Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds.

In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.

Bangladesh is a secular democracy with almost 90% of its population constituting of Muslims and the rest 10% constituting of the minority groups that includes Hindus, Christians, Buddhists, Ahmadi Muslims, Shia, Sufi, LGBT groups and Atheists. In recent years, Bangladesh has experienced an increase in attacks by religious extremist groups, such as IS and AQIS affiliates, hate-groups and politically motivated violence. Attacks have also become indiscriminate, with assailants targeting a wide variety of individuals, including religious minorities and foreigners. According to the telecoms regulator, the number of internet users in Bangladesh now stands at over 66.8 million reaching 41% penetration. Of them, 63 million access the internet through mobile phones. Facebook, with the usage of about 97.2%, is the most used social network in Bangladesh.

In this research, local academics with cultural expertise collaborated to locate and download content from 292 Facebook groups organized under three (3) major umbrella types: Religious Terrorist Violence, Political Intolerance and Issue, and Target-based Intolerance between June2016 - December 2016 period. Dates of real extremist attacks were aligned with corresponding Facebook message streams, identified posts and comments related to the targets and perpetrators of the attacks, and proceeded to use the context of the attacks, their effects, the nature and structure of underlying extremist and counter-violent extremist networks, to study the narratives and trends over time.

With the recent expansion in the use of wearable technology, a large number of users access personal data with these smart devices. The consumer market of wearables includes smartwatches, health and fitness bands, and gesture control armbands. These smart devices enable users to communicate with each other, control other devices, relax and work out more effectively. As part of their functionality, these devices store, transmit, and/or process sensitive user personal data, perhaps biological and location data, making them an abundant source of confidential user information. Thus, prevention of unauthorized access to wearables is necessary. In fact, it is important to effectively authenticate users to prevent intentional misuse or alteration of individual data. Current authentication methods for the legitimate users of smart wearable devices utilize passcodes, and graphical pattern based locks. These methods have the following problems: (1) passcodes can be stolen or copied, (2) they depend on conscious user inputs, which can be undesirable to a user, (3) they authenticate the user only at the beginning of the usage session, and (4) they do not consider user behavior or they do not adapt to evolving user behavior.

In this thesis, an approach is presented for developing software for continuous authentication of the legitimate user of a smart wearable device. With this approach, the legitimate user of a smart wearable device can be authenticated based on the user's behavioral biometrics in the form of motion gestures extracted from the embedded sensors of the smart wearable device. The continuous authentication of this approach is accomplished by adapting the authentication to user's gesture pattern changes. This approach is demonstrated by using two comprehensive datasets generated by two research groups, and it is shown that this approach achieves better performance than existing methods.

Most current database management systems are optimized for single query execution.

Yet, often, queries come as part of a query workload. Therefore, there is a need

for index structures that can take into consideration existence of multiple queries in a

query workload and efficiently produce accurate results for the entire query workload.

These index structures should be scalable to handle large amounts of data as well as

large query workloads.

The main objective of this dissertation is to create and design scalable index structures

that are optimized for range query workloads. Range queries are an important

type of queries with wide-ranging applications. There are no existing index structures

that are optimized for efficient execution of range query workloads. There are

also unique challenges that need to be addressed for range queries in 1D, 2D, and

high-dimensional spaces. In this work, I introduce novel cost models, index selection

algorithms, and storage mechanisms that can tackle these challenges and efficiently

process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular,

I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces),

and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently

handle range query workload and the unique challenges arising from their respective

spaces. I experimentally show the effectiveness of the above proposed index structures

by comparing with state-of-the-art techniques.

The game held by National Basketball Association (NBA) is the most popular basketball event on earth. Each year, tons of statistical data are generated from this industry. Meanwhile, managing teams, sports media, and scientists are digging deep into the data ocean. Recent research literature is reviewed with respect to whether NBA teams could be analyzed as connected networks. However, it becomes very time-consuming, if not impossible, for human labor to capture every detail of game events on court of large amount. In this study, an alternative method is proposed to parse public resources from NBA related websites to build degenerated game-wise flow graphs. Then, three different statistical techniques are tested to observe the network properties of such offensive strategy in terms of Home-Away team manner. In addition, a new algorithm is developed to infer real game ball distribution networks at the player level under low-rank constraints. The ball-passing degree matrix of one game is recovered to the optimal solution of low-rank ball transition network by constructing a convex operator. The experimental results on real NBA data demonstrate the effectiveness of the proposed algorithm.

Visual Question Answering (VQA) is a new research area involving technologies ranging from computer vision, natural language processing, to other sub-fields of artificial intelligence such as knowledge representation. The fundamental task is to take as input one image and one question (in text) related to the given image, and to generate a textual answer to the input question. There are two key research problems in VQA: image understanding and the question answering. My research mainly focuses on developing solutions to support solving these two problems.

In image understanding, one important research area is semantic segmentation, which takes images as input and output the label of each pixel. As much manual work is needed to label a useful training set, typical training sets for such supervised approaches are always small. There are also approaches with relaxed labeling requirement, called weakly supervised semantic segmentation, where only image-level labels are needed. With the development of social media, there are more and more user-uploaded images available

on-line. Such user-generated content often comes with labels like tags and may be coarsely labelled by various tools. To use these information for computer vision tasks, I propose a new graphic model by considering the neighborhood information and their interactions to obtain the pixel-level labels of the images with only incomplete image-level labels. The method was evaluated on both synthetic and real images.

In question answering, my research centers on best answer prediction, which addressed two main research topics: feature design and model construction. In the feature design part, most existing work discussed how to design effective features for answer quality / best answer prediction. However, little work mentioned how to design features by considering the relationship between answers of one given question. To fill this research gap, I designed new features to help improve the prediction performance. In the modeling part, to employ the structure of the feature space, I proposed an innovative learning-to-rank model by considering the hierarchical lasso. Experiments with comparison with the state-of-the-art in the best answer prediction literature have confirmed

that the proposed methods are effective and suitable for solving the research task.

With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, groups and organization leaders. Finally, the experimental results are presented and a subset of relevant features is identified that lead to a generalizable model. Detection of tiny number of groups from large network is achieved with 0.8 precision, 0.75 recall and 0.77 F1 score. The domain independent network and behavioral features and models developed here are suitable for solving twitter account classification problem in any context.

Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and

the central idea is that multiple tenant applications can be developed using compo

nents stored in the SaaS infrastructure. Recently, MTA has been extended where

a tenant application can have its own sub-tenants as the tenant application acts

like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy

Architecture ). In STA, each tenant application not only need to develop its own

functionalities, but also need to prepare an infrastructure to allow its sub-tenants to

develop customized applications. This dissertation formulates eight models for STA,

and proposes a Variant Point based customization model to help tenants and sub

tenants customize tenant and sub-tenant applications. In addition, this dissertation

introduces Crowd- sourcing to become the core of STA component development life

cycle. To discover fit tenant developers or components to help building and com

posing new components, dynamic and static ranking models are proposed. Further,

rank computation architecture is presented to deal with the case when the number of

tenants and components becomes huge. At last, an experiment is performed to prove

rank models and the rank computation architecture work as design.

The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested with data from a different distribution. Variations in vision based data can be attributed to the following reasons, viz., differences in image quality (resolution, brightness, occlusion and color), changes in camera perspective, dissimilar backgrounds and an inherent diversity of the samples themselves. Machine learning techniques like transfer learning are employed to adapt computational models across distributions. Domain adaptation is a special case of transfer learning, where knowledge from a source domain is transferred to a target domain in the form of learned models and efficient feature representations.

The dissertation outlines novel domain adaptation approaches across different feature spaces; (i) a linear Support Vector Machine model for domain alignment; (ii) a nonlinear kernel based approach that embeds domain-aligned data for enhanced classification; (iii) a hierarchical model implemented using deep learning, that estimates domain-aligned hash values for the source and target data, and (iv) a proposal for a feature selection technique to reduce cross-domain disparity. These adaptation procedures are tested and validated across a range of computer vision applications like object classification, facial expression recognition, digit recognition, and activity recognition. The dissertation also provides a unique perspective of domain adaptation literature from the point-of-view of linear, nonlinear and hierarchical feature spaces. The dissertation concludes with a discussion on the future directions for research that highlight the role of domain adaptation in an era of rapid advancements in artificial intelligence.

The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.

Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.