Augmenting Authentication with Context-Specific Behavioral Biometrics

Behavioral biometrics, being non-intrusive and cost-efficient, have the potential to assist user identification and authentication. However, user behaviors can vary significantly for different hardware, software, and applications. Research of behavioral biometrics is needed in the context of a specific application. Moreover, it is hard to collect user data in real world settings to assess how well behavioral biometrics can discriminate users. This work aims to improving authentication by behavioral biometrics obtained for user groups. User data of a webmail application are collected in a large-scale user experiment conducted on Amazon Mechanical Turk. Used in a continuous authentication scheme based on user groups, off-line identity attribution and online authentication analytic schemes are proposed to study the applicability of application-specific behavioral biometrics. Our results suggest that the useful user group identity can be effectively inferred from users’ operational interaction with the email application

On the Benefits of Stochastic Economic Dispatch in Real-Time Electricity Markets

Independent system operators (ISOs) in the US clear their real-time electricity market every five minutes, optimizing energy and reserve dispatches to minimize operating costs while meeting technical and regulatory constraints. Currently, many real-time markets are cleared by solving a single-period, deterministic, security-constrained economic dispatch (SCED). Nevertheless, given the growing penetration of renewable generation and the forthcoming retirement of conventional generation units, it becomes increasingly challenging to manage operational uncertainty at the real-time stage via the current SCED formulations. This limitation is best illustrated by the recent introduction into the real-time market of multiple short-term ramping products, which aim at bridging the gap between deterministic and stochastic formulations. In contrast, this paper explores the scalability and potential benefits of explicitly considering uncertainty in real-time market formulations by combining multi-period look-ahead dispatch (LAD) and stochastic look-ahead (SLAD) formulations. An accelerated Benders' decomposition is presented to solve the resulting problems efficiently. The paper conducts extensive numerical experiments on a real, industry-size transmission grid that benchmarks the proposed approaches and evaluates their benefits. The results demonstrate that stochastic optimization is now tractable enough to be used in real-time markets. Furthermore, the combination of multi-period and stochastic look-ahead offers significant benefits in both reliability and cost, as SLAD can better position online generators to accommodate future ramping needs, thereby reducing future operational costs and violations. Overall, SLAD reduces import costs and the risk of transmission violation and saves an average of more than 2% of costs compared to SCED

Understanding stories via event sequence modeling

Understanding stories, i.e. sequences of events, is a crucial yet challenging natural language understanding (NLU) problem, which requires dealing with multiple aspects of semantics, including actions, entities and emotions, as well as background knowledge. In this thesis, towards the goal of building a NLU system that can model what has happened in stories and predict what would happen in the future, we contribute on three fronts: First, we investigate the optimal way to model events in text; Second, we study how we can model a sequence of events with the balance of generality and specificity; Third, we improve event sequence modeling by joint modeling of semantic information and incorporating background knowledge. Each of the above three research problems poses both conceptual and computational challenges. For event extraction, we find that Semantic Role Labeling (SRL) signals can be served as good intermediate representations for events, thus giving us the ability to reliably identify events with minimal supervision. In addition, since it is important to resolve co-referred entities for extracted events, we make improvements to an existing co-reference resolution system. To model event sequences, we start from studying within document event co-reference (the simplest flow of events); and then extend to model two other more natural event sequences along with discourse phenomena while abstracting over the specific mentions of predicates and entities. We further identify problems for the basic event sequence models, where we fail to capture multiple semantic aspects and background knowledge. We then improve our system by jointly modeling frames, entities and sentiments, yielding joint representations of all these semantic aspects; while at the same time incorporate explicit background knowledge acquired from other corpus as well as human experience. For all tasks, we evaluate the developed algorithms and models on benchmark datasets and achieve better performance compared to other highly competitive methods

Risk Assessment Matrix of Operational Safety (RAMOS): Aviation Safety with a MATLAB® Design Toolkit

Safety is the priority of the aviation industry that requires continuous support and improvement. While the Safety Management Systems (SMS) is mandatory for the Federal Aviation Administration (FAA) Federal Aviation Regulation (FAR) Part 121 air carriers and Part 139 airports in the United States, SMS remains optional to General Aviation (GA) due to various reasons including limited budget and manpower associated with technologies. This paper aims to promote the adoption of MATLAB® to develop a low-cost Risk Assessment Matrix of Operational Safety (RAMOS) (risk calculation and control) for GA operators. A case is presented to demonstrate the application of MATLAB® for the safety committee’s usage when going through risk assessment, control options, and decision making via a computer, tablet, or smartphone. A future comprehensive risk management toolkit can be expected with the introduction of RAMOS using MATLAB®

Understanding Security Behavior of Real Users: Analysis of a Phishing Study

This paper presents a set of statistical analyses on an empirical study of phishing email sorting by real online users. Participants were assigned to multitasking and/or incentive conditions in unattended web-based tasks that are the most realistic in any comparable study to date. Our three stages of analyses included logistic regression models to identify individual phishing “cues” contributing to successful classifications, statistical significance tests assessing the links between participants’ training experience and self-assessments of success to their actual performance, significance tests searching for significant demographic factors influencing task completion performance, and lastly k-means clustering based on a range of performance measures and utilizing participants’ demographic attributes. In particular, the results indicate that multitasking and incentives create complex dynamics while demographic traits and cybersecurity training can be informative predictors of user security behavior. These findings strongly support the benefits of security training and education and advocate for customized and differentiated interventions to increase users’ success of correctly identifying phishing emails

On truth and polarity in negation processing: language-specific effects in non-linguistic contexts

IntroductionThis study examines how negation is processed in a nonverbal context (e.g., when assessing ▲ ≠ ▲) by speakers of a truth-based system like Mandarin and a polarity-based system like English. In a truth-based system, negation may take longer to process because it is typically attached to the negation as a whole (it is not true that triangle does not equal triangle), whereas in polarity-based systems, negation is processed relatively faster because it is attached to just the equation symbol (triangle does not equal triangle), which is processed relatively faster. Our hypothesis was that negation processing routines previously observed for verbal contexts, namely that speakers of Mandarin get slowed down more when processing negative stimuli than positive stimuli compared to speakers of English, also extend to contexts when language use is not obligatory.MethodsTo test this, we asked participants to agree/disagree with equations comprising simple shapes and positive ‘=’ or negative ‘≠’ equation symbols. English speakers showed a response-time advantage over Mandarin speakers in negation conditions. In a separate experiment, we also tested the contribution of equation symbols ‘≠’/‘=’ to the cognitive demands by asking participants to judge shape sameness in symbol-free trials, such as ▲ ■. This comparison allowed us to test whether crosslinguistic differences arise not because of shape congruence judgement but arguably due to negation attachment.Results and discussionThe effect of the ‘≠’ symbol on shape congruence was language-specific, speeding up English speakers but slowing down Mandarin speakers when the two shapes differed. These findings suggest language-specific processing of negation in negative equations, interpreted as novel support for linguistic relativity