Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.

Technological advances have allowed for the assimilation of a variety of data, driving a shift away from the use of simpler and constrained patterns to more complex and diverse patterns in retrieval and analysis of such data. This shift has inundated the conventional techniques and has stressed the need for intelligent mechanisms that can model the complex patterns in the data. Deep neural networks have shown some success at capturing complex patterns, including the so-called attentioned networks, have significant shortcomings in distinguishing what is important in data from what is noise. This dissertation observes that the traditional neural networks primarily rely solely on gradient-based learning to model deep features maps while ignoring the key insight in the data that can be leveraged as complementary information to help learn an accurate model. In particular, this dissertation shows that the localized multi-scale features (captured implicitly or explicitly) can be leveraged to help improve model performance as these features capture salient informative points in the data.

This dissertation focuses on “working with the data, not just on data”, i.e. leveraging feature saliency through pre-training, in-training, and post-training analysis of the data. In particular, non-neural localized multi-scale feature extraction, in images and time series, are relatively cheap to obtain and can provide a rough overview of the patterns in the data. Furthermore, localized features coupled with deep features can help learn a high performing network. A pre-training analysis of sizes, complexities, and distribution of these localized features can help intelligently allocate a user-provided kernel budget in the network as a single-shot hyper-parameter search. Additionally, these localized features can be used as a secondary input modality to the network for cross-attention. Retraining pre-trained networks can be a costly process, yet, a post-training analysis of model inferences can allow for learning the importance of individual network parameters to the model inferences thus facilitating a retraining-free network sparsification with minimal impact on the model performance. Furthermore, effective in-training analysis of the intermediate features in the network help learn the importance of individual intermediate features (neural attention) and this analysis can be achieved through simulating local-extrema detection or learning features simultaneously and understanding their co-occurrences. In summary, this dissertation argues and establishes that, if appropriately leveraged, localized features and their feature saliency can help learn high-accurate, yet cheaper networks.

The burden of adaptation has been a major limiting factor in the adoption rates of new wearable assistive technologies. This burden has created a necessity for the exploration and combination of two key concepts in the development of upcoming wearables: anticipation and invisibility. The combination of these two topics has created the field of Anticipatory and Invisible Interfaces (AII)

In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main categories of consideration in the development of devices which are anticipatory and invisible:

• Idiosyncratic Design: How do can a design encapsulate the unique characteristics of the individual in the design of assistive aids?

• Adaptation to Intrapersonal Variations: As individuals progress through the various stages of a disability
eurological disorder, how can the technology adapt thresholds for feedback over time to address these shifts in ability?

• Context Aware Invisibility: How can the mechanisms of interaction be modified in order to reduce cognitive load?

The concepts proposed in this framework can be generalized to a broad range of domains; however, there are two primary applications for this work: rehabilitation and assistive aids. In preliminary studies, the framework is applied in the areas of Parkinsonian freezing of gait anticipation and the anticipation of body non-compliance during rehabilitative exercise.

The development of the internet provided new means for people to communicate effectively and share their ideas. There has been a decline in the consumption of newspapers and traditional broadcasting media toward online social mediums in recent years. Social media has been introduced as a new way of increasing democratic discussions on political and social matters. Among social media, Twitter is widely used by politicians, government officials, communities, and parties to make announcements and reach their voice to their followers. This greatly increases the acceptance domain of the medium.

The usage of social media during social and political campaigns has been the subject of a lot of social science studies including the Occupy Wall Street movement, The Arab Spring, the United States (US) election, more recently The Brexit campaign. The wide

spread usage of social media in this space and the active participation of people in the discussions on social media made this communication channel a suitable place for spreading propaganda to alter public opinion.

An interesting feature of twitter is the feasibility of which bots can be programmed to operate on this platform. Social media bots are automated agents engineered to emulate the activity of a human being by tweeting some specific content, replying to users, magnifying certain topics by retweeting them. Network on these bots is called botnets and describing the collaboration of connected computers with programs that communicates across multiple devices to perform some task.

In this thesis, I will study how bots can influence the opinion, finding which parameters are playing a role in shrinking or coalescing the communities, and finally logically proving the effectiveness of each of the hypotheses.

Internet memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. Internet memes most commonly take the form of an image which is a combination of image, text, and humor, making them a powerful tool to deliver information. Image memes are used in viral marketing and mass advertising to propagate any ideas ranging from simple commercials to those that can cause changes and development in the social structures like countering hate speech.

This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-based meme for any given sentence using an encoder-decoder architecture. For a given input sentence, a meme is generated by combining a meme template image and a text caption where the meme template image is selected from a set of popular candidates using a selection module and the meme caption is generated by an encoder-decoder model. An encoder is used to map the selected meme template and the input sentence into a meme embedding space and then a decoder is used to decode the meme caption from the meme embedding space. The generated natural language caption is conditioned on the input sentence and the selected meme template.

The model learns the dependencies between the meme captions and the meme template images and generates new memes using the learned dependencies. The quality of the generated captions and the generated memes is evaluated through both automated metrics and human evaluation. An experiment is designed to score how well the generated memes can represent popular tweets from Twitter conversations. Experiments on Twitter data show the efficacy of the model in generating memes capable of representing a sentence in online social interaction.

In the presence of big data analysis, large volume of data needs to be systematically indexed to support analytical tasks, such as feature engineering, pattern recognition, data mining, and query processing. The volume, variety, and velocity of these data necessitate sophisticated systems to help researchers understand, analyze, and dis- cover insights from heterogeneous, multidimensional data sources. Many analytical frameworks have been proposed in the literature in recent years, but challenges to accuracy, speed, and effectiveness remain hence a systematic approach to perform data signature computation and query processing in multi-dimensional space is in people’s interest. In particular, real-time and near real-time queries pose significant challenges when working with large data sets.

To address these challenges, I develop an innovative robust multi-variate fea- ture extraction algorithm over multi-dimensional temporal datasets, which is able to help understand and analyze various real-world applications. Furthermore, to an- swer queries over these features, I develop a novel resource-aware indexing framework to approximately solve top-k queries by leveraging onion-layer indexing in conjunc- tion with locality sensitive hashing. The proposed indexing scheme allows people to answer top-k queries by only accessing a bounded amount of data, which optimizes big data small for queries.

In the last decade, the immense growth of computational power, enhanced data storage capabilities, and the increasing popularity of online learning systems has led to adaptive learning systems becoming more widely available. Parallel to infrastructure enhancements, more researchers have started to study the adaptive task selection systems, concluding that suggesting tasks appropriate to students' needs may increase students' learning gains.

This work built an adaptive task selection system for undergraduate organic chemistry students using a deep learning algorithm. The proposed model is based on a recursive neural network (RNN) architecture built with Long-Short Term Memory (LSTM) cells that recommends organic chemistry practice questions to students depending on their previous question selections.

For this study, educational data were collected from the Organic Chemistry Practice Environment (OPE) that is used in the Organic Chemistry course at Arizona State University. The OPE has more than three thousand questions. Each question is linked to one or more knowledge components (KCs) to enable recommendations that precisely address the knowledge that students need. Subject matter experts made the connection between questions and related KCs.

A linear model derived from students' exam results was used to identify skilled students. The neural network based recommendation system was trained using those skilled students' problem solving attempt sequences so that the trained system recommends questions that will likely improve learning gains the most. The model was evaluated by measuring the predicted questions' accuracy against learners' actual task selections. The proposed model not only accurately predicted the learners' actual task selection but also the correctness of their answers.

The recent proliferation of online platforms has not only revolutionized the way people communicate and acquire information but has also led to propagation of malicious information (e.g., online human trafficking, spread of misinformation, etc.). Propagation of such information occurs at unprecedented scale that could ultimately pose imminent societal-significant threats to the public. To better understand the behavior and impact of the malicious actors and counter their activity, social media authorities need to deploy certain capabilities to reduce their threats. Due to the large volume of this data and limited manpower, the burden usually falls to automatic approaches to identify these malicious activities. However, this is a subtle task facing online platforms due to several challenges: (1) malicious users have strong incentives to disguise themselves as normal users (e.g., intentional misspellings, camouflaging, etc.), (2) malicious users are high likely to be key users in making harmful messages go viral and thus need to be detected at their early life span to stop their threats from reaching a vast audience, and (3) available data for training automatic approaches for detecting malicious users, are usually either highly imbalanced (i.e., higher number of normal users than malicious users) or comprise insufficient labeled data.

To address the above mentioned challenges, in this dissertation I investigate the propagation of online malicious information from two broad perspectives: (1) content posted by users and (2) information cascades formed by resharing mechanisms in social media. More specifically, first, non-parametric and semi-supervised learning algorithms are introduced to discern potential patterns of human trafficking activities that are of high interest to law enforcement. Second, a time-decay causality-based framework is introduced for early detection of “Pathogenic Social Media (PSM)” accounts (e.g., terrorist supporters). Third, due to the lack of sufficient annotated data for training PSM detection approaches, a semi-supervised causal framework is proposed that utilizes causal-related attributes from unlabeled instances to compensate for the lack of enough labeled data. Fourth, a feature-driven approach for PSM detection is introduced that leverages different sets of attributes from users’ causal activities, account-level and content-related information as well as those from URLs shared by users.

In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting of news headlines, tweets, and the price of the cryptocurrency. I fed this data into a Long Short-Term Memory Neural Network and built a model that predicted Bitcoin price for a new timeframe. The model correctly predicted 75% of test set price trends on 3.25 hour time intervals. This is higher than the 53.57% accuracy tested with a Bitcoin price model without sentiment data. I concluded public reaction to Bitcoin news headlines has an effect on the short-term price direction of the cryptocurrency. Investors can use my model to help them in their decision-making process when making short-term Bitcoin investment decisions.

Social media is a medium that contains rich information which has been shared by many users every second every day. This information can be utilized for various outcomes such as understanding user behaviors, learning the effect of social media on a community, and developing a decision-making system based on the information available. With the growing popularity of social networking sites, people can freely express their opinions and feelings which results in a tremendous amount of user-generated data. The rich amount of social media data has opened the path for researchers to study and understand the users’ behaviors and mental health conditions. Several studies have shown that social media provides a means to capture an individual state of mind. Given the social media data and related work in this field, this work studies the scope of users’ discussion among online mental health communities. In the first part of this dissertation, this work focuses on the role of social media on mental health among sexual abuse community. It employs natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; how online response differs from traditional response found in a physical world. To answer these questions, this work extracts Reddit posts on rape to understand the nature of user responses for this stigmatized topic. In the second part of this dissertation, this work expands to a broader range of online communities. In particular, it investigates the potential roles of social media on mental health among five major communities, i.e., trauma and abuse community, psychosis and anxiety community, compulsive disorders community, coping and therapy community, and mood disorders community. This work studies how people interact with each other in each of these communities and what these online forums provide a resource to users who seek help. To understand users’ behaviors, this work extracts Reddit posts on 52 related subcommunities and analyzes the linguistic behavior of each community. Experiments in this dissertation show that Reddit is a good medium for users with mental health issues to find related helpful resources. Another interesting observation is an interesting topic cluster from users’ posts which shows that discussion and communication among users help individuals to find proper resources for their problem. Moreover, results show that the anonymity of users in Reddit allows them to have discussions about different topics beyond social support such as financial and religious support.