ASTRO: Reducing COVID-19 Exposure through Contact Prediction and Avoidance

The fight against the COVID-19 pandemic has highlighted the importance and benefits of recommending paths that reduce the exposure to and the spread of the SARS-CoV-2 coronavirus by avoiding crowded indoor or outdoor areas. Existing path discovery techniques are inadequate for coping with such dynamic and heterogeneous (indoor and outdoor) environments—they typically find an optimal path assuming a homogeneous and/or static graph, and hence they cannot be used to support contact avoidance. In this article, we pose the need for Mobile Contact Avoidance Navigation and propose ASTRO ( A ccessible S patio- T emporal R oute O ptimization), a novel graph-based path discovering algorithm that can reduce the risk of COVID-19 exposure by taking into consideration the congestion in indoor spaces. ASTRO operates in an A * manner to find the most promising path for safe movement within and across multiple buildings without constructing the full graph. For its path finding, ASTRO requires predicting congestion in corridors and hallways. Consequently, we propose a new grid-based partitioning scheme combined with a hash-based two-level structure to store congestion models, called CM-Structure , which enables on-the-fly forecasting of congestion in corridors and hallways. We demonstrate the effectiveness of ASTRO and the accuracy of CM-Structure ’s congestion models empirically with realistic datasets, showing up to one order of magnitude reduction in COVID-19 exposure.

PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment

Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into direct contact with patients with COVID-19. However, we find that the existing systems lack functionality and flexibility , which may hurt the success of contact tracing. Specifically, they cannot detect indirect contact (e.g., people may be exposed to COVID-19 by using a contaminated sheet at a restaurant without making direct contact with the infected individual); they also cannot flexibly change the rules of “risky contact,” such as the duration of exposure or the distance (both spatially and temporally) from a patient with COVID-19 that is considered to result in a risk of exposure, which may vary with the environmental situation. In this article, we propose an efficient and secure contact tracing system that enables us to trace both direct contact and indirect contact. To address the above problems, we need to utilize users’ trajectory data for PCT, which we call trajectory-based PCT . We formalize this problem as a spatiotemporal private set intersection that satisfies both the security and efficiency requirements. By analyzing different approaches such as homomorphic encryption, which could be extended to solve this problem, we identify the trusted execution environment (TEE) as a candidate method to achieve our requirements. The major challenge is how to design algorithms for a spatiotemporal private set intersection under the limited secure memory of the TEE. To this end, we design a TEE-based system with flexible trajectory data encoding algorithms. Our experiments on real-world data show that the proposed system can process hundreds of queries on tens of millions of records of trajectory data within a few seconds.

Another Look at Privacy-Preserving Automated Contact Tracing

In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have been proposed, and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this article, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability, and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing Bluetooth Low Energy based ACT solutions, including centralized and decentralized ones. In addition, we examine the existing venue-based ACT solutions and identify their privacy and security concerns. Then, we propose a generic venue-based ACT solution and a concrete instantiation based on Bluetooth Low Energy technology. Our solution monitors users’ contacting history only in virus-spreading-prone venues and offers higher-level protection for both security and privacy than its predecessors. Finally, we evaluate our solution from security, privacy, and efficiency perspectives, and also highlight how to reduce false positives in some specific indoor environments.

Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

Bayesian Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays

This article presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralization, and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximize the agreement between the modeled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an a priori spatial structure that embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.

Social-Spatial Group Queries with Keywords

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

CityLightSense: A Participatory Sensing-based System for Monitoring and Mapping of Illumination levels

Adequate nighttime lighting of city streets is necessary for safe vehicle and pedestrian movement, deterrent of crime, improvement of the citizens’ perceptions of safety, and so on. However, monitoring and mapping of illumination levels in city streets during the nighttime is a tedious activity that is usually based on manual inspection reports. The advancement in smartphone technology comes up with a better way to monitor city illumination using a rich set of smartphone-equipped inexpensive but powerful sensors (e.g., light sensor, GPS, etc). In this context, the main objective of this work is to use the power of smartphone sensors and IoT-cloud-based framework to collect, store, and analyze nighttime illumination data from citizens to generate high granular city illumination map. The development of high granular illumination map is an effective way of visualizing and assessing the illumination of city streets during nighttime. In this article, an illumination mapping algorithm called Street Illumination Mapping is proposed that works on participatory sensing-based illumination data collected using smartphones as IoT devices to generate city illumination map. The proposed method is evaluated on a real-world illumination dataset collected by participants in two different urban areas of city Kolkata. The results are also compared with the baseline mapping techniques, namely, Spatial k-Nearest Neighbors, Inverse Distance Weighting, Random Forest Regressor, Support Vector Regressor, and Artificial Neural Network.

Combining Polygon Schematization and Decomposition Approaches for Solving the Cavity Decomposition Problem

The cavity decomposition problem is a computational geometry problem, arising in the context of modern electronic CAD systems, that concerns detecting the generation and propagation of electromagnetic noise into multi-layer printed circuit boards. Algorithmically speaking, the problem can be formulated so as to contain, as sub-problems, the well-known polygon schematization and polygon decomposition problems. Given a polygon P and a finite set C of given directions, polygon schematization asks for computing a C -oriented polygon P ′ with “low complexity” and “high resemblance” to P , whereas polygon decomposition asks for partitioning P into a set of basic polygonal elements (e.g., triangles) whose size is as small as possible. In this article, we present three different solutions for the cavity decomposition problem, which are obtained by suitably combining existing algorithms for polygon schematization and decomposition, by considering different input parameters, and by addressing both methodological and implementation issues. Since it is difficult to compare the three solutions on a theoretical basis, we present an extensive experimental study, employing both real-world and random data, conducted to assess their performance. We rank the proposed solutions according to the results of the experimental evaluation, and provide insights on natural candidates to be adopted, in practice, as modules of modern printed circuit board design software tools, depending on the observed performance and on the different constraints on the desired output.

VRPDiv: A Divide and Conquer Framework for Large Vehicle Routing Problems

The Vehicle Routing Problem (VRP) is an NP hard problem where we need to optimize itineraries for agents to visit multiple targets. When considering real-world travel (road-network topology, speed limits and traffic), modern VRP solvers can only process small instances with a few hundred targets. We propose a framework (VRPDiv) that can scale any solver to support larger VRP instances with up to ten thousand targets (10k) by dividing them into smaller clusters. VRPDiv supports the multiple VRP scenarios and contains a pool of clustering algorithms from which it chooses the ideal one depending on properties of the instance. VRPDiv assigns agents based on cluster demand and targets compatibility (i.e. realizable time-windows and capacity limitations). We incorporate the framework into the Bing Maps Multi-Itinerary Optimization (MIO) 1 online service. This architecture allows MIO to scale up from solving instances with a few hundred to over 10k targets in under 10 minutes. We evaluate our framework on public datasets and publish a new dataset ourselves, as large enough instances supporting real-world travel were impossible to find. We investigate multiple clustering methods and show that choosing the correct one is critical with differences of up to 60% in quality. We compare with relevant baselines and report a 40% improvement in target allocation and a 9.8% improvement in itinerary durations. We compare with existing scores and report an average delta of 10%, with lower values (<5%) in instances with low workload (few targets per agent), which are acceptable for an online service.

A Square Equal-Area Map Projection with Low Angular Distortion, Minimal Cusps, and Closed-Form Solutions

A novel square equal-area map projection is proposed. The projection combines closed-form forward and inverse solutions with relatively low angular distortion and minimal cusps, a combination of properties not manifested by any previously published square equal-area projection. Thus, the new projection has lower angular distortion than any previously published square equal-area projection with a closed-form solution. Utilizing a quincuncial arrangement, the new projection places the north pole at the center of the square and divides the south pole between its four corners; the projection can be seamlessly tiled. The existence of closed-form solutions makes the projection suitable for real-time visualization applications, both in cartography and in other areas, such as for the display of panoramic images.