Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater re...
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Simulink is a widely used model-based development environment for embedded systems. Stateflow is a component of Simulink for modeling event-driven control via hierarchical state machines and flow charts. However, Stat...
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SMT solvers are utilized to check the satisfiability of logic formulas and have been applied in various crucial domains, including software verification, test case generation, and program synthesis. However, bugs hidd...
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ISBN:
(纸本)9798350329964
SMT solvers are utilized to check the satisfiability of logic formulas and have been applied in various crucial domains, including software verification, test case generation, and program synthesis. However, bugs hidden in SMT solvers can lead to severe consequences, causing erroneous results in these domains. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Despite several testing approaches proposed for SMT solvers, generating effective test formulas to comprehensively test SMT solvers remains a challenge. To address this challenge, in this study, we propose to port large language models (LLMs) to generate SMT formulas for fuzzing solvers. Specifically, the study presents a novel retrain-finetune pipeline to unleash the potential of language models to generate effective SMT formulas and improve their generation performance through data augmentation. We implemented our approach as a practical fuzzing tool, named LaST, and then extensively tested the state-of-the-art SMT solvers, namely Z3, cvc5, and Bitwuzla. To date, LaST has successfully uncovered 65 genuine bugs for the solvers, of which 45 have been fixed by the developers.
Reasoning is the process of inferring new knowledge and identifying inconsistencies within ontologies. Traditional techniques often prove inadequate when reasoning over large Knowledge Bases containing millions or bil...
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Reasoning is the process of inferring new knowledge and identifying inconsistencies within ontologies. Traditional techniques often prove inadequate when reasoning over large Knowledge Bases containing millions or billions of facts. This article introduces NORA, a persistent and scalable OWL reasoner built on top of Apache Spark, designed to address the challenges of reasoning over extensive and complex ontologies. NORA exploits the scalability of NoSQL databases to effectively apply inference rules to Big Data ontologies with large ABoxes. To facilitate scalable reasoning, OWL data, including class and property hierarchies and instances, are materialized in the Apache Cassandra database. Spark programs are then evaluated iteratively, uncovering new implicit knowledge from the dataset and leading to enhanced performance and more efficient reasoning over large-scale ontologies. NORA has undergone a thorough evaluation with different benchmarking ontologies of varying sizes to assess the scalability of the developed solution.
D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy ***,the ma...
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D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy ***,the mass assignments given by unknown information sources are *** to measure the difference between the mass assignments has aroused people’s *** this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass *** method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass *** the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation ***,in the process of information fusion,the reliability of each source could be quantified through ***,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information ***,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.
Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainst...
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With the rapid development of enterprise digital transformation and the widespread use of cloud-native architecture applications, the complexity of enterprise-level systems is getting higher and higher. This requires ...
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With the rapid development of cloud-network integration, in order to ensure the normal operation of devices, networks, and services in the cloud, anomaly detection algorithms have received increasing attention. The ex...
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Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic...
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Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge *** offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth *** partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop *** optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation *** reward formulation primarily considers taskrequired computing resources and action-applied allocation *** defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated *** simulation for the control rule installation is conducted using Mininet and Ryu SDN *** delay and task delivery/drop ratios are used as the key performance metrics.
Person re-identification (ReID) aims to retrieve a target person across non-overlapping cameras. Due to the uncontrollable environment and the privacy concerns, the diversity and scale of real-world training data are ...
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