Realistic texture perception in Extended Reality (XR) requires tactile feedback that simultaneously addresses the fundamental perceptual dimensions of hardness, roughness, and warmth. However, current haptic solutions often rely solely on vibrotactile feedback, which alone cannot convey the full spectrum of tactile sensations. This paper presents a multimodal texture rendering method that combines three distinct haptic modalities—force, vibration, and temperature—to provide all required perceptual dimensions. We developed a multimodal fingertip haptic display to deliver these stimuli, and evaluated its performance through perceptual user studies within a Virtual Reality (VR) experimental framework. This framework assesses the modalities, both in isolation and in combination, to examine the robustness of the mappings between stimuli and their corresponding dimensions. Furthermore, we demonstrate our multimodal method by rendering real-world materials such as wood, metal, and water. A subjective evaluation with 48 participants indicates that users can reliably identify most perceptual dimensions, and that the integration of more modalities consistently improved texture realism compared to representations with fewer. These findings validate the effectiveness of our multimodal texture rendering method and advance the design of immersive, texture-rich XR experiences.

This paper introduces a touch-based reading interface for tablets designed to support vocabulary acquisition, text comprehension, and reduction of reading anxiety. Touch interaction is leveraged to allow direct replacement of words with synonyms, easy access to word definitions and seamless dialogue with a personalized model of the reader's vocabulary. We discuss how fluid interaction and direct manipulation coupled with natural language processing can help address the reading needs of audiences such as school-age children and English as Second Language learners.

Many NLP applications entail that texts are classified based on their semantic distance (how similar or different the texts are). For example, comparing the text of a new document to that of documents of known topics can help identify the topic of the new text. Typically, a distributional distance is used to capture the implicit semantic distance between two pieces of text. However, such approaches do not take into account the semantic relations between words. In this article, we introduce an alternative method of measuring the semantic distance between texts that integrates distributional information and ontological knowledge within a network flow formalism. We first represent each text as a collection of frequency-weighted concepts within an ontology. We then make use of a network flow method which provides an efficient way of explicitly measuring the frequency-weighted ontological distance between the concepts across two texts. We evaluate our method in a variety of NLP tasks, and find that it performs well on two of three tasks. We develop a new measure of semantic coherence that enables us to account for the performance difference across the three data sets, shedding light on the properties of a data set that lends itself well to our method.

We demonstrate the benefits of a multilingual approach to automatic lexical semantic verb classification based on statistical analysis of corpora in multiple languages. Our research incorporates two interrelated threads. In one, we exploit the similarities in the crosslinguistic classification of verbs, to extend work on English verb classification to a new language (Italian), and to new classes within that language, achieving an accuracy of 86.4% (baseline 33.9%). Our second strand of research exploits the differences across languages in the syntactic expression of semantic properties, to show that complementary information about English verbs can be extracted from their translations in a second language (Chinese). The use of multilingual features improves classification performance of the English verbs, achieving an accuracy of 83.5% (baseline 33.3%).