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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 realtime 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 computer's 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.

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 realworld 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.

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 Dijkstra's 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.

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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 company's 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.

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