Social relationship classification based on interaction data from smartphones.

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.1Chapter 2 --- Research Background --- p.7Chapter 2.1 --- Related work of social relationship analysis --- p.7Chapter 2.1.1 --- Community detection in social network --- p.8Chapter 2.1.2 --- Social influence analysis --- p.10Chapter 2.1.3 --- Modeling social interaction data --- p.10Chapter 2.1.4 --- Social relationship prediction --- p.12Chapter 2.2 --- Classification methodologies --- p.14Chapter 2.2.1 --- Algorithms for social relationship classification --- p.14Chapter 2.2.2 --- Algorithms for dimensionality reduction --- p.16Chapter 3 --- Problem Formulation of Relationship Classicification --- p.19Chapter 3.1 --- Multi-modal data in smartphones --- p.20Chapter 3.2 --- Formulation of relationship classification problem --- p.21Chapter 3.3 --- Refinement of feature definition and energy efficiency --- p.27Chapter 3.4 --- Chapter summary --- p.28Chapter 4 --- Social Interaction Data Acquisition --- p.30Chapter 4.1 --- Social interaction data collection campaign overview --- p.31Chapter 4.2 --- Format of raw interaction data --- p.33Chapter 4.3 --- Building social interaction matrix with real-life interaction data --- p.37Chapter 4.4 --- Chapter summary --- p.43Chapter 5 --- Statistical Analysis of Social Interaction Data --- p.45Chapter 5.1 --- Coverage of social interaction data --- p.46Chapter 5.2 --- Social relationships statistics --- p.48Chapter 5.3 --- Social relationship interaction patterns --- p.52Chapter 5.4 --- Chapter summary --- p.59Chapter 6 --- Automatic Social Relationship Classification Based on Smartphone Interaction Data --- p.61Chapter 6.1 --- Comparison of different classification algorithms --- p.62Chapter 6.2 --- Advantages of multi-modal interaction data --- p.65Chapter 6.3 --- Comparison of interaction data in different communication channels --- p.67Chapter 6.4 --- Dimensionality reduction on social interaction data --- p.72Chapter 6.5 --- Discussions in deploying social relationship classification application --- p.80Chapter 6.5.1 --- Considerations of user privacy --- p.81Chapter 6.5.2 --- Saving smartphone resources --- p.82Chapter 6.6 --- Chapter summary --- p.83Chapter 7 --- Conclusion and Future Work --- p.86Bibliography --- p.9

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Smartphone apps usage patterns as a predictor of perceived stress levels at workplace

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

The Relationship Between Smartphone Addiction and Forward Head Posture in Junior High School Students in North Denpasar

Background:Nowadays, the smartphone has become an important requirement. The number of smartphone users and the duration of smartphone use is increasing rapidly, and the side effects can be detrimental one of them is forward head posture. The prevalence of forward head posture is greater women (24.1%) than men (9.1%).Methods:This study was an analytical observational study with a cross-sectional design.This research was conducted in April 2019 in SMP Negeri 2 Denpasar and SMP Negeri 4 Denpasar andincluding 56 samples, who were recruited through simple random sampling.Variables studied are smartphone addition was measured using the Smartphone Addiction Scale questionnaire, and forward head posture was measured by measuring craniovertebral angles. Data analysis was done using the Chi-Square test. Result: Based on this study, smartphone addiction was related to forward head posture. The research shows that as many as 45 samples had Smartphone Addiction (80.35%) while those who had forward head posture were 29 samples (51.78%).Conclusion : Ignorance of how to sort and choose the effects of globalization, especially smartphone use, which can lead to posture disorders. The factor recognized factors for the occurrences of the forward head posture the lack of education about ergonomic positions when using a smartphone. Parents are advised to set the right smartphone usage for children as early as possible

Survey and Systematization of Secure Device Pairing

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications Surveys & Tutorials 2017 (Volume: PP, Issue: 99

Enriched elderly virtual profiles by means of a multidimensional integrated assessment platform

The pressure over Healthcare systems is increasing in most developed countries. The generalized aging of the population is one of the main causes. This situation is even worse in underdeveloped, sparsely populated regions like Extremadura in Spain or Alentejo in Portugal. The authors propose to use the Situational-Context, a technique to seamlessly adapt Internet of Things systems to the needs and preferences of their users, for virtually modeling the elderly. These models could be used to enhance the elderly experience when using those kind of systems without raising the need for technical skills or the costs of implementing such systems by the regional healthcare systems. In this paper, the integration of a multidimensional integrated assessment platform with such virtual profiles is presented. The assessment platform provides and additional source of information for the virtual profiles that is used to better adapt existing systems to the elders needs