5086368
Marker-controlled perception-based mesh segmentation
This paper introduces an interactive marker-controlled mesh segmentation algorithm. In many applications, we need to partition a given mesh according to human perception or particular requirements. The algorithm presented in this paper is based on fast marching watersheds. We refer to the experience of image segmentation area and introduce interactive marker selection. Thus we obviously overcome the difficulty in intuitional segmentation control, and improve the quality of segmentation. The output results show the effectiveness and practicability of the algorithm.
23948133
Building Domain Ontology Based on Web Data and Generic Ontology
The automatic or semi-automatic construction of ontology has become a research topic of interest in recent years. This paper describes a mechanism for constructing domain specific ontologies automatically based on web data and generic ontology. Firstly, we employ the hierarchical agglomerative clustering(HAC) algorithm, clustering web pages hierarchically and resulting in a binary tree. Then an algorithm is proposed, which selectd from the binary tree the significative nodes as topics implying concepts of domain interests. Lastly, the Chinese generic ontology, HowNet, is introduced to evolve the topics (together with their hierarchical structures) into domain ontology. We experiment our method in the field of computer hardware based on web pages collected from Chinese BtoC web sites. An in-depth discussion on the experiment results is also given.
36918365
C3: A New Learning Scheme to Improve Classification of Rare Category Emails
This paper proposes C3, a new learning scheme to improve classification performance of rare category emails in the early stage of incremental learning. C3 consists of three components: the chief-learner, the co-learners and the combiner. The chief-learner is an ordinary learning model with an incremental learning capability. The chief-learner performs well on categories trained with sufficient samples but badly on rare categories trained with insufficient samples. The co-learners that are focused on the rare categories are used to compensate for the weakness of the chief-learner in classifying new samples of the rare categories. The combiner combines the outputs of both the chief-learner and the co-learner to make a finial classification. The chief-learner is updated incrementally with all the new samples overtime and the co-learners are updated with new samples from rare categories only. After the chief-learner has gained sufficient knowledge about the rare categories, the co-learners become unnecessary and are removed. The experiments on customer emails from an e-commerce company have shown that the C3 model outperformed the Naive Bayes model on classifying the emails of rare categories in the early stage of incremental learning.
15560929
UNDER DEVELOPMENTUCD in Chinese IT enterprises
A zoom lens system includes a first lens unit having a negative optical power, located closer to an object side, and a second lens unit having a positive optical power, located farther from the object side. Zooming is accomplished by varying the spacing between the first and second lens units. The surface of at least one lens element in the two lens units is a diffractive optical surface. At least one surface of the lens element is an aspherical surface.
16577876
FAME: Adding Multi-Level Authentication to Shibboleth
The paper describes the design of FAME (Flexible Access Middleware Extension) architecture aimed at providing multi-level user authentication service for Shibboleth, which is endorsed by the Joint Information Systems Committee (JISC) as the next generation authentication and authorisation infrastructure for the UK Higher Education community. FAME derives authentication assurance level based upon the strength of the authentication token and protocol used by the user when authenticating and feeds it to the PERMIS authorisation decision engine via Shibboleth to enable more fine-grained access control. In this way, access to resources is controlled according to the strength of the authentication method so that more sensitive resources may require users to identify themselves using a higher level of authentication.
5391610
A study of fairness for ATM ABR service based on cell delay
A general goal of the asynchronous transfer mode (ATM) network is to support connections across local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs). However, in the available bit rate (ABR) service, different treatments are offered according to different round trip times (RTTs) of connections. To improve the above "unfairness", this paper presents an ABR delay mechanism, in which three reference parameters for cell delay are defined, and reflect on the messages of resource management (RM) cells. To evaluate our mechanism, we execute simulations on a simple ATM network model where six TCP connections and a background traffic with different RTTs share the bandwidth of a bottleneck link. The simulation results, based on TCP goodput and efficiency, clearly show that the ABR delay mechanism improves the fairness among TCP connections, although the considered implementation of ABR transfer capacity is simple.
6831292
Graph Cuts in Content-Based Image Classification and Retrieval with Relevance Feedback
Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.
1660866
A General Fuzzified CMAC Controller with Eligibility
This paper presents an online neural network controller. Cerebellar Model Articulation Controller (CMAC) is suitable to online control due to its fast learning speed. By integrating the CMAC address scheme with fuzzy logic concept, a general fuzzified CMAC (GFAC) is proposed. Then by incorporating the concept of eligibility into the GFAC, a GFAC controller with eligibility is presented, named FACE. A learning algorithm for the FACE is derived to tune the model parameters. To achieve online control, an efficient implementation of the proposed FACE method is presented. As an example, the proposed FACE is applied to a ship steering control system. The simulation results show that the ship course can be properly controlled under the disturbances of wave, wind and current.