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Contourlet Transform Based Reduced-Reference Image Quality Assessment

Contourlet Transform Based Reduced-Reference Image Quality A...

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international symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Fengping Xu College of information technology Nantong Normal College Nantong China

ISBN: (纸本)9781665428903

In this paper, we propose a reduced-reference image quality assessment method based on Contourlet transform, due to its properties of multi-resolution, multi-scale, multi-directional and anisotropy. It can capture the image edge profile effectively by using a small number of coefficients. First, we perform three-scale and four-level Contourlet decomposition for the reference and test images. Then we calculate energy eigenvectors on each scale and subsequently obtain the angel of energy eigenvector between the reference and test images. Finally, a weighted summation is used to predict the image quality score. Various experiments based on the LIVE2 database verify the performance of the proposed algorithm, which has high consistency with human visual system.

关键词： Image quality Image edge detection Transforms Visual systems Prediction algorithms Visual databases Data mining

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Protecting Resources Against Volumetric and Non-volumetric Network Attacks

Protecting Resources Against Volumetric and Non-volumetric N...

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international Conference on parallel and distributed systems (ICPADS)

作者： Rajorshi Biswas Jie Wu Information Sciences and Technology Penn State Berks Reading PA USA Temple University Philadelphia PA USA

ISBN: (纸本)9781665408790

Cyber attacks are growing with the increase in internet usage. In a volumetric attack, the target resource is taken down with a huge amount of traffic. distributed denial-of-service and link flooding attacks are examples of these types of attacks. In a non-volumetric attack, the attackers try to steal or get illegal authorization of some resources in a network. This type of attack can be severe even with a small amount of traffic. Non-volumetric attacks can be stopped by applying a moving target defense approach at the nodes on the attack path. An attack path is a series of steps and the attacker needs to succeed in all of those steps to gain access to the resources. In this paper, we propose an architecture to defend against both types of attacks. We formulate a problem to minimize the damage caused by the volumetric attack by using a limited number of blockage at some routers. This problem is NP-hard and we provide a greedy solution and provide an approximation ratio of it. We formulate another optimization problem to minimize the damage while securing the resources by deploying the minimum number of moving target defense methods. We provide a dynamic programming based solution to this problem. We conduct an extensive simulation to support our proposed models.

关键词： Authorization databases Conferences Information filters File servers Dynamic programming Floods

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17th international symposium on Applied Reconfigurable Computing, ARC 2021

17th International Symposium on Applied Reconfigurable Compu...

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17th international symposium on Applied Reconfigurable Computing, ARC 2021

ISBN: (纸本)9783030790240

The proceedings contain 25 papers. The special focus in this conference is on Applied Reconfigurable Computing. The topics include: Multi-layered NoCs with Adaptive Routing for Mixed Criticality systems;preface;fast Approximation of the Top-k Items in Data Streams Using a Reconfigurable Accelerator;PDU Normalizer Engine for Heterogeneous In-Vehicle Networks in Automotive Gateways;StreamGrid - An AXI-Stream-Compliant Overlay Architecture;Increasing Side-Channel Resistance by Netlist Randomization and FPGA-Based Reconfiguration;moving Target and Implementation Diversity Based Countermeasures Against Side-Channel Attacks;clone-Resistant Secured Booting Based on Unknown Hashing Created in Self-Reconfigurable Platform;transparent Near-Memory Computing with a Reconfigurable Processor;a Dataflow Architecture for Real-Time Full-Search Block Motion Estimation;providing Tamper-Secure SoC Updates Through Reconfigurable Hardware;graviton: A Reconfigurable Memory-Compute Fabric for Data Intensive Applications;Dynamic Spatial Multiplexing on FPGAs with OpenCL;exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in distributed databases;Accelerating Convolutional Neural Networks in FPGA-based SoCs using a Soft-Core GPU;Evaluating the Design Space for Offloading 3D FFT Calculations to an FPGA for High-Performance Computing;FPGA Implementation of Custom Floating-Point Logarithm and Division;On the Suitability of Read only Memory for FPGA-Based CAM Emulation Using Partial Reconfiguration;Domain-Specific Modeling and Optimization for Graph Processing on FPGAs;Covid4HPC: A Fast and Accurate Solution for Covid Detection in the Cloud Using X-Rays;Evaluation of Different Manual Placement Strategies to Ensure Uniformity of the V-FPGA;Timing Optimization for Virtual FPGA Configurations;Hardware Based Loop Optimization for CGRA Architectures.

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D³: Deterministic Data Distribution for Efficient Data Reconstruction in Erasure-Coded distributed Storage systems 33

D<SUP>3</SUP>: Deterministic Data Distribution for Efficient...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Li, Zhipeng Lv, Min Xu, Yinlong Li, Yongkun Xu, Liangliang Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781728112466

Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However, the commonly used random data placement in storage systems based on erasure codes induces to heavy cross-rack traffic, load imbalance, and random access, which slow down the recovery process upon failures. In this paper, with orthogonal arrays, we define a Deterministic Data Distribution (D-3) of blocks to nodes and racks, and propose an efficient failure recovery approach based on D-3. D3 not only uniformly distributes data/parity blocks among storage servers, but also balances the repair traffic among racks and storage servers for failure recovery. Furthermore, D-3 also minimizes the cross-rack repair traffic for data layouts against a single rack failure and provides sequential access for failure recovery. We implement D-3 in Hadoop distributed File System (HDFS) with a cluster of 28 machines. Our experiments show that D-3 significantly speeds up the failure recovery process compared with random data distribution, e.g., 2.21 times for (6, 3)-RS code in a system consisting of eight racks and three nodes in each rack.

关键词： Bandwidth distributed databases Arrays Maintenance engineering Layout Fault tolerance Fault tolerant systems

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SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees

SwiftAgg: Communication-Efficient and Dropout-Resistant Secu...

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IEEE international symposium on Information Theory

作者： Tayyebeh Jahani-Nezhad Mohammad Ali Maddah-Ali Songze Li Giuseppe Caire Electrical Engineering and Computer Science Department Technische Universit&#x00E4 t Berlin Department of Electrical Engineering Sharif University of Technology IoT Thrust The Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N distributed users, each of size L, trained on their local data, in a privacy-preserving manner. Compared with state-of-the-art secure aggregation protocols, SwiftAgg significantly reduces the communication overheads without any compromise on security. Specifically, in presence of at most D dropout users, SwiftAgg achieves a server communication load of (T +1)L and a per-user communication load of up to (T+D+1)L, with a worst-case information-theoretic security guarantee, against any subset of up to T semi-honest users who may also collude with the curious server. The key idea of SwiftAgg is to partition the users into groups of size T+D+1, then in the first phase, secret sharing and aggregation of the individual models are performed within each group, and then in the second phase, model aggregation is performed on T +D+1 sequences of users across the groups. If a user in a sequence drops out in the second phase, the rest of the sequence remains silent. This design allows only a subset of users to communicate with each other, and only the users in a single group to directly communicate with the server, eliminating the requirements of 1) all-to-all communication network across users; and 2) all users communicating with the server, for other secure aggregation protocols. This helps to substantially slash the communication costs of the system.

关键词： Protocols distributed databases Collaborative work Data models Servers Cryptography Communication networks

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Clustered Federated Multi-Task Learning with Non-IID Data

Clustered Federated Multi-Task Learning with Non-IID Data

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international Conference on parallel and distributed systems (ICPADS)

作者： Yao Xiao Jiangang Shu Xiaohua Jia Hejiao Huang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng National Laboratory Department of New NetworksShenzhenChina City University of Hong Kong Hong Kong China

ISBN: (纸本)9781665408790

Federated Learning enables the collaborative learning in cross-client scenarios while keeping the clients' data local for privacy. The presence of non-IID data is one of major challenges in federated learning. To deal with this statistic challenge, federated multi-task learning considers the local training for each client as a single task. However, all the clients must participate in each training round, and it is inapplicable to mobile or IOT devices with constrained communication capability. To achieve the communication-efficiency and high accuracy with non-IID data, we propose a clustered federated multi-task learning by exploring client clustering and multi-task learning. We measure the similarities of local data among clients indirectly through their models' parameters, and design a client clustering strategy to enable clients with similar data distribution into a same group. The limitation of full-participation can be eliminated through the way of model training for groups instead of individual clients. The convergence analysis and experimental evaluation on real-world datasets shows that our work outperforms the basic federated learning in accuracy and is also more communication-efficient than the existing federated multi-task learning.

关键词： Training Data privacy distributed databases Multitasking Collaborative work Particle measurements Data models

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Logic-Based Approach to Incremental Monitoring and Optimization on Strongly distributed Data Streams 1

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11th international symposium on Foundations of Information and Knowledge systems (FoIKS)

作者： Ravve, Elena V. Ort Braude Coll Karmiel Israel

ISBN: (数字)9783030399511

ISBN: (纸本)9783030399511;9783030399504

In this paper, we systematically adopt logical reduction techniques to monitoring and optimization on distributed data streams. The first technique: Feferman-Vaught reductions, which describe how the queries over a disjoint union of data streams can be computed from queries over the components and queries over the index set. The second one: the syntactically defined translation schemes, which describe possible transformations of data. Combination of these two techniques allows us to consider not only monitoring and optimization on disjoin unions of data streams but rather on much richer compositions. We call them strongly distributed data streams. Our approach is applicable to both homogeneous and heterogeneous data streams. While, as a rule, the known approaches provide some approximation of the solution of the original problem, our method derives solutions over the components of a strongly distributed data stream, such that their further proceeding gives a result that is equivalent to the solution of the original problem on the given data stream.

关键词： Data streams Incremental parallel and distributed monitoring Logical reductions Syntactically defined translation schemes

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A Flexible and distributed Runtime System for High-Throughput Constrained Data Streams Generation 33

A Flexible and Distributed Runtime System for High-Throughpu...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Godard, Paul Loechner, Vincent Bastoul, Cedric Soulier, Frederic Muller, Guillaume Caldera Eckbolsheim France Univ Strasbourg Strasbourg France INRIA Rocquencourt France

ISBN: (纸本)9781538655559

Major research topics on parallel and distributed frameworks focus on reliability, performance and programmability of large scale systems for, e.g., HPC or Big Data. The solutions proposed are often directly impacted by the large scale nature of the problems. Differently, high-throughput data stream generation is an important challenge for many scientific and industrial applications which is typically well suited for small to medium scale systems, and which has to respect specific constraints about, e.g., speed, throughput or output location. In this paper we present a framework dedicated to this class of problems. We propose a performance-oriented runtime system architecture able to generate constrained data streams issued from jobs dynamically submitted by the user. Our architecture is designed to scale from a single host to a medium-sized cluster with large topology flexibility to achieve high throughput capabilities while being widely adaptive to a variety of problems. We provide experimental evidence of the ability of our framework to meet high-throughput constraints on an industrial use-case, i.e., professional digital printing, that may require tens of Gbit/s sustained output rates. We show in our measurements that our system scales and reaches data rates close to the maximum throughput of our experimental cluster.

关键词： Task analysis Runtime Big Data distributed databases Hardware Throughput Streaming media

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Pufferscale: Rescaling HPC Data Services for High Energy Physics Applications 20

Pufferscale: Rescaling HPC Data Services for High Energy Phy...

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20th IEEE/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Cheriere, Nathanael Dorier, Matthieu Antoniu, Gabriel Wild, Stefan M. Leyffer, Sven Ross, Robert Univ Rennes INRIA CNRS IRISA Rennes France Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9781728160955

User-space HPC data services are emerging as an appealing alternative to traditional parallel file systems, because of their ability to be tailored to application needs while eliminating unnecessary overheads incurred by POSIX compliance. The High Energy Physics (HEP) community is progressively turning towards such services to enable high-throughput accesses under heavy concurrency to billions of event data produced by instruments and consumed by subsequent analysis workflows. Such services would benefit from the possibility to be rescaled up and down to adapt to changing workloads, as experimental campaigns progress, in order to optimize resource usage. This paper formalizes rescaling a distributed storage system as a multi objective optimization problem considering three criteria: load balance, data balance, and duration of the rescaling operation. We propose a heuristic for rapidly finding a good approximate solution, while allowing users to weight the criteria as needed. The heuristic is evaluated with Pufferscale, a new rescaling manager for microservice-based distributed storage systems. To validate our approach in a real-world ecosystem, we showcase the use of Pufferscale as a means to enable storage malleability in the HEPnOS storage system for HEP applications.

关键词： distributed Storage System Elasticity Rescaling Load balancing High Energy Physics

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Towards a synthetic load generator framework for validating schedules in HPC environment 19

Towards a synthetic load generator framework for validating ...

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INFOTEH-JAHORINA 19th international symposium (INFOTEH)

作者： Krpic, Zdravko Lukic, Ivica Kohler, Mirko JJ Strossmayer Univ Osijek FERIT Osijek Dept Software Engn Osijek Croatia

ISBN: (纸本)9781728147758

Most of the scientific parallel and distributed applications require significant amount of time to complete on huge and expensive computing systems. Often, these applications are executed multiple times, making their scheduling a critical process for the runtime performance. One way to validate effectiveness of a schedule is to run the application on the actual hardware, which takes a lot of time and resources and is largely unfeasible. In this work we present a baseline for an adjustable-scale graph-based parallel workload executor framework for executing custom-scaled parallel applications given as graphs using synthetic load for the tasks, the GRAPPLER. The idea behind GRAPPLER is to operate as a schedule validator whose execution traces can provide valuable insights into runtime bottlenecks of the application, platform and the scheduler.

关键词： High performance computing scheduling graph-based parallel workloads DAG

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