion Laboratory /Center for Advanced Computing Research California Institute of Technology Pasadena CA USA

The Hybrid Technology Multi-Threading project is a long-term study of the feasibility of combining several emerging technologies to reach 1 petaFLOPS within ten years. HTMT will combine high-speed superconductor processors, semiconductor memories with built-in processors, high-speed optical interconnects, and high-density holographic storage. While there are major challenges in all aspects of this project, those in processor architecture are the focus of this paper. Fundamental differences between RSFQ circuits and conventional semiconductor circuits, including a radical jump in clock speed, make today's processor design approaches inappropriate for HTMT. Sequential instruction dispatching, even within the lowest programming unit (a strand), will lead to unacceptably high latencies, hence poor performance. We propose alternative processor designs which use fine-grain synchronizations between individual instructions in order to avoid these bottlenecks.

关键词： Random access memory Optical buffering Holography Holographic optical components Delay computer architecture Optical interconnections Electrical capacitance tomography Quantum computing Clocks

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sNPU: Trusted Execution Environments on Integrated NPUs

sNPU: Trusted Execution Environments on Integrated NPUs

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Annual International Symposium on computer Architecture, ISCA

作者： Erhu Feng Dahu Feng Dong Du Yubin Xia Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Department of Precision Instrument Tsinghua University Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-only TEEs to FPGA and GPU TEE systems. However, existing TEE systems exhibit inadequate and inefficient support for an emerging (and significant) processing unit, NPU. For instance, commercial TEE systems resort to coarse-grained and static protection approaches for NPUs, resulting in notable performance degradation (10%–20%), limited (or no) multitasking capabilities, and suboptimal resource utilization. In this paper, we present a secure NPU architecture, known as sNPU, which aims to mitigate vulnerabilities inherent to the design of NPU architectures. First, sNPU proposes NPU Guarder to enhance the NPU’s access control. Second, sNPU defines new attack surfaces leveraging in-NPU structures like scratchpad and NoC, and designs NPU Isolator to guarantee the isolation of scratchpad and NoC routing. Third, our system introduces a trusted software module called NPU Monitor to minimize the software TCB. Our prototype, evaluated on FPGA, demonstrates that sNPU significantly mitigates the runtime costs associated with security checking (from upto 20% to 0%) while incurring less than 1% resource costs.

关键词： Costs Runtime Prototypes computer architecture Routing Software Resource management

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EDDNet: An Efficient and Accurate Defect Detection Network for the Industrial Edge Environment

EDDNet: An Efficient and Accurate Defect Detection Network f...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Runbing Qin Ningjiang Chen Yihui Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.

关键词： Image edge detection Computational modeling Neural networks Software quality Object detection Feature extraction Transformers

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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A science gateway getting ready for serving the international molecular simulation community 2

A science gateway getting ready for serving the internationa...

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2012 EGI Community Forum / EMI 2nd Technical Conference, EGICF-EMITC 2012

作者： Gesing, Sandra Herres-Pawlis, Sonja Birkenheuer, Georg Brinkmann, André Grunzke, Richard Kacsuk, Peter Kohlbacher, Oliver Kozlovszky, Miklos Krüger, Jens Müller-Pfefferkorn, Ralph Schäfer, Patrick Steinke, Thomas Center for Bioinformatics Department of Computer Science University of Tübingen Sand 14 Tübingen72076 Germany Department of Chemistry Ludwig-Maximilians-University Munich Butenandtstr. 5-13 München81377 Germany Paderborn Center for Parallel Computing University of Paderborn Warburger Str. 100 Paderborn33089 Germany Johannes Gutenberg-University Mainz Mainz55099 Germany Center for Information Services and High Performance Computing Technische Universität Dresden Zellescher Weg 12-14 Germany Laboratory of Parallel and Distributed Systems MTA SZTAKI Kende Street 13-17 Budapest1111 Hungary Zuse Institute Berlin Takustraße 7 Berlin14195 Germany

The project MoSGrid (Molecular Simulation Grid) has been developing a web-based science gateway supporting the community with various services for quantum chemistry, molecular modeling, and docking. Users gain access to distributed computing infrastructures (DCIs) via intuitive user interfaces for sophisticated tools, specialized workflows, and distributed repositories. Currently, the MoSGrid community consists of about 120 users from a number of fields related to chemistry and bioinformatics located in Germany. However, the underlying security infrastructure is generally applicable and can be deployed in arbitrary projects. MoSGrid intends to address the international community by participating in the EU-projects SCI-BUS (Scientific gateway Based User Support) and ER-flow (Building an European research Community through Interoperable Workflows and Data), and collaborating with the EU-project EDGI (European Desktop Grid Initiative). © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence.

关键词： Quantum chemistry

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, centerNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, centerNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices

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Procedia computer Science 2020年 174卷 2-8页

作者： Zhiqiang Liu Jingfei Jiang Guoqing Lei Kai Chen Buyue Qin Xiaoqiang Zhao Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems.

关键词： Processor RISC-V Accelerator AI (Artificial Intelligence) CNN (Convolutional Neural Network) IoT (Internet of Things)

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