以GAAC分群法提升既有桌面檢索系統之表現－以Google Desktop Search為例

With the development of search engines (e.g. Google, Bing, Yahoo, etc.), people is ambitiously expecting higher quality and improvements of current technologies. Bringing human intelligence features to these tools, like the ability to find implicit information through semantics, is one of the must prominent research lines in Computer Science. Information semantics is a very wide concept, as wide as the human capability to interpret, in particular, the analysis of geographical semantics gives the possibility to associate information with a place. It is estimated that more than 70\% of all information in the world has some kind of geographic features \cite{Jones04}. In 2012, Ed Parsons, a GeoSpatial Technologist from Google, reported that between 30\% and 40\% of the user queries at Google search engine contain geographic references \cite{Parsons12}. This thesis addresses the field of geographic information extraction and retrieval in unstructured texts. This process includes the identification of spatial features in textual documents, the data indexing, the manipulation of the relevance of the identified geographic entities and the multi-criteria retrieval according to the thematic and geographic information. The main contributions of this work include a custom geographic knowledge base, built from the combination of GeoNames and WordNet; a Natural Language Processing and knowledge based heuristics for Toponym Recognition and Toponym Disambiguation; and a geographic relevance weighting model that supports non-spatial indexing and simple ranking combination approaches. The validity of each one of these components is supported by practical experiments that show their effectiveness in different scenarios and their alignment with state of the art solutions. In addition, it also constitutes a main contribution of this work GEIR, a general purpose GIR framework that includes the implementations of the above described components and brings the possibility of implementing new ones and test their performance within an end to end GIR system.

Poe in Cyberspace: The “Mobile-Friendly” Sites

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Application of a feature selection algorithm to a textual data set can improve the performance of some classifiers. Due to the characteristics, specifically the size, of textual data sets researchers have traditionally relied on a family of simple heuristics to perform feature selection. These heuristics, which in practice are quite effective, use functions of individual feature statistics, that we refer to as feature ranking functions, to order the feature set. We are interested in identifying the most effective feature ranking functions. To do this we begin by defining a feature set evaluation methodology. Traditionally the performance of feature selection algorithms has been measured by comparing the performance of classification algorithms before and after feature selection. Instead, we measure various criteria of the selected feature set itself, including measures of separation, noise, size, and robustness. We demonstrate that many of these criteria are competing, and show how the tools of multicriteria optimization can be employed to rank the performance of feature selection algorithms. Using this methodology we evaluate the performance of a large set of feature ranking functions, including a function that measures the rareness of a feature assuming that relevant and irrelevant documents are generated by two independent stochastic processes. Motivated by the results, we identify the defining characteristics of the functions that are most successful, noting that many of these can be written as ratios of measures of separation to measures of noise. Next we introduce a set of axioms which we believe that feature ranking functions should satisfy, and study the set of these functions that can be represented as a linear combination of some finite set of basis functions. We demonstrate that many of the functions or approximations to the functions that we studied are members of this set. Next consider the set of coefficient vectors of this set and show that it is convex, bounded, and not empty. We conclude by investigating the performance of other approaches to feature selection including greedy and ensemble algorithms that use feature ranking functions.

Semantic web works on producing machine readable data and aims to deal with large amount of data. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that consists in the existing web pages. Semantic search engines are extensions to traditional engines and overcome the difficulties faced by them. This paper summarizes semantic web, concept of traditional and semantic search engines and infrastructure. Also semantic search approaches are detailed. A summary of the literature is provided by touching on the trends. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging. Multimedia retrieval is a newly scope of semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia retrieval is analyzed within semantic search.

Environmental influences on the epidemiology of fibropapillomatosis in green turtles (Chelonia mydas) and consequences for management of inshore areas of the Great Barrier Reef

Background Availability of numerous oil deposits with a wide variety of the conditions of occurrence, reservoir properties and physical-chemical properties of fluids makes important the need of grouping of development objects and identification of relatively uniform groups by certain criteria. Grouping allows to define similarities and differences between deposits in determining development objects, and to substantiate systems of development. Analysis of large data sets on objects characterized by the measured or estimated parameters is labor-intensive, requiring a lot of time and effort, so there is a question of dimension reduction, i.e. of reducing the original data set to a smaller number of parameters, and grouping of objects into relatively uniform groups. Here the parameters can be selected from the original ones or obtained by calculation and conversion (with the least loss of information about the objects under study). An effective tool of analysis that can help in this matter is the method of principal component analysis. Aims and Objectives Apply the method of principal component analysis to reduce the number of parameters which define objects of study, so that to find effective ways to develop new oil and gas deposits, and fast and correct solution of practical problems through the use of gained summary experience and assessment of similarities and differences of oil and gas reservoirs by combinations of geological-field attributes. Methods The problem of ranking oil deposits in Western Siberia, is solved by the method of principal component analysis, which is a linear transformation of features in a new set of «independent» random values, which are arranged in descending order of their variances, and the main components are determined by the eigenvectors of the correlation matrix obtained based on input parameters characterizing the objects under investigation. Having selected the components that comprise the bulk of the variances, one can calculate them for different types of objects and classify by clusterable points. Grouping of objects of research was carried out using built-in functions of computer statistics programs (a system of statistical analysis Statistica, analytical applications for Microsoft Excel XLStat). The same calculation was performed on the basis of the theory of mathematical statistics. The calculation results coincided. Results The method of principal component analysis allowed selection from the original data set comprising 50 research objects characterized by 12 parameters of six relatively uniform groups using only 4 factors (principal components) which, however, contain much more information than the individual parameters. As a result of the carried out analysis data were systematized and the dimension of the initial population (transition from multidimensional space to the four-dimensional) was reduced, with no significant loss of information about research objects. Conclusion Based on the proposed algorithm, and relatively uniform groups obtained there is a possibility for inclusion of new deposits into one of these groups and selection of the nearest to it analogous object, that will simplify the design of development system and identify ways and means of improving system’s effectiveness.

The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75­25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree.

Variation in Results of Three Biology‐Focused Search Engines: A Case Study Using North American Tree Species

For health and safety monitoring in civil constructions such as bridges, roads and pavements, segmenting the regions of interest is the fundamental requirement for image analysis at high-level semantic. One of the major structural problems in concrete and asphalt surfaces are cracks, which start with harming the visual aspect of the construction and further lead to failure of the construction. This study makes four contributions to extract the image foreground from the background in order to address the crack detection task on the asphalt and concrete surfaces. Specifically, we model cracks as foreground objects and concrete or asphalt surfaces as textured background. The first contribution of this research is a hybrid image processing method for crack detection. In this method, the cracks are modelled as linear structures on the background of textured concrete or asphalt surfaces, which can be extracted by combining structure extraction with global pattern distribution. There are two phases in this model towards creating the final structure-texture map. The first phase is extracting strong structures or edges using relative total variation measures, which produces a structure feature map by preserving the edges and suppressing the background noises. The second phase calculates the spatial distribution of textures across the image. A bag-of-words model is used in this phase to quantise the texture pattern, which in crack detection application is the widely distributed road texture background. The local structure map and the global distribution map extract the crack structure and the textured background, respectively. The final crack is extracted by fusing these two maps and applying binarisation as the post-processing step. This model achieved a better result compared with the local structure extraction and the saliency method. As the second contribution, a large-scale dataset of asphalt and concrete crack images is prepared, including images with their corresponding high-resolution pixel-wise labels. To the best of our knowledge, until the completion of this thesis, there has not been any crack image dataset with pixel-wise ground truth labels available. The original crack image set includes 2532 images of cracks on brick and asphalt surfaces. This image set is split into training, validation and testing sets with the ratio of 50/25/25. Two augmentation techniques of rotation and ipping are applied to only the training set while validation and testing sets are locked in order to prevent the data leakage. All models are learned on the training set after fine-tuning the hyper-parameters. After each tuning, an early estimate of the model accuracy is obtained using the validation set. Finally, an unbiased performance estimation of the fitted model is provided on the testing set. The third contribution of this thesis is developing two encoder-decoder networks by exploring the recent advances of deep learning research for crack detection and applying them on our crack dataset. The first network is inspired by DeepLab, which is a modified ResNet architecture. In this network the last pooling layer is replaced with an Atrous Spatial Pyramid Pooling (ASPP) module. This encoder-decoder structure is designed to classify each image pixel into two foreground cracks or textured background. The second model is inspired by Full Resolution Residual Network (FRRN), which is a ResNetlike network with two residual and pooling streams to extract the high- and low-level features, respectively. The combination of di erent level features in this model improves the localization of crack pixels as well as the recognition of the crack structure as a whole. As FRRN outperforms DeepLab on crack classification, we select it as the baseline for further research. In our last contribution, we optimise the FRRN model by reducing the number of parameters. Inspired by the Inception module which significantly improved the utilization of the computing resources inside the GoogLeNet, we proposed Incepted FRRN (I-FRRN) network by embedding the Inception module inside the FRRN. Combining these two structures, our proposed model records 88.14% accuracy in classifying the positive class with 0.22% improvement, while having less than half the number of parameters compared with FRRN. The results show that the proposed architecture achieves significant computational e ciency gains and comparable or higher class-accuracy in crack classification task over the baseline model.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Data-Planet. Usage Statistics: Sessions | Channel: Conquest | Organization: Babson College | Product: Data Planet, 08/2014 - 05/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 077-001-001 Dataset: Reports number of user sessions. Reports usage statistics for Data-Planet subscribing institutions. Sessions, searches, and retrievals are reported for Statistical Datasets and Statistical Ready Reference interfaces. Category: Education Subject: Source: Data-Planet Data-Planet Statistical Datasets is a web-based research solutions tool that provides fast and easy access to data from licensed and public domain datasets within an easy-to-use interface. With this dynamic tool, you can scan the contents of the collection, select subjects and variables of interest, and view your data in side-by-side tables and charts. The Data-Planet repository contains more than 35 billion data points from 4.9 billion time series sourced from more than 70 source organizations. Billions of charts, maps, views, rankings, time series and tables are available for use in the Data-Planet repository. All of the data have been standardized and structured, and are described with up to 37 fields of metadata. http://www.data-planet.com/Data-Planet. Usage Statistics: Retrievals | Channel: Conquest | Organization: Babson College | Product: Data Planet | FileName: Retrievals, EASI Analytics, InfoGroup, 08/2014 - 05/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 077-001-003 Dataset: Reports number of retrievals. Reports usage statistics for Data-Planet subscribing institutions. Sessions, searches, and retrievals are reported for Statistical Datasets and Statistical Ready Reference interfaces. Category: Education Subject: Source: Data-Planet Data-Planet Statistical Datasets is a web-based research solutions tool that provides fast and easy access to data from licensed and public domain datasets within an easy-to-use interface. With this dynamic tool, you can scan the contents of the collection, select subjects and variables of interest, and view your data in side-by-side tables and charts. The Data-Planet repository contains more than 35 billion data points from 4.9 billion time series sourced from more than 70 source organizations. Billions of charts, maps, views, rankings, time series and tables are available for use in the Data-Planet repository. All of the data have been standardized and structured, and are described with up to 37 fields of metadata. http://www.data-planet.com/

A novel query expansion algorithm is proposed in this paper. It is based on a model of personalized web search system. The new system, as a middleware between a user and a Web search engine, is set up on the client machine. It can learn a user's preference implicitly and then generate the user profile automatically. When the user inputs query keywords, more personalized expansion words are generated by the proposed algorithm, and then these words together with the query keywords are submitted to a popular search engine such as Baidu or Google. These expansion words can help a search engine retrieval information for a user according to his/her implicit search intentions. The new Web search model can make a common search engine personalized, that is, throughout personalized query expansion the search engine can return different search results to different users who input the same keywords. The experimental results show the effect and applicability of the presented work for personalized information service of a search engine.

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.

There are different methods to predict survival time, including traditional statistical regression, and modern neural network. Still, precise prediction of survival time remains as a challenge. In recent years algebraic topology was introduced as a novel method to infer information from high dimensional data set, where a mapper uses an algorithm to obtain geometric or topological features of the data set, and associate the features with the predicted variables. In this study, the data set includes gender, age, AFP, GGT, PPT, TB, ALT, tumor size, tumor number, BCLC stage, number of liver nodules, type of surgical approach etc., total 21 diagnostic variables. We use these variables to predicted OS and DFS, and the data set contains 329 records. In this study, the topology mapper is executed to acquire the topological features of all the data (including training and test sets), since this task is unsupervised, so the target values are not considered. Our method is to infer three new features of each data record, including the location of the record on the main branch of the topology, whether it is a branch node (i.e. has more than 2 branches), and whether it is an endpoint. A simple neural network with one hidden layer is designed and implemented with Tensorflow, and the three features will be added to the data set and provide valuable information for prediction of the target values. Without adding the new features from topology mapper, we have 72% accuracy (in predicting the survival time is greater or less than the mean value) on test set data when predicting OS, and we have 75% accuracy on test set data when predicting the DFS. With the new features from topology mapper, we have 83% accuracy on test set data when predicting OS, and 84% accuracy on test set data when predicting the DFS. The three new features inferred from the topology mapper have a significant effect on the accuracy of prediction and provide better accuracy. The topology based data analysis method in this study could be applied to other classification problems related to clinical trial data, and will need further verification. Citation Format: Cheng Huang, Hui-Chuan Sun, Junda Chen, Lin Shi, Fengqing Li, Yihui Lin, Hanyan Yang. A topology based data analysis method to predict survival time [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1634.