This study is a design science research (DSR) project in which a description of the development and evaluation process for several novel technological artifacts will be communicated. Specifically, this study will establish: 1) an ontology of recommender systems issues, 2) an ontology of financial capability goals, and 3) a framework for a Personal Financial Recommender System (PFRS) application designed to improve user financial capability, called FinPathlight. The impetus for the RecSys Issues Ontology is to address a gap in the literature by providing researchers with a comprehensive knowledge classification of the issues and limitations inherent to recommender systems research. The development of a Financial Capability Goals Ontology will contribute domain knowledge classification for technological systems within the domain of finance and serves as a recommendation item knowledgebase for our PFRS. The FinPathlight framework provides the architecture and principles of implementation for a novel, financial-technology (FinTech) PFRS. FinPathlight is designed to improve the financial capability of its users through the recommendation, tracking and assistance with achieving financial capability enhancing goals. This research is notable in that it expands the influence and furthers the relevance of information systems research by providing an explicitly applicable research solution to an area of significant socio-economic importance, financial capability, a heretofore unsolved “wicked problem” (Churchman 1967) domain. In light of current financial conditions, recommender systems research that addresses a problem such as consumer financial capability is a step towards ensuring that information systems research continues to matter and retain its influence and relevance in everyday practice.

Educators and researchers have long considered parental involvement to be an integral factor in the success of students. However, parental involvement is low in many U.S. schools. Guided by Epstein's parental model, the purpose of this case study was to examine teachers' perceptions and experiences of parental involvement at an inner-city K-8 center in the United States which has had low parental involvement over the last 5 years. The overarching research question concerned teacher perceptions and experiences regarding communicating with parents, encouraging learning at home, and parents volunteering. Data sources consisted of interviews, questionnaires, and unobtrusive data. Purposeful sampling was used to identify the 11 teacher participants. Data were transcribed, coded and analyzed for various themes. The findings indicated that teachers perceive parental involvement to be important when they communicate with parents, when parents encourage learning at home, and when parents volunteer. The themes that emerged from the data were (a) the importance of parental involvement, (b) reinforcing learning at home, (c) communication, (d) encouraging parental involvement at school, and (e) increasing parental involvement. Based on the findings, a policy recommendation was developed to enhance the Parent Teacher Student Association (PTSA) currently in place at the study site. This project could lead to positive social change by assisting the staff at the K-8 center in developing a PTSA program that may encourage parents to become more involved. Their efforts may provide a model for other schools struggling with a lack of parental involvement.

Au cours de la dernière décennie, les réseaux (les graphes) se sont révélés être un outil efficace pour modéliser des systèmes complexes. La problématique de détection de communautés est une tâche centrale dans l’analyse des réseaux complexes. La majeur partie des travaux dans ce domaine s’intéresse à la structure topologique des réseaux. Cependant, dans plusieurs cas réels, les réseaux complexes ont un ensemble d’attributs associés aux nœuds et/ou aux liens. Ces réseaux sont dites : réseaux attribués. Mes activités de recherche sont basées principalement sur la détection des communautés dans les réseaux attribués. Pour aborder ce problème, on s’est intéressé dans un premier temps aux attributs relatifs aux liens, qui sont un cas particulier des réseaux multiplexes. Un multiplex est un modèle de graphe multi-relationnel. Il est souvent représenté par un graphe multi-couches. Chaque couche contient le même ensemble de nœuds mais encode une relation différente. Dans mes travaux de recherche, nous proposons une étude comparative des différentes approches de détection de communautés dans les réseaux multiplexes. Cette étude est faite sur des réseaux réels. Nous proposons une nouvelle approche centrée "graine" pour la détection de communautés dans les graphes multiplexes qui a nécessité la redéfinition des métriques de bases des réseaux complexes au cas multiplex. Puis, nous proposons une approche de clustering dans les réseaux attribués qui prend en considération à la fois les attributs sur les nœuds et sur les liens. La validation de mes approches a été faite avec des indices internes et externes, mais aussi par une validation guidée par un système de recommandation que nous avons proposé et dont la détection de communautés est sa tâche principale. Les résultats obtenus sur ces approches permettent d’améliorer la qualité des communautés détectées en prenant en compte les informations sur les attributs du réseaux. De plus, nous offrons des outils d’analyse des réseaux attribués sous le langage de programmation R. / In complex networks analysis field, much effort has been focused on identifying graphs communities of related nodes with dense internal connections and few external connections. In addition to node connectivity information that are mostly composed by different types of links, most real-world networks contains also node and/or edge associated attributes which can be very relevant during the learning process to find out the groups of nodes i.e. communities. In this case, two types of information are available : graph data to represent the relationship between objects and attributes information to characterize the objects i.e nodes. Classic community detection and data clustering techniques handle either one of the two types but not both. Consequently, the resultant clustering may not only miss important information but also lead to inaccurate findings. Therefore, various methods have been developed to uncover communities in networks by combining structural and attribute information such that nodes in a community are not only densely connected, but also share similar attribute values. Such graph-shape data is often referred to as attributed graph.This thesis focuses on developing algorithms and models for attributed graphs. Specifically, I focus in the first part on the different types of edges which represent different types of relations between vertices. I proposed a new clustering algorithms and I also present a redefinition of principal metrics that deals with this type of networks.Then, I tackle the problem of clustering using the node attribute information by describing a new original community detection algorithm that uncover communities in node attributed networks which use structural and attribute information simultaneously. At last, I proposed a collaborative filtering model in which I applied the proposed clustering algorithms.

In 2015, there were 257,524 women with cervical cancer (CC) in the United States (U.S.). CC is preventable; screening detects early-stage cancer when treatment is most successful. This study aimed to identify predictors for CC screening adherence among U.S. women, describe predictors for screening adherence by marital status, and examine physician recommendation for CC screening and adherence to those recommendations. Predictors were grouped as demographic, acculturation, access to care, chronic conditions, and health behaviors. Descriptive analyses were performed on a sample of 10,667 women from the 2015 National Health Interview Survey, and multiple logistic regression models determined predictors of CC screening adherence, physician recommendations, and adherence to physician recommendations. Overall, 81.7% (95%CI=80.7-82.7%) of U.S. women adhered to CC screening guidelines. Adherence declined with increasing age after 39 years old. Never married women (adjusted odds ratio[aOR]=0.67, CI=0.56-0.79) or current smokers (aOR=0.70, CI=0.59-0.84) had lower odds, while college-educated women had greater odds (aOR=1.38, CI=1.14-1.67) of CC screening adherence. Among unmarried women, 78.6% adhered to CC screening. Unmarried women who were unemployed (aOR=0.48, CI=0.38-0.62), had no physician visits (aOR=0.58, CI=0.40-0.85), no usual source of care (aOR=0.67, CI=0.50-0.89), never heard of HPV (aOR=0.59, CI=0.46-0.76), never received HPV vaccine (aOR=0.50, CI=0.34-0.75), no birth control use (aOR=0.33, CI=0.23-0.47), no flu shot (aOR=0.62, CI=0.48-0.80), and perceived low breast cancer risk (aOR=0.66, CI=0.47-0.92) had lower odds of adherence. Among women with a physician, 55.6% received screening recommendations. Race/ethnicity, access to care, HPV knowledge and vaccine receipt, age when first child was born, and flu shot were significant predictors of physician recommendation for CC screening. Significant predictors of adherence to physician recommendation included education, employment, English proficiency, outpatient clinic visits, usual source of care, age when first child was born, birth control, alcohol use, smoking status, flu shot, and health status. Based on our results, two levels of intervention should be explored. First, targeted interventions are needed for women who are unmarried, have low socio-economic status, and limited access to care to reduce cervical cancer risk. Second, interventions for physicians to increase screening recommendations to all eligible women are needed to improve national screening rates.

近年來，推薦系統（recommendation system）相關研究是一個很熱門的議題，當使用者看到一篇文章，對該文章所描述的事件很感興趣，想要了解該事件的全貌，此時想要得到是該事件的通盤的見解，而非局部的意見，也就是以不同角度去解析此事件的文章清單時，若以過去傳統推薦系統的作法，推薦與這篇文章相似的文章給使用者就未必合適，因為相似文章只能反映對此事件相同角度，而非對此事件不同角度的文章。因此，本研究擬使用社群性標籤（social tag）解決以上問題。透過不同使用者標註標籤反映不同看法的機制，我們可以從文章中選出代表性的標籤，透過該標籤組與文章分數計算，找出對此事件不同角度的文章清單推薦給使用者。實驗結果顯示，若文章有較高的可信度擁有多種角度，則使用我們提出的演算法確實擁有較好的準確度。

Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a computational perspective. Traditionally, fuzzy logic is used to deal with fuzziness among single linguistic terms in documents. However, linguistic terms may be related to other types of uncertainty. For instance, different users search ‘cheap hotel’ in a search engine, they may need distinct pieces of relevant hidden information such as shopping, transportation, weather, etc. Therefore, this research work focuses on studying granular words and developing new algorithms to process them to deal with uncertainty globally. To precisely describe the granular words, a new structure called Granular Information Hyper Tree (GIHT) is constructed. Furthermore, several technologies are developed to cooperate with computing with granular words in spam filtering and query recommendation. Based on simulation results, the GIHT-Bayesian algorithm can get more accurate spam filtering rate than conventional method Naive Bayesian and SVM; computing with granular word also generates better recommendation results based on users’ assessment when applied it to search engine.

Increasing number of software vendors are offering or planning to offer their applications as a Software-as-a-Service (SaaS) to leverage the benefits of cloud computing and Internet-based delivery. Therefore, potential clients will face increasing number of providers that satisfy their requirements to choose from. Consequently, there is an increasing demand for automating such a time-consuming and error-prone task. In this work, we develop an architecture for automated service discovery and selection in cloud computing environment. The system is based on an algorithm that recommends service choices to users based on both functional and non-functional characteristics of available services. The system also derives automated ratings from monitoring results of past service invocations to objectively detect badly-behaving providers. We demonstrate the effectiveness of our approach using an early prototype that was developed following object-oriented methodology and implemented using various open-source Java technologies and frameworks. The prototype uses a Chord DHT as its distributed backing store to achieve scalability.

Books and printed materials have been used as a major learning content for thousands of years. Nowadays, Smartphone is considered as an important tool for mobile learning. This study designed a learning environment with paper and Smartphone which seamlessly integrates printed materials and digital materials. The idea is to augment the traditional paper-based materials with plenty of digital materials available on the Internet. Furthermore, because both book and Smartphone are with very good mobility, the designed system is also very suitable for mobile learning. Two special mechanisms were designed for supporting learning activities, and their effects on learning performance were evaluated. The first one is learning recommendation which is generated based on the learning portfolio. The second one is automated content connection which can reduce the loading of context switching between printed materials and digital materials so as learners can be more concentrated on learning tasks. A system was designed and implemented for conducting an experiment and data collection. The statistic analysis shows that learning recommendation has a significant positive effect on learning performance; however, the effect of automated content connection on learning performance is not significant. Besides, the questionnaire survey also shows that learners have positive attitude toward the acceptance of the learning system designed in this study. Based on the results, some implications and suggestions are provided for researchers and instructors.

Solving linear systems of equations is an integral part of most scientific simulations. In recent years, there has been a considerable interest in large scale scientific simulation of complex physical processes. Iterative solvers are usually preferred for solving linear systems of such magnitude due to their lower computational requirements. Currently, computational scientists have access to a multitude of iterative solver options available as "plug-and- play" components in various problem solving environments. Choosing the right solver configuration from the available choices is critical for ensuring convergence and achieving good performance, especially for large complex matrices. However, identifying the "best" preconditioned iterative solver and parameters is challenging even for an expert due to issues such as the lack of a unified theoretical model, complexity of the solver configuration space, and multiple selection criteria. Therefore, it is desirable to have principled practitioner-centric strategies for identifying solver configuration(s) for solving large linear systems. The current dissertation presents a general practitioner-centric framework for (a) problem independent retrospective analysis, and (b) problem-specific predictive modeling of performance data. Our retrospective performance analysis methodology introduces new metrics such as area under performance-profile curve and conditional variance-based finetuning score that facilitate a robust comparative performance evaluation as well as parameter sensitivity analysis. We present results using this analysis approach on a number of popular preconditioned iterative solvers available in packages such as PETSc, Trilinos, Hypre, ILUPACK, and WSMP. The predictive modeling of performance data is an integral part of our multi-stage approach for solver recommendation. The key novelty of our approach lies in our modular learning based formulation that comprises of three sub problems: (a) solvability modeling, (b) performance modeling, and (c) performance optimization, which provides the flexibility to effectively target challenges such as software failure and multiobjective optimization. Our choice of a "solver trial" instance space represented in terms of the characteristics of the corresponding "linear system", "solver configuration" and their interactions, leads to a scalable and elegant formulation. Empirical evaluation of our approach on performance datasets associated with fairly large groups of solver configurations demonstrates that one can obtain high quality recommendations that are close to the ideal choices.

Interpersonal relationship and recommendation are the important relation and popular mechanism. Living in the information-overloading age, the original information searching mechanisms, which require the specification of keywords, are ineffective and impractical. Moreover, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, especially in online stores. Among different recommendation techniques proposed in the literature, the content-based and collaborative filtering approaches have been broadly adopted by membership stores that maintain users¡¦ long term interest. For short-term interest, by far the content-based approach is the most popular one for recommendation. However, most of the proposed recommendation approaches do not take the social information as an important factor. In this study, we proposed several social network-based recommendation approaches that take into account the similarities of items with respect to their social closeness for meeting users¡¦ short term interests. Our experiment evaluation results show that social network-based approaches perform better than the content-based counterpart, if the user¡¦s short term interest profile contains articles of similar content. Nonetheless, content-based approach becomes better when articles in the profile are diversified in their content. Besides, contrast to content-based approach, social network-based approach is less likely to recommend articles which readers do not value. Finally, the desired articles recommended by content-based approach are very different from those by social network-based approach. This suggests the development of some hybrid recommendation method that utilizes both content and social network when making recommendations.