In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty or information, posing a challenge for real-world applications such as 3D-GS SLAM. We propose to quantify information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design, which is a classical solution widely used in literature. By restructuring information quantification of 3D-GS through optimal experimental design, we arrive at multiple solutions, of which T-Optimality and D-Optimality perform the best quantitatively and qualitatively as measured on two popular datasets. Additionally, we propose a block diagonal covariance approximation which provides a measure of correlation at the expense of a greater computation cost.

There is always demand for integrating data into microeconomic decision making. Participatory sensing deals with how real-world data may be extracted with stakeholder participation and resolves a problem of Big Data, which is concerned with monetizing data extracted from individuals without their participation. We present how Decentralized Physical Infrastructure Networks (DePINs) extend participatory sensing. We discuss the threat models of these networks and how DePIN cryptoeconomics can advance participatory sensing.

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. To address this gap, we propose a method which advances the literature of continuous semantic mapping from voxels to ellipsoids, combining the precise structure of 3D-GS with the ability to quantify uncertainty of probabilistic robotic maps. Given a set of images, our algorithm performs a probabilistic semantic update directly on the 3D ellipsoids to obtain an expectation and variance through the use of conjugate priors. We also propose a probabilistic rasterization which returns per-pixel segmentation predictions with quantifiable uncertainty. We compare our method with similar probabilistic voxel-based methods to verify our extension to 3D ellipsoids, and perform ablation studies on uncertainty quantification and temporal smoothing.

Urban navigation systems traditionally optimize for efficiency, thereby overlooking environmental factors and personal preferences. This paper introduces Routify, a novel multi-criteria recommender system for personalized urban route selection. Routify advances the state-of-the-art in route recommendation through: (1) integration of diverse environmental data (air quality, noise levels, green spaces) with real-time IoT inputs; (2) a dynamic weighting algorithm allowing fine-grained user control over routing parameters; (3) transparent decision-making via an interactive interface displaying edge-level information; and (4) a community-driven feedback mechanism that continuously refines recommendations. We have implemented Routify as an open-source platform, facilitating further research and development. Experimental results from 1000 origin-destination pairs demonstrate significant improvements in user-defined environmental metrics (up to 32.98% increase in green index for walking and 16.91% reduction in noise levels for cycling) compared to traditional routing systems. These improvements come with slight trade-offs in distance, especially in walking mode. Our research contributes a comprehensive framework for multi-criteria route recommendation, balancing individual preferences, environmental factors, and community insights in urban navigation.

In the past few years, Internet of Things (IoT) has emerged as an outstanding technology and can be considered as the backbone of the smart world era as it connects physical devices with the internet. On the other hand there are these intelligent conversation interfaces that use a dialogue system to have a conversation with the users and are already used widely. In this paper, we focus on integration of chatbots and IoT to address a critical problem like air quality awareness. We present the architecture of the chatbot and its implementation on an instant messaging application. The chatbot not only provides air quality, temperature and humidity information to the users but also provides services like subscription to air quality monitoring nodes in a particular area or any part of the service region, alarm services, threshold settings, geoquery and recommendation based on the pollutant levels. A detailed explanation of the scenario of use, challenges identified during development process and possible future directions of this integration have also been addressed.

No abstract available.

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an anomaly detection framework (ADF) for large-scale, real-world environmental sensing systems. The framework is composed of four modules: 1) time-sliced anomaly detection (TSAD), which detects spatial, temporal, and spatio-temporal anomalies in the real-time sensor measurement data stream; 2) real-time emission detection, which detects potential regional emission sources; 3) device ranking, which provides a ranking for each sensing device; and 4) malfunction detection, which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.

There is a growing concern over the issues related to online privacy due to large availability of high quality images. To tackle the privacy concerns a face swapping application is proposed. There is a library of face images, which is created by downloading images from different sources on Internet. For any given image, first of all facial landmarks are detected. The second image is rotated and scaled so that it can properly fit over the input image. To make sure that the new image looks natural, color balance adjustment is done. After that blending of features from the second image onto the input image is done. It is also shown how this system can be used to creating appealing and funny photographs for entertainment purposes. We conclude with a study that shows the high quality of images produced by this system as compared to existing face swap applications and also limitations of this system.

In this paper, we present an energy, data and cost efficient model for mobile opportunistic PM2.5 sensing via bicycles. To facilitate the implementation of such systems, we first investigate the accuracy issue of different Inertial Monitoring Unit (IMU) built into the Arduino 101 for stop detection. Then, by curve fitting and optimization calculation on system parameter tuning and modeling, each sensor could start up at the optimum time in order to achieve minimum energy consumption and maximum data usability. Also we propose a formula that can count the minimum number of required PM2.5 sensors under the condition of total experiment time spent and total expected number of sampling point. Finally, we conduct a field experiment to evaluate the proposed model in a real world setting. The results show that total time spent of PM2.5 data collection is similar to the expected time derived from the system modeling.