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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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High–Fidelity Haptic Rendering Through Implicit Neural Force Representation 14th

High–Fidelity Haptic Rendering Through Implicit Neural Forc...

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14th International Conference on Human Haptic Sensing and Touch Enabled computer Applications, EuroHaptics 2024

作者： Vlachos, Christoforos Moustakas, Konstantinos Department of Electrical and Computer Engineering University of Patras Rion-Patras Greece

ISBN: (纸本)9783031700576

Recent research has demonstrated that neural networks using periodic nonlinearities may be used for implicit representation and reconstruction of continuous-time signals. Starting with a previously published network for representing the Signed Distance Function (SDF) of a mesh surface, we extend the concept and lay the foundation for introducing the additional representation of the Unit Normal Function (UNF). With the representation of these two functions at hand, we construct a penalty-based haptic rendering method. Our experiments suggest that this proposed method is able to handle very large meshes better than other competing alternatives, producing high-fidelity forces, free of discontinuities, by sampling a continuous implicit force function at the desired spatial accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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Detection and Visualization of User Facial Expressions 17th

Detection and Visualization of User Facial Expressions

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17th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Wojnar, Martyna Grzejszczak, Tomasz Bartosiak, Natalia Silesian Tech Univ Gliwice Poland

ISBN: (纸本)9783031430770;9783031430787

The work covers topics in face detection, prediction of the position of the face landmarks as well as control of the graphical model. The purpose of the work is to create a vision system that detects the user's facial expressions and visualizes them on the created computer model. The scope of work includes detection of the face and face landmarks, creation of a graphic model of a character whose facial expressions can be modified, and control of the created model using data obtained from the camera image. The main objectives of the project are easy accessibility, simplicity of use and low cost of the tools used.

关键词： facial expression face landmarks visualization computer model 3D modeling human-computer interaction

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Design and Implementation of Deep Learning Real-Time Streaming Video Data Processing System 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Zhao, Qiming Wu, Jing Wu, Xiang Fan, Jie Wang, Linhao Wu, Fengling College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Parameterized Complexity of Generalizations of Edge Dominating Set 50th

Parameterized Complexity of Generalizations of Edge Domina...

引用

50th International Conference on Current Trends in Theory and Practice of computer science, SOFSEM 2025

作者： Aute, Shubhada Panolan, Fahad Saha, Souvik Saurabh, Saket Upasana, Anannya Department of Computer Science and Engineering IIT Hyderabad Kandi India School of Computer Science University of Leeds Leeds United Kingdom The Institute of Mathematical Sciences HBNI Chennai India University of Bergen Bergen Norway

ISBN: (纸本)9783031826696

The objective of this article is to propose two natural generalizations of covering edges by edges (Edge Dominating Set) and study these problems from the multivariate lens. The first is simply considering Edge Dominating Set on hypergraphs, called Hyperedge Dominating Set. Given a hypergraph H=(U,F), a set F⊆F is called a hyperedge dominating set if all hyperedges intersect with at least one hyperedge e∈F. The objective of the Hyperedge Dominating Set problem is to determine whether a hyperedge dominating set of size at most k exists. We find it quite surprising that such generalization is missing from the literature. The second extension we consider is the t-Path Edge Dominating Set problem. In this problem, the input consists of a graph G and an integer k, and the goal is to find a set P of at most k paths, each of length at most t, such that for every edge in G, at least one of its endpoints belongs to the vertex set V(P) for some P∈P. We show the following results and add to the literature on Edge Dominating Set. Hyperedge Dominating Set is FPT parameterized by k+d, where d is the maximum size of a hyperedge in the input hypergraph.A kernel of size O(kd) can be obtained for the Hyperedge Dominating Set problem, where d is the maximum size of a hyperedge in the input *** problem of finding a Hyperedge Dominating Set is computationally difficult;specifically it is W[2]-hard when parameterized by k. This hardness result holds even when each vertex is contained in at most 2 hyperedges and the intersection between any two hyperedges is at most 1.t-Path Edge Dominating Set is FPT when parameterized by k+t. Additionally, it has a kernel of size O(k3t3). Hyperedge Dominating Set is FPT parameterized by k+d, where d is the maximum size of a hyperedge in the input hypergraph. A kernel of size O(kd) can be obtained for the Hyperedge Dominating Set problem, where d is the maximum size of a hyperedge in the input hypergraph. The problem of finding a Hyperedge Dom

关键词： Parameterization

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Toward a QoS-Guaranteed Cloud Through Elastic Resource Scaling and Request Updating 24th

Toward a QoS-Guaranteed Cloud Through Elastic Resource Scal...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Shen, Bingchen Liu, Jiawei Zhao, Gongming Xu, Hongli Bao, Jianfeng School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China

ISBN: (纸本)9789819615414

In multi-tenant clouds, service providers offer services to tenants and generate profits by deploying network function (e.g., NAT, VPN) instances. Tenants access services by forwarding their requests to corresponding instances. In practice, traffic dynamics typically result in load imbalances and overloads among instances, severely impacting QoS. Existing works primarily address these issues through request updating and elastic resource scaling. Request updating is a fast and feasible method but may incur significant costs since these methods need prepurchasing enough servers. Elastic resource scaling is an approach to save infrastructure costs but may degrade user QoS when adjusting the number of instances. This paper presents a problem called ESRU, aimed at providing a QoS-guaranteed and cost-effective elastic resource scaling and request updating in clouds. Furthermore, we propose the ESRU-A algorithm, which features a bounded approximation factor based on randomized rounding and a greedy strategy. Performance analysis indicates our algorithm can achieve optimal value with high certainty. Experiments conducted across various topologies and traffic dynamic scenarios have demonstrated that our proposed algorithm reduces infrastructure costs by up to 45.17% while increasing throughput by 58.17%, compared to state-of-the-art benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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On Minimization of Nonlinear Energies Using FEM in MATLAB 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Moskovka, Alexej Valdman, Jan Vohnoutová, Marta Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň30100 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic The Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague18208 Czech Republic

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Two minimization problems are added to the Moskovka and Valdman MATLAB package (2022): a Ginzburg-Landau (scalar) problem and a topology optimization (both scalar and vector) problem in linear elasticity. Both problems are described as nonlinear energy minimizations that contain the first gradient of the unknown field. Their energy functionals are discretized by finite elements, and the corresponding minima are searched using the trust-region method with a known Hessian sparsity or the Quasi-Newton method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finite element method

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Afra: An Eclipse-Based Tool with Extensible Architecture for Modeling and Model Checking of Rebeca Family Models 1

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10th IPM International Conference on Fundamentals of Software Engineering, FSEN 2023

作者： Khamespanah, Ehsan Sirjani, Marjan Khosravi, Ramtin School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Science Reykjavik University Reykjavik Iceland School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, exporting a model and its transition system to some other formats facilities are features of Afra. Rebeca family provides actor-based modeling languages which are designed to bridge the gap between formal methods and software engineering. Faithfulness to the system being modeled, and the usability of Rebeca family languages help in ease of modeling and analysis of the model, together with the synthesis of the system based on the model. In this paper, architectural decisions and design strategies we made in the development of Afra are presented. This makes Afra an extensible and reusable application for the modeling and analysis of Rebeca family models. Here, we show how different compilers can be developed for the family of languages which are the same in general language constructs but have some minor differences. Then we show how the model checking engine for these different languages is designed. Despite the fact that Afra has a layered object-oriented design and is developed in Java technology, we use C++ codes for developing its model checking for the performance purposes. This decision made the design of the application even harder. © 2023, IFIP International Federation for Information Processing.

关键词： Model checking

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EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Guangtao Li, Jun Xie, Jie Xu, Jianhua Yang, Bo School of Computer and Electronic Information Nanjing Normal University Nanjing210023 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Face recognition

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Generalization Gap in Data Augmentation: Insights from Illumination 27th

Generalization Gap in Data Augmentation: Insights from Ill...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xiao, Jianqiang Guo, Weiwen Liu, Junfeng Li, Mengze South China University of Technology Guangzhou China Co. Ltd. Guangzhou China Co. Ltd. Guangzhou China Research Center for Frontier Fundamental Studies Zhejiang Lab Hangzhou China

ISBN: (纸本)9783031784439

In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques. However, regarding the generalization capabilities of models, the difference in artificial features generated by data augmentation and natural visual features has not been fully revealed. This study introduces the concept of "visual representation variables" to define the possible visual variations in a task as a joint distribution of these variables. We focus on the visual representation variable "illumination", by simulating its distribution degradation and examining how data augmentation techniques enhance model performance on a classification task. Our goal is to investigate the differences in generalization between models trained with augmented data and those trained under real-world illumination conditions. Results indicate that after applying various data augmentation methods, model performance has significantly improved. Yet, a noticeable generalization gap still exists after utilizing various data augmentation methods, emphasizing the critical role of feature diversity in the training set for enhancing model generalization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Vision Data Augmentation Generalization

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