Uncovering perceived identification accuracy of in-vehicle biometric sensing

Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustere

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

EVALUATING THE USE OF UNMANNED AERIAL SYSTEMS (UAS) FOR COLLECTING THEMATIC MAPPING ACCURACY ASSESSMENT REFERENCE DATA IN NEW ENGLAND FOREST COMMUNITIES

To overcome the main drivers of global environmental change, such as land use and land cover change, evolving technologies must be adopted to rapidly and accurately capture, process, analyze, and display a multitude of high resolution spatial variables. Remote sensing technologies continue to advance at an ever-increasing rate to meet end-user needs, now in the form of unmanned aerial systems (UAS or drones). UAS have bridged the gap left by satellite imagery, aerial photography, and even ground measurements in data collection potential for all matters of information. This new platform has already been deployed in many data collection scenarios, being modified to the needs of the end user. With modern remote sensing optics and computer technologies, thematic mapping of complex communities presents a wide variety of classification methods, including both pixel-based and object-based classifiers. One essential component of using the derived thematic data as decision-making information is first validating its accuracy. The process of assessing thematic accuracy over the years has come a long way, with site-specific multivariate analysis error matrices now being the premier evaluation mechanism. In order to perform any evaluation of certainty, or correctness, a comparison to a known standard must be made, this being reference data. Methods for reference data collection in both pixel-based and object-based classification assessments are indeterminate, but can all become quite limiting due to their immense costs. This research project set out to evaluate if the new, low cost UAS platform could collect reference data for use in thematic mapping accuracy assessments. We also evaluated several collection process methods for their efficiency and effectiveness, as the use of UAS is still relatively unknown in its ability to acquire data in densely vegetated landscapes. Collected imagery was calibrated and stitched together by way of structure-from-motion (SfM), attempting calibration and configuration in both Agisoft PhotoScan and Pix4DMapper Pro to form orthomosaic models. Our results showed that flying heights below 100m above the focus area surface, while acquiring ultra-high-detailed imagery, only resulted in a maximum of 62% image calibration when generating spatial models. Flying at our legal maximum flying height of 120m above the surface (just below 400ft), we averaged 97.49% image calibration, and a gsd of 3.23cm/pixel over the 398 ha. sampled. Using a classification scheme based on judging the percent coniferous composition of the sampled units, our results during optimal UAS sampling showed a maximum of 71.43% overall accuracy and 85.71% overall accuracy, respectively, for pixel-based and object-based thematic accuracy assessments, in direct comparison to ground sampled locations. Other randomly sampled procedures for each approach achieved slightly less agreement with ground data classifications. Despite the minor drawbacks brought about by the complexity of the environment, the classification results demonstrated OBIA acquiring exceptional accuracy in reference data collection. Future expansion of the project across more study areas, and larger forest landscapes could uncover increased agreement and efficiency of the UAS platform

Smart Washers May Clean Your Clothes, But Hacks Can Clean Out Your Privacy, and Underdeveloped Regulations Could Leave You Hanging on a Line, 32 J. Marshall J. Info. Tech. & Privacy L. 259 (2016)

A house is equipped with a smart clothes washer, an intelligent HVAC system and a video enabled home security system, all running through the home network - it reduces the noise by doing laundry when no one is at home, saves energy costs by automatically changing the temperature depending who is in a room, lets the owner remotely see the kids walk in the door after school, and keeps the house safe - the owner is maximizing the use of the Internet of Things (“IoT”) devices (i.e. a network of everyday objects connected to the Internet and to each other). However, the home owner has also created at least four points for data vulnerabilities, giving a hacker four opportunities to enter the home. A single hack can allow a wrongdoer to determine when no one is home and access an empty house, spy on the children and collect PIN numbers and any sensitive data recorded by any or all of the IoT service providers, like credit card numbers. When such a data breach happens, what legal protections does a consumer have? What regulatory infrastructure is in place to prevent this type of intrusion, what data is considered protectable personal identifying information (PII), what obligations do the manufacturers have to prevent hacks, and what remedies are available to those whose privacy has been corrupted? This paper attempts to address the growing infiltration of the IoT into everyday life and to answer some of these questions by looking at the current US legal framework addressing privacy

Policy implications of ubiquitous technologies in the car : privacy, data ownership, and regulation

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2006.Includes bibliographical references (leaves 58-61).Motor vehicle travel is the primary means of transportation in the United States, providing freedom in travel and enterprise for many people. However, motor vehicle accidents are the largest component of unintentional injuries and contribute to a high degree of morbidity and mortality for all ages. This thesis analyzes the relationship between feedback technologies and driver behavior. Based on the findings, policy recommendations were made to help ensure that the privacy and trust of the public are not compromised, as ubiquitous technologies become a reality in automobiles. The thesis provides an overview of the most modem mechanisms available in cars today. Furthermore, this thesis takes the first steps to combining existing technologies into a single system that not only tracks driver behavior, but also provides feedback in the hopes of improving drive performance and safety. The qualitative discussion includes a stakeholder analysis of the prime interests and effects of all parties that are impacted by ubiquitous technologies in the car. The qualitative discussion also contains the results of four focus groups that were conducted to gain first hand insights about the view of the drivers about monitoring technologies in the car.(cont.) This study finds that most drivers have a symbiotic relationship with the technologies that exist in their car; however, drivers feel uncomfortable with a fully automated system. Their concerns rise from the belief that fully automated systems take control away from the driver. Drivers were also concerned about the privacy and security of the data collected and stored by these technologies in their vehicles. These concerns can be addressed within the existing legal framework, but additional regulations also need to be designed because as the technology changes so will the concerns. Therefore, it is important to design policies that are flexible, rather than completely depending on current regulations to address future concerns.by Alex Fernando Narváez Bustamante.S.M

Usable Authentication for Mobile Banking

Mobile banking is attractive because it allows people to do banking anytime, anywhere. One of the requirements of performing a mobile banking transaction is that users are required to login before use. The current mobile banking login method is PIN authentication; however, results from other research studies have found that there are usability concerns of using PINs. To overcome some of the concerns, researchers have suggested the use graphical passwords. In this research, we argue that another alternative input technique can be utilized. We explore a novel password input approach, called gesture passwords, of using 3-dimensional discrete gesture motions as password elements. As a result, three systems (PINs, graphical passwords and gesture passwords) were compared. This dissertation describes the design of two mobile authentication techniques: combinational graphical passwords and gesture passwords. These systems were implemented as prototypes. The prototypes along with a PIN authenticator were evaluated with users. User experience and password retention were evaluated to determine the usability and users’ acceptance of each system. Experiments were conducted to evaluate the above. Results from the experiments show that users were able to use all of the testing systems; however, the results reveal that users are more proficient and preferred to use PINs for mobile banking authentication than the other two systems

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems