This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification. Its modularity allows flexibility to test methods, datasets, and ensure rigorous evaluations. A concrete use case demonstrates its effectiveness.

We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the error between the original image and its reconstruction,second, on the support estimation of the normal image distribution in the latent space, and third, on the error between the originalimage and a restored version of the reconstructed image. These methods are evaluated on the industrial image database MVTecADand compared to two competitive state-of-the-art methods.

Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an approach based on language models and evaluate the impact on the results of different preprocessings and matching techniques. For task 3, we have developed an information extraction system yielding very encouraging results accuracy-wise. We have experimented two different approaches, one based on the exclusive use of neural networks, the other based on a linguistic analysis.

English uses the passive voice more frequently than French. One method of translating the passive includes rendering the sentence as active by using an active verb, and changing the placement of the verb’s arguments. We are studying extra-syntactic features that predict where this method of translating the passive voice is used, in-cluding animacy and information status. We have obtained data from examining the Hansard, the transactions of the Canadian Parliament, which is published in both languages. This paper presents the results of a small mechanized corpus analysis on the relevance of the relative animacy of the agent (or experiencer) and the theme. This information will help to achieve desired stylistic output in a bilingual surface realizer.

The few available French resources for evaluating linguistic models or algorithms on other linguistic levels than morpho-syntax are either insufficient from quantitative as well as qualitative point of view or not freely accessible. Based on this fact, the FREEBANK project intends to create French corpora constructed using manually revised output from a hybrid Constraint Grammar parser and annotated on several linguistic levels (structure, morpho-syntax, syntax, coreference), with the objective to make them available on-line for research purposes. Therefore, we will focus on using standard annotation schemes, integration of existing resources and maintenance allowing for continuous enrichment of the annotations. Prior to the actual presentation of the prototype that has been implemented, this paper describes a generic model for the organization and deployment of a linguistic resource archive, in compliance with the various works currently conducted within international standardization initiatives (TEI and ISO/TC 37/SC 4).

Diagrammatic, analogical or iconic representations are often contrasted with linguistic or logical representations, in which the shape of the symbols is arbitrary. The aim of this paper is to make a case for the usefulness of diagrams in inferential knowledge representation systems. Although commonly used, diagrams have for a long time suffered from the reputation of being only a heuristic tool or a mere support for intuition. The first part of this paper is an historical background paying tribute to the logicians, psychologists and computer scientists who put an end to this formal prejudice against diagrams. The second part is a discussion of their characteristics as opposed to those of linguistic forms. The last part is aimed at reviving the interest for heterogeneous representation systems including both linguistic and diagrammatic representations.

We proposed in previous papers an extension and an implementation of the STROBE model, which regards the Agents as Scheme interpreters. These Agents are able to interpret messages in a dedicated environment including an interpreter that learns from the current conversation therefore representing evolving meta-level Agent's knowledge. When the Agent's interpreter is a nondeterministic one, the dialogues may consist of subsequent refinements of specifications in the form of constraint sets. The paper presents a worked out example of dynamic service generation - such as necessary on Grids - by exploiting STROBE Agents equipped with a nondeterministic interpreter. It shows how enabling dynamic specification of a problem. Then it illustrates how these principles could be effective for other applications. Details of the implementation are not provided here, but are available.