fondazione bruno kessler

About: fondazione bruno kessler is a facility organization based out in Trento, Italy. It is known for research contribution in the topics: Silicon photomultiplier & Detector. The organization has 1145 authors who have published 4730 publications receiving 94404 citations. The organization is also known as: Trentino Institute of Culture.

The FBK Participation in the WMT15 Automatic Post-editing Shared Task

Abstract: In this paper, we describe the “FBK EnglishSpanish Automatic Post-editing (APE)” systems submitted to the APE shared task at the WMT 2015. We explore the most widely used statistical APE technique (monolingual) and its most significant variant (context-aware). In this exploration, we introduce some novel task-specific dense features through which we observe improvements over the default setup of these approaches. We show these features are useful to prune the phrase table in order to remove unreliable rules and help the decoder to select useful translation options during decoding. Our primary APE system submitted at this shared task performs significantly better than the standard APE baseline.

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

Abstract: In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics.

Learning Deep NBNN Representations for Robust Place Categorization

Abstract: This letter presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that by considering features derived from pretrained convolutional neural networks (CNNs) in combination with part-based classification models, high recognition accuracy can be achieved, even in the presence of occlusions and severe viewpoint changes. Inspired by these works, we propose to exploit local deep representations, representing images as set of regions applying a Naive Bayes nearest neighbor (NBNN) model for image classification. As opposed to previous methods, where CNNs are merely used as feature extractors, our approach seamlessly integrates the NBNN model into a fully CNN. Experimental results show that the proposed algorithm outperforms previous methods based on pretrained CNN models and that, when employed in challenging robot place recognition tasks, it is robust to occlusions, environmental and sensor changes.

Outsourcing FrameNet to the Crowd

Abstract: We present the first attempt to perform full FrameNet annotation with crowdsourcing techniques. We compare two approaches: the first one is the standard annotation methodology of lexical units and frame elements in two steps, while the second is a novel approach aimed at acquiring frames in a bottom-up fashion, starting from frame element annotation. We show that our methodology, relying on a single annotation step and on simplified role definitions, outperforms the standard one both in terms of accuracy and time.

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Abstract: We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.