Systems and methods for automated detection of application vulnerabilities

*/Board of Regents, University of Texas Syste

COVID-19: The Information Warfare Paradigm Shift

In Kuhn's The Structure of Scientific Revolutions, the critical term is paradigm-shift when it suddenly becomes evident that earlier assumptions no longer are correct and the plurality of the scientific community that studies this domain accepts the change. These types of events can be scientific findings or as in social science system shock that creates a punctured equilibrium that sets the stage in the developments. In information warfare, recent years studies and government lines of efforts have been to engage fake news, electoral interference, and fight extremist social media as the primary combat theater in the information space, and the tools to influence a targeted audience. The COVID-19 pandemic generates a rebuttal of these assumptions. Even if fake news and extremist social media content may exploit fault lines in our society and create a civil disturbance, tensions between federal and local government, and massive protests, it is still effects that impact a part of the population. What we have seen with COVID-19, as an indicator, is that what is related to public health is far more powerful to swing public sentiment and create reactions within the citizenry that are trigger impact at a larger magnitude that has rippled through society in multiple directions

COVID-19: The Information Warfare Paradigm Shift

Thomas Kuhn\u27s The Structure of Scientific Revolutions highlights the critical term “paradigm shift,” which occurs when it suddenly becomes evident that earlier assumptions are no longer correct. The plurality of the scientific community studying this domain accepts the change. These paradigm-shifting events can be scientific findings or, as in the social sciences, a system shock that creates a punctured equilibrium, triggering a leap forward acquiring new knowledge. In information warfare, the government lines of effort have been to engage fake news, intercept electoral interference, fight extremist social media as the primary combat theater in the information space, and use the tools to influence a targeted audience to defend against an adversary that seeks to influence our population. The COVID-19 pandemic generates a rebuttal, or at least a challenge, of the information warfare assumption that our government’s authority, legitimacy, and control are mainly challenged by tampering with the electoral system, fueling extremist views, and distributing fake political news. The fake news and extremist social media content exploit fault lines in our society and create civil disturbances, tensions between federal and local government, and massive protests that impact only a fraction of the population. We have seen with COVID-19, for example, public health has a far more powerful effect on public sentiment and is more likely to create reactions of larger magnitude within the citizenry, which ripple out

Progressive One-shot Human Parsing

Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in fashion analysis. In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set of reference classes defined by any single reference example. During training, only base classes defined in the training set are exposed, which can overlap with part of reference classes. In this paper, we devise a novel Progressive One-shot Parsing network (POPNet) to address two critical challenges , i.e., testing bias and small sizes. POPNet consists of two collaborative metric learning modules named Attention Guidance Module and Nearest Centroid Module, which can learn representative prototypes for base classes and quickly transfer the ability to unseen classes during testing, thereby reducing testing bias. Moreover, POPNet adopts a progressive human parsing framework that can incorporate the learned knowledge of parent classes at the coarse granularity to help recognize the descendant classes at the fine granularity, thereby handling the small sizes issue. Experiments on the ATR-OS benchmark tailored for OSHP demonstrate POPNet outperforms other representative one-shot segmentation models by large margins and establishes a strong baseline. Source code can be found at https://github.com/Charleshhy/One-shot-Human-Parsing.Comment: Accepted in AAAI 2021. 9 pages, 4 figure

Towards access control for visual Web model management

2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task.

Driver fatigue is a prevalent problem and a major risk for road safety accounting for approximately 20-40% of all motor vehicle accidents. One strategy to prevent fatigue related accidents is through the use of countermeasure devices. Research on countermeasure devices has focused on methods that detect physiological changes from fatigue, with the fast temporal resolution from brain signals, using the electroencephalogram (EEG) held as a promising technique. This paper presents the results of nonlinear analysis using sample entropy and second-order difference plots quantified by central tendency measure (CTM) on alert and fatigue EEG signals from a driving simulated task. Results show that both sample entropy and second-order difference plots significantly increases the regularity and decreases the variability of EEG signals from an alert to a fatigue state