Modern travel planning requires knowledge graphs (KGs) that semantically integrate heterogeneous datasets while prioritizing niche attractions and real-world constraints. This paper introduces a Travel Planning Knowledge Graph (KG-ITP) combining Geographic Names Information Systems (GNIS) natural features, Institute of Museum and Library Services (IMLS) cultural sites, Yelp dining taxonomies, and Valley Metro transit networks through schema alignment and OWL2 ontological modeling. The KG-ITP’s novel architecture establishes spatial-temporal dependencies between activities (e.g., museum hours), transit accessibility (e.g., light rail buffers), and user constraints (e.g., dietary needs) using a query language for processing geospatial data called GeoSPARQL, its proximity functions and Resource Description Framework (RDF).By resolving semantic conflicts between authoritative and crowd-sourced datasets, the knowledge graph improves coverage of underrepresented attractions often omitted by commercial platforms through semantic alignment and spatial reasoning. The prototype implementation of the travel planner system demonstrates efficient query processing of complex multi-criteria requests like identifying vegetarian-friendly restaurants within 500m of transit-accessible cultural sites. The modular design enables seamless integration of third-party datasets through shared vocabularies. This work contributes to tourism informatics by illustrating how ontology-driven knowledge graphs can enhance personalization accuracy compared to traditional keyword-based systems while maintaining W3C standards compliance for spatial-semantic reasoning.

Handed Shearing Auxetics (HSA) are a promising structure for making electrically driven robots with distributed compliance that convert a motors rotation and torque into extension and force. These structures expand and contract by changing an internal angle between links, the evolution of the structure as this angle changes is known as the auxetic trajectory. We overcome past limitations on the range of actuation, blocked force, and stiffness by focusing on two key design parameters: the point of an HSA's auxetic trajectory that is energetically preferred, and the number of cells along the HSAs length. Modeling the HSA as a programmable spring, we characterize the effect of both on blocked force, minimum energy length, spring constant, angle range and holding torque. We also examined the effect viscoelasticity has on actuation forces over time. By varying the preferred auxetic trajectory point, we were able to make actuators that can push, pull, or do both. We expanded the range of forces possible from 5 N to 150 N, and the range of stiffness from 2 N/mm to 89 N/mm. For a fixed point on the auxetic trajectory, we found decreasing length can improve force output, at the expense of needing higher torques, and having a shorter throw. We also found that the viscoelastic effects can limit the amount of force a 3D printed HSA can apply over time.

Even with several advancements in multilingual modeling, it is challenging to recognize multiple languages using a single neural model, without knowing the input language and most multilingual models assume the availability of the input language. In this work, we propose a novel bilingual end-to-end (E2E) modeling approach, where a single neural model can recognize both languages and also support switching between the languages, without any language input from the user. The proposed model has shared encoder and prediction networks, with language-specific joint networks that are combined via a self-attention mechanism. As the language-specific posteriors are combined, it produces a single posterior probability over all the output symbols, enabling a single beam search decoding and also allowing dynamic switching between the languages. The proposed approach outperforms the conventional bilingual baseline with 13.3%, 8.23% and 1.3% word error rate relative reduction on Hindi, English and code-mixed test sets, respectively.

Visual surveillance has numerous applications. It can support public safety, traffic monitoring, and facility protection to name just a few. Networked digital surveillance devices are revolutionizing the surveillance industry by supporting high quality images, remote monitoring, and advanced image processing. However, they also raise serious privacy/security concerns. To address the issue, the surveillance industry has recently begun to provide basic support for secure communications. In this paper, we present a new protocol to significantly enhance the security and performance compared with the state-of-the-art baseline method widely taken in the video surveillance industry. Through extensive experiments for performance evaluation, our approach is shown to substantially reduce the delay to execute cryptographic mechanisms and increase the supported bit rate compared with the baseline, while providing desirable security features.