In Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping o...
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Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitiv...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisfaction. In this paper, we propose a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty level of a target-reaching task. With this we systematically quantify and model the effect of task difficulty (i.e. size and distance of target) and robot autonomy on task performance and operators' cognitive load and trust levels. Our empirical results (N=24) not only show that both task difficulty and robot autonomy influence task performance, but also that the performance can be modelled using these parameters, which may allow for the generalization of this relationship across more diverse setups. We also found that the users' perceived cognitive load and trust were influenced by these factors. Given the challenges in directly measuring cognitive load in real-time, our adapted Fitts' model presents a potential alternative approach to estimate cognitive load through determining the difficulty level of the task, with the assumption that greater task difficulty results in higher cognitive load levels. We hope that these insights and our proposed framework inspire future works to further investigate the generalizability of the method, ultimately enabling the benchmarking and systematic assessment of shared control quality and user impact, which will aid in the development of more effective and adaptable systems.
With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is p...
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ISBN:
(数字)9798331520113
ISBN:
(纸本)9798331520120
With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is proposed, combining data distillation and model distillation. Data distillation is achieved through KMeans clustering and refinement, while model distillation leverages a random forest (RF) teacher model to guide a simplified multi-layer perceptron (MLP) student model. The approach is validated on CICIDS2017 and TON-IoT datasets, where dataset sizes are reduced to 20% and 40%, respectively. Experimental results demonstrate that the proposed method enhances MLP accuracy on CICIDS2017 from 93.17% to 99.66% and on TON-IoT from 89.00% to 93.14%. Moreover, the method significantly reduces model parameters compared to the RF teacher model while maintaining competitive performance, making it highly suitable for resource-constrained IoT environments.
The use of the polarization property of light to evaluate the birefringence property of tissues as well as changes due to pathological conditions has been gaining interest over the past years with the introduction of ...
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Indirect speech acts (ISAs) are a natural pragmatic feature of human communication, allowing requests to be conveyed implicitly while maintaining subtlety and flexibility. Although advancements in speech recognition h...
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For a robot to perform personalised human support in a home, it is essential to uniquely identify the people it is working for. Each individual has their own attributes, knowledge of the environment, and experiences w...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
For a robot to perform personalised human support in a home, it is essential to uniquely identify the people it is working for. Each individual has their own attributes, knowledge of the environment, and experiences which the robot should learn and keep in its memory. One of the current challenges is for a robot to identify and re-identify an individual while dealing with visual occlusion, changes in appearance, and identity switching. This paper presents a novel approach to address these challenges in robot vision by extending current state-of-the-art solutions to enhance person memorisation by taking multiple viewpoints to extract features for matching and maintaining this information in a world model of the robot.
Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the perf...
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ISBN:
(数字)9798331530983
ISBN:
(纸本)9798331530990
Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the performance of five U-Net variants in cardiac MRI segmentation using the Automated Cardiac Diagnosis Challenge (ACDC) dataset and a hybrid loss function combining Cross-Entropy and dice losses. Among the variants, the Feature Pyramid U-Net achieved the best performance, with Dice coefficients of 0.9388 (Left Ventricle), 0.8759 (Right Ventricle), and 0.8426 (Myocardium), showcasing its superior ability to capture multi-scale features and segment complex anatomical structures. The comprehensive and standardized evaluation conducted in this study provides valuable insights into the strengths and limitations of these architectures for cardiac segmentation.
We consider the problem of vertex recoloring: we are given n vertices with their initial coloring, and edges arrive in an online fashion. The algorithm is required to maintain a valid coloring by means of vertex recol...
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We consider the problem of vertex recoloring: we are given n vertices with their initial coloring, and edges arrive in an online fashion. The algorithm is required to maintain a valid coloring by means of vertex recoloring, where recoloring a vertex incurs a cost. The problem abstracts a scenario of job placement in machines (possibly in the cloud), where vertices represent jobs, colors represent machines, and edges represent "anti affinity" (disengagement) constraints. online coloring in this setting is a hard problem, and only a few cases were analyzed. One family of instances which is fairly well-understood is bipartite graphs, i.e., instances in which two colors are sufficient to satisfy all constraints. In this case it is known that the competitive ratio of vertex recoloring is Θ(log n). In this paper we propose a generalization of the problem, which allows using additional colors (possibly at a higher cost), to improve overall performance. Concretely, we analyze the simple case of bipartite graphs of bounded largest bond (a bond of a connected graph is an edge-cut that partitions the graph into two connected components). From the upper bound perspective, we propose two algorithms. One algorithm exhibits a trade-off for the uniform-cost case: given Ω(log β) ≤ c ≤ O(log n) colors, the algorithm guarantees that its cost is at most O(logn/c ) times the optimal offline cost for two colors, where n is the number of vertices and β is the size of the largest bond of the graph. The other algorithm is designed for the case where the additional colors come at a higher cost, D > 1: given ∆ additional colors, where ∆ is the maximum degree in the graph, the algorithm guarantees a competitive ratio of O(log D). From the lower bounds viewpoint, we show that if the cost of the extra colors is D > 1, no algorithm (even randomized) can achieve a competitive ratio of o(log D). We also show that in the case of general bipartite graphs (i.e., of unbounded bond size), any determinis
Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerab...
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