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.

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their selected points of interest and generate the routes and schedules for the robots according to uncertain spatial and time estimation. A large neighborhood search algorithm is developed to find sub-optimal low-cost solutions for the SMRP efficiently. The scalability and optimality of the multi-robot planner are first evaluated computationally under different environment sizes and numbers of humans and robots. Then, a photo-realistic multi-robot simulation platform was developed based on Habitat-AI to verify the tour guiding performance in an uncertain indoor environment. Results demonstrate that the proposed centralized tour planner is scalable, makes a smooth tradeoff in the plans under different environmental constraints, and can lead to robust performance with inaccurate uncertainty estimations (within a certain margin).

Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance

This paper presents an approach to enhance electronic traveling aids (ETAs) for people who are blind and severely visually impaired (BSVI) using indoor orientation and guided navigation by employing social outsourcing of indoor route mapping and assistance processes. This type of approach is necessary because GPS does not work well, and infrastructural investments are absent or too costly to install for indoor navigation. Our approach proposes the prior outsourcing of vision-based recordings of indoor routes from an online network of seeing volunteers, who gather and constantly update a web cloud database of indoor routes using specialized sensory equipment and web services. Computational intelligence-based algorithms process sensory data and prepare them for BSVI usage. In this way, people who are BSVI can obtain ready-to-use access to the indoor routes database. This type of service has not previously been offered in such a setting. Specialized wearable sensory ETA equipment, depth cameras, smartphones, computer vision algorithms, tactile and audio interfaces, and computational intelligence algorithms are employed for that matter. The integration of semantic data of points of interest (such as stairs, doors, WC, entrances/exits) and evacuation schemes could make the proposed approach even more attractive to BVSI users. Presented approach crowdsources volunteers’ real-time online help for complex navigational situations using a mobile app, a live video stream from BSVI wearable cameras, and digitalized maps of buildings’ evacuation schemes.

Citizen Participation via Mobile Applications

Participatory smartphone apps empower citizens to interact with the city's administration. The purpose of this case study is to investigate the current state of participatory apps in Germany. Within this study, we examined 248 applications aimed at strengthening citizen participation. These apps were found in Google Playstore and Apple Appstore using search terms extracted from the relevant literature. Many of the apps give users the opportunity to report problems within their cities, such as broken street lamps or potholes. The information created and disseminated by the citizens through the app mainly includes the topics “mobility” and “environment.” Information provided by the city itself is much more diverse. Topics such as “Points of Interest,” “News and Events,” “Government” or “City Services” can be identified here. In the southern part of Germany, there is a significantly larger number of municipalities which have a citizen participation app. None of the apps examined uses gamification, although the use of game elements is very promising to foster the engagement and motivation of citizens.

Spatial Analysis on Networks: Towards identifying similarity of routes

Finding an optimal path in a road network is a method of planning and decision-making that is mainly related to transportations and emergency response. The paper presents an algorithm for finding optimal paths in spatial networks, through the utilization of open source GIS and mathematical analysis of Networks using Graph Theory as well as using geographical proximity attributes of network nodes. The geometric and spatial information of the network as well as its relations with points of interest (POI) of the study areas located at the nodes and edges of the network, are transformed into spatial information, which by applying spatial queries in a geographical database (Postgis/Pgrouting) give query-enabled paths. The case study for the application of the algorithm and finding a route based on spatial queries is the island of Lesvos. This island combines intense topography and a complex road network with multiple geometric relationships. The area also has points of interest such as cultural, tourist and social. The final route will be a synthesis of morphological, tourist and cultural elements similar to the spatial search queries. Finally, the methodology as well as the search algorithm can be applied to any Spatial Network (transportations, environment, energy) described by its geographical features, considering all kinds of geographical issues, thus solving spatial problems and contributing to local development.

Operational Deflection Shapes Magnification and Visualization Using Optical-Flow-Based Image Processing

Much information can be derived from operational deflection shapes of vibrating structures and the magnification of their motion. However, the acquisition of deflection shapes usually requires a manual definition of an object’s points of interest, while general motion magnification is computationally inefficient. We propose easy extraction of operational deflection shapes straight from vision data by analyzing and processing optical flow information from the video and then, based on these graphs, morphing source data to magnify the shape of deflection. We introduce several processing routines for automatic masking of the optical flow data and frame-wise information fusion. The method is tested based on data acquired both in numerical simulations and real-life experiments in which cantilever beams were subjected to excitation around their natural frequencies.

Hybrid Physics-Constrained and Data-Driven Approach for Interwell Saturation Estimation from Well Logs

Combining physics-based models for well log analysis with artificial intelligence (AI) advanced algorithms is crucial for wellbore studies. Data-driven methods do not generalize well and lack theoretical knowledge accumulated in the field. Estimating well saturation significantly improves if predictions from physical models are used to constrain data-driven algorithms in outlined primary fluid channels and other important points of interest. Saturation propagations in the reservoirs interwell region also generalize better under using combination of models. This work addresses combined usage of theoretical and data-driven models by aggregating them into single hybrid model. Multiple physical and data-driven models are under study, their parameters are optimized using observations. Weighted sum is used to predict water saturation at every point with weights being recomputed at each step. Model outputs are compared in terms accuracy and cumulative loss. A synthesized reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data is used for the validation of the algorithms. Aggregated model for estimating interwell saturation shows improved prediction accuracy compared both to physics-based or data-driven approaches separately.