For the telephone broadcast model, an O(n log n)-time algorithm for constructing an optimal broadcasting scheme in a star of cliques with a total of n vertices was recently presented by Ambashankar and Harutyunyan at ...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
The current increase in the number of large, open sets of unstructured textual data has created both opportunities and challenges for social scientists. Here, we explore if and how we can use such data by looking at a...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection b...
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Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.
Modeling of on-chip interconnects in microarchitectural simulations is becoming more important. Chips continue to increase their number of cores, i.e., via 3D stacking and multi-chiplet integration, and their performa...
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The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate t...
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The network security analyzers use intrusion detection systems(IDSes)to distinguish malicious traffic from benign *** deep learning-based(DL-based)IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction ***,this new generation of IDSes still needs to overcome a number of challenges to be employed in practical *** of the main issues of an applicable IDS is facing traffic concept drift,which manifests itself as new(i.e.,zero-day)attacks,in addition to the changing behavior of benign users/***,a practical DL-based IDS needs to be conformed to a distributed(i.e.,multi-sensor)architecture in order to yield more accurate detections,create a collective attack knowledge based on the observations of different sensors,and also handle big data challenges for supporting high throughput *** paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings,considering a more practical scenario(i.e.,online adaptable IDSes).This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local *** addition,a federated learning approach is proposed for sharing and exchanging local knowledge between different ***,the proposed framework implements sequential packet labeling for each flow,which provides an attack probability score for the flow by gradually observing each flow packet and updating its *** evaluate the proposed framework by employing different deep models(including CNN-based and LSTM-based)over the CICIDS2017 and CSE-CIC-IDS2018 *** extensive evaluations and experiments,we show that the proposed distributed framework is well adapted to the traffic concept *** precisely,our results indicate that the CNNbased models are well suited for continually adapting to the traffic concept drift(i.e.,achieving
We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a ...
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We present a graded modal type theory, a dependent type theory with grades that can be used to enforce various properties of the code. The theory has II-types, weak and strong ς-types, natural numbers, an empty type, ...
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Content-based image retrieval (CBIR) using visual saliency in the pixel domain has shown promising retrieval results at lesser computational cost as features are extracted only from salient regions. CBIR in the JPEG c...
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