In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own righ...
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In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to ***,it is necessary to reinvigorate the relationship between computerscience and other academic disciplines and celebrate exploration and creativity in *** do this,the structures of the academic department have to act as supporting scaffolding rather than *** examples are given that show the efforts being made at the University of Cambridge to approach this problem.
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.
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shorte...
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Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.
Automated machine learning(AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning *** the focal areas of AutoML,neural architecture search(NAS) stands out,aiming to...
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Automated machine learning(AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning *** the focal areas of AutoML,neural architecture search(NAS) stands out,aiming to systematically explore the complex architecture space to discover the optimal neural architecture configurations without intensive manual *** has demonstrated its capability of dramatic performance improvement across a large number of real-world *** core components in NAS methodologies normally include(ⅰ) defining the appropriate search space,(ⅱ)designing the right search strategy and(ⅲ) developing the effective evaluation *** early NAS endeavors are characterized via groundbreaking architecture designs,the imposed exorbitant computational demands prompt a shift towards more efficient paradigms such as weight sharing and evaluation estimation,***,the introduction of specialized benchmarks has paved the way for standardized comparisons of NAS ***,the adaptability of NAS is evidenced by its capability of extending to diverse datasets,including graphs,tabular data and videos,etc.,each of which requires a tailored *** paper delves into the multifaceted aspects of NAS,elaborating on its recent advances,applications,tools,benchmarks and prospective research directions.
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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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 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.
The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band *** such systems,all participants r...
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The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band *** such systems,all participants related to commercial and industrial systems must communicate and generate ***,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their ***,as the number of participants increases,the size of generated data also ***,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and *** address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central ***,they only share updated training models that are trained over their private *** the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security ***,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed ***,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and ***,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving *** proposed solution uses the impetus term to speed up model convergence during the model training ***,it establishes a secure communication channel between IoT devices and the *** a
In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attribu...
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In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.
The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has be...
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Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system ***,the Internet of Things(IoT)has revolutionized the Fourth...
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Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system ***,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human *** paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain *** integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be ***,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated *** integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain *** platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data *** results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.
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