Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these proce...
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Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.
Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizin...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the p...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
Fuzzing is the most widely used method for uncovering software security vulnerabilities, and many fuzzing implementations (fuzzers) are available on Linux. On Windows, however, only a few fuzzers are available;in part...
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Interpretable visual recognition is essential for decision-making in high-stakes situations. Recent advancements have automated the construction of interpretable models by leveraging Visual Language Models (VLMs) and ...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model perfor...
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Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention(SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss(SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset(detection track) show that our detector can achieve average precision(AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile,reducing 39.8% of the computational cost.
The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a c...
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The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a conceptual phase rooted in science fiction to a dynamic and transformative digital environment impacting various sectors including gaming,education,healthcare,and *** paper introduces the metaverse,details its historical development,and introduces key technologies that enable its existence such as virtual and augmented reality,blockchain,and artificial *** this work explores diverse application scenarios,future trends,and critical challenges including data privacy,technological limitations,and integration issues that must be addressed for the metaverse to reach its full *** significance of this study lies in its comprehensive nature,providing insights not only for researchers and practitioners but also for policymakers aiming to navigate the complexities of the metaverse and leverage its capabilities for societal ***,the paper forecast the future where the metaverse plays an integral role in reshaping human interaction,commerce,and creativity,thus emphasizing the need for ongoing research and collaborative efforts to unlock its vast possibilities.
Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading ***,in the absence of appropr...
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The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading ***,in the absence of appropriate pricing guidance,service providers often devise pricing strategies solely based on their own interests,potentially hindering the maximization of overall market *** challenge is even more severe in edge computing scenarios,as different edge service providers are dispersed across various regions and influenced by multiple factors,making it challenging to establish a unified pricing *** paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge ***,an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction,achieving optimal ***,an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three *** optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary *** results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations,while also demonstrating the effectiveness of our algorithm in resolving game problems.
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