Personality Based Latent Friendship Mining
Methods and apparatus for detecting imperfections in adhesive coatings formed on fingers of beams are disclosed. The present invention relates, in one aspect, to a method for preparing a first work piece for joining with a second work piece. The method involves forming a finger in the first work piece, then applying an adhesive coating on the finger. A determination is made to determine whether the adhesive coating is defective through the used of a sensing mechanism which is arranged to scan across a portion of the finger. In one embodiment, a defect in the adhesive coating on the finger is defined as a void.

title: SNPMiner: A Domain-Specific Deep Web Mining Tool
abstract: In this paper, we propose a novel query-oriented, mediator-based biological data querying tool, SNPMiner. The system searches and queries single nucleotide polymorphisms (SNPs) data from eight widely used web accessible databases. The system provides a domain-specific search utility, which can access and collect data from the deep web. This is a web-based system, so any user can use the system by accessing our server from their own computers. The system includes three important components, which are the Web server interface, the dynamic query planner, and the Web page parser. The Web server interface can provide end users a unified and friendly interface. The dynamic query planner can automatically schedule an efficient query order on all available databases according to user's query request. The Web page parser analyzes the layout of HTML files and extracts desired data from those files. The final results of the query are organized in a tabular format, which can be reviewed by a biological researcher.

title: Query Planning for Searching Inter-dependent Deep-Web Databases
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming what is referred to as the deep web. It is desirable to have systems that can provide a high-level and simple interface for users to query such data sources, and can automate data retrieval from the deep web. However, such systems need to address the following challenges. First, in most cases, no single database can provide all desired data, and therefore, multiple different databases need to be queried for a given user query. Second, due to the dependencies present between the deep-web databases, certain databases must be queried before others. Third, some database may not be available at certain times because of network or hardware problems, and therefore, the query planning should be capable of dealing with unavailable databases and generating alternative plans when the optimal one is not feasible. 
 
This paper considers query planning in the context of a deep-web integration system. We have developed a dynamic query planner to generate an efficient query order based on the database dependencies. Our query planner is able to select the topKquery plans. We also develop cost models suitable for query planning for deep web mining. Our implementation and evaluation has been made in the context of a bioinformatics system, SNPMiner. We have compared our algorithm with a naive algorithm and the optimal algorithm. We show that for the 30 queries we used, our algorithm outperformed the naive algorithm and obtained very similar results as the optimal algorithm. Our experiments also show the scalability of our system with respect to the number of data sources involved and the number of query terms.

title: SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources
abstract: A recent and emerging trend in scientific data dissemination involves online databases that are hidden behind query forms, thus forming what is referred to as the deep web . In this paper, we propose SEEDEEP, a System for Exploring and quErying scientific DEEP web data sources. SEEDEEP is able to automatically mine deep web data source schemas, integrate heterogeneous data sources, answer cross-source keyword queries, and incorporates features like caching and fault-tolerance. Currently, SEEDEEP integrates 16 deep web data sources in the biological domain. We demonstrate how an integrated model for correlated deep web data sources is constructed, how a complex cross-source keyword query is answered efficiently and correctly, and how important performance issues are addressed.

title: Extracting Output Metadata from Scientific Deep Web Data Sources
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming the deep web. The popularity of this new medium for data dissemination is leading to new problems in data integration. Particularly, to enable data integration from multiple deep web data sources, one needs to obtain the metadata for each of the data sources. Obtaining the metadata, particularly, the output schema, can be very challenging. This is because, given an input query, many deep web data sources only return a subset of the output schema attributes, i.e, the ones that have a non-NULL value for the corresponding input. In this paper, we propose two approaches, which are the sampling model approach and the mixture model approach, respectively, to efficiently obtain an approximately complete set of output schema attributes from a deep web data source. Our experiments show while each of the above two approaches has limitations, a hybrid strategy, where we combine the two approaches, achieves high recall with good precision for most data sources.

title: Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources
abstract: Increasingly, biological data is being shared over the deep web. Many biological queries can only be answered by successively searching a number of distinct web-sites. This paper introduces a system that exploits parallelization for accelerating search over multiple deep web data sources. An interactive, two-stage multi-threading system is developed to achieve task parallelization, thread parallelization, and pipelined parallelization. We show the effectiveness of our system by considering a number of queries involving SNP datasets. We show that most of the queries can be accelerated significantly by exploiting these three forms of parallelism.

title: A Study on the Application of GA-BP Neural Network in the Bridge Reliability Assessment

title: Spectrum Sharing in Cognitive Radio Networks
abstract: In this paper, we present a novel joint power/channel allocation scheme that uses a distributed pricing strategy to improve the network's performance. According to this scheme, the spectrum allocation problem is modeled as a non-cooperative game. A price-based iterative water-filling (PIWF) algorithm is proposed, which allows users to converge to the Nash Equilibrium (NE). This PIWF algorithm can be implemented distributively, with CRs repeatedly negotiating their best transmission powers and spectrum. We propose a protocol that implements our price- based resource allocation algorithm. The proposed MAC protocol allows multiple CR pairs to first contend through an admission phase, and then to iteratively negotiate their transmission powers and spectrum via control-packet exchanges. Subsequently, CRs proceed concurrently with their data transmissions. Simulations are used to study the performance of our protocol and demonstrate its effectiveness in improving the overall network throughput and reducing the average transmission power.

title: Deployment of an AEC industry sector product model
abstract: CIMsteel Integration Standard, Version 2 (CIS/2) is an industry-developed product model based on ISO-STEP technology that has been widely adopted within the steel construction industry. CIS/2 is an early success story of broad use of a product model for both data exchange and improving the productivity of those companies taking advantage of its capabilities. Here, we review the history of CIS/2, the methods and issues arising from its deployment, the benefits it has thus far realized and the research issues these activities have identified.

title: A New Neighborhood Based on Improvement Graph for Robust Graph Coloring Problem
abstract: In this paper, we propose a new neighborhood structure based on the improvement graph for solving the Robust Graph Coloring Problem, an interesting extension of classical graph coloring Different from the traditional neighborhood where the color of only one vertex is modified, the new neighborhood involves several vertices In addition, the questions of how to select the modified vertices and how to modify them are modelled by an improvement graph and solved by a Dynamic Programming method The experimental results clearly show that our new improvement graph based k-exchange cycle neighborhood improves the accuracy significantly, especially for large scale heuristic search.

title: HRPD-A downlink voice/IP and data throughput and delay analysis
abstract: HRPD-A adds new forward and reverse link rates and a forward link multi-user packet capability at the MAC layer. Completed simulations reveal the performance characteristics of this system in the 1.9 GHz frequency band. These characteristics consist of capacities of voice/IP traffic and mixed data and voice/IP traffic. Downlink data traffic models consist of FTP, HTTP (Web browsing), WAP, and video streaming models. The simulation assumptions include the ITU channel models and 3GPP2 data traffic models