Matching Items (7)
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- All Subjects: Computer Science
- All Subjects: Distracted Driving
- Genre: Doctoral Dissertation
- Status: Published
Description
Node-link diagrams are widely used to visualize the relational structure of real world datasets. As identical data can be visualized in infinite ways by simply changing the spatial arrangement of the nodes, one of the important research topics of the graph drawing community is to visualize the data in the way that can facilitate people's comprehension. The last three decades have witnessed the growth of algorithms for automatic visualization. However, despite the popularity of node-link diagrams and the enthusiasm in improving computational efficiency, little is known about how people read these graphs and what factors (layout, size, density, etc.) have impact on their effectiveness (the usability aspect of the graph, e.g., are they easy to understand?). This thesis is comprehensive research to investigate the factors that affect people's understanding of node-link diagrams using eye-tracking methods. Three experiments were conducted, including 1) a pilot study with 22 participants to explore the layout and size effect; 2) an eye tracking experiment with 43 participants to investigate the layout, size and density effect on people's graph comprehension using abstract node-link diagram and generic tasks; and 3) an eye tracking experiment with the same participants to investigate the same effects using a real visualization analytic application. Results showed that participants' spatial reasoning ability had significant impact on people's graph reading performance. Layout, size, and density were all found to be significant effects under different task circumstances. The applicability of the eye tracking methods on visualization evaluation has been confirmed by providing detailed evidence that demonstrates the cognitive process of participants' graph reading behavior.
ContributorsLiu, Qing (Author) / McKenna, Anna (Thesis advisor) / Jennifer, Jennifer (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.
ContributorsAlomari, Jamil (Author) / Mayyas, AbdRaouf (Thesis advisor) / Cooke, Nancy J. (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.
ContributorsMcNabb, Jaimie Christine (Author) / Gray, Dr. Rob (Thesis advisor) / Branaghan, Dr. Russell (Committee member) / Becker, Dr. Vaughn (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.
ContributorsCheeks, Loretta H. (Author) / Gaffar, Ashraf (Thesis advisor) / Wald, Dara M (Committee member) / Ben Amor, Hani (Committee member) / Doupe, Adam (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking.
This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.
This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.
ContributorsLu, Yafeng (Author) / Maciejewski, Ross (Thesis advisor) / Cooke, Nancy J. (Committee member) / Liu, Huan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2017
Description
Cyber threats are growing in number and sophistication making it important to continually study and improve all dimensions of cyber defense. Human teamwork in cyber defense analysis has been overlooked even though it has been identified as an important predictor of cyber defense performance. Also, to detect advanced forms of threats effective information sharing and collaboration between the cyber defense analysts becomes imperative. Therefore, through this dissertation work, I took a cognitive engineering approach to investigate and improve cyber defense teamwork. The approach involved investigating a plausible team-level bias called the information pooling bias in cyber defense analyst teams conducting the detection task that is part of forensics analysis through human-in-the-loop experimentation. The approach also involved developing agent-based models based on the experimental results to explore the cognitive underpinnings of this bias in human analysts. A prototype collaborative visualization tool was developed by considering the plausible cognitive limitations contributing to the bias to investigate whether a cognitive engineering-driven visualization tool can help mitigate the bias in comparison to off-the-shelf tools. It was found that participant teams conducting the collaborative detection tasks as part of forensics analysis, experience the information pooling bias affecting their performance. Results indicate that cognitive friendly visualizations can help mitigate the effect of this bias in cyber defense analysts. Agent-based modeling produced insights on internal cognitive processes that might be contributing to this bias which could be leveraged in building future visualizations. This work has multiple implications including the development of new knowledge about the science of cyber defense teamwork, a demonstration of the advantage of developing tools using a cognitive engineering approach, a demonstration of the advantage of using a hybrid cognitive engineering methodology to study teams in general and finally, a demonstration of the effect of effective teamwork on cyber defense performance.
ContributorsRajivan, Prashanth (Author) / Cooke, Nancy J. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2014