16445417
Multiple Viewpoints Based Ontology Integration
Ontology integration is a focus on ontology application field. Ontology can be viewed as a kind of software product. Ontology integration needs to be directed by methodology. In many applications, we need to integrate existed ontologies for a unified ontology for application requirements. The ontology to be integrated can be viewed as a viewpoint of the unified ontology. A multiple-viewpoints-based ontology integration approach is introduced by multiple viewpoints theory in requirement engineering. We define ontology viewpoint by characters of ontology and use conceptual graph to represent semantic in ontology. We discuss the inconsistency checking inner ontology viewpoint and among ontology viewpoints. At last, we use a concept lattice to construct the concept hierarchy.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
1936453
A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing
Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
42520367
Extending metadata with scenarios in adaptive distributed system
The traditional metadata cannot provide enough support for the adaptive distributed systems. In this paper, we propose a metadata extension that uses the scenarios as semantic aspect, describing the expected interaction sequence based on XML Schema. With such constraints, resource waste and further damages can be avoided by preventing the illegal interactions at runtime. This extension is more valid and user-friendly than the majority of the existing proposals, avoiding the potential inconsistency. A prototype has been developed to illustrate the applicability of the extended metadata. The experiments demonstrate that the prototype introduces an average overhead ratio of 6.4% in the inter-process interactions and only 0.27% in the remote interactions. The approach presented in this paper is suitable for the adaptive distributed systems and affords more guarantee of reliability.
2401406
Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification
A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.
4342705
Weighted Ontology-Based Search Exploiting Semantic Similarity
This paper is concerned with the problem of semantic search. By semantic search, we mean searching for instances from knowledge base. Given a query, we are to retrieve ‘relevant’ instances, including those that contain the query keywords and those that do not contain the keywords. This is contrast to the traditional approaches of generating a ranked list of documents that contain the keywords. Specifically, we first employ keyword based search method to retrieve instances for a query; then a proposed method of semantic feedback is performed to refine the search results; and then we conduct re-retrieval by making use of relations and instance similarities. To make the search more effective, we use weighted ontology as the underlying data model in which importances are assigned to different concepts and relations. As far as we know, exploiting instance similarities in search on weighted ontology has not been investigated previously. For the task of instance similarity calculation, we exploit both concept hierarchy and properties. We applied our methods to a software domain. Empirical evaluation indicates that the proposed methods can improve the search performance significantly.
8110472
Methodology for Mode Selection in MIMO-OFDM System
Mode selection problem in MIMO-OFDM system is addressed. A general formulation is introduced to decompose the problem into two decoupled sub-problems: modulation and coding scheme (MCS) selection, and space-time processing (STP) optimization, each of which can be tackled more systematically. Mode selection algorithms can then be developed using a channel quality indicator (CQI) metric. The methodology is illustrated using the platform of IEEE 802.11n. The qualities of multiple CQI metrics are compared in terms of "coherency" and their performances are evaluated in terms of system goodput. This has been shown that the metric we proposed outperforms the other metrics.
43377731
Software as a Service: An Integration Perspective
Software as a Service (SaaS) is gaining momentum in recent years with more and more successful adoptions. Though SaaS is delivered over Internet and charged on per-use basis, it is software application in essence. SaaS contains business data and logics which are usually required to integrate with other applications deployed by a SaaS subscriber. This makes Integration become one of the common requirements in most SaaS adoptions. In this paper, we analyze the key functional and non-functional SaaS integration requirements from an industry practitioner point of view; and summarize the SaaS integration patterns and existing offerings; then point out the gaps from both technology and tooling perspectives; finally we introduce a SaaS integration framework to address those gaps. Considering there is no much academic work on SaaS service modeling, we come up with a SaaS service description framework as an extension of Web Service description, so as to model SaaS unique features in a unified way. With the supported tooling and runtime platform, the framework can facilitate the SaaS integration lifecycle in a model-driven approach.