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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Apriori Algorithm Big Data Cloud-Native Architectures Data Mining Distributed Computing Maritime Serverless Computing

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Kangaroo Tail-Inspired Variable Stiffness Executive Mechanism for Rescue Robots

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IEEE Robotics and Automation Letters 2025年第6期10卷 5943-5950页

作者： Lu, Maoshi Zhao, Yanzhi Ma, Feixiang Shan, Yu Zhang, Bowen Yanshan University Key Laboratory of Parallel Robots and Mechatronic Systems Qinhuangdao066004 China Yanshan University College of Mechanical Engineering Qinhuangdao066004 China Dalian University of Technology College of Mechanical Engineering Dalian116024 China

In the field of casualty extraction and rescue, a key challenge is avoiding secondary injuries to the human body during rescue operations. Currently, most rescue robot executive mechanisms are rigid, which increases the risk of contact-related injuries. To address this issue, a rescue executive mechanism with variable stiffness, inspired by the kangaroo tail, is proposed. This mechanism can adapt to the human body's contours with flexible contact while providing sufficient rigidity and load-bearing capacity. By applying the layer jamming principle, the mechanism achieves variable stiffness, enabling it to self-adapt to the human body profile and support large loads. Drawing inspiration from the rigid support and shape adaptability of the kangaroo tail, a 3D model and a prototype of the executive mechanism were developed using bionic principles. The mechanism's performance was validated through variable stiffness tests, contour adaptation tests, and human model holding and loading experiments. The results demonstrate that the rescue robot executive mechanism exhibits high load-bearing capacity (load 32.17 kg), strong adaptability, and enhanced safety, effectively addressing the limitations of rigid mechanisms that may harm the human body. © 2016 IEEE.

关键词： Tail Jamming Pins Rescue robots Robots Wire Injuries Muscles Friction Shape

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Wireless-Aware TSN Engineering: Implications for 5G and Upcoming 6G Networks

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IEEE Network 2025年第3期39卷 99-107页

作者： Egger, Simon Durr, Frank Varga, Balazs De Andrade, Marilet Sharma, Gourav Prateek Sachs, Joachim Harmatos, Janos Gross, James University of Stuttgart Institute for Parallel and Distributed Systems Stuttgart70569 Germany Ericsson Research Budapest1117 Hungary Ericsson Research Stockholm16440 Sweden KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm114 28 Sweden Ericsson Research Aachen52134 Germany

The importance of time- and mission-critical applications is increasing as industries and society are advancing in their digitalization. As such applications introduce stringent quality of service (QoS) requirements on the underlying network infrastructure, open standards like time-sensitive networking (TSN) and DetNet are being developed to provide dependable and time-sensitive communication in bridged LAN networks and IP networks, respectively. More recently, the extension to support wireless time-sensitive connectivity has received special interest due to improved network deployment flexibility with mobile devices. These efforts have led to the standardized support of TSN/DetNet in 5G, allowing the 5G system and its mobile devices to be integrated transparently into TSN/DetNet networks while conforming to their user and control plane protocols. Nevertheless, the induced packet delays– and in particular the packet delay variations (PDVs)– of 5G are significantly larger than in their wired counterparts. In this work, we illustrate that established methods to configure TSN/DetNet networks insufficiently address major runtime uncertainties, and that they would consequently result in QoS impairments or scalability issues. We therefore advocate for two systematic approaches to improve traffic shaping in this setting: first, we propose Packet Delay Correction where the PDV is corrected within the 5G system. Second, we introduce "wireless-aware" TSN scheduling to account for the packet delay characteristics of 5G. We demonstrate that wireless-aware TSN scheduling and Packet Delay Correction are complementary in their design and can be combined to provide formal end-to-end reliability guarantees at scale. In addition, being a key aspect to support wireless-aware TSN scheduling in 5G and future 6G networks, we introduce a data-driven framework to predict the packet delay characteristics of the wireless systems over a finite time horizon. Finally, we identify open research

关键词： Quality of service

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st Computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments

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Procedia Computer Science 2025年 257卷 452-459页

作者： Mahtab Shahin Saeed Rahimpour Tara Ghasempouri Sanja Bauk Thomas Fahringer Dirk Draheim Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

关键词： Cloud-Native Architectures Serverless Computing Data Mining Apriori Algorithm Distributed Computing Big Data Maritime

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ESTIMATING AND OPTIMIZING PERFORMANCE FOR parallel PROGRAMS

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COMPUTER 1995年第11期28卷 47-&页

作者： FAHRINGER, T Institut for software Technology and Parallel Systems University of Technology Vienna Austria

(PT)-T-3, a Parameter-based Performance Prediction Tool, is an interactive performance estimator that helps users tune scientific Fortran programs. It detects bottlenecks in the program, identifies the causes of performance problems, and guides users in selecting effective program transformations to gain performance. Four of the most critical aspects of parallel programs are automatically estimated: load balance, data locality, communication, and computation overhead. Operating as an integrated tool of the Vienna Fortran Compilation System, P3T is enabled to aggressively exploit considerable knowledge about the compiler's analysis information and code restructuring strategies. The author evaluates existing features and describes substantial enhancements in three key areas: the graphical user interface, performance parameters, and input programs. The tool's graphical user interface directs the user to bottlenecks in a computation that prevent the program from performing well. In addition, it allows performance data to be filtered and visualized at various levels of detail. The author discusses a new performance parameter that models computation time. Experiments show how (PT)-T-3 can successfully guide the user in performance tuning of parallel programs. The results demonstrate the feasibility of automatic performance prediction for parallel programs.

关键词： Runtime Programming profession Performance analysis Information analysis Distribution strategy Program processors Optimizing compilers Instruments Software performance Performance gain

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Compile-time estimation of communication costs for data parallel programs

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 1996年第1期39卷 46-65页

作者： Fahringer, T Institute for Software Technology and Parallel Systems University of Vienna Liechtensteinstrasse 22 A-1090 Vienna Austria

Most of the current compiler projects for distributed memory architectures leave the critical and time-consuming problem of finding performance-efficient data distributions and profitable program transformations for a given parallel program almost entirely to the programmer. Performance estimators provide critical performance information to both programmers and parallelizing compilers, the most crucial part of which involves determining the communication overhead induced by a program. In this paper, we present a very practical approach to the problem of compile-time estimation of communication costs for regular codes that includes analytical methods to model the number of messages exchanged, data volume transferred, transfer time, and network contention. In order to achieve high estimation accuracy, our estimator aggressively exploits compiler analysis and optimization information. It is assumed that machine parameters and problem size are known at compile time. We conducted a variety of experiments to validate the estimation accuracy and the ability to support both the programmer and compiler in the effort of performance tuning of parallel programs. We believe that our approach can be automatically applied to a large class of regular codes. (C) 1996 Academic Press, Inc.

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Vienna Fortran and the path towards a standard parallel language

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IEICE TRANSACTIONS ON INFORMATION AND systems 1997年第4期E80D卷 409-416页

作者： Chapman, BM Mehrotra, P Zima, HP Institute for Software Technology and Parallel Systems University of Vienna A-1090 Vienna Liechtensteinstr. 22 Austria

Highly parallel scalable multiprocessing systems (HMPs) are powerful tools for solving large-scale scientific and engineering problems. However, these machines are difficult to program since algorithms must exploit locality in order to achieve high performance. Vienna Fortran was the first fully specified data-parallel language for HMPs that provided features for the specification of data distribution and alignment at a high level of abstraction. In this paper we outline the major elements of Vienna Fortran and compare it to High Performance Fortran (HPF), a de-facto standard in this area. A significant weakness of HPF is its lack of support for many advanced applications, which require irregular data distributions and dynamic load balancing. We introduce HPF+, an extension of HPF based on Vienna Fortran, that provides the required functionality.

关键词： high performance computing distributed-memory architectures data distribution, parallelization Fortran extensions

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