This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial ev...
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In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochast...
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In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochastic systems in state space form,subject to an average cost constraint.C_(FB) is the maximum rate of encoding signals or messages into randomized controller-encoder strategies with feedback,which control the state of the system,and reproducing the messages at the output of the system using a decoder or estimator with arbitrary small asymptotic error *** the first part of the paper,the authors characterize C_(FB) by an information theoretic optimization problem of maximizing directed information from the inputs to the outputs of the system,over randomized strategies(controller-encoders).The authors derive equivalent characterizations of C_(FB),using randomized strategies generated by either uniform or arbitrary distributed random variables(RVs),sufficient statistics,and a posteriori distributions of nonlinear filtering *** the second part of the paper,the authors analyze C_(FB) for linear-quadratic Gaussian partially observable stochastic systems(LQG-POSSs).The authors show that randomized strategies consist of control,estimation and signalling parts,and the sufficient statistics are,two Kalman-filters and an orthogonal innovations *** authors prove a semi-separation principle which states,the optimal control strategy is determined explicitly from the solution of a control matrix difference Riccati equation(DRE),independently of the estimation and signalling ***,the authors express the optimization problem of C_(FB) in terms of two filtering matrix DREs,a control matrix DRE,and the covariance of the innovations *** the paper,the authors illustrate that the expression of C_(FB) includes as degenerate cases,problems of stochastic optimal control and channel capacity of information transmission.
The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context ...
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The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.
Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can...
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The growing prevalence of Internet of Things (IoT) devices has heightened vulnerabilities to botnet-based cyberattacks, necessitating robust detection mechanisms. This paper proposes DenseRSE-ASPPNet, an advanced deep...
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Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for buildings (V2B) and the grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2...
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Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
In this paper, we present a Deep Neural Network(DNN) based framework that employs Radio Frequency(RF) hologram tensors to locate multiple Ultra-High Frequency(UHF) passive Radio-Frequency Identification(RFID) tags. Th...
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In this paper, we present a Deep Neural Network(DNN) based framework that employs Radio Frequency(RF) hologram tensors to locate multiple Ultra-High Frequency(UHF) passive Radio-Frequency Identification(RFID) tags. The RF hologram tensor exhibits a strong relationship between observation and spatial location, helping to improve the robustness to dynamic environments and equipment. Since RFID data is often marred by noise, we implement two types of deep neural network architectures to clean up the RF hologram tensor. Leveraging the spatial relationship between tags, the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping. In contrast to fingerprinting-based localization systems that use deep networks as classifiers, our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints. We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors. The proposed framework is implemented using commodity RFID devices, and its superior performance is validated through extensive experiments.
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1...
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.
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