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  • × author_ss:"Zhang, H."
  1. Wenyin, L.; Chen, Z.; Li, M.; Zhang, H.: ¬A media agent for automatically builiding a personalized semantic index of Web media objects (2001) 0.00 
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    Abstract
    A novel idea of media agent is briefly presented, which can automatically build a personalized semantic index of Web media objects for each particular user. Because the Web is a rich source of multimedia data and the text content on the Web pages is usually semantically related to those media objects on the same pages, the media agent can automatically collect the URLs and related text, and then build the index of the multimedia data, on behalf of the user whenever and wherever she accesses these multimedia data or their container Web pages. Moreover, the media agent can also use an off-line crawler to build the index for those multimedia objects that are relevant to the user's favorites but have not accessed by the user yet. When the user wants to find these multimedia data once again, the semantic index facilitates text-based search for her.
    Type
    a
  2. Zhang, H.; Qiu, B.; Ivanova, K.; Giles, C.L.; Foley, H.C.; Yen, J.: Locality and attachedness-based temporal social network growth dynamics analysis : a case study of evolving nanotechnology scientific collaboration networks (2010) 0.00 
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    Abstract
    The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fast-evolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.
    Type
    a
  3. Zhai, Y.; Ding, Y.; Zhang, H.: Innovation adoption : broadcasting versus virality (2021) 0.00 
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    Abstract
    Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co-authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively.
    Type
    a
  4. Chen, Z.; Wenyin, L.; Zhang, F.; Li, M.; Zhang, H.: Web mining for Web image retrieval (2001) 0.00 
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    Abstract
    The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user-intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low-level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low-level and high-level features, which is extremely useful for assigning the low-level features' weights in similarity assessment
    Type
    a
  5. Cui, H.; Heidorn, P.B.; Zhang, H.: ¬An approach to automatic classification of text for information retrieval (2002) 0.00 
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    Type
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