The workflow model is the abstract expression of the workflow or the business process. Following the WfMC reference model, a PKI-based lightweight workflow model named as PBLW is put forward in this paper. The framewo...
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Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Geno...
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Untraceability is an important aspect of RFID security. A BRS-based approach for modeling RFID untraceability is provided in this paper. Elements in a RFID protocol can be represented as bigraphs, communications betwe...
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Untraceability is an important aspect of RFID security. A BRS-based approach for modeling RFID untraceability is provided in this paper. Elements in a RFID protocol can be represented as bigraphs, communications between elements can be represented as reaction rules. RFID untraceability can be represented as behavioral congruences. We take a RFID air interface protocol as a case study and show the usability of this approach.
With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of informa...
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With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.
Essential graph is a graphical representation for Markov equivalence classes of Bayesian networks. Learning essential graph can avoid some problems in traditional Bayesian networks learning algorithms: (1) the number ...
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The configuration problem in manufacture is more complicated than most other fields. Therefore, the design of modeling and reasoning module for product configuration manager in manufacture is very important and comple...
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In order to solve the problem of low intrusion detection rate and weak generalization ability of Intrusion Detection System(IDS), it proposes a new hybrid method based on the relationship of feature and spatial corr...
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ISBN:
(纸本)9781509038237;9781509038220
In order to solve the problem of low intrusion detection rate and weak generalization ability of Intrusion Detection System(IDS), it proposes a new hybrid method based on the relationship of feature and spatial correlation for IDS. The proposed IDS reduces the dimension of network data flow by spatial correlation-based dimension reduction method(SCDR). It improves the effectiveness of intrusion detection, and the negative feedback learning method can also improve the generalization ability of IDS. Experiments show that the method in this paper can improve the detection rate notably and also enhance the detection ability of unknown attacks.
Mobile ambients is a process calculus for modeling mobile agents in wide-area networks. It has important theoretical and practical values in studying concurrent and mobile computation as well as the security of intera...
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Clustering is an important data processing tool, which can be used to reveal the distribution structure of unfamiliar domain data, or as preprocess methods to magnify data object to accelerate subsequent processing or...
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Computer science labs in universities have different demands in cloud computing compared with typical IT companies. They differ greatly in software requirements and resource utilization. As an instance, we propose bui...
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