Maritime transport and vessel monitoring rely on multiple systems for positioning, such as the Automatic Identification System, electro-optical systems, and shore-based radar systems, to improve safety and efficiency in vessel tracking. However, each system has inherent limitations, including coverage gaps, reliance on vessel compliance, and limited real-time monitoring capabilities. As a complementary approach to existing methods and systems, this paper presents the SeaSentry system, a passive sensor network designed to detect, position, and track vessels in real time, thus eliminating the need for onboard installations. The sensors detect radar pulses emitted by the vessels' rotating radar antennas and compute time stamps as the radar beams pass over them. Geometric constraints can be derived from time differences of arrival to localize the vessels, with time error and synchronization demands in the millisecond range. Along with some initial results, this paper discusses the SeaSentry setup and data processing pipeline.

We present a novel statistical approach to incorporate uncertainty awareness in model-free distributional deep reinforcement learning for mission and safety-critical robotics. Deep learning predictions are influenced by uncertainties in the data, termed as aleatoric uncertainties, as well as uncertainties in the learning process and model structure, known as epistemic uncertainties. The proposed algorithm, called as Calibrated Evidential Quantile Regression in Deep-Q Networks (CEQR-DQN), addresses key challenges associated with separately estimating aleatoric and epistemic uncertainty in stochastic robotic environments. It combines deep evidential learning with quantile calibration based on the principles of conformal inference to provide explicit, sample-free computations of global uncertainty as opposed to local estimates based on simple variance. Thereby, the proposed approach overcomes limitations of traditional methods in computational and statistical efficiency and handling of out-of-distribution (OOD) observations. Tested on a suite of representative miniaturized Atari games (i.e., MinAtar), CEQR-DQN is shown to surpass similar existing frameworks in scores and learning speed. Its ability to rigorously evaluate uncertainties improves exploration strategies and can serve as a blueprint for other uncertainty-aware robotic algorithms.

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.

In recent years, an evolutionary change occurred in the digital world when various social networking sites became popular. This led to a big increase of users who share their activities in digital form. A huge amount of digital information has become available, providing researchers with a unique insight into the behavior and activities of entire population. The use of Geo-tagged social media data became an emerging trend to represent user actions and behaviour at specific geographical locations. However, the inaccuracies of Geo-tagged data from social media often limits the utility of this data source in micro scale analysis (at the street or place of interest level (POI)). Our study supports how social media data could be matched accurately with specific physical locations. More specifically, our investigation includes Geo-tweet data and image data from flickr to map accurately physical location using machine leaning and deep learning techniques. In this paper, a preliminary discussion of this work is provided, using Geo-tagged data from Twitter and Open StreetMap in cities such as San Francisco and London as well as for image mapping using Flickr data in cities such as San Francisco and Kyoto.

The vast and growing amount of publicly available real-time information from social network services such as Twitter could provide many benefits for improving public health and safety, especially towards the area of crime prevention. While prior studies have leveraged such data to help in the prediction of criminal incidents, we have developed a crime investigation tool which utilizes Twitter data to aid in crime analysis. The tool provides contextual information about crime incidents by visualizing the spatial and time-based characteristics of a crime and its context using data from nearby tweets and from the criminal history of a target place. In addition, sentiment analysis is also carried out with the identified tweets to further examine the negative characteristics of the spatial areas related to the different crimes in question. A demonstration prototype of this tool was developed as a web application for the area of San Francisco.

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one's physical health. However, it is generally difficult to motivate people to adhere to their fitness regimens and persist in such activities. To help address this problem, we developed a Gamified Acoustic AR running support system where we use the previous running records of a user to project a "Virtual runner" into an augmented reality space for runners to compete against. The presence of the virtual runner is conveyed through the sound of running footsteps and breathing: users hear the sound of the virtual runner while they are running and are able to virtually compete against themselves in real time. In this paper, we describe how such a system could be implemented and discuss the results of an experiment study which highlights the effectiveness of our Gamified AR system.