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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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Demystifying Decentralized Matrix Communication network: Ecosystem and Security 29

Demystifying Decentralized Matrix Communication Network: Eco...

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

作者： Li, Hao Wu, Yanbo Huang, Ronghong Mi, Xianghang Hu, Chengyu Guo, Shanqing Shandong University China Chinese Academy of Sciences Institute of Information Engineering China School of Cyber Security University of Chinese Academy of Sciences China University of Science and Technology of China China Quancheng Laboratory China

ISBN: (纸本)9798350330717

With the emergence of Web3, decentralized network protocol technologies have been vigorously developed. As a pioneer for decentralized real-time communication systems, Matrix is an open standard based on a federation specification protocol. Anyone can set up a self-hosted homeserver to participate in the global Matrix network and communicate with others in chat rooms. In this paper, we conduct the first in-depth measurement and exploratory research on Matrix's ecosystem and security. We designed and implemented several investigation techniques to empirically delve into Matrix federation from various aspects (homeservers, rooms, and users). In the end, we identified a number of interesting findings and potential vulnerabilities, including anti-decentralization phenomena, cybersecurity threats in homeservers, and the confidentiality of encrypted rooms being compromised. © 2023 IEEE.

关键词： decentralization ecosystem Matrix security

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MiddleCache: Accelerating TCP based In-memory Key-value Stores using eBPF 29

MiddleCache: Accelerating TCP based In-memory Key-value Stor...

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

作者： Pang, Yiren Chen, Sheng Li, Wenxin Liu, Hao Li, Yulong He, Xin Zhang, Song Guan, Zewei Suo, Lide Liu, Yuan Tianjin University Tianjin300350 China

ISBN: (纸本)9798350330717

In-memory key-value stores are widely used in modern web services to support large-scale user requests by caching popular data. Their performance is critical, and BMC, the state-of-the-art work, builds an in-kernel cache and processes requests before the stack using eBPF to reduce the overhead of the kernel network stack. However, BMC fails to support stateful protocol TCP because pre-stack processing creates TCP state bias between the client and *** is widely used by in-memory key-value stores, is even the only choice for some applications (e.g., Redis), and also suffers from performance issues. In this work, we present MiddleCache, a TCP-enabled in-memory key-value store acceleration design. Our key observation is that the TCP state bias of the client and server can be inferred and eliminated with packet length. The design of MiddleCache has two key parts: (i) A compact TCP state maintenance mechanism that accumulates packet lengths and applies corrections to the packet header, which realize TCP support within the constrains of eBPF. (ii) Lock-free accumulation counters that support high-performance concurrent access by utilizing Receive Side Scaling (RSS). Our experiments show that, compared with Memcached, MiddleCache reduces 56% processing latency on cache hit and achieves a 3.8× throughput improvement on Facebook-like small-size requests workload. © 2023 IEEE.

关键词： eBPF in-memory key-value store memcached

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Ancient Chinese Character Image Retrieval based on Fusing Deep Features and Skeleton Features 29

Ancient Chinese Character Image Retrieval Based on Fusing De...

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

作者： Wang, Xinhui Tian, Xuedong Hebei University School of Cyber Security and Computer Baoding071002 China Hebei University Institute of Intelligent Image and Document Information Processing Baoding071002 China

ISBN: (纸本)9798350330717

The complex glyph structures and diverse writing styles of ancient Chinese character images lead to suboptimal performance when existing image retrieval methods are directly applied to datasets of these images. Addressing the impact on retrieval caused by the complex glyph structure and rich detail information characteristic of ancient Chinese character images, a multi-layer feature adaptive fusion model for ancient Chinese character image retrieval is designed. Firstly, a local feature extraction module is constructed to obtain low-level feature maps with different spatial receptive fields. Secondly, to better fuse features of varying scales, an improved ASFF(Adaptively Spatial Feature Fusion) method is employed to build the PSASFF(Pixel Shuffle Adaptively Spatial Feature Fusion) module for adaptive fusion of multi-layer features extracted by the designed network. Finally, in order to reduce the influence of the writing style of ancient Chinese characters on retrieval, the cosine similarity scores of image retrieval before and after fine processing are weighted and used as the ultimate similarity scores. The retrieval method proposed achieves an average retrieval accuracy of mAP@50 and mAP@30 of 0.8537 and 0.9576 respectively on the ancient Chinese character image dataset. Experimental results demonstrate the effectiveness of this method for ancient Chinese character image retrieval. © 2023 IEEE.

关键词： adaptively feature fusion ancient Chinese character image retrieval local feature extraction weighted similarity scores

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Simulation of Intelligent Operation and Maintenance Optimization Model of Digital Distribution network based on Decision Tree Algorithm

Simulation of Intelligent Operation and Maintenance Optimiza...

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2023 International conference on Internet of Things, Robotics and distributed Computing, ICIRDC 2023

作者： Hu, Yundong Zhou, Meng Li, Mengyuan Wang, Gang State Grid Shanghai Electric Power Research Institute Shanghai200433 China State Grid Shanghai Energy Interconnection Institute Co. Ltd. Shanghai201203 China State Grid Corporation of China Beijing100032 China

ISBN: (纸本)9798350375275

With the continuous development of digital distribution network (DDN) technology, intelligent operation and maintenance (OM) of power system has become an important means to improve the efficiency and reliability of system operation. based on Decision Tree (DT) algorithm, this paper constructs an intelligent OM optimization model of DDN, and verifies its performance through simulation experiments. In the model construction, we make full use of the interpretability and adaptability of DT, and realize the intelligent support for the monitoring, fault diagnosis and maintenance decision of power equipment through reasonable selection of features. In the simulation experiment, the model is trained and tested by using the actual distribution network data. The experimental results show that the proposed intelligent OM model based on DT has achieved remarkable advantages in real-time alarm, fault diagnosis and maintenance decision. In order to meet the demand of large-scale data processing in DDN, the optimization strategy of parallel computing is also discussed. By means of data parallelism and feature parallelism, the training efficiency of the model is significantly improved. This provides practical technical support for deploying intelligent OM system in large-scale DDN. This model is not only practical, but also has a wide application prospect in improving the reliability of power system and reducing the OM cost. © 2023 IEEE.

关键词： Electric power distribution

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Sequence-to-Sequence Knowledge Graph Completion based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Architecture Design of Intelligent Education and Teaching System based on Machine Learning Algorithm 3rd

Architecture Design of Intelligent Education and Teaching Sy...

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3rd EAI International conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Zhu, Libo Ma, Xin Liang, Xinlong Zhang, Jun Zhou, Yufei Zibo Normal College Shandong Zibo255100 China Yunnan University of Business Management Yunnan Anning650300 China

ISBN: (纸本)9783031631382

With the great success of machine learning (ML) algorithms represented by deep learning in vision, speech recognition and other fields, while looking forward to and promoting the large-scale educational application of artificial intelligence (AI), data center network is an important infrastructure supporting big data and cloud computing platforms, which is widely used in data-intensive and large-scale modular parallel computing tasks. AI has entered an unprecedented period of rapid adoption and stability. At the beginning of architecture design, the traditional distributed big data processing system didn't optimize the ML task, but ensured the training convergence efficiency, improved the iterative computing speed and improved the model quality. The application of AI education is limited by the common sense and symbolic grounding and transfer of AI technology. based on the above analysis, this paper aims to solve the efficiency bottleneck of distributed system in dealing with ML tasks, and design and implement a high-performance distributed ML system for heterogeneous environment in data centers, so as to promote the intelligent development of education and teaching by applying heuristic AI teaching ideas. Overall, the degree of automation that AI technology can achieve in education is still very limited. It is more realistic to strengthen teacher design than to replace it. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Efficiency

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OOCL-DDQN: Online Evaluation and Offline Training-based Clipped Double DQN for Automated Anesthesia Control 29

OOCL-DDQN: Online Evaluation and Offline Training-Based Clip...

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

作者： Li, Huijie Lin, Wei Huo, Jian Luo, Wuman Macao Polytechnic University China Shenzhen United Scheme Technology Co. Ltd China

ISBN: (纸本)9798350330717

Anesthesia control is critical in surgical procedures. Nowadays, various methods have been proposed for automated anesthesia control. However, traditional control model-based methods and PK/PD-based methods cannot fully capture the individual characteristics of patients. Machine learning-based methods are not suitable for dealing with large-scale clinical anaesthesia data. The existing deep reinforcement learning-based (DRL) methods cannot provide both stable and reliable anesthesia strategies while ensuring the agent's adaptability to environmental changes. To address these issues, in this paper, we propose a deep reinforcement learning model named OOCL-DDQN for automated anesthesia control. Specifically, we propose an online evaluation and offline training mechanism to well avoid excessive reliance on the environment models. To enhance the model stability, we design a clip method to optimize the Bellman equation in Double DQN. Besides, we propose a fixed-order random sampling method to enable the agent to effectively learn the relationship between anesthesia state and anesthetic dosage in different patients. In data preprocessing, we design a same-frequency resampling method to ensures that the resampled data obey the Markov property. To evaluate the performance of our proposed method, we conduct extensive experiments on a real-world dataset. The experiment results show that our proposed model gets better performances than the other state-of-the-art methods. © 2023 IEEE.

关键词： Automated Anesthesia Control Deep Reinforcement Learning Double Deep Q-network (DDQN)

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CustTest: An Effective Testbed for Testing Customized Routing in Integrated Heterogeneous networks 29

CustTest: An Effective Testbed for Testing Customized Routin...

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

作者： Zhang, Jialei Xie, Xiaoxuan Yan, Zheng Wang, Haiguang Li, Tieyan Xidian University School of Cyber Engineering Xi'an China Singapore Research Center Huawei Technologies Shield Lab. Singapore

ISBN: (纸本)9798350330717

With the rapid development of the internet and the increasing demand for communication, customized routing has become one of the hot research topics in future network. In order to better meet various communication requirements, it is necessary to test and evaluate existing customized routing algorithms to optimize and explore better customized algorithms. However, existing research on customized routing simulation testing cannot provide a customized routing testbed that supports universality, stability, efficiency, and reliability to test and evaluate different customized routing algorithms. To address this issue, we proposes a testbed called CustTest. CustTest is designed based on Software Defined network (SDN) and network Simulator 3 (NS3), and consists of two sub platforms: a customized routing calculation platform and a customized routing simulation platform. The customized routing calculation platform is used to calculate customized routes and distribute routing tables. This platform can not only flexibly deploy different customized routing algorithms, but also calculate customized routes based on user needs and convert these routes into customized routing tables. The customized routing simulation platform can effectively evaluate the performance of different customized routing algorithms using statistical analysis tools in NS3. We ensure the stability, efficiency, and reliability of CustTest by adding a pool structure and daemons. In addition, we conduct functional and performance tests on CustTest to demonstrate its ability to effectively execute customized routing calculations and its excellent performance in high concurrency environments, as well as the effectiveness of testing the performance of different customized routing algorithms. © 2023 IEEE.

关键词： Customized Routing Computing Platform Customized Routing Simulation Platform Customized Routing Testbed Integrated Heterogeneous network

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