With the technological advances in creation and capture of 3D spatial data, new emerging applications are being developed. Digital Twins, metaverse and extended reality (XR) based immersive environments can be enriched by leveraging geocoded 3D spatial data. Unlike 2D spatial queries, queries involving 3D immersive environments need to take the query user’s viewpoint into account. Spatio-visual queries return objects that are visible from the user’s perspective.In this paper, we propose enhancing 3D spatio-visual queries with large language models (LLM). These kinds of queries allow a user to interact with the visible objects using a natural language interface. We have implemented a proof-of-concept prototype and conducted preliminary evaluation. Our results demonstrate the potential of truly interactive immersive environments.

Out-of-distribution (OOD) aware classification aims to classify in-distribution samples into their respective classes while simultaneously detecting OOD samples. Previous works have largely focused on the image domain, where images from an unrelated dataset can serve as auxiliary OOD training data. In this work, we address OOD-aware classification for tabular data, where an unrelated dataset cannot be used as OOD training data. A potential solution to OOD-aware classification involves filtering out OOD samples using an outlier detection method and classifying the remaining samples with a traditional classification model. However, seamlessly integrating this approach into downstream optimization tasks is challenging due to the employment of multiple methods. Our approach is turning OOD-aware classification into traditional classification by augmenting the in-distribution training data with synthesized OOD data. This approach continues leveraging traditional classification methods while detecting OOD samples, and the learned model retains the same mathematical properties as traditional classification models, thus, it can be easily integrated into downstream tasks. We evaluate these benefits empirically using real-life datasets. Code is available at https://github.com/ah-ansari/OCT.

In recent years, with the increased usage of the Internet of Things (IoT) devices, cyber-attacks have become a serious threat over the Internet. These devices have low memory capacity and processing power, which makes them easy targets for attackers. The research community has proposed different approaches to deal with emerging variants of attacks on IoT devices using various machine learning techniques. However, these approaches rely heavily on the classifier’s categorization of a given record while ignoring its confidence. This paper proposes a verification-based scheme to reject IoT attacks by utilizing the classifier’s confidence. At the same time, existing studies are evaluated using traditional cross-validation approaches (e.g., k-fold), thus, not tested against unknown attacks. We propose using the leave-one-attack-out (LOAO) cross-validation scheme to evaluate the generalizability of the application to unknown attacks. The experiments are performed on Med BIoT, a publicly available dataset consisting of three IoT attacks. The system’s robustness is evaluated in terms of Receiver Operating Curves (ROC) and Equal Error rates (EERs). The results indicate a lower false-positive rate of 12.6% using the proposed verification-based approach in comparison to k-fold cross-validation.