To fully leverage contextual information for the precise segmentation of objects in remote sensing images, while addressing the challenges associated with substantial object scale variations and complex backgrounds, we propose a lightweight cross-domain coupling network (LCCN) tailored for semantic segmentation of high-resolution remote sensing images (HRSIs). To standardize feature selection and fusion procedures, the LCCN incorporates an innovative Encoder-Coupler-Decoder architecture designed to facilitate key feature extraction and optimization. A cross-domain coupling module (CDCM) is created in the Coupler to conduct preliminary features screening of spaces and dimensions based on channel and spatial attention. It performs multi-scale feature extraction and global information modeling through the feature grouping and loop aggregation. This helps to extract key features while reducing the computational overhead. To further decrease the interference from complex backgrounds, a secondary optimization of the key features is carried out: a lightweight fully-featured mapping attention module (LFMAM) is designed within the Decoder. LFMAM utilizes an interactive fusion strategy and a lightweight linear self-attention mechanism, comprehensively considering all interactions between global-to-global, global-to-local, local-to-local, and local-to-global processes. By capturing the effective correlations and variances among features to further refine them, it enables the network to further optimize the crucial information while ensuring light weight. We have conducted extensive comparison experiments and ablation experiments on the ISPRS Vaihingen and ISPRS Potsdam datasets. The extensive experimental results demonstrate that our proposed LCCN can obtain superior performance compared to other advanced semantic segmentation models.

Understanding users’ safety perception of the credibility of web-based information has become increasingly important in the context of new retailing. This study extends the existing literature by exploring the factors influencing information credibility in the context of new retailing. Based on the technology acceptance model and the rational behavior theory, a theoretical model for the assessment of information credibility in new retailing was developed. We analyzed the factors influencing users’ safety preference toward information communication procedures and information credibility in new retailing based on two aspects: perceived information quality and user judgment motivation. The reliability and validity of the model measure were analyzed, and structural equation modeling was used to test the model hypotheses. The following results were obtained: (1) Authenticity, accuracy, and practicability positively affected the perceived information quality of new retailing information; (2) User judgment motivation had a positive impact on information users’ safety preference and information credibility; (3) Users’ safety preference positively affected information credibility; (4) Information acquisition, social interaction, and self-identity positively affected the perceived credibility of new retailing information.

Name ambiguity is a critical problem in many applications, in particular in the online bibliography systems, such as DBLP and CiteSeer. Previously, several clustering based methods have been proposed although, the problem still presents to be a big challenge for both research and industry communities. In this paper, we present a complementary study to the problem from another point of view. We propose an approach of finding atomic clusters to improve the performance of existing clustering-based methods. We conducted experiments on a dataset from a real-world system: Arnetminer.org. Experiments results show that significant improvements can be obtained by using the proposed atomic clusters finding approach (about +8% and +27% improvements depending on different clustering methods).

In order to manage the increasing amount of RDF data, an RDF repository should provide not only necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards this goal, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System II, to pursue a better tradeoff among the system scalability, the query efficiency, and the inference capabilities. System II takes the hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD* semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining new algebra operators. The results of performance evaluation on the LUBM benchmark show that System II has a better combined metric value than the other comparable systems.

To manage the increasing amount of RDF data, an RDF repository should provide not only necessary scalability and efficiency, but also sufficient inference capabilities. In this paper, we propose a native RDF repository, System, to pursue a better tradeoff among the above requirements. System takes the hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. In addition, a set of efficient semantic query processing techniques are designed. The results of performance evaluation on the LUBM benchmark show that System has a better combined metric value than the other comparable systems.

This paper is concerned with the problem of name disambiguation. By name disambiguation, we mean distinguishing persons with the same name. It is a critical problem in many knowledge management applications. Despite much research work has been conducted, the problem is still not resolved and becomes even more serious, in particular with the popularity of Web 2.0. Previously, name disambiguation was often undertaken in either a supervised or unsupervised fashion. This paper first gives a constraint-based probabilistic model for semi-supervised name disambiguation. Specifically, we focus on investigating the problem in an academic researcher social network (http://arnetminer.org). The framework combines constraints and Euclidean distance learning, and allows the user to refine the disambiguation results. Experimental results on the researcher social network show that the proposed framework significantly outperforms the baseline method using unsupervised hierarchical clustering algorithm.

To guarantee the quality of component composition is an acute and important problem in component-based software development. This paper proposes a multidimension composition (MDC) model which views component composition from four aspects of time, space, data and state. This model is implemented as two data structure: ISP(internal structural property) hierarchical tree and BP (behavioral property) directed-graph respectively. MDC model solves the limit of traditional hierarchical component model and extends the flexibility of composition granularity.

Current web search mostly relies on the keywords in the web pages. This method lacks of semantics ii many ways. For example, a search for a person by the person's name means to find the web pages that contain the text of the name. On the contrary, search semantic is to find the information about the person in the real world. It is hard to achieve this goal in current content based web search engines because text is not useful during inference. The semantic web brings semantic to current web with formalized knowledge and data that computers can process. Therefore search can be benefit from the inference which is supported by ontology. In this paper, we propose a novel method to enhance the search semantic using ontology language DAML+OIL. Experiment shows preferable results comparing to the traditional search. The conclusion and future work will also be discussed in this paper.

More and more infrastructure software for semantic web has emerged with the popularity of Semantic Web. Now Semantic Web applications are calling for different infrastructure software to support essential ontology operations, such as ontology persistence, ontology consistency, ontology query, ontology management, reasoning and so on. This paper brings forward the vision about integration of ontology operations based on the unified semantic web software platform. It introduces a model in which varied operations of ontology are added, updated and deleted dynamically. Furthermore, a USWP ("Unified Semantic Web Platform") is implemented according to this model. As an ontology platform using the standard of RDF and RQL, the key characteristic of the USWP is that the platform has a "total solution" for the semantic web applications built on an expansible, flexible, scalable and open architecture. Third part's ontology operation modules can be deployed into USWP with a wrapper under the standard of OOU ("Ontology Operation Unit") and can share data flow with other modules using DOOD ("Dynamical Operatin Ontology Domain") dynamically.

Device diversity is a most popular research topic of ubiquitous computing applications that have to adapt automatically to the context. In this paper, we propose an architecture for the device-independent mobile Web applications, which separates reusable interface components from adapting logic and expand each with evaluators to enable intelligent adaptations. We also propose an object-oriented framework design and implementation to facilitate accessing and managing the evaluator-combined components as plug-ins in order to meet the rapid evolvement of consumer equipments.

Mobile commerce has attracted increasingly attention recently and Web based service plays more and more important role in the future. However, many problems need to be solved when building this kind of service. In that, device diversity is the most troublesome one. In this article, we introduce a project called VMSDT, which provides a device independent usual development environment to construct Web-based mobile service. It provides solutions for the diversity of device language. It stresses on the extensibility not only in visual design and result file conversion. In this article, we first analyze design issues resulted from the diversity of device language and introduce related works. Then, we present the system architecture of this tool and discuss the implementation strategies and algorithms of the two core components IEE and converter in detail. In the end, we draw a conclusion and specify the future work.