Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Strategic positioning of Taiwan in the outsourcing market: Evidence from information technology services and electronic manufacturing outsourcing to China

In outsourcing relationships with China, the Electronic Manufacturing (EM) and Information Technology Services (ITS) industry in Taiwan may possess such advantages as the continuing growth of its production value, complete manufacturing supply chain, low production cost and a large-scale Chinese market, and language and culture similarity compared to outsourcing to other countries. Nevertheless, the Council for Economic Planning and Development of Executive Yuan (CEPD) found that Taiwan's IT services outsourcing to China is subject to certain constraints and might not be as successful as the EM outsourcing (Aggarwal, 2003; CEPD, 2004a; CIER, 2003; Einhorn and Kriplani, 2003; Kumar and Zhu, 2006; Li and Gao, 2003; MIC, 2006). Some studies examined this issue, but failed to (1) provide statistical evidence about lower prevalence rates of IT services outsourcing, and (2) clearly explain the lower prevalence rates of IT services outsourcing by identifying similarities and differences between both types of outsourcing contexts. This research seeks to fill that gap and possibly provide potential strategic guidelines to ITS firms in Taiwan. This study adopts Transaction Cost Economics (TCE) as the theoretical basis. The basic premise is that different types of outsourcing activities may incur differing transaction costs and realize varying degrees of outsourcing success due to differential attributes of the transactions in the outsourcing process. Using primary data gathered from questionnaire surveys of ninety two firms, the results from exploratory analysis and binary logistic regression indicated that (1) when outsourcing to China, Taiwanese firms' ITS outsourcing tends to have higher level of asset specificity, uncertainty and technical skills relative to EM outsourcing, and these features indirectly reduce firms' outsourcing prevalence rates via their direct positive impacts on transaction costs; (2) Taiwanese firms' ITS outsourcing tends to have lower level of transaction structurability relative to EM outsourcing, and this feature indirectly increases firms' outsourcing prevalence rates via its direct negative impacts on transaction costs; (3) frequency does influence firms' transaction costs in ITS outsourcing positively, but does not bring impacts into their outsourcing prevalence rates, (4) relatedness does influence firms' transaction costs positively and prevalence rates negatively in ITS outsourcing, but its impacts on the prevalence rates are not caused by the mediation effects of transaction costs, and (5) firm size of outsourcing provider does not affect firms' transaction costs, but does affect their outsourcing prevalence rates in ITS outsourcing directly and positively. Using primary data gathered from face-to-face interviews of executives from seven firms, the results from inductive analysis indicated that (1) IT services outsourcing has lower prevalence rates than EM outsourcing, and (2) this result is mainly attributed to Taiwan's core competence in manufacturing and management and higher overall transaction costs of IT services outsourcing. Specifically, there is not much difference between both types of outsourcing context in the transaction characteristics of reputation and most aspects of overall comparison. Although there are some differences in the feature of firm size of the outsourcing provider, the difference doesn't cause apparent impacts on firms' overall transaction costs. The medium or above medium difference in the transaction characteristics of asset specificity, uncertainty, frequency, technical skills, transaction structurability, and relatedness has caused higher overall transaction costs for IT services outsourcing. This higher cost might cause lower prevalence rates for ITS outsourcing relative to EM outsourcing. Overall, the interview results are consistent with the statistical analyses and provide support to my expectation that in outsourcing to China, Taiwan's electronic manufacturing firms do have lower prevalence rates of IT services outsourcing relative to EM outsourcing due to higher transaction costs caused by certain attributes. To solve this problem, firms' management should aim at identifying alternative strategies and strive to reduce their overall transaction costs of IT services outsourcing by initiating appropriate strategies which fit their environment and needs.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

Automating Hospitality Information: Network Technology and Systems Management

The local area network (LAN) interconnecting computer systems and soft- ware can make a significant contribution to the hospitality industry. The author discusses the advantages and disadvantages of such systems.

Differential Impacts of Information Technology Services in the Korean Hotel Industry: A Study of Management Perceptions

Successful introduction of information technology applications in various operations of hotel management is vital to most service firms. In recent decades, technologies of information, automation, and communication are increasingly recognized as essential components of a hotel company’s strategic plan. In this study, 62 super-deluxe hotels (5 star), deluxe hotels (4 star), and tourist hotels (3 star) in Korea are examined for differences in the impact of information technology services on guest’ satisfaction, guest convenience, and operational efficiency. The findings generally suggest that the impacts of information technology-enhanced services vary according to the category of hotels in Korea. The results of the study are expected to assist managers in the selections and implementation of information technology systems in their hotel.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.