Nnanna Ekedebe

The SoundsRight CAPTCHA: an improved approach to audio human interaction proofs for blind users

In this paper we describe the development of a new audio CAPTCHA called the SoundsRight CAPTCHA, and the evaluation of the CAPTCHA with 20 blind users. Blind users cannot use visual CAPTCHAs, and it has been documented in the research literature that the existing audio CAPTCHAs have task success rates below 50% for blind users. The SoundsRight audio CAPTCHA presents a real-time audio-based challenge in which the user is asked to identify a specific sound (for example the sound of a bell or a piano) each time it occurs in a series of 10 sounds that are played through the computers audio system. Evaluation results from three rounds of usability testing document that the task success rate was higher than 90% for blind users. Discussion, limitations, and suggestions for future research are also presented.

A threat monitoring system for smart mobiles in enterprise networks

With the development of modern mobile operating systems, computing and communication technologies, smart mobile devices have been widely used to support rich applications and have been integrated to enterprise networks for various organizations. With accessing sensitive personal and business information, the security of smart mobile devices has become a serious problem for enterprise networks. To address this issue, we developed a threat monitoring system to monitor and detect threats on mobile devices in enterprise networks. In our system, the detection related information will be collected by mobile devices and transmitted to the operation center, which will further monitor and detect threats by using both signature and anomaly based detection schemes. Using real-world benign and malware samples, our experimental data shows that our developed system can accurately and effectively detect malware on the Android platform with a low overhead to the system in terms of energy and CPU usage.

On a simulation study of cyber attacks on vehicle-to-infrastructure communication (V2I) in Intelligent Transportation System (ITS)

An intelligent transportation system (ITS) is one typical cyber-physical system (CPS) that aims to provide efficient, effective, reliable, and safe driving experiences with minimal congestion and effective traffic flow management. In order to achieve these goals, various ITS technologies need to work synergistically. Nonetheless, ITS’s reliance on wireless connectivity makes it vulnerable to cyber threats. Thus, it is critical to understand the impact of cyber threats on ITS. In this paper, using real-world transportation dataset, we evaluated the consequences of cyber threats – attacks against service availability by jamming the communication channel of ITS. In this way, we can have a better understanding of the importance of ensuring adequate security respecting safety and life-critical ITS applications before full and expensive real-world deployments. Our experimental data shows that cyber threats against service availability could adversely affect traffic efficiency and safety performances evidenced by exacerbated travel time, fuel consumed, and other evaluated performance metrics as the communication network is compromised. Finally, we discuss a framework to make ITS secure and more resilient against cyber threats.

Towards experimental evaluation of intelligent Transportation System safety and traffic efficiency

Traffic efficiency and safety are major hallmarks of Intelligent Transportation Systems (ITS). To accurately validate and investigate the effectiveness of traffic efficiency and safety application of ITSs, realistic studies are highly demanded [1]. In this paper, using real-world traffic and simulation data, we developed a realistic ITS test bed and a mobile application known as the Incident Warning Application (IWA) with the view of answering the following question: what is the traffic efficiency and safety benefits of Vehicle-to-Infrastructure (V2I) communications in a realistic ITS environment? Our real-world dataset consists of six weeks road traffic data of the Maryland (MD)/Washington DC and Virginia (VA) areas from August 8th, 2012 to September 27th, 2012. Our evaluation data shows that vehicles running our IWA application show improvements in almost all of the performance metrics evaluated. Specifically, our data shows that improvements in travel time (139.89%), fuel consumption (11.77%), and environmental emissions - carbon dioxide [CO2] (11.77%), etc. can be achieved through V2I communication.

On an efficient and effective Intelligent Transportation System (ITS) using field and simulation data

Intelligent transportation system (ITS) applications are expected to provide a more efficient, effective, reliable, and safe driving experience, which can minimize road traffic congestion resulting in a better traffic flow management. To efficiently manage traffic flows, in this paper, we compare the effectiveness of two well-known vehicle routing algorithms: the Dijkstras shortest path algorithm and the A* (Astar) algorithm in terms of the total travel time and the travel distance. To this end, we built a generic ITS test-bed and created several real-world driving scenarios using field and simulation data to evaluate the performance of these two routing algorithms. The dataset used in our simulation is six weeks traffic volume data from 08/01/2012 to 09/27/2012 in the Maryland (MD)/Washington DC and Virginia (VA) area. Our simulation data shows that an increase in network size results in scalability problems as the efficiency and effectiveness of these algorithms diminishes in larger road networks with greater traffic volume densities, flow rates, and congested conditions. In addition, the imprecision of the road network increases as the network size and the traffic volume density increases. Our study shows that the ability of these vehicular routing algorithms to adaptively route traffic depends on the size and type of road networks, and the current roadway conditions.

Automated analysis and evaluation of SEC documents

This paper presents an intelligent corporate governance analysis and rating system, called AAE System, capable of retrieving SEC required documents of public companies and performing analysis and rating in terms of recommended corporate governance practices. With Machine Learning, local knowledge bases, databases, and semantic networks, the AAE system is able to automatically evaluate the strengths, deficiencies, and risks of a companys corporate governance practices and board of directors based on the documents stored in the SEC EDGAR database[1]. The produced score reduces a complex corporate governance process and related policies into a single number which enables concerned government agencies, investors and legislators to assess the governance characteristics of individual companies.

A threat monitoring system in enterprise networks with smart mobiles

With the development of modern mobile operating systems, computing, and communication technologies, smart mobile devices have been widely used to support rich applications and have been integrated into enterprise networks for organisations to improve business operations. When accessing sensitive pieces of personal and business information, the lack of strong security in smart mobile devices has become a serious issue. In this paper, we developed a threat monitoring system to monitor and detect threats in enterprise networks with mobile devices. We implemented both signature and anomaly based schemes to monitor and detect threats. To evaluate the effectiveness of our threat monitoring system, we used real-world samples of benign apps and malware samples to conduct experiments on Android mobile devices. Our experimental data shows that our developed system can accurately and effectively detect malware on the Android platform while incurring low overhead to the system in terms of energy and CPU usage.

On an investigation into Intelligent Transportation System (ITS) safety and traffic efficiency applications

Vehicle-to-X (V2X) ( vehicle-to-vehicle [V2V], and vehicle-to-infrastructure [V2I]) communication, used in intelligent transportation system (ITS)/vehicular ad hoc networks (VANETs), promises improved traffic efficiency, road safety, and provision of infotainment services, etc. However, the levels of these improvements have not been clearly researched and documented especially in realistic environments [2]. Consequently, using field and simulation data, we investigate the safety and traffic efficiency application benefits of V2V communication applications in a realistic scenario. In order to do this, we built a real-world simulation test-bed using real-world/field traffic data of the Maryland (MD)/Washington DC and Virginia (VA) area from July 2012 to December 2012. In addition, we developed an application called incident warning application (IWA) of which IWA-equipped vehicles make use of it to bypass a compound road accident, slippery roadway caused by ice, and reduced visibility as a result of fog; unequipped/classic vehicles are unaware of this and hence suffer adverse effects. On the average, our results show that, indeed, tangible benefits/improvements with respect to travel time (126.78%), average speed (56.12%), fuel consumption (8.05%), CO2 emissions (8.05%) together with other evaluated performance metrics are derivable from V2V communication especially at specific IWA-equipped vehicles penetration rates.

On an efficient and effective intelligent transportation system (ITS) safety and traffic efficiency application with corresponding driver behavior

Driver distraction could result in safety compromises attributable to distractions from in-vehicle equipment usage [1]. The effective design of driver-vehicle interfaces (DVIs) and other human-machine interfaces (HMIs) together with their usability, and accessibility while driving become important [2]. Driving distractions can be classified as: visual distractions (any activity that takes your eyes away from the road), cognitive distraction (any activity that takes your mind away from the course of driving), and manual distractions (any activity that takes your hands away from the steering wheel [2]). Besides, multitasking during driving is a distractive activity that can increase the risks of vehicular accidents. To study the driver’s behaviors on the safety of transportation system, using an in-vehicle driver notification application, we examined the effects of increasing driver distraction levels on the evaluation metrics of traffic efficiency and safety by using two types of driver models: young drivers (ages 16-25 years) and middle-age drivers (ages 30-45 years). Our evaluation data demonstrates that as a drivers distraction level is increased, less heed is given to change route directives from the in-vehicle on-board unit (OBU) using textual, visual, audio, and haptic notifications. Interestingly, middle-age drivers proved more effective/resilient in mitigating the negative effects of driver distraction over young drivers [2].

An evaluation into the efficiency and effectiveness of machine learning algorithms in realistic traffic pattern prediction using field data

Accurate and timely knowledge is critical in intelligent transportation system (ITS) as it leads to improved traffic flow management. The knowledge of the past can be useful for the future as traffic patterns normally follow a predictable pattern with respect to time of day, and day of week. In this paper, we systematically evaluated the prediction accuracy and speed of several supervised machine learning algorithms towards congestion identification based on six weeks real-world traffic data from August 1st, 2012 to September 12th, 2012 in the Maryland (MD)/Washington DC, and Virginia (VA) area. Our dataset consists of six months traffic data pattern from July 1, 2012 to December 31, 2012, of which 6 weeks was used as a representative sample for the purposes of this study on our reference roadway – I-270. Our experimental data shows that with respect to classification, classification tree (Ctree) could provide the best prediction accuracy with an accuracy rate of 100% and prediction speed of 0.34 seconds. It is pertinent to note that variations exist respecting prediction accuracy and prediction speed; hence, a tradeoff is often necessary respecting the priority of the applications in question. It is also imperative to note from the outset that, algorithm design and calibration are important factors in determining their effectiveness.