abstract: With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data. This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick. In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information? In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information. The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

With the advancement in multimedia systems and the increased interest in haptics to be used in interpersonal communication systems, where users can see, show, hear, tell, touch and be touched, mouse and keyboard are no longer dominant input devices. Touch, speech and vision will soon be the main methods of human computer interaction. Moreover, as interpersonal communication usage increases, the need for securing user authentication grows. In this research, we examine a user's identification and verification based on haptic information. We divide our research into three main steps. The first step is to examine a pre-defined task, namely a handwritten signature with haptic information. The user target in this task is to mimic the legitimate signature in order to be verified. As a second step, we consider the user's identification and verification based on user drawings. The user target is predefined, however there are no restrictions imposed on the order or on the level of details required for the drawing. Lastly, we examine the feasibility and possibility of distinguishing users based on their haptic interaction through an interpersonal communication system. In this third step, there are no restrictions on user movements, however a free movement to touch the remote party is expected. In order to achieve our goal, many classification and feature reduction techniques have been discovered and some new ones were proposed. Moreover, in this work we utilize evolutionary computing in user verification and identification. Analysis of haptic features and their significance on distinguishing users is hence examined. The results show a utilization of visual features by Genetic Programming (GP) towards identity verification, with a probability equal to 50% while the remaining haptic features were utilized with a probability of approximately 50%. Moreover, with a handwritten signature application, a verification success rate of 97.93% with False Acceptance Rate (FAR) of 1.28% and @11.54% False Rejection Rate (FRR) is achieved with the utilization of genetic programming enhanced with the random over sampled data set. In addition, with a totally free user movement in a haptic-enabled interpersonal communication system, an identification success rate of 83.3% is achieved when random forest classifier is utilized.

Yes / Building on the global branding literature, brand relationship theory and social identity theory, this study investigates the relationship between perceived brand globalness (PBG) and user engagement (active/passive) on global social networks (GSN). Additionally, the study investigates the mediating effects of two distinct forms of user identification (i.e., user identification with the GSN brand and user identification with the GSN community) as well as the moderating effects of user global identity on the relationship between PBG and user engagement with such brands. Covariance-based structural equation modeling was used to analyse data collected from users of a GSN (i.e., Facebook) in the United Kingdom (UK) and India. The results indicate that PBG significantly influences both active and passive user engagement. This relationship is mediated by users' identification with a GSN brand and community. Additionally, the findings indicate that the associations between PBG and user engagement (active/passive) on GSN vary as a function of users' global identity. The results also demonstrate some country-specific variations in key relationships. Finally, the study offers useful recommendations for social media managers to rethink and redesign their user engagement strategies, keeping in mind global cultural diversity.

<p dir="ltr">The proliferation of mobile devices and big data has put digital forensic investigators at a disadvantage. Despite all the technological advances, the tools and methods used during the investigations must catch up. With smartphones becoming integral to crime scenes, often containing multiple instances, courts and law enforcement offices greatly depend on their data. In addition to traditional data on smartphones, such as call logs, text messages, and emails, sensor data can drastically increase the chances of resolving and painting the complete picture of the events required for a successful investigation. While sensor data are collected frequently, it often creates a lot of noise due to the amount of entries over some time. In attempting to decipher the data and link them to the relevant events, digital forensics investigators are prone to missing or simply disregarding the data extracted from smartphones. Interpreting sensor data such as location and various phone activities already collected and extracted can lead to finding two main links required for the investigation: time and location. Knowing an individual's time and location can significantly improve the investigation process and aid in the final outcome. Despite smartphones being capable of collecting sensor data and discovering these two variables, data interpretation and correlation between them still need to be improved. The statement is particularly true for smartphones with newer operating system versions. Due to the special forensic software required to extract the data and the ability to interpret them, digital forensic investigators are either strained for time or are unequipped for processing them.</p><p dir="ltr">In order to mitigate the gap, automation of the process capable of handling large amounts of data while classifying the time and the location appropriate for the investigation is necessary. Reducing investigation times and increasing prediction accuracy will allow faster resolving times while freeing up desperately needed resources for digital forensic investigators. Therefore, this study presents a novel approach to identifying and predicting user locations using machine learning based on various sensor data collected from multiple smartphones. As the first step in achieving the goal, a user study was conducted, collecting real-world data for training and testing of the machine learning models. The process includes engineering the necessary procedures and methodologies required to extract raw data and process them for successful model training. The results showed that the models are capable of differentiating between the three different locations using XGBoost with score test accuracy over 0.88. Additionally, Random Forest Entropy and Random Forest Gini achieved accuracy over 0.85. As for for the results where only two locations were predicted Random Forest Entropy and Random Forest Gini achieved accuracy test score per model over 0.97. </p>

Fallende Hardwarepreise sowie eine zunehmende Offenheit gegenüber neuartigen Interaktionsmodalitäten haben in den vergangen Jahren den Einsatz von wandgroßen interaktiven Displays möglich gemacht, und in der Folge ist ihre Anwendung, unter anderem in den Bereichen Visualisierung, Bildung, und der Unterstützung von Meetings, erfolgreich demonstriert worden. Aufgrund ihrer Größe sind Wanddisplays für die Interaktion mit mehreren Benutzern prädestiniert. Gleichzeitig kann angenommen werden, dass Zugang zu persönlichen Daten und Einstellungen — mithin personalisierte Interaktion — weiterhin essentieller Bestandteil der meisten Anwendungsfälle sein wird. Aktuelle Benutzerschnittstellen im Desktop- und Mobilbereich steuern Zugriffe über ein initiales Login. Die Annahme, dass es nur einen Benutzer pro Bildschirm gibt, zieht sich durch das gesamte System, und ermöglicht unter anderem den Zugriff auf persönliche Daten und Kommunikation sowie persönliche Einstellungen. Gibt es hingegen mehrere Benutzer an einem großen Bildschirm, müssen hierfür Alternativen gefunden werden. Die daraus folgende Forschungsfrage dieser Dissertation lautet: Wie können wir im Kontext von Mehrbenutzerinteraktion mit wandgroßen Displays personalisierte Schnittstellen zur Verfügung stellen? Die Dissertation befasst sich sowohl mit personalisierter Interaktion in der Nähe (mit Touch als Eingabemodalität) als auch in etwas weiterer Entfernung (unter Nutzung zusätzlicher mobiler Geräte). Grundlage für personalisierte Mehrbenutzerinteraktion sind technische Lösungen für die Zuordnung von Benutzern zu einzelnen Interaktionen. Hierzu werden zwei Alternativen untersucht: In der ersten werden Nutzer via Kamera verfolgt, und in der zweiten werden Mobilgeräte anhand von Ultraschallsignalen geortet. Darauf aufbauend werden Interaktionstechniken vorgestellt, die personalisierte Interaktion unterstützen. Diese nutzen zusätzliche Mobilgeräte, die den Zugriff auf persönliche Daten sowie Interaktion in einigem Abstand von der Displaywand ermöglichen. Einen weiteren Teil der Arbeit bildet die Untersuchung der praktischen Auswirkungen der Ausgabe- und Interaktionsmodalitäten für personalisierte Interaktion. Hierzu wird eine qualitative Studie vorgestellt, die Nutzerverhalten anhand des kooperativen Mehrbenutzerspiels Miners analysiert. Der abschließende Beitrag beschäftigt sich mit dem Analyseprozess selber: Es wird das Analysetoolkit für Wandinteraktionen GIAnT vorgestellt, das Nutzerbewegungen, Interaktionen, und Blickrichtungen visualisiert und dadurch die Untersuchung der Interaktionen stark vereinfacht. / An increasing openness for more diverse interaction modalities as well as falling hardware prices have made very large interactive vertical displays more feasible, and consequently, applications in settings such as visualization, education, and meeting support have been demonstrated successfully. Their size makes wall displays inherently usable for multi-user interaction. At the same time, we can assume that access to personal data and settings, and thus personalized interaction, will still be essential in most use-cases. In most current desktop and mobile user interfaces, access is regulated via an initial login and the complete user interface is then personalized to this user: Access to personal data, configurations and communications all assume a single user per screen. In the case of multiple people using one screen, this is not a feasible solution and we must find alternatives. Therefore, this thesis addresses the research question: How can we provide personalized interfaces in the context of multi-user interaction with wall displays? The scope spans personalized interaction both close to the wall (using touch as input modality) and further away (using mobile devices). Technical solutions that identify users at each interaction can replace logins and enable personalized interaction for multiple users at once. This thesis explores two alternative means of user identification: Tracking using RGB+depth-based cameras and leveraging ultrasound positioning of the users' mobile devices. Building on this, techniques that support personalized interaction using personal mobile devices are proposed. In the first contribution on interaction, HyDAP, we examine pointing from the perspective of moving users, and in the second, SleeD, we propose using an arm-worn device to facilitate access to private data and personalized interface elements. Additionally, the work contributes insights on practical implications of personalized interaction at wall displays: We present a qualitative study that analyses interaction using a multi-user cooperative game as application case, finding awareness and occlusion issues. The final contribution is a corresponding analysis toolkit that visualizes users' movements, touch interactions and gaze points when interacting with wall displays and thus allows fine-grained investigation of the interactions.

Fallende Hardwarepreise sowie eine zunehmende Offenheit gegenüber neuartigen Interaktionsmodalitäten haben in den vergangen Jahren den Einsatz von wandgroßen interaktiven Displays möglich gemacht, und in der Folge ist ihre Anwendung, unter anderem in den Bereichen Visualisierung, Bildung, und der Unterstützung von Meetings, erfolgreich demonstriert worden. Aufgrund ihrer Größe sind Wanddisplays für die Interaktion mit mehreren Benutzern prädestiniert. Gleichzeitig kann angenommen werden, dass Zugang zu persönlichen Daten und Einstellungen — mithin personalisierte Interaktion — weiterhin essentieller Bestandteil der meisten Anwendungsfälle sein wird. Aktuelle Benutzerschnittstellen im Desktop- und Mobilbereich steuern Zugriffe über ein initiales Login. Die Annahme, dass es nur einen Benutzer pro Bildschirm gibt, zieht sich durch das gesamte System, und ermöglicht unter anderem den Zugriff auf persönliche Daten und Kommunikation sowie persönliche Einstellungen. Gibt es hingegen mehrere Benutzer an einem großen Bildschirm, müssen hierfür Alternativen gefunden werden. Die daraus folgende Forschungsfrage dieser Dissertation lautet: Wie können wir im Kontext von Mehrbenutzerinteraktion mit wandgroßen Displays personalisierte Schnittstellen zur Verfügung stellen? Die Dissertation befasst sich sowohl mit personalisierter Interaktion in der Nähe (mit Touch als Eingabemodalität) als auch in etwas weiterer Entfernung (unter Nutzung zusätzlicher mobiler Geräte). Grundlage für personalisierte Mehrbenutzerinteraktion sind technische Lösungen für die Zuordnung von Benutzern zu einzelnen Interaktionen. Hierzu werden zwei Alternativen untersucht: In der ersten werden Nutzer via Kamera verfolgt, und in der zweiten werden Mobilgeräte anhand von Ultraschallsignalen geortet. Darauf aufbauend werden Interaktionstechniken vorgestellt, die personalisierte Interaktion unterstützen. Diese nutzen zusätzliche Mobilgeräte, die den Zugriff auf persönliche Daten sowie Interaktion in einigem Abstand von der Displaywand ermöglichen. Einen weiteren Teil der Arbeit bildet die Untersuchung der praktischen Auswirkungen der Ausgabe- und Interaktionsmodalitäten für personalisierte Interaktion. Hierzu wird eine qualitative Studie vorgestellt, die Nutzerverhalten anhand des kooperativen Mehrbenutzerspiels Miners analysiert. Der abschließende Beitrag beschäftigt sich mit dem Analyseprozess selber: Es wird das Analysetoolkit für Wandinteraktionen GIAnT vorgestellt, das Nutzerbewegungen, Interaktionen, und Blickrichtungen visualisiert und dadurch die Untersuchung der Interaktionen stark vereinfacht. / An increasing openness for more diverse interaction modalities as well as falling hardware prices have made very large interactive vertical displays more feasible, and consequently, applications in settings such as visualization, education, and meeting support have been demonstrated successfully. Their size makes wall displays inherently usable for multi-user interaction. At the same time, we can assume that access to personal data and settings, and thus personalized interaction, will still be essential in most use-cases. In most current desktop and mobile user interfaces, access is regulated via an initial login and the complete user interface is then personalized to this user: Access to personal data, configurations and communications all assume a single user per screen. In the case of multiple people using one screen, this is not a feasible solution and we must find alternatives. Therefore, this thesis addresses the research question: How can we provide personalized interfaces in the context of multi-user interaction with wall displays? The scope spans personalized interaction both close to the wall (using touch as input modality) and further away (using mobile devices). Technical solutions that identify users at each interaction can replace logins and enable personalized interaction for multiple users at once. This thesis explores two alternative means of user identification: Tracking using RGB+depth-based cameras and leveraging ultrasound positioning of the users' mobile devices. Building on this, techniques that support personalized interaction using personal mobile devices are proposed. In the first contribution on interaction, HyDAP, we examine pointing from the perspective of moving users, and in the second, SleeD, we propose using an arm-worn device to facilitate access to private data and personalized interface elements. Additionally, the work contributes insights on practical implications of personalized interaction at wall displays: We present a qualitative study that analyses interaction using a multi-user cooperative game as application case, finding awareness and occlusion issues. The final contribution is a corresponding analysis toolkit that visualizes users' movements, touch interactions and gaze points when interacting with wall displays and thus allows fine-grained investigation of the interactions.

<p>Despite the many security and privacy concerns associated with Internet-of-Things (IoT) devices, we continue to be barraged by new IoT devices every day. These devices have infiltrated almost every aspect of our lives, from government and corporations to our homes, and now, on and within our person, in the form of smartphones and wearables. These personal IoT devices can collect some of the most intimate pieces of data about their user. For instance, a smartwatch can record its wearer's heart rate, skin temperature, physical activity, and even GPS location data. At the same time, a smartphone has access to almost every piece of information related to its user, including text messages, social media activity, web browser history, and application-specific data. Due to the quantity and quality of data these personal IoT devices record, these devices have become critical sources of evidence during forensic investigations. However, there are instances in which digital forensic investigators need to make doubly sure that the data obtained from these smart devices, in fact, belong to the alleged owner of the smart device and not someone else. To that end, this dissertation provides the first look at using personal IoT device handling as a user identification technique with machine learning models to aid forensic investigations. The results indicated that this technique is capable of significantly differentiating device owners with performance metrics of .9621, .9618, and .9753, for accuracy, F1, and AUC, respectively, when using a smartwatch with statistical time-domain features. When considering the smartphone performance, the performance was only marginally acceptable with accuracy, F1, and AUC values of .8577, .8560, and .8891, respectively.  The results also indicate that female users handled their devices notably differently from male users. This study thus lays the foundation for performing user identification during a forensic investigation to determine whether the smart device owner did, in fact, use the device at the time of the incident.</p>

The aim of the diploma thesis is to map the information provided by web browsers, which can be used in practice to identify users on websites. The work focuses on obtaining and subsequent analysis of information about devices, browsers and side effects caused by web extensions that mask the identity of users. The acquisition of information is realized by a designed and implemented library in the TypeScript language, which was deployed on 4 commercial websites. The analysis of the obtained information is carried out after a month of operation of the library and focuses on the degree of information obtained, the speed of obtaining information and the stability of information. The dataset shows that up to 94 % of potentially different users have a unique combination of information. The main contribution of this work lies in the created library, design of new methods of obtaining information, optimization of existing methods and the determination of quality and poor quality information based on their level of information, speed of acquisition and stability over time.