Black- and White-Box Self-testing COTS Components

Development of a software system from existing components can surely have various benefits, but can also entail a series of problems. One type of problems is caused by a limited exchange of information between the developer and user of a component, i.e. the developer of a componentbased system. A limited exchange of information cannot only require the testing by the user but it can also complicate this tasks, since vital artifacts, source code in particular, might not be available. Self-testing components can be one response in such situation. This paper describes an enhancement of the Self-Testing COTS Components (STECC) Method so that an appropriately enabled component is not only capable of white-box testing its methods but also capable of black-box testing

On Systematic Design of Protectors for Employing OTS Items

Off-the-shelf (OTS) components are increasingly used in application areas with stringent dependability requirements. Component wrapping is a well known structuring technique used in many areas. We propose a general approach to developing protective wrappers that assist in integrating OTS items with a focus on the overall system dependability. The wrappers are viewed as redundant software used to detect errors or suspicious activity and to execute appropriate recovery when possible; wrapper development is considered as a part of system integration activities. Wrappers are to be rigorously specified and executed at run time as a means of protecting OTS items against faults in the rest of the system, and the system against the OTS item's faults. Possible symptoms of erroneous behaviour to be detected by a protective wrapper and possible actions to be undertaken in response are listed and discussed. The information required for wrapper development is provided by traceability analysis. Possible approaches to implementing “protectors” in the standard current component technologies are briefly outline

The Science of Disguise

Technological advances have made digital cameras ubiquitous, to the point where it is difficult to purchase even a mobile phone without one. Coupled with similar advances in face recognition technology, we are seeing a marked increase in the use of biometrics, such as face recognition, to identify individuals. However, remaining unrecognized in an era of ubiquitous camera surveillance remains desirable to some citizens, notably those concerned with privacy. Since biometrics are an intrinsic part of a person\u27s identity, it may be that the only means of evading detection is through disguise. We have created a comprehensive database of high-quality imagery that will allow us to explore the effectiveness of disguise as an approach to avoiding unwanted recognition. Using this database, we have evaluated the performance of a variety of automated machine-based face recognition algorithms on disguised faces. Our data-driven analysis finds that for the sample population contained in our database: (1) disguise is effective; (2) there are significant performance differences between individuals and demographic groups; and (3) elements including coverage, contrast, and disguise combination are determinative factors in the success or failure of face recognition algorithms on an image. In this dissertation, we examine the present-day uses of face recognition and their interplay with privacy concerns. We sketch the capabilities of a new database of facial imagery, unique both in the diversity of the imaged population, and in the diversity and consistency of disguises applied to each subject. We provide an analysis of disguise performance based on both a highly-rated commercial face recognition system and an open-source algorithm available to the FR community. Finally, we put forth hypothetical models for these results, and provide insights into the types of disguises that are the most effective at defeating facial recognition for various demographic populations. As cameras become more sophisticated and algorithms become more advanced, disguise may become less effective. For security professionals, this is a laudable outcome; privacy advocates will certainly feel differently

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t

Fault diversity among off-the-shelf SQL database servers

Fault tolerance is often the only viable way of obtaining the required system dependability from systems built out of "off-the-shelf" (OTS) products. We have studied a sample of bug reports from four off-the-shelf SQL servers so as to estimate the possible advantages of software fault tolerance - in the form of modular redundancy with diversity - in complex off-the-shelf software. We checked whether these bugs would cause coincident failures in more than one of the servers. We found that very few bugs affected two of the four servers, and none caused failures in more than two. We also found that only four of these bugs would cause identical, undetectable failures in two servers. Therefore, a fault-tolerant server, built with diverse off-the-shelf servers, seems to have a good chance of delivering improvements in availability and failure rates compared with the individual off-the-shelf servers or their replicated, nondiverse configurations

Bio-Inspired Robotic Fish With Vision Based Target Tracking

The lionfish is an invasive species that out-competes and overcrowds native sh species along the eastern seaboard of the United States and down into the Caribbean. Lionfish populations are growing rapidly. Current methods of monitoring lionfish populations are costly and time intensive. A bio-inspired robotic fish was built to use as an autonomous lionfish tracking platform. Lionfish are tracked visually using an onboard processor. Five different computer vision methods for identification and tracking are proposed and discussed. These include: background subtraction, color tracking, mixture of Gaussian background subtraction, speeded up robust feature (SURF), and CamShift based tracking. Each of these methods were compared and their accuracy analyzed. CamShift based tracking is determined to be the most accurate for this application. Preliminary experiments for system identification and control design are discussed