In this paper, an extension to rules-based fault detection is demonstrated utilizing properties of the Koopman operator. The Koopman operator is an infinite-dimensional, linear operator that captures nonlinear, finite dimensional dynamics. The definition of the Koopman operator enables algorithms that can evaluate the magnitude and coincidence of time-series data. Using spectral properties of this operator, diagnostic rule signals generated from building management system (BMS) trend data can be decomposed into components that allow the capture of device behavior at varying time-scales and to a granular level. As it relates to the implementation of fault detection (FDD), this approach creates additional spatial and temporal characterizations of rule signals providing additional data structure and increasing effectiveness with which classification techniques can be applied to the analysis process. The approach permits a knowledge base to be applied in a similar manner to that of a rules-based approach, but the introduced extensions also facilitate the definition of new kinds of diagnostics and overall provide increased analysis potential.

IntroductionPoint-Of-Sale (POS) systems are used globally to accept payment from consumers using credit or debit cards to purchase goods or services. The most popular form of POS reader, especially in the United States (US), involves swiping a credit or debit card through a magnetic scanner (Smart Card Alliance, 2011). This "traditional" POS (TPOS) system requires a dedicated, standalone card reader deployed solely for the purpose of processing transactions using credit/debit cards. Such systems have been used for many years and most consumers are familiar with them.Recently, a new POS system was introduced to the market, the mobile POS (MPOS) system (Johnson, 2012). Manufactured by several venders, including Square®, Intuit GoPayment® and Paypal Here®, MPOS systems take two forms. One type, hardware-based MPOS systems, consists of a small reader that plugs into a mobile device such as a smart phone or tablet. Consumers swipe their credit/debit cards through the device to make a payment. Merchants typically use the mobile device not just to accept payments but also for various other personal or business purposes. Payments are processed through software apps stored on the device. The other type of MPOS system is software based and usually requires manual entry of card information onto the phone or tablet. In some cases, the merchant can photograph the credit/debit card instead of entering data found on the card (www.card.io).A large body of research suggests consumers may have anxiety, fear, or concern about their personal safety and security when using new technology, especially when they are sharing personal or secure information such as credit or debit card numbers using that technology (Liu, 2012; Meuter et al., 2003; Perea y Monsuwe, Dellaert, & de Ruyter, 2004). Such anxiety was reported by consumers, for example, when making Internet-based credit card purchases a decade ago (Perea y Monsuwe et al, 2004) and more recently in using self-service bank machines (Liu, 2012). Given recent mass media publicity about cyber-crime and the potential for personal data to be stolen electronically (e.g., Whitaker, 2014), MPOS systems may introduce particular concern about crime victimization among consumers for several reasons. These include: (a) the electronic devices are used for multiple purposes by merchants, including personal matters, which may introduce greater risk of consumer information being stolen or misused; (b) they are small, mobile devices connected wirelessly and often insecurely to the Internet, which may raise fear of ready access by criminals to personal information; (c) third-party apps are both easily and typically stored on MPOS host devices and may contain malware; and (d) credit card theft has been featured prominently by national US media outlets in recent years.Given the rapid increase recently in the use of MPOS systems, the potential for high levels of consumer anxiety about crime victimization while using them based on research with similar technologies, and the dearth of empirical research on consumer anxiety about crime and personal security surrounding MPOS systems and the implications of that anxiety for industry, this study was designed to evaluate consumer fear, anxiety, and discomfort using MPOS devices at local merchants. Specifically, we tested four hypotheses: (a) consumers will generally feel comfortable using either MPOS or TPOS systems, but (b) consumers will report less concern using TPOS systems than with using MPOS systems; (c) consumers will report greater trust in TPOS systems over MPOS systems; and (d) consumers will find TPOS systems as convenient as MPOS systems. To test these hypotheses, we conducted a clustered case vs. control survey research study. Consumers making purchases at two small businesses, an ice cream shop that used a TPOS reader and a sandwich shop that used an MPOS reader, were surveyed.MethodsResearch SitesStudy sites were selected to meet the following criteria: (a) large and diverse consumer population, (b) high levels of consumer traffic during peak hours, (c) located geographically (

As the scope of building design and construction increases and building systems become more integrated, the use of building energy models has become increasingly widespread in evaluating and predicting building performance. Despite the growing sophistication of building modeling tools, errors can arise from approximations that are made by a practitioner during model creation. This paper examines the process of model zoning, i.e., how the volume of a building is divided into regions where properties are assumed to be uniform. Zoning is performed during model creation to decrease model complexity. Flowever, accuracy reduces when dissimilar regions of a building are defined by a single zone. In this paper, a systematic approach to creating zoning approximations is introduced. Utilizing the Koopman operator, the time-series output produced by a building simulation can be decomposed into spatial modes which capture the thermal behavior of a building at different time-scales. Identification of spatial structures within these modes forms a framework for the creation of simplified models of varying levels of granularity. In this paper, a detailed model is analyzed, and model accuracy is studied as coarser building representations are created using the introduced method. Published by Elsevier B.V.

The security of computer systems often relies upon decisions and actions of end users. In this paper, we set out to investigate user-centered security by concentrating at the most fundamental component governing user behavior - the human brain. We introduce a novel neuroscience-based study methodology to inform the design of user-centered security systems. Specifically, we report on an fMRI study measuring users' security performance and the underlying neural activity with respect to two critical security tasks: (1) distinguishing between a legitimate and a phishing website, and (2) heeding security (malware) warnings. At a higher level, we identify neural markers that might be controlling users' performance in these tasks, and establish relationships between brain activity and behavioral performance as well as between users' personality traits and security behavior. Our results provide a largely positive perspective towards users' capability and performance vis-a-vis these crucial security tasks. First , we show that users exhibit significant brain activity in key regions associated with decision-making, attention, and problem-solving (phishing and malware warnings) as well as language comprehension and reading (malware warnings), which means that users are actively engaged in these security tasks. Second , we demonstrate that certain individual traits, such as impulsivity measured via an established questionnaire, can have a significant negative effect on brain activation in these tasks. Third , we discover a high degree of correlation in brain activity (in decision-making regions) across phishing detection and malware warnings tasks, which implies that users' behavior in one task may potentially be predicted by their behavior in the other task. Finally , we discuss the broader impacts and implications of our work on the field of user-centered security, including the domain of security education, targeted security training, and security screening.

Whole building energy models have found widespread use in estimating the energy consumption of building systems. Within these models, usage profiles are assumed when capturing the influence of processes such as occupancy, lighting, and equipment operation. Usage profiles are defined hourly, but are repetitive in the sense that their shapes are periodic, typically, from week to week. When evaluating a design, the use of periodic usage profiles is accepted since detailed knowledge of building operation is usually unknown. In the case of an existing building, however, this approach may not accurately capture building behavior and cause an error in prediction from the resulting mismatch in operation between the actual and modeled building. In this work, fluctuations in space occupation of a multi-use university building is studied. By decomposing building temperature data using wavelets, room occupancy states (i.e. the status of a room being occupied or vacant) are estimated. From this estimate, usage profiles are generated which better capture the actual behavior of the building. Predictions of energy usage from a model simulation which utilizes this implementation is compared to building utility data as well as a simulation where conventional usage profiles are assumed.

Existing captcha solutions on the Internet are a major source of user frustration. Game captchas are an interesting and, to date, little-studied approach claiming to make captcha solving a fun activity for the users. One broad form of such captchas -- called Dynamic Cognitive Game (DCG) captchas -- challenge the user to perform a game-like cognitive task interacting with a series of dynamic images. We pursue a comprehensive analysis of a representative category of DCG captchas. We formalize, design and implement such captchas, and dissect them across: (1) fully automated attacks, (2) human-solver relay attacks, and (3) usability. Our results suggest that the studied DCG captchas exhibit high usability and, unlike other known captchas, offer some resistance to relay attacks, but they are also vulnerable to our novel dictionary-based automated attack.

As the scope of building construction increases and de- signs become more integrated, building energy models have found widespread use in evaluating building per- formance. Despite the growing sophistication of build- ing modelling tools, errors can arise from the approxima- tions that are made during model creation. This paper ad- dresses model zoning, i.e., how the volume of a building is divided into regions where properties are assumed to be uniform. Zoning is important during the creation of a model because the accuracy of prediction from simu- lating a model reduces when dissimilar zones are lumped together. In this paper, a systematic approach to creat- ing zoning approximations is introduced to investigate the effect of zoning on simulation accuracy. Applying the Koopman operator, an infinite-dimensional, linear oper- ator that captures nonlinear, finite-dimensional dynamics without linearization, a detailed building model is stud- ied. Using the Koopman operator, the temperature history of rooms produced by a building simulation can be de- composed into Koopman modes. These modes identify dynamically significant behavior which will form a basis for the creation of zoning approximations. An implemen- tation of this technique is illustrated in a building model of an actual building designed with both mechanical and natural conditioning.