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2nd International Conference on computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Song, Shuang Xiao, Ruoxiu Lu, Tong Zhu, Hang Beihang University School of Computer Science and Engineering Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Visual 3D Medical Science and Technology Development Co. Ltd Beijing China The Sixty Medical Center of People's Liberation Army General Hospital Senior Department of Cardiology Beijing China

ISBN: (纸本)9781665492508

X-ray angiographic images are regarded as the gold standard in the intra-operation of vascular diseases due to their high imaging quality and speed. However, due to many vascular- similar structures existing in the images, the accurate segmentation of the coronary artery is challenging. In this paper, we proposed a width-aware U-Net model by supervising the segmentation procedure using the label mask and centerline- derived width map. In our model, the centerlines of label masks are extracted and width maps are then computed. Together with the label mask, the width maps are then utilized to supervise the segmentation network. By comparing the proposed network with other state-of-the-art models, we can achieve superior results in discarding non-vascular artefacts and preserving the integrity of the whole vascular tree. © 2022 IEEE.

关键词： Image segmentation

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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Multi-Classification Segmentation Method of Gastric Cancer Pathological Images Based on Deep Learning

Multi-Classification Segmentation Method of Gastric Cancer P...

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Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Hehu Zhou Jingshan Pan Li Na Qingyan Ding Chengjun Zhou Wantong Du Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Gastric cancer is a serious health threat, and pathological imaging is important in detecting it. These images can assist doctors in accurately determining the location of the cancer, thereby providing an important reference for clinical decision-making. In the field of image processing, deep learning technology is leading to more and more excellent segmentation models. The Trans-Unet model has achieved success in the image segmentation. However, when applying this model to gastric cancer pathological section data, the segmentation boundary appears jagged. We propose three possible solutions. First, we designed an attention connection module to replace the skip connections in the model to enhance the prediction accuracy of the model. Second, we designed a prediction processing unit that takes the model’s prediction results as input and uses a Conditional Random Field (CRF) for further prediction calculations. To enable the model to meet the requirements of multi-classification of images, we designed a dynamic weight selection module to enhance the model’s accuracy for multi-classification tasks. After our optimization, the optimized model improved by 8% on the DSC evaluation index and 39% on the HD evaluation index. In addition, the jagged boundary problem in the prediction results has also been effectively improved. Through comparative experiments and erosion experiments, we found that the improved method enhances the accuracy of model prediction and reduces the jagged results at the boundary.

关键词： Training Image segmentation Pathology Adaptation models Accuracy Computational modeling Predictive models Data models Conditional random fields Cancer

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Arithmetic Billiard Paths Revisited: Escaping from a Rectangular Room

Arithmetic Billiard Paths Revisited: Escaping from a Rectang...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Somnath Kundu Yeganeh Bahoo Onur Çağırıcı Steven M. LaValle Department of Computer Science Toronto Metropolitan University (Formerly Ryerson University) Toronto Canada Center for Ubiquitous Computing University of Oulu Oulu Finland

In this work, we consider a problem where a robot can move in a straight line inside a 2D rectangular room with integer lengths until it hits any part of the wall of the room. If the robot hits any part of a wall other than the corners or any point of an opening, then the robot bounces off the wall and follows a new direction in another straight line following the laws of symmetric reflection. The robot needs to escape through an opening on the wall that has a minimum length of one unit. The robot can only escape through the opening if it reaches any point of the opening with a non-zero *** present an efficient algorithm for which the robot is guaranteed to find the opening if there is any or declare that there is none. We prove that the algorithm works if and only if the sides of the rectangle are co-prime. As a by-product of our main result, we also provide some interesting results related to the coverage of the interior of the rectangle when the robot follows similar algorithms to escape from the rectangular room.

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A Performance Analysis of Task Scheduling for UQ Workflows on HPC Systems

A Performance Analysis of Task Scheduling for UQ Workflows o...

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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Chung Ming Loi Anne Reinarz Linus Seelinger William Hornsby James Buchanan Mikkel Lykkegaard Department of Computer Science Durham University Durham UK Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany UKAEA-CCFE Abingdon UK Danish Technological Institute Aarhus Denmark

ISBN: (数字)9783982633619

Uncertainty Quantification (UQ) workloads are becoming increasingly common in science and engineering. They involve the submission of thousands or even millions of similar tasks with potentially unpredictable runtimes, where the total number is usually not known a priori. A static one-size-fits-all batch script would likely lead to suboptimal scheduling, and native schedulers installed on High-Performance computing (HPC) systems such as SLURM often struggle to efficiently handle such workloads. In this paper, we introduce a new load balancing approach suitable for UQ workflows. To demonstrate its efficiency in a real-world setting, we focus on the GS2 gyrokinetic plasma turbulence simulator. Individual simulations can be computationally demanding, with runtimes varying significantly—from minutes to hours—depending on the high-dimensional input parameters. Our approach uses the UQ and Modelling Bridge, which offers a language-agnostic interface to a simulation model, combined with HyperQueue which works alongside the native scheduler. In particular, deploying this framework on HPC systems does not require system-level changes. We benchmark our proposed framework against a standalone SLURM approach using GS2 and a Gaussian Process surrogate thereof. Our results demonstrate a reduction in scheduling overhead by up to three orders of magnitude and a maximum reduction of 38% in CPU time for long-running simulations compared to the naïve SLURM approach, while making no assumptions about the job submission patterns inherent to UQ workflows.

关键词： Adaptation models Runtime Uncertainty Processor scheduling Computational modeling High performance computing Gaussian processes Load management Servers Load modeling

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Data Augmentation Techniques for Medical Image Segmentation - A Review

Data Augmentation Techniques for Medical Image Segmentation ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Kozah, Naney Dornaika, Fadi Charafeddine, Jinan El Jaam, Jihad University of the Basque Country UPV/EHU Department of Computer Science and Artificial Intelligence San Sebastian Spain University of the Basque Country UPV/EHU Ikerbasque Basque Foundation for Science San Sebastian Spain Léonard de Vinci Pôle Universitaire Research Center Paris La Défense92916 France School of Computing and Data Liverpool J. Mores University Liverpool United Kingdom

ISBN: (纸本)9798350367560

Segmentation of medical images is crucial for accu-rate diagnosis, treatment planning and intervention. However, the scarcity of data and the variability of different imaging modalities, including MRI, CT, PET and ultrasound, pose a major challenge for deep learning models. This review discusses traditional and advanced data augmentation techniques used to improve segmentation performance. In addition to geometric transformations such as rotation and scaling, advanced approaches such as Generative Adversarial Networks (GANs) and superpixel-based augmentations are also evaluated. The review also highlights novel methods such as Local-and-Contour Aware Grid Mixing (LCAMix) and Hard and Soft Mixing (HSMix) that preserve anatomical boundaries and effectively augment datasets. The analysis concludes with practical insights for clinical applications and suggests integrating these techniques into data pipelines to increase the generalizability of models. Future research directions focus on DICOM-specific augmentation strategies and optimization of computational requirements to improve clinical applicability. © 2024 IEEE.

关键词： Image segmentation

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Transferability Estimation Based On Principal Gradient Expectation

arXiv

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arXiv 2022年

作者： Qi, Huiyan Cheng, Lechao Chen, Jingjing Yu, Yue Song, Xue Fengg, Zunlei Jiang, Yu-Gang School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Zhejiang Lab China Zhejiang University China

Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning or linear probing. Especially, selecting a proper source domain for a specific target domain under predefined tasks is crucial for improving efficiency and effectiveness. It is conventional to solve this problem via estimating transferability. However, existing methods can not reach a trade-off between performance and cost. To comprehensively evaluate estimation methods, we summarize three properties: stability, reliability and efficiency. Building upon them, we propose Principal Gradient Expectation (PGE), a simple yet effective method for assessing transferability. Specifically, we calculate the gradient over each weight unit multiple times with a restart scheme, and then we compute the expectation of all gradients. Finally, the transferability between the source and target is estimated by computing the gap of normalized principal gradients. Extensive experiments show that the proposed metric is superior to state-of-the-art methods on all properties. © 2022, CC BY.

关键词： Economic and social effects

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MULTI-CROSSRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction

arXiv

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arXiv 2023年

作者： Bassignana, Elisa Ginter, Filip Pyysalo, Sampo van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark TurkuNLP Department of Computing University of Turku Finland MaiNLP Center for Information and Language Processing LMU Munich Germany

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose MULTI-CROSSRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. MULTI-CROSSRE is a machine translated version of CrossRE (Bassignana and Plank, 2022a), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and-as sanity check-over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset. © 2023, CC BY.

关键词： Extraction

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Richelieu: self-evolving LLM-based agents for AI diplomacy 24

Richelieu: self-evolving LLM-based agents for AI diplomacy

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhenyu Guan Xiangyu Kong Fangwei Zhong Yizhou Wang Institute for Artificial Intelligence Peking University College of Computer Science Beijing Information Science and Technology University and State Key Laboratory of General Artificial Intelligence BIGAI School of Artificial Intelligence Beijing Normal University and State Key Laboratory of General Artificial Intelligence BIGAI Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9798331314385

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI's potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection; 2) goal-oriented negotiation with social reasoning; and 3) augmenting memory through self-play games for self-evolution without human in the loop. Project page: https://***/view/richelieu-diplomacy.

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