A framework for reasoning about the human in the loop

Many secure systems rely on a \u27human in the loop\u27 to perform security-critical functions. However, humans often fail in their security roles. Whenever possible, secure system designers should find ways of keeping humans out of the loop. However, there are some tasks for which feasible or cost effective alternatives to humans are not available. In these cases secure system designers should engineer their systems to support the humans in the loop and maximize their chances of performing their security-critical functions successfully. This paper proposes a framework for reasoning about the human in the loop that provides a systematic approach to identifying potential causes for human failure. This framework can be used by system designers to identify problem areas before a system is built and proactively address deficiencies. System operators can also use this framework to analyze the root cause of security failures that have been attributed to \u27human error.\u27 Examples are provided to illustrate the applicability of this framework to a variety of secure systems design problems, including anti-phishing warnings and password policies

The evolution of tropos: Contexts, commitments and adaptivity

Software evolution is the main research focus of the Tropos group at University of Trento (UniTN): how do we build systems that are aware of their requirements, and are able to dynamically reconfigure themselves in response to changes in context (the environment within which they operate) and requirements. The purpose of this report is to offer an overview of ongoing work at UniTN. In particular, the report presents ideas and results of four lines of research: contextual requirements modeling and reasoning, commitments and goal models, developing self-reconfigurable systems, and requirements awareness

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed