Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

Formalization of the static semantics of contracts using attribute grammars

Le travail présenté dans ce mémoire concerne le langage Contracts. Les systèmes orientés objet consistent en des groupes d'objets reliés qui coopèrent afin de réaliser certaines tâches ou de maintenir certains invariants. Le langage Contracts de Helm est une technique de spécification de coopérations interobjets [Helm 90]. Un contrat, dans Contracts, spécifie les coopérations en termes d'objets participants, d'obligations des participants, de dépendances entre participants et de l'instanciation de ceux-ci. De plus, un contrat contient des invariants que les participants coopèrent à maintenir. Les résultats de ce mémoire sont la formalisation de la sémantique statique du langage Contracts en utilisant la notation de grammaires d'attributs [Wait 85], selon la syntaxe publiée dans [Holl 92], et l'implémentation de l'analyse syntaxique et lexicale de Contracts en utilisant Lex et Yace

System combination with extra alignment information

This paper provides the system description of the IHMM team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). Our work is based on a confusion network-based approach to system combination. We propose a new method to build a confusion network for this: (1) incorporate extra alignment information extracted from given meta data, treating them as sure alignments, into the results from IHMM, and (2) decode together with this information. We also heuristically set one of the system outputs as the default backbone. Our results show that this backbone, which is the RBMT system output, achieves an 0.11% improvement in BLEU over the backbone chosen by TER, while the extra information we added in the decoding part does not improve the results