Towards a holistic approach to the socio-historical analysis of vernacular photos

Although one of the most popular practices in photography since the end of the 19th century, an increase in scholarly interest in family photo albums dates back to the early 1980s. Such collections of photos may reveal sociological and historical insights regarding specific cultures and times. They are, however, in most cases scattered among private homes and only available on paper or photographic film, thus making their collection and analysis by historians, socio-cultural anthropologists, and cultural theorists very cumbersome. Computer-based methodologies could aid such a process in various ways, speeding up the cataloging step, for example, with the use of modern computer vision techniques. We here investigate such an approach, in- troducing the design and development of a multimedia application that may automatically catalog vernacular pictures drawn from family photo albums. To this aim, we introduce the IMAGO dataset, which is composed of photos belonging to family albums assembled at the University of Bologna’s Rimini campus since 2004. Ex- ploiting the proposed application, IMAGO has offered the opportunity of experimenting with photos taken between the years 1845 and 2009. In particular, it has been possible to estimate their socio-historical content, i.e., the dates and contexts of the images, without resorting to any other sources of information. Exceeding our initial expectations, such an approach has revealed its merit not only in terms of performance but also in terms of the foreseeable implications for the benefit of socio-historical research. To the best of our knowledge, this contribution is among the few that move along this path at the intersection of socio-historical studies, multimedia computing, and artificial intelligence

Optimizing resource allocation in computational sustainability: Models, algorithms and tools

The 17 Sustainable Development Goals laid out by the United Nations include numerous targets as well as indicators of progress towards sustainable development. Decision-makers tasked with meeting these targets must frequently propose upfront plans or policies made up of many discrete actions, such as choosing a subset of locations where management actions must be taken to maximize the utility of the actions. These types of resource allocation problems involve combinatorial choices and tradeoffs between multiple outcomes of interest, all in the context of complex, dynamic systems and environments. The computational requirements for solving these problems bring together elements of discrete optimization, large-scale spatiotemporal modeling and prediction, and stochastic models. This dissertation leverages network models as a flexible family of computational tools for building prediction and optimization models in three sustainability-related domain areas: 1) minimizing stochastic network cascades in the context of invasive species management; 2) maximizing deterministic demand-weighted pairwise reachability in the context of flood resilient road infrastructure planning; and 3) maximizing vertex-weighted and edge-weighted connectivity in wildlife reserve design. We use spatially explicit network models to capture the underlying system dynamics of interest in each setting, and contribute discrete optimization problem formulations for maximizing sustainability objectives with finite resources. While there is a long history of research on optimizing flows, cascades and connectivity in networks, these decision problems in the emerging field of computational sustainability involve novel objectives, new combinatorial structure, or new types of intervention actions. In particular, we formulate a new type of discrete intervention in stochastic network cascades modeled with multivariate Hawkes processes. In conjunction, we derive an exact optimization approach for the proposed intervention based on closed-form expressions of the objective functions, which is applicable in a broad swath of domains beyond invasive species, such as social networks and disease contagion. We also formulate a new variant of Steiner Forest network design, called the budget-constrained prize-collecting Steiner forest, and prove that this optimization problem possesses a specific combinatorial structure, restricted supermodularity, that allows us to design highly effective algorithms. In each of the domains, the optimization problem is defined over aspects that need to be predicted, hence we also demonstrate improved machine learning approaches for each.Ph.D