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 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.
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.
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Accurate liver tumor diagnosis in clinical practice relies on precisely delineating the liver and identifying potential tumors in Computed Tomography scans. This study aims to develop a lightweight liver and tumor seg...
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Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous vali...
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Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model ***, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.
Modern apps require high computing resources for real-time data processing, allowing app users (AUs) to access real-time information. Edge computing (EC) provides dynamic computing resources to AUs for real-time data ...
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Modern apps require high computing resources for real-time data processing, allowing app users (AUs) to access real-time information. Edge computing (EC) provides dynamic computing resources to AUs for real-time data processing. However, due to resources and coverage constraints, edge servers (ESs) in specific areas can only serve a limited number of AUs. Hence, the app user allocation problem (AUAP) becomes challenging in the EC environment. This paper proposes a quantum-inspired differential evolution algorithm (QDE-UA) for efficient user allocation in the EC environment. The quantum vector is designed to provide a complete solution to the AUAP. The fitness function considers the minimum use of ES, user allocation rate (UAR), energy consumption, and load balance. Extensive simulations and hypotheses-based statistical analyses (ANOVA, Friedman test) are performed to show the significance of the proposed QDE-UA. The results indicate that QDE-UA outperforms the majority of the existing strategies with an average UAR improvement of 112.42%, and 140.62% enhancement in load balance while utilizing 13.98% fewer ESs. Due to the higher UAR, QDE-UA shows 59.28% higher total energy consumption on average. However, the lower energy consumption per AU is evidence of its energy efficiency. IEEE
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across *** studies frequently focus on single-use situations and lack a comprehensive understandin...
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A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across *** studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and *** gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment *** this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly *** propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal *** methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance *** for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are *** investigation’s scope,mad,and methods are described,but the primary results are *** work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy *** medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote ***-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple *** discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain *** framework helps academics and practitioners identify,adapt,and innovate LLMs for different *** work
ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and det...
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In the enormous field of Natural Language Processing (NLP), deciphering the intended significance of a word among a multitude of possibilities is referred to as word sense disambiguation. This process is essential for...
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