Hedonic and Transcendent Conceptions of Value

In this paper we introduce a conceptual distinction between a hedonic and transcendent conception of value. We posit three linguistic earmarks by which one can distinguish these conceptions of value. We seek validation for the conceptual distinctions by examining the language contained in reviews of cars and reviews of paintings. In undertaking the empirical examination, we draw on the work of M.A.K. Halliday to identify clauses as fundamental units of meaning and to specify process types that can be mapped onto theoretical distinctions between the two conceptions of value. Extensions of this research are discussed

3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary version was published in ACM MM'14 conferenc

Attentive Tensor Product Learning

This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach

An Evaluation of Inter-Organizational Workflow Modelling Formalisms

This paper evaluates the dynamic aspects of the UML in the context of inter-organizational workflows. Two evaluation methodologies are used. The first one is ontological and is based on the BWW (Bunge-Wand-Weber) models. The second validation is based on prototyping and consists in the development of a workflow management system in the aerospace industry. Both convergent and divergent results are found from the two validations. Possible enhancements to the UML formalism are suggested from the convergent results. On the other hand, the divergent results suggest the need for a contextual specification in the BWW models. Ce travail consiste en une évaluation des aspects dynamiques du language UML dans un contexte de workflow inter-organisationnel. Le choix du language par rapport à d'autres est motivé par sa richesse grammaticale lui offrant une très bonne adaptation à ce contexte. L'évaluation se fait par une validation ontologique basée sur les modèles BWW (Bunge-Wand-Weber) et par la réalisation d'un prototype de système de gestion de workflows inter-organisationnels. À partir des résultats convergents obtenus des deux différentes analyses, des améliorations au formalisme UML sont suggérées. D'un autre coté, les analyses divergentes suggèrent une possibilité de spécifier les modèles BWW à des contextes plus particuliers tels que ceux des workflows et permettent également de suggérer d'autres améliorations possibles au langage.Ontology, Conceptual study, Prototype Validation, UML, IS development methods and tools., Ontologie, étude conceptuelle, validation du prototype, UML, méthodes et outils de développement IS

The Long-Short Story of Movie Description

Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII Movie Description allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the challenging setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these visual classifiers we learn how to generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD dataset. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task