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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 1249 条记录，以下是81-90 订阅

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Performance study of HPC applications on an Arm-based cluster using a generic efficiency model 28

Performance study of HPC applications on an Arm-based cluste...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Banchelli, Fabio Peiro, Kilian Querol, Andrea Ramirez-Gargallo, Guillem Ramirez-Miranda, Guillem Vinyals, Joan Vizcaino, Pablo Garcia-Gasulla, Marta Mantovani, Filippo Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728165820

HPC systems and parallel applications are increasing their complexity. therefore the possibility of easily study and project at large scale the performance of scientific applications is of paramount importance. In this paper we describe a performance analysis method and we apply it to four complex HPC applications. We perform our study on a pre-production HPC system powered by the latest Arm-based CPUs for HPC, the Marvell thunderX2. For each application we spot inefficiencies and factors that limit their scalability. the results show that in several cases the bottlenecks do not come from the hardware but from the way applications are programmed or the way the system software is configured.

关键词： Performance analysis High Performance Computing parallel Applications Arm thunderX2

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TCN: Pioneering Topological-based Convolutional networks for Planetary Terrain Learning 36

TCN: Pioneering Topological-Based Convolutional Networks for...

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36th AAAI conference on Artificial Intelligence / 34th conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Chen, Yuzhou Marchetti, Yuliya Sizikova, Elena Gel, Yulia R. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA NYU Ctr Data Sci New York NY 10011 USA Univ Texas Dallas Dept Math Sci Richardson TX 75080 USA Lawrence Berkeley Natl Lab Energy Storage & Distributed Resources Div Berkeley CA 94720 USA

ISBN: (纸本)9781577358763

Implementations of artificial intelligence (AI) based on deep learning (DL) have proven to be highly successful in many domains, from biomedical imaging to natural language processing, but are still rarely applied in the space industry, particularly for onboard learning of planetary surfaces. In this project, we discuss the utility and limitations of DL, enhanced with topological footprints of the sensed objects, for multiclass classification of planetary surface patterns, in conjunction with tactile and embedded sensing in rover exploratory missions. We consider a Topological Convolutional network (TCN) model with a persistence-based attention mechanism for supervised classification of various landforms. We study TCN's performance on the Barefoot surface pattern dataset, a novel surface pressure dataset from a prototype tactile rover wheel, known as the Barefoot Rover tactile wheel. Multiclass pattern recognition in the Barefoot data has neither been ever tackled before with DL nor assessed with topological methods. We provide insights into advantages and restrictions of topological DL as the early-stage concept for onboard learning and planetary exploration.

关键词： Deep learning

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Self-supervised Multi-task Distillation for Few-shot Classification 29

Self-supervised Multi-task Distillation for Few-shot Classif...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ji, Enze Chen, Shi Ji, Tiandong Li, Jing Chen, Zhikui Chinese Academy of Sciences Computer Network Information Center Beijing China Hong Kong University of Science and Technology Big Data Institute Hong Kong Hong Kong Dalian University of Technology School of Software Technology Dalian China State Grid Liaoning Marketing Service Center Shenyang China

ISBN: (纸本)9798350330717

Few-shot classification has gained significant attention owing to the effectiveness in classifying unseen classes with a few annotated images. Although previous works achieve encouraging classification performance, they heavily rely on one-hot labels during the meta-learning process, which may result in the supervision collapse and limited generalization. To address these challenges, the few-shot classification based on a self-supervised multi-task distillation (SMD) is proposed for mitigating the nuisance arising from one-hot labels. Specifically, SMD formulates multiple auxiliary tasks to enhance the cross entropy classification in a multi-task learning manner, including the self-supervised classification task and the self-distilled classification task. these auxiliary tasks do not rely on one-hot labels in meta-learning, which can effectively enhance generalization performance of the model. Finally, extensive experiment results on two benchmark datasets, i.e., CIFAR-FS and FC-100, demonstrate the superiority and effectiveness of SMD. © 2023 IEEE.

关键词： few-shot classification multi-task learning self distillation self supervision

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Adversarial Attack and Defense for Webshell Detection on Machine Learning Models 12

Adversarial Attack and Defense for Webshell Detection on Mac...

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12th International conference on Cyber-Enabled distributed Computing and Knowledge Discovery (CyberC)

作者： Zhang, Qian Chen, Lishen Yan, Qiao Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350331547

Machine learning (ML) models can be used for the automated processing and analysis of source codes, thus improving the detection of webshell malware source codes, which can enhance the security of the whole network. However, despite the successes of ML-based models in webshell detection, these models lack large amounts of data for training and are vulnerable to adversarial examples. We have built a larger and more precise dataset containing 2015 manually labeled webshell malware. A detection model trained with this dataset can achieve higher detection accuracy. We have also proposed a method to generate adversarial examples for the programming language without changing its logic. the main idea of our method is to insert perturbation codes that do not modify the webshell program's semantics, thereby creating an adversarial example that can bypass the model's detection. this method can effectively attack the existing webshell malware ML detection models without changing the original malicious functions. Experiments have shown that our method can generate webshell malware adversarial examples that evade model detection while obtaining the model's confidence output. To defend against such attacks, we have applied retraining and adversarial fine-tuning.

关键词： adversarial examples webshell malware network security

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A p2p environment to validate ensemble-based approaches in the cybersecurity domain 28

A p2p environment to validate ensemble-based approaches in t...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Folino, Francesco Folino, Gianluigi Pontieri, Luigi CNR Inst High Performance Comp & Networking ICAR Arcavacata Di Rende CS Italy

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

the main data-driven techniques for detecting cybersecurity attacks are based on the analysis of network traffic data and/or of application/system logs (stored in a host or in some other kind of device). A wide range of machine-learning techniques (and possible alternative configurations of them) have been proposed in the literature so far, for this purpose, but none of them has been proven to consistently overcome the others across different datasets. In order to ensure better accuracy and stability, the ensemble paradigm can be exploited as an effective solution for combining such techniques. However, as attack detection problems are hard to cope with and, usually, entail the analysis of large and fast streams of data, different types of ensemble (and of base algorithms composing the ensemble) should have experimented, exploiting distributed architecture to suitably reduce the high-execution times necessary to run them. In order to handle all these issues, a p2p environment to validate ensemble-based approaches in the cybersecurity domain is proposed in this paper. Two case studies are analyzed by using this framework, which concern the detection of intrusions in network-traffic data and of deviant process instances. Preliminary scalability results demonstrate that the framework is a viable solution for these challenging kind of problems.

关键词： Ensemble-based Systems p2p frameworks cybersecurity applications

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Inter-Server RSS: Extending Receive Side Scaling for Inter-Server Workload Distribution 28

Inter-Server RSS: Extending Receive Side Scaling for Inter-S...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oeldemann, Andreas Biersack, Franz Wild, thomas Herkersdorf, Andreas Tech Univ Munich Chair Integrated Syst Munich Germany

ISBN: (纸本)9781728165820

network Function Virtualization enables operators to schedule diverse network processing workloads on a general-purpose hardware infrastructure. However, short-lived processing peaks make an efficient dimensioning of processing resources under stringent tail latency constraints challenging. To reduce dimensioning overheads, several load balancing approaches, which either adaptively steer network traffic to a group of servers or to their internal CPU cores, have separately been investigated. In this paper, we present Inter-Server RSS (isRSS), a hardware mechanism built on top of Receive Side Scaling in the network interface card, which combines intra- and inter-server load balancing. In a first step, isRSS targets a balanced utilization of processing resources by steering packet bursts to CPU cores based on per-core load feedback. If all local CPU cores are highly loaded, isRSS avoids high queueing delays by redirecting newly arriving packet bursts to other servers, which execute the same network functions, exploiting that processing peaks are unlikely to occur at all servers at the same time. Our evaluation based on real-world network traces shows that compared to Receive Side Scaling, the joint intra- and inter-server load balancing approach is able to reduce the processing capacity dimensioned for network function execution by up to 38.95% and limit packet reordering to 0.0589% while maintaining tail latencies.

关键词： network function virtualization load balancing receive side scaling

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Blockchain-based Multi-layer Data Security Sharing Model 12

Blockchain-based Multi-layer Data Security Sharing Model

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12th International conference on Cyber-Enabled distributed Computing and Knowledge Discovery (CyberC)

作者： Wei, Yishan Cao, Xiaomei Nanjing Univ Posts & Telecommun Coll Comp Nanjing Peoples R China

ISBN: (纸本)9798350331547

Facing the era of big data, the traditional data sharing model has many problems such as low execution efficiency, opaque data sharing process, and inflexible processing, which can no longer meet the demand for secure data sharing. So we present a blockchain-based multi-layer data security sharing model (ML-DSM). the model not only combines blockchain and attribute-based access control technology, but also introduces malicious access behavior judgment and user rating mechanism. Judging malicious access behavior helps detect malicious users in time at the early stage of access control, and effectively prevents malicious attacks such as DoS. In the user rating mechanism, the smart contract evaluates the user's reputation level based on the user's current and historical reputation scores and judges whether the reputation level is higher than the level of the requested data. the rating mechanism not only dynamically adjusts the access rights of the requestor but also effectively reduces the average judgment time of access control. the final experimental analysis results show that the data sharing model in this paper can improve the execution efficiency of access control while ensuring the security of the shared data. thus ML-DSM achieves more secure and efficient data sharing.

关键词： blockchain smart contract data sharing attribute-based access control reputation level

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Efficient Wavefront parallel processing for HEVC CABAC Decoding 28

Efficient Wavefront Parallel Processing for HEVC CABAC Decod...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Habermann, P. Juurlink, B. Chi, C. C. Alvarez-Mesa, M. TU Berlin Embedded Syst Architecture Berlin Germany Spin Digital Video Technol GmbH Berlin Germany

ISBN: (纸本)9781728165820

Context-based Adaptive Binary Arithmetic Coding (CABAC) is the only compute-intensive task in the High Efficiency Video Coding (HEVC) Standard that does not contain significant data-level parallelism. As a result, it is often a throughput bottleneck for the overall decoding process, especially for high-quality videos. Consequently, the use of high-level parallelization techniques is inevitable to reach throughput requirements for CABAC decoding. Multiple high-level parallelization tools are specified in HEVC, amongst which Wavefront parallel processing (WPP) has only small losses in coding efficiency. However, it lacks in parallel efficiency due to a ramp-up and -down in active parallel threads within a frame. this is a serious problem for systems that cannot process multiple frames at the same time due to performance or memory constraints (e.g. mobile devices), and also for low-delay applications such as video conferencing. To address this issue, we present three improved WPP implementations for HEVC CABAC decoding. they differ in the granularity at which dependency checks are performed. the improvement comes from increased parallel efficiency of the WPP implementation while using the same number of threads as conventional WPP. the proposed implementations allow speedups up to 1.83 x with very little implementation overhead.

关键词： HEVC H.265 CABAC Wavefront parallel processing

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Natural language processing approach for distributed health data management 28

Natural language processing approach for distributed health ...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Forestiero, Agostino Papuzzo, Giuseppe CNR ICAR Via Pietro Bucci 8-9 C I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781728165820

Today's health domain is characterized by heterogeneous, numerous, highly dynamics and geographically distributed information sources. Moreover, the increasing use of digital health data, like electronic health records (EHRs), has led to store an unprecedented amount of information. Managing this large amount of data can, often, introduce issues of information overload, with potential negative consequences on clinical work, such as errors of omission, delays, and overall patient safety. Innovative techniques, approaches and infrastructures are needed to investigate data featured by high velocity, volume and variability. this paper introduces a distributed and self-organizing algorithm for building a management system for big data in highly dynamic environments like healthcare domain. Health data are represented with vectors obtained through the Doc2Vec model, a Natural Language processing (NLP) approach able to capture the semantic context representing documents in dense vectors namely word embeddings. Doc2Vec is an unsupervised algorithm to generate vectors starting from sentences/documents based on word2vec approach which can generate vectors for words. the servers of a clinical distributed system, by performing autonomous and local operations, organize themselves in a sorted overlay network, so that resource management operations become faster and efficient. the effectiveness of the approach was proved performing a set of preliminary experiments exploiting a tailored implemented simulator.

关键词： Electronic Health Records Natural language processing Semantic overlay network Self-organization

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Cloud and network Resource Schedule based on Hardware Acceleration 12th

Cloud and Network Resource Schedule Based on Hardware Acce...

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12th International conference on Computer Engineering and networks, CENet 2022

作者： Li, Xiangning Zong, Jiaying Wang, Qingtian Liu, Haitao Liu, Yang Yang, Fengyi China Telecom Research Institution Beijing China

ISBN: (纸本)9789811969003

With the development of 5th generation plus and 6 th generation (5G+ & 6G) wireless communication networks, the cloud computing and wireless access network combine tightly to meet diversified industry demands in different deployment and service scenario. As the computing force demand of cloud network increasing, AAL (Acceleration Abstraction Layer) is introduced to the network architecture to unload the computing task from CPU to GPU/ FPGA and other hardware accelerators, which could accelerate the task processing with parallel dealing with tasks in hardware accelerators to save the CPU resource to deal with other tasks. However the resource scheduling of network and computing resource have not been considered together. We view the network slice resource, the computing resource and storage of cloud-network system and design three algorithm to optimize the resource scheduling, the key of each algorithm is weighted the hardware computing and storage resource and the radio wireless slice resource to schedule together. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Abstracting

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