Hardware description language (HDL) is widely used to model the structure and behavior of digital systems ranging from simple hardware building blocks to complete systems in today's hardware design. To tackle with...
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ISBN:
(纸本)0780386531
Hardware description language (HDL) is widely used to model the structure and behavior of digital systems ranging from simple hardware building blocks to complete systems in today's hardware design. To tackle with ever-increasing complexity of process models, higher level of abstraction needs to be exploited. We propose an ontology-based architecture in the process of hardware design using HDLs. The architecture combines the traditional HDL design processes with ontology models and offers a semi-automatic process utilizing the ontology reasoning abilities of OWL (Web ontology language). Based on such ontology assistant architecture, more flexible and automatic processes can be devised, which are of crucial importance to meet current market demands.
Ontology based approach has been popularized by current semantic Web researches. However, ontology building by hand has proven to be a very hard and error-prone task and become the bottleneck of ontology acquiring pro...
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ISBN:
(纸本)0780384032
Ontology based approach has been popularized by current semantic Web researches. However, ontology building by hand has proven to be a very hard and error-prone task and become the bottleneck of ontology acquiring process. WordNet, an electronic lexical database, is considered to be the most important resource available to researchers in computational linguistics. The paper proposes an ontology learning approach, which uses WordNet lexicon resources to build a standard OWL ontology model. The approach can the automation of ontology building and be very useful in ontology-based applications.
Mobile agent need migrate to several hosts for accomplishing its task. Migration strategy is responsible for planning out an optimal migration path, which ensures mobile agent to complete its task correctly and effici...
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ISBN:
(纸本)0780378652
Mobile agent need migrate to several hosts for accomplishing its task. Migration strategy is responsible for planning out an optimal migration path, which ensures mobile agent to complete its task correctly and efficiently at the minimum cost. The paper introduces the concept of itinerary graph and presents three migration strategies based on it. Itinerary graph not only describes the migration semantics of mobile agent but also reflects the changes of software and hardware environment where mobile agent lives. During its travel, mobile agent equipped with itinerary graph perceives these changes, react quickly, modify its migration path autonomously and accomplish its task according to certain specified criterions, which embody the reactivity and autonomy of agent. Besides improving the efficiency, these migration strategies enhances the performance of mobile agent system because they avoid migration failure resulted from network disconnection or node crashing.
In this paper, we explore the links between two well-known models in spatial and temporal reasoning - Allen's interval algebra in temporal reasoning and the RCC model in spatial reasoning. We show that the tempora...
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ISBN:
(纸本)0780378652
In this paper, we explore the links between two well-known models in spatial and temporal reasoning - Allen's interval algebra in temporal reasoning and the RCC model in spatial reasoning. We show that the temporal relations identified by interval algebra are actually a combination of RCC-8 relations with directional relations. The work divides RCC-8 relations into two distinct sets with respect to directional factors. This result can be utilized to combine topology and directional relations in particular applications.
Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction perf...
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Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.
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