We present Large Sign Language Models (LSLM), a novel framework for translating 3D American Sign Language (ASL) by leveraging Large Language Models (LLMs) as the backbone, which can benefit hearing-impaired individuals’ virtual communication. Unlike existing sign language recognition methods that rely on 2D video, our approach directly utilizes 3D sign language data to capture rich spatial, gestural, and depth information in 3D scenes. This enables more accurate and resilient translation, enhancing digital communication accessibility for the hearing-impaired community. Beyond the task of ASL translation, our work explores the integration of complex, embodied multimodal languages into the processing capabilities of LLMs, moving beyond purely text-based inputs to broaden their understanding of human communication. We investigate both direct translation from 3D gesture features to text and an instruction-guided setting where translations can be modulated by external prompts, offering greater flexibility. This work provides a foundational step toward inclusive, multimodal intelligent systems capable of understanding diverse forms of language.

Understanding what makes people happy is a central topic in psychology. Prior work has mostly focused on developing self-reporting assessment tools for individuals and relies on experts to analyze the periodic reported assessments. One of the goals of the analysis is to understand what actions are necessary to encourage modifications in the behaviors of the individuals to improve their overall well-being. In this paper, we outline a complementary approach; on the assumption that the user journals her happy moments as short texts, a system can analyze these texts and propose sustainable suggestions for the user that may lead to an overall improvement in her well-being. We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER)module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described. This component is implemented as a neural network model with two encoders, one for the user input and one for the candidate actionable suggestion, with additional layers to capture psychologically significant features in the happy moment and suggestion. Our model achieves an AU-ROC of 0.831 and outperforms our baseline as well as the current state-of-the-art Textual Entailment model from AllenNLP by more than 48% of improvements, confirming the uniqueness and complexity of the HER task.

We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, friendly staff) and combine these with objective attributes, such as price and location. Voyageur also highlights interesting facts and tips about the services the user is considering to provide them with further insights into their choices.

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

In a distributed grid environment with ambitious service demands the job submission and management interfaces provide functionality of major importance. Emerging e-science and grid infrastructures such as EGEE and DEISA rely on highly available services that are capable of managing scientific jobs. It is the adoption of emerging open standard interfaces which allows the distribution of grid resources in such a way that their actual service implementation or grid technologies are not isolated from each other, especially when these resources are deployed in different e-science infrastructures that consist of different types of computational resources. This paper motivates the interoperability of these infrastructures and discusses solutions. We describe the adoption of various open standards that recently emerged from the open grid forum (OGF) in the field of job submission and management by well-known grid technologies, respectively gLite and UNICORE. This has a fundamental impact on the interoperability between these technologies and thus within the next generation e-science infrastructures that rely on these technologies.

Many existing Grid technologies and resource management systems lack a standardized job submission interface in Grid environments or e-Infrastructures. Even if the same language for job description is used, often the interface for job submission is also different in each of these technologies. The evolvement of the standardized Job Submission and Description Language (JSDL) as well as the OGSA - Basic Execution Services (OGSA-BES) pave the way to improve the interoperability of all these technologies enabling cross-Grid job submission and better resource management capabilities. In addition, the BytelO standards provide useful mechanisms for data access that can be used in conjunction with these improved resource management capabilities. This paper describes the integration of these standards into the recently released UNICORE 6 Grid middleware that is based on open standards such as the Web Services Resource Framework (WS-RF) and WS-Addressing (WS-A).