無線通信和移動技術已經從根本上改變了人和人之間相互通信的方式，隨著像智能手機這樣功能強大的移動設備不斷普及，現在我們有更多的機會去監測用戶的運動狀態、社交情況和地理位置等信息。近期，越來越多的基於智能手機的傳感研究相繼出現，這些研究利用智能手機中的多種傳感、定位以及近距離無線設備來識別手機用戶當前的活動狀態和周圍環境。一些可識別用戶活動狀態和監控身體健康狀況的移動應用程式已經被開發并投入使用。儘管如此，當前大部份關於智能手機的研究忽視了這樣一個問題，智能手機是用戶與外界通信的一個指令中心。移動用戶可以使用智能手機用很多種方式聯繫他們的朋友，例如打電話、發送短消息、電子郵件、或者通過即時通信程序或者社交網絡，這些多渠道的通信方式和人與人之間面對面的交流一樣重要，因此智能手機是識別用戶和其他聯繫人的社會關係的關鍵。在本論文中，我們提出用智能手機中 獨有的多渠道用戶通信數據來對用戶的的社會關係進行分類。作為我們研究的開始，我們生成人工的通信數據並且用社交矩陣來為人與人之間的通信建立模型，這也幫助我們測試了很多可以應用在此類問題的數據挖掘算法。接下來，我們通過招募真實用戶來採集他們的各種社交通信數據，這些數據包括手機通話記錄、電子郵件、社交網絡（Facebook和Renren）和面對面的交流。通過在社交矩陣上應用不同的分類算法，我們發現SVM的分類性能要超過KNN和決策樹算法，SVM對於社會關係的分類準確率可以達到82.4%。我們也對來自不同渠道的通信數據進行了比較，最終發現來自社交網絡和面對面交流的數據在社交關係分類中起更大的作用。另外，我們通過使用降低維度算法可以把社交矩陣從65維度映射到9維度，關係分類的準確率卻沒有明顯降低，在降低維度的過程中我們也可以提取出用戶主要的通信特徵，從而更好地解釋社會關係分類的原理。最後，我們也應用了CUR矩陣分解算法從社交矩陣65列中選出13列建立新的社交矩陣，關係分類的準確率從82.4%降低到77.7%，但是我們卻可以通過 CUR來選擇合適的傳感器抽樣採集頻率，這樣可以在利用手機採集數據過程中節省更多手機電量。 / Wireless Communications and Mobile Computing have fundamentally changed the way people interact and communicate with each other. The proliferation of powerful and programmable mobile devices, smartphones in particular, has offered an unprecedented opportunity to continuously monitor the physical, social and geographical activities of their users. Recently, much research has been done on smartphone-based sensing which leverages the rich set of sensing, positioning and short-range radio capabilities of the smartphones to identify the context of user activities and ambient environment conditions. Mobile applications for personal behavior tracking and physical wellness monitoring have also been developed. Despite that, most of the existing work in mobile sensing has neglected the role of smartphone as the command-center of the user’s communications with the outside world. As mobile users contact their friends via phone, SMS, emails, instant messaging, and other online social-networking applications, these multi-modal communication activities are as equally important as physical activities in proling one’s life. They also hold the key to understand the user’s social relationship with other people of interest. In this thesis, we propose to use the unique multi-model interaction data from smartphone to classify social relationships. To jump start our study, we generate articial interaction data and build social interaction matrix to modeMl the interaction between people. This also helps us in testing a wide range of data mining analysis techniques for this type of problem. We then carry out a social interaction data collection campaign with a group of real users to obtain real-life multi-modal communication data, e.g., phone call, Email, online social network(Facebook and Renren), and physical location/proximity. After applying different classification algorithms on social interaction matrix, we find that SVM outperforms KNN and decision tree algorithms, with a classification accuracy of 82.4% (the accuracies of KNN and decision tree are 79.9% and 77.6%, respectively). We also compare the data from different interaction channels and finally find that on-line social network and location/proximity data contribute more to the overall classification accuracy. Additionally, with dimensionality reduction algorithms, the social interaction matrix can be embedded from 65 to 9 dimensional space while preserving the high classification accuracy and we also get principle interaction features as by-product. At last, we use CUR decomposi¬tion to select 13 out of the 65 features in the social interaction matrix. The classification accuracy drops from 82.4% to 77.7% after CUR decomposition. But it can help to determine the right sensor sampling frequency so as to enhance energy efficiency for social data collection. / Detailed summary in vernacular field only. / Sun, Deyi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 90-96). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Research Background --- p.7 / Chapter 2.1 --- Related work of social relationship analysis --- p.7 / Chapter 2.1.1 --- Community detection in social network --- p.8 / Chapter 2.1.2 --- Social influence analysis --- p.10 / Chapter 2.1.3 --- Modeling social interaction data --- p.10 / Chapter 2.1.4 --- Social relationship prediction --- p.12 / Chapter 2.2 --- Classification methodologies --- p.14 / Chapter 2.2.1 --- Algorithms for social relationship classification --- p.14 / Chapter 2.2.2 --- Algorithms for dimensionality reduction --- p.16 / Chapter 3 --- Problem Formulation of Relationship Classicification --- p.19 / Chapter 3.1 --- Multi-modal data in smartphones --- p.20 / Chapter 3.2 --- Formulation of relationship classification problem --- p.21 / Chapter 3.3 --- Refinement of feature definition and energy efficiency --- p.27 / Chapter 3.4 --- Chapter summary --- p.28 / Chapter 4 --- Social Interaction Data Acquisition --- p.30 / Chapter 4.1 --- Social interaction data collection campaign overview --- p.31 / Chapter 4.2 --- Format of raw interaction data --- p.33 / Chapter 4.3 --- Building social interaction matrix with real-life interaction data --- p.37 / Chapter 4.4 --- Chapter summary --- p.43 / Chapter 5 --- Statistical Analysis of Social Interaction Data --- p.45 / Chapter 5.1 --- Coverage of social interaction data --- p.46 / Chapter 5.2 --- Social relationships statistics --- p.48 / Chapter 5.3 --- Social relationship interaction patterns --- p.52 / Chapter 5.4 --- Chapter summary --- p.59 / Chapter 6 --- Automatic Social Relationship Classification Based on Smartphone Interaction Data --- p.61 / Chapter 6.1 --- Comparison of different classification algorithms --- p.62 / Chapter 6.2 --- Advantages of multi-modal interaction data --- p.65 / Chapter 6.3 --- Comparison of interaction data in different communication channels --- p.67 / Chapter 6.4 --- Dimensionality reduction on social interaction data --- p.72 / Chapter 6.5 --- Discussions in deploying social relationship classification application --- p.80 / Chapter 6.5.1 --- Considerations of user privacy --- p.81 / Chapter 6.5.2 --- Saving smartphone resources --- p.82 / Chapter 6.6 --- Chapter summary --- p.83 / Chapter 7 --- Conclusion and Future Work --- p.86 / Bibliography --- p.90

Previous research suggests that self-reports of the frequency of events can vary dramatically. Minor changes in question format can result in major changes in the obtained results. The purpose of this study is to examine how changes in reference period, memory cue, and measurement scale affect participants' self-reports of conflict frequency. Additionally, the role of cognitive effort was examined to gain insight into the recall strategy used for different measures of conflict. Participants include 175 college undergraduates between the ages of 18-24, enrolled in psychology courses at Florida Atlantic University. Results indicate that reference period and memory cue form a significant interaction to create changes in reports of conflict frequency. Both reference period and memory cue act differently within the different conflict measurement scales. In the 0-10 or more scale, memory cue was statistically significant with higher rates of conflict reported in the cued condition than the uncued. In the open (fill in the blank) scale, there was a significant interaction between reference period and memory cue with the highest amount of conflict being reported in the one day/cued condition. The Likert scale behaved differently than the other two absolute frequency scales. Within the Likert scale there was a significant interaction between reference period and memory cue, however, the highest amount of conflict reported was in the two weeks/uncued condition. Finally, cognitive effort varied as a product of reference period, within both the 0-10 or more scale and the open scale with the two weeks condition eliciting higher reports of effort than the one day condition. These cognitive effort findings suggest that participants used enumeration as a recall strategy for the absolute frequency scales and estimation for the Likert scale. / by Justin Puder. / Thesis (M.A.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

D.Litt et Phil. (Clinical Psychology) / Please refer to full text to view abstract

M.A. / Irritable bowl syndrome (IBS) is one of the common conditions referred to gastroentorologists, but one of the least understood. Part of the reason for this is the real lack of consensus of opinion regarding the nature of the complaint. The problem is confounded by the absence of objective disease makers, as well as the variation in symptom presentation. The term (IBS) describes a cluster of symptoms which include chronic abdominal pain and altered bowel habits (diarrhoea, constipation, or alternating diarrhoea and constipation) in the absence of a known structural cause for the symptom (Toner, Garfinkel, Jeejeebhoy, Scher, Shulhan & Di Gasbarro, 1990). The symptoms of IBS mimic those of many other gastrointestinal diseases and the challenge to medical doctors is to establish a confident diagnosis based on the symptomatology of the individual, without the need to carry out multiple investigations to eliminate organic disease of the bowel. The pathogenesis of a condition remains a mystery. Most doctors would not consider IBS to a 'proper" disease at all, but view it as a physiological alteration in intestinal function brought about by psychological disturbance (Read, 1985).

D.Cur. / South Africa is currently undergoing radical transformational changes in all facets of its society. This is an era immediately following the first democratic elections in the country. The new Government, the Government of National Unity (GNU) which is dominated by the African National Congress (ANC), has introduced what is known as the Reconstruction and Development Programme (RDP)(ANC, 1994), which seeks to redress disparities of the past. This programme has significant social, political and economical implications for the South African community. It proposes that statutory bodies and institutions should be rationalized and restructured to reflect the diversity of the South African people and should be better able to promote and protect the standards of health care and training. It aims to, inter alia, promote mental health and to increase the accessibility, quality and quantity of mental health support and counselling services. In line with this goal and based on problems that arise because of anxieties and fears that are inherent in change, the mental health of nurse educators and nursing students of a selected nursing college was investigated utilizing a qualitative, theory – generative design which is exploratory, descriptive and contextual in nature. The study was conducted in two phases. In Phase 1, in-depth phenomenological interviews were conducted individually with ten nurse educators and nine nursing students to explore and describe their experience of interaction with one another. Follow-up interviews were also conducted with some of the participants. Data was analysed using Tesch's method. Based on the results of the analysis, the major concept, understanding was identified as the essence of a model for constructive nurse educator - student interaction envisaged. In Phase 2, a theory -generative design was utilized to develop a constructive nurse educator - student interaction model, founded on a mental health approach. The identified concept was examined in existing writings to determine essential criteria of the concept. In addition, a model case was written in which the concept was constructed to the best of the researcher's understanding. Essential criteria of the concept were identified and a concept map was drawn to depict the essential criteria in relation to each other. The related concepts were identified and portrayed in a structural form. The visual model depicts nurse educators and nursing students who function as a family, as the recipients of activity, and the advanced psychiatric nurse practitioner, who facilitates their interaction, as the agent. By utilizing the deductive reasoning strategy, relationship statements were inferred from the model. Guidelines were described for the advanced psychiatric nurse practitioner with regard to all three phases, namely, the Interaction Initiation Phase, the Interaction Cohesiveness Phase and the Interaction Dissolution Phase, in terms of objectives, strategies and proposed activities.

Indiana University-Purdue University Indianapolis (IUPUI) / The substantial interpersonal and economic costs of depression make it imperative to better understand the predictors and moderators of depressive symptoms. The ability to use social support protects people from depressive symptoms, but individuals high in attachment avoidance tend not to use others as sources of support. Research has found that attachment avoidance is related to depressive symptoms in some samples but not in others (Mikulincer & Shaver, 2007; Shea, 2011). Thus, there appear to be factors that moderate the relationship between attachment avoidance and depressive symptoms. The present study examined if cognitive abilities that facilitate effective emotion regulation strategies moderate the relationship between attachment avoidance and depressive symptoms. Using a sample of college students, attachment avoidance, cognitive abilities, depressive symptoms, and other indices of psychological distress and well-being were measured and examined for evidence of moderation via hierarchical linear regression. The hypothesis that cognitive abilities moderate the relationship between attachment avoidance and depressive symptoms was not supported (ΔR2 = 0.02, p = .68). Factors contributing to the null findings are discussed and conceptual and methodological suggestions are offered for future research.

Indiana University-Purdue University Indianapolis (IUPUI) / Background: Research has repeatedly shown that individuals with severe mental illness desire interpersonal and romantic relationships and that social support (including spousal relationships) is beneficial. In addition, social deficits in mental disorders can often get in the way of developing fulfilling relationships. However, there is little currently available to help clinicians and researchers assess romantic relationship functioning in those with mental illness. The aim of this pilot study was to examine reliability and validity of a new measure of functioning in romantic relationships, the Romantic Relationship Functioning Scale (RRFS). Method: The RRFS was constructed based on theory proposed by Redmond, Larkin, and Harrop (2010). In an analog study, we tested the measure in a sample of college students (N=387), examining reliability, stability over time, factor structure, and relationships with measures of psychopathology and related measures of social functioning to assess convergent validity. Results: The RRFS exhibited a hierarchical four-factor structure, supporting the use of a total score. Although subscales were supported in the factor analysis, other psychometric evidence was weaker, and the use of a total score is advocated. Internal consistency and test-retest reliability were acceptable for the total scale (>.8). The RRFS had moderate to large correlations in the expected direction with all psychopathology measures. In predictive models, overall mental health, social functioning, and fewer interpersonal difficulties predicted higher romantic relationship functioning. Conclusions: The RRFS total score shows preliminary evidence of reliability and validity. The RRFS has potential to be of use in treatment centers for undergraduates and for individuals with diagnosed mental disorders. Future research should further investigate the RRFS subscales and the measure’s performance in clinical samples.