We present ``Together about Dementia'', a mobile health app that aims to help persons with dementia when they get lost through the help of caregivers, relatives, and volunteering citizens. When the app detects that a person with dementia is disoriented, wandering, and getting lost, it triggers an alarm that activates a relative and possibly a volunteer in the proximity. A backend system based on a microservice and serverless architecture performs the detection of wandering and the subsequent coordination of users that are put on a mission to the rescue of the person with dementia. The backend system implements an AI technique for spatio-temporal anomaly detection based on location data recorded by the frontend system installed on the portable device of the person with dementia.

Recent approaches have proven the effectiveness of local outlier factor-based outlier detection when applied over traffic flow probability distributions. However, these approaches used distance metrics based on the Bhattacharyya coefficient when calculating probability distribution similarity. Consequently, the limited expressiveness of the Bhattacharyya coefficient restricted the accuracy of the methods. The crucial deficiency of the Bhattacharyya distance metric is its inability to compare distributions with non-overlapping sample spaces over the domain of natural numbers. Traffic flow intensity varies greatly, which results in numerous non-overlapping sample spaces, rendering metrics based on the Bhattacharyya coefficient inappropriate. In this work, we address this issue by exploring alternative distance metrics and showing their applicability in a massive real-life traffic flow data set from 26 vital intersections in The Hague. The results on these data collected from 272 sensors for more than two years show various advantages of the Earth Mover's distance both in effectiveness and efficiency.

Collaborative learning has been widely used to foster students’ communication and joint knowledge construction. However, the classification of learners into well-structured groups is one of the most challenging tasks in the field. The aim of this study is to propose a novel method to form intra-heterogeneous and inter-homogeneous groups based on relevant student characteristics. Such a method allows for the consideration of multiple student characteristics and can handle both numerical and categorical characteristic types simultaneously. It assumes that the teacher provides an order of importance of the characteristics, then it solves the grouping problem as a lexicographic optimization problem in the given order. We formulate the problem in mixed integer linear programming (MILP) terms and solve it to optimality. A pilot experiment was conducted with 29 college freshmen considering three general characteristics (i.e., 13 specific features) including knowledge level, demographic information, and motivation. Results of such an experiment demonstrate the validity and computational feasibility of the algorithmic approach. Large-scale studies are needed to assess the impact of the proposed grouping method on students’ learning experience and academic achievement.

Urban traffic data consists of observations like number and speed of cars or other vehicles at certain locations as measured by deployed sensors. These numbers can be interpreted as traffic flow which in turn relates to the capacity of streets and the demand of the traffic system. City planners are interested in studying the impact of various conditions on the traffic flow, leading to unusual patterns, i.e., outliers. Existing approaches to outlier detection in urban traffic data take into account only individual flow values (i.e., an individual observation). This can be interesting for real time detection of sudden changes. Here, we face a different scenario: The city planners want to learn from historical data, how special circumstances (e.g., events or festivals) relate to unusual patterns in the traffic flow, in order to support improved planing of both, events and the layout of the traffic system. Therefore, we propose to consider the sequence of traffic flow values observed within some time interval. Such flow sequences can be modeled as probability distributions of flows. We adapt an established outlier detection method, the local outlier factor (LOF), to handling flow distributions rather than individual observations. We apply the outlier detection online to extend the database with new flow distributions that are considered inliers. For the validation we consider a special case of our framework for comparison with state-of-the-art outlier detection on flows. In addition, a real case study on urban traffic flow data showcases that our method finds meaningful outliers in the traffic flow data.

We consider the problem of finding cheapest flight routes through free route airspaces in a 2D setting. We subdivide the airspace into regions determined by a Voronoi subdivision around the points from a weather forecast. This gives rise to a regular grid of rectangular regions (quads) with every quad having an associated vector-weight that represents the wind magnitude and direction. Finding a cheapest path in this setting corresponds to finding a piece-wise linear path determined by points on the boundaries of the quads. In our solution approach, we discretize such boundaries by introducing border points and only consider segments connecting border points belonging to the same quad. While classic shortest path graph algorithms are available and applicable to the graphs originating from these border points, we design an algorithm that exploits the geometric structure of our scenario and show that this algorithm is more efficient in practice than classic graph-based algorithms. In particular, it scales better with the number of quads in the subdivision of the airspace, making it possible to find more accurate routes or to solve larger problems.

Flight routes are paths calculated on a network of waypoints representing 3D-coordinates. A common approach is first to calculate a path in a 2D-network, taking into account feasibility constraints, and then to optimize the altitude of the flight.

We consider an integrated biomass inventory and energy production problem that arises from scenario analysis in national energy planning. It addresses together two decision stages that have previously been kept distinct: the decisions on the purchase of biomass and the decisions on the production of electricity and heat of each power plant. We model the problem as a stochastic mixed 0-1 integer linear programming problem. Since practical instances of this problem are very large, we experimentally assess a relaxed formulation to obtain near-optimal solutions. In addition, we study a Benders decomposition that exploits the problem structure of the relaxed formulation. In a distributed computing architecture this decomposition allows to increase the number of included scenarios and thus to better address the uncertainty of the data. Computational results indicate that at parity of information the approximation provided by the relaxed model is good. However, by allowing to increase the amount of information treated it can provide more accurate predictions. On a multicore computing architecture a state-of-the-art MIP solver operating on the undecomposed model is sufficient to achieve similar performance as the Benders decomposition. However, the use of the solver in a distributed computing environment is not obvious and the Benders decomposition is a more easily implementable and scalable approach.

We consider the 2-edge-connectivity augmentation problem: given a graph S = (V,E) which is not 2-edge-connected and a set of new edges E′ ⊆ V × V with nonnegative weights, find a minimum cost subset X of E′ such that adding the edges of X to S results in a 2-edge-connected graph. A practical application is the extension of an existing telecommunication network to become robust against single link failures. We compare, experimentally, different algorithms for solving general and large-scale instances. This includes exact methods based on mathematical programming, simple construction heuristics, and metaheuristics. As part of the design of heuristics, we consider different neighborhood structures for local search, among which is a very large scale neighborhood. In all cases, we exploit approaches through the graph formulation as well as through an equivalent set covering formulation. The results indicate that exact solutions by means of a basic integer programming model can be obtained in reasonably short time even on networks with 800 vertices and around 287,000 edges. Alternatively, an advanced heuristic algorithm based on subgradient optimization and iterated greedy often finds the optimal solution and is very fast. All previous benchmark instances are easily solved to optimality and new, larger instances are introduced and studied. © 2010 Wiley Periodicals, Inc. NETWORKS, 2010