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IEEE International Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Encoding Temporal Change of Longitudinal EBV DNA for Dynamic Risk Prediction of Nasopharyngeal Carcinoma 7

Encoding Temporal Change of Longitudinal EBV DNA for Dynamic...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Yang, Fangfang Zhen, Zicheng Lin, Li Liu, Yuyu Gao, Rui Zhang, Taoyu Lu, Lijun Lv, Wenbing School of Information Yunnan University Yunnan Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650504 China Department of Radiation Oncology Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou510060 China School of Software Yunnan University Yunnan Kunming650504 China School of Biomedical Engineering Southern Medical University 1023 Shatai Road Guangzhou510515 China

ISBN: (纸本)9783800762910

—To accurately predict the risk of recurrence and metastasis of nasopharyngeal carcinoma (NPC) patients, an image-encoded risk prediction model was proposed. 752 patients (training vs. testing: 527 vs. 225) were retrospectively collected, and the plasma EBV DNA copies were tested 13 times during treatment. Based on 13 times plasma EBV DNA copies, three kinds of image encoding methods were employed to characterize the linear and nonlinear dynamic change of longitudinal EBV DNA, i.e. Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Recurrence Plot (RP). The Cox proportional hazard regression model was constructed by incorporating both image and clinical features as covariates to determine the risk factors and prediction models for disease-free survival (DFS). On the test set, the proposed model combining image and clinical features achieved C-index and AUC of 0.777 and 0.776, respectively outperforming the model relying solely on clinical features (C-index = 0.667, AUC = 0.675). In conclusion, the dynamic risk prediction model with image encoding can monitor changes in plasma EBV DNA copies during treatment, aiding doctors in assessing whole-body tumor burden, accurately predicting the dynamic risk of recurrence and metastasis, and planning individualized treatments in NPC patients. © VDE VERLAG GMBH.

关键词： Gene encoding

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Two-Stream Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

DNN Partitioning and Assignment for Distributed Inference in...

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International Conference on Distributed computing Systems

作者： Yuepeng Li Deze Zeng Lin Gut Song Guo Albert Y. Zomaya School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Distributed Deep Neural Network (DNN) inference is a promising technology to explore the distributed resources in edge cloud to realize edge intelligence. Meanwhile the inherent resource sharing nature of edge cloud infrastructure also raises serious concerns on security and privacy. software Guard Ex-tensions (SGX) emerges as a potential hardware-level solution but its limited secure memory (i.e., enclave page cache) imposes new challenges, especially in contrast to memory-hungry DNN models. A task's performance will be severely affected when its memory footprint is beyond the enclave page cache size, due to expensive secure page swapping. In this case, how to appropriately partition a DNN model and assign the partitions to distributed edge servers to efficiently utilize edge resources for fast secure inference becomes a challenging problem. In this paper, we first show that this problem is NP-hard. We further propose a MEmory -aware Distributed Inference Acceleration (MEDIA) algorithm, whose guaranteed approximation ratio is also formally analyzed. We have implemented a prototype system and applied some well-known representative DNN models to evaluate MEDIA's performance. Through extensive experiments, we verify the efficiency of MEDIA by the fact that it reduces the inference time by 19.5%-38.1 % in comparison with state-of-the-art approaches.

关键词： Computational modeling Prototypes Artificial neural networks Media Approximation algorithms Inference algorithms software

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Categorical Data Clustering via Value Order Estimated Distance Metric Learning

arXiv

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arXiv 2024年

作者： Zhang, Yiqun Zhao, Mingjie Jia, Hong Lu, Yang Li, Mengke Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong

Categorical data composed of qualitative valued attributes are ubiquitous in machine learning tasks. Due to the lack of well-defined metric space, categorical data distributions are difficult to be intuitively understood. Clustering is a popular data analysis technique suitable for data distribution understanding. However, the success of clustering often relies on reasonable distance metrics, which happens to be what categorical data naturally lack. This paper therefore introduces a new finding that the order relation among attribute values is the decisive factor in clustering accuracy, and is also the key to understanding categorical data clusters, because the essence of clustering is to order the clusters in terms of their admission to samples. To obtain the orders, we propose a new learning paradigm that allows joint learning of clusters and the orders. It alternatively partitions the data into clusters based on the distance metric built upon the orders and estimates the most likely orders according to the clusters. The algorithm achieves superior clustering accuracy with a convergence guarantee, and the learned orders facilitate the understanding of the non-intuitive cluster distribution of categorical data. Extensive experiments with ablation studies, statistical evidence, and case studies have validated the new insight into the importance of value order and the method proposition. The source code is temporarily opened in https://***/r/OCL-demo. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Aqib Hassan Vengas Memon Farhan Bashir Shaikh Saad Alahmari Mansoor Iqbal Farooq Azam School of computing and mathematical sciences University of Greenwich London United Kingdom Faculty of Information and Communication Tech. Universiti Tunku Abdul Rahman Perak Malaysia Department of Computer Science Northern Border University Arar Saudi Arabia Department of Computer Science University of Wah Wah Cantt Pakistan Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability.

关键词： TCP Transport protocols Protocols Force Packet loss Throughput Real-time systems Stability analysis Reliability Low latency communication

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Understanding the use of message passing interface in exascale proxy applications

Understanding the use of message passing interface in exasca...

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作者： Sultana, Nawrin Rüfenacht, Martin Skjellum, Anthony Bangalore, Purushotham Laguna, Ignacio Mohror, Kathryn Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science and Engineering University of Tennessee at Chattanooga ChattanoogaTN United States Department of Computer Science University of Alabama at Birmingham BirminghamAL United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States

The Exascale computing Project (ECP) focuses on the development of future exascale-capable applications. Most ECP applications use the message passing interface (MPI) as their parallel programming model with mini-apps serving as proxies. This paper explores the explicit usage of MPI in such ECP proxy applications. We empirically analyze 14 proxy applications from the ECP Proxy Apps Suite. We use the MPI profiling interface (PMPI) to collect MPI usage patterns in ECP proxy apps. Our analysis shows that a small subset of features from MPI is commonly used in the proxies of exascale-capable applications, even when they reference third-party libraries. This study is intended to provide a better understanding of the use of MPI in current exascale applications. The findings can help focus software investments made for exascale systems in the MPI middleware including optimization, fault-tolerance, tuning, and hardware-offload. © 2020 John Wiley & Sons Ltd

关键词： Message passing

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A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery 20

A Light-Weight Object Detection Framework with FPA Module fo...

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4th High Performance computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Gu, Xi Kong, Lingbin Wang, Zhicheng Li, Jie Yu, Zhaohui Wei, Gang Department of Computing Science Tongji University China Department of Software Engineering Tsinghua University China Shanghai Aerospaces Electronic Technology Institute Shanghai China

ISBN: (纸本)9781450375603

With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we design the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection. © 2020 ACM.

关键词： Object detection

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BcIIS: Blockchain-Based Intelligent Identification Scheme of Massive IoT Devices

BcIIS: Blockchain-Based Intelligent Identification Scheme of...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yi Sun Tong Xu Ali Kashif Bashir Jie Liu Marwan Omar School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Department of Computing and Mathematics Manchester United Kingdom Woxsen School of Business Woxsen University India Information Technology and Management Illinois Institute of Technology USA

With the rapid development of loT technology, various loT devices enter our daily life. The continuously increasing scale of the massive loT devices in both of numbers and types further bring heavy pressure on loT network management and security. Therefore, how to accurately identify and efficiently manage so massive loT devices has become a challenge. In this paper, we propose BcllS, Blockchain-based Intelligent Identification Scheme of Massive loT Devices. It applies a decentralized identification architecture and realizes learning sustainably as well as efficiently identifying by updating the identification model constantly according to the ledger which is maintained by all gateways collaboratively. Experiments show that the identification accuracy can achieve up to 99.5 %.

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