Background: This paper critically discusses the use and merits of global indices, in particular, the Global Health Security Index (GHSI; Cameron et al.https://www.ghsindex.org/#l-section--map) in times of an imminent crisis, such as the current pandemic. This index ranked 195 countries according to their expected preparedness in the case of a pandemic or other biological threat. The coronavirus disease 2019 (Covid-19) pandemic provides the background to compare each country's predicted performance from the GHSI with the actual performance. In general, there is an inverted relation between predicted versus actual performance, i.e. the predicted top performers are among those that are the worst hit. Obviously, this reflects poorly on the potential policy uses of this index in imminent crisis management.Methods: The paper analyses the GHSI and identifies why it may have struggled to predict actual pandemic preparedness as evidenced by the Covid-19 pandemic. The paper also uses two different data sets, one from the Worldmeter on the spread of the Covid-19 pandemics, and the other from the International Network for Government Science Advice (INGSA) Evidence-to-Policy Tracker, to draw comparisons between the actual introduction of pandemic response policies and the corresponding death rate in 29 selected countries.Results: This paper analyses the reasons for the poor match between prediction and reality in the index, and mentions six general observations applying to global indices in this respect. These observations are based on methodological and conceptual analyses. The level of abstraction in these global indices builds uncertainties upon uncertainties and hides implicit value assumptions, which potentially removes them from the policy needs on the ground.Conclusions: From the analysis, the question is raised if the policy community might have better tools for decision-making in a pandemic. On the basis of data from the INGSA Evidence-to-Policy Tracker, and with backing in studies from social psychology and philosophy of science, some simple heuristics are suggested, which may be more useful than a global index.

As we move towards improving the skill of computers to play games like chess against humans, the ability to accurately perceive real-world game boards and game states remains a challenge in many cases, hindering the development of game-playing robots. In this paper, we present a computer vision algorithm developed as part of a chess robot project that detects the chess board, squares, and piece positions in relatively unconstrained environments. Dynamically responding to lighting changes in the environment, accounting for perspective distortion, and using accurate detection methodologies results in a simple but robust algorithm that succeeds 100% of the time in standard environments, and 80% of the time in extreme environments with external lighting. The key contributions of this paper are a dynamic approach to the Hough line transform, and a hybrid edge and morphology-based approach for object/occupancy detection, that enable the development of a robot chess player that relies solely on the camera for sensory input.

This paper presents a project that allows the Baxter humanoid robot to play chess against human players autonomously. The complete solution uses three main subsystems: computer vision based on a single camera embedded in Baxter's arm to perceive the game state, an open-source chess engine to compute the next move, and a mechatronics subsystem with a 7-DOF arm to manipulate the pieces. Baxter can play chess successfully in unconstrained environments by dynamically responding to changes in the environment. This implementation demonstrates Baxter's capabilities of vision-based adaptive control and small-scale manipulation, which can be applicable to numerous applications, while also contributing to the computer vision chess analysis literature.

The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.

In an age of increasing camera-based surveillance, there is also an increase in privacy concerns being voiced by the surveilled. Camera surveillance systems are expanding beyond law enforcement and public safety applications towards "video analytics" scenarios that act as a source of data for business and planning decisions. It is necessary for us to consider how perceptions of privacy may change under different conditions, so that appropriate safeguards can be designed for the relevant systems. Through an on-line survey, we determine that privacy concerns are not universal, and that they are heavily dependent on the specific context being considered. We find a wide distribution of perception between the respondents, and identify some of the underlying factors that lead to differences in opinion between different contexts.