In this work, we explore route discovery in private payment channel networks. We first determine what "ideal" privacy for a routing protocol means in this setting. We observe that protocols achieving this st...
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While mitigating link-flooding attacks on the Internet has become an essential task, little research has been done on how an attacker can further attack and abuse the mitigation solutions themselves. In this paper, we...
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ISBN:
(纸本)9783031649530;9783031649547
While mitigating link-flooding attacks on the Internet has become an essential task, little research has been done on how an attacker can further attack and abuse the mitigation solutions themselves. In this paper, we propose a two-wave attack with collateral damage of millions (or Carom), a new link-flooding attack that poses a mitigation dilemma for multiple simultaneously attacked networks, which must either endure the flooding attack or suffer unwanted side effects in mitigating the attack. Composed of practical components, the Carom attack aims to maximize the burden on attack mitigation systems and the collateral damage to defending networks, thereby wreaking havoc on large swaths of the Internet. After modeling real-world mitigation solutions, we evaluated the attack against the mitigation solutions with real-world datasets, showing the feasibility of the attack and quantifying the amount of damage it can inflict on today's Internet. We hope that this work can motivate the improvement of existing link-flooding mitigation solutions.
In the domain of image annotation, the involvement of human annotators presents a series of intricate challenges tied to the complexities of visual perception. The manual labeling process demands an understanding of c...
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ISBN:
(纸本)9783031809453;9783031809460
In the domain of image annotation, the involvement of human annotators presents a series of intricate challenges tied to the complexities of visual perception. The manual labeling process demands an understanding of context and visual intricacies, all susceptible to human subjectivity. Furthermore, the presence of visual distractions compromises annotation quality, not only impeding annotation precision but also escalating time expenditures as annotators navigate through visual noise to discern pertinent details. Traditional image annotation pipelines underscore these challenges in favor of automatic or semiautomatic annotation, emphasizing the critical necessity for innovative approaches in annotation tasks where the human annotator role is fundamental. Within this context, the Grounded SAM model, emerges as a potent tool for text-prompt-based panoptic segmentation. This paper proposes a novel annotation pipeline employing Grounded SAM and LaMa cleaner models to augment the indispensable role of human annotators by enhancing annotation efficiency through natural language-based attention mining for visual distractions elimination and preannotation techniques. The effectiveness of distraction elimination is demonstrated through an annotation task involving human annotators, with half of the images processed through our pipeline and the remaining unmodified. With our current approach and with the current data analyzed, image annotation times of 70% of the annotators were reduced by 15.88%, while global annotation time was reduced by a 6.93%.
Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled non-linear ordinary differential equations. In many applications, for example, in systems b...
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ISBN:
(纸本)9783031751066;9783031751073
Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled non-linear ordinary differential equations. In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs to steer the system toward a given target. Unfortunately, the resulting optimal control problem is non-linear, therefore, computationally very challenging. We address this issue by introducing an optimality-preserving reduction algorithm for CRNs. The algorithm partitions the original state variables into a reduced set of macro-variables for which one can define a reduced optimal control problem with provably identical optimal values. The reduction algorithm runs with polynomial time complexity in the size of the CRN. We use this result to reduce verification and control problems of large-scale vaccination models over real-world networks.
The proceedings contain 37 papers. The special focus in this conference is on Applied Innovations in IT. The topics include: Use of Heterogeneous Special Purpose Telecommunication networks for Provision of Convergent ...
ISBN:
(纸本)9783031892950
The proceedings contain 37 papers. The special focus in this conference is on Applied Innovations in IT. The topics include: Use of Heterogeneous Special Purpose Telecommunication networks for Provision of Convergent Services;methodology for Choosing the Best Network Topology for the Multiple Objects Network;turing Machine Development for High Protected Remote Control of the IoT Mobile Platform;distributed Multi-agent systems Based on the Mixture of Experts Architecture in the Context of 6G Wireless Technologies;technological Principles for Building a Network Architecture of Service Data Processing Centers;Comparison of Performance and Power Consumption in Sigfox, NB-IoT, and LTE-M;approach to Effective Query Execution;possible Features of Designing Telemedicine networks and Telemedicine Stations;applying Adaptive Learning Techniques for Studying of Installation Process an Operating System on a Personal computer;determination the Conversion Power of the Most Popular Switching Converters for computersystems;achieving Cyber-Physical Consistency for Immersive Robot Controlling;quantifying the Economic Impact of Investment Activities: Methods and Applications;analysis of Modern Solutions for the Identification of Anonymous Users;ontology-Driven Approach to the Structuring of Information Resources Describing a Subject Domain;a Comprehensive Integration of Practical Strategies in DevOps;methods of Spline Functions in Information Technologies;adaptive Clustering for Distribution Parameter Estimation in Technical Diagnostics;semi-Autonomous Object Retrieval with Robot Arm and Depth Camera for Quadruped Robots;power-to-X Strategies: A Key Driver for Decarbonization and Renewable Energy Integration in Economies;Estimation of the Repeater Span Length of OTH Transmission System with QAM Modulation;automated 3D Sign Language Animation Using Machine Learning Algorithms.
Recent development of federated learning based smart healthcare techniques facilitates distributed processing of patient data while protecting privacy. However, two significant challenges can notably degrade model lea...
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The proceedings contain 8 papers. The special focus in this conference is on From Data to Models and Back. The topics include: Extracting Cyber Threat Intelligence from Social Media with Case Studies in ...
ISBN:
(纸本)9783031872167
The proceedings contain 8 papers. The special focus in this conference is on From Data to Models and Back. The topics include: Extracting Cyber Threat Intelligence from Social Media with Case Studies in Twitter/X and Reddit;attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction systems;preliminary Results on Shapley Value Notions and Propagation Methods for Boolean networks;towards a Flexible Approach for Understanding and Comparing Traces;modelling and Verification of an Application for Managing Sensitive Health Data;evaluating Large Language Models and Prompt Variants on the Task of Detecting Cease and Desist Violations in German Online Product Descriptions.
This study aims to address the challenges of delivering adequate ultrasound examinations in remote areas. It focuses on developing a long-distance teleoperation system for ultrasound examinations using commercially av...
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PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One...
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PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network *** developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).ResultsThe results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical *** work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.
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% r...
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