699,452 research outputs found
Fortunes of the firefly: Sciascia's art of detection
Two of the principal features of Leonardo Sciascia’s works are the treatment of historical events and the adoption of the techniques of the detective novel. It is the latter characteristic which is the object of this study, for which I shall examine in particular the five novels, Il giorno della civetta, 1961 (The Day Of The Owl), A ciascuno il suo, 1966 (To Each His Own), Il contesto, 1971 (Equal Danger), Todo modo, 1974 (One Way Or Another), and Il cavaliere e la morte, 1988 (The Knight and Death), and the short story, Sciascia’s final ‘giallo’ (detective story), Una storia semplice, 1989 (A Straightforward Tale)
Using Global Constraints and Reranking to Improve Cognates Detection
Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of the art methods for performing cognates detection and results in
significant performance improvements beyond current state of the art
performance on publicly available datasets with different language pairs and
various conditions such as different levels of baseline state of the art
performance and different data size conditions, including with more realistic
large data size conditions than have been evaluated with in the past.Comment: 10 pages, 6 figures, 6 tables; published in the Proceedings of the
55th Annual Meeting of the Association for Computational Linguistics, pages
1983-1992, Vancouver, Canada, July 201
Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges
Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted
A Review of Object Detection Models based on Convolutional Neural Network
Convolutional Neural Network (CNN) has become the state-of-the-art for object
detection in image task. In this chapter, we have explained different
state-of-the-art CNN based object detection models. We have made this review
with categorization those detection models according to two different
approaches: two-stage approach and one-stage approach. Through this chapter, it
has shown advancements in object detection models from R-CNN to latest
RefineDet. It has also discussed the model description and training details of
each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl
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Sarcasm detection on Twitter
State-of-the-art approaches for sarcasm detection in social media combine lexical clues with contextual information surrounding the potentially sarcastic posting including author information. This article presents detailed methods for performing contextualizing sarcasm detection on Twitter, including data extraction, feature engineering and classification model settings. I reproduce the state-of-the-art results reported by Bamman and Smith (2015).Informatio
S4ND: Single-Shot Single-Scale Lung Nodule Detection
The state of the art lung nodule detection studies rely on computationally
expensive multi-stage frameworks to detect nodules from CT scans. To address
this computational challenge and provide better performance, in this paper we
propose S4ND, a new deep learning based method for lung nodule detection. Our
approach uses a single feed forward pass of a single network for detection and
provides better performance when compared to the current literature. The whole
detection pipeline is designed as a single Convolutional Neural Network
(CNN) with dense connections, trained in an end-to-end manner. S4ND does not
require any further post-processing or user guidance to refine detection
results. Experimentally, we compared our network with the current
state-of-the-art object detection network (SSD) in computer vision as well as
the state-of-the-art published method for lung nodule detection (3D DCNN). We
used publically available CT scans from LUNA challenge dataset and showed
that the proposed method outperforms the current literature both in terms of
efficiency and accuracy by achieving an average FROC-score of . We also
provide an in-depth analysis of our proposed network to shed light on the
unclear paradigms of tiny object detection.Comment: Accepted for publication at MICCAI 2018 (21st International
Conference on Medical Image Computing and Computer Assisted Intervention
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour
detection. For this task, we prioritise the effective utilization of the
high-level abstraction capability of a ResNet, which leads to state-of-the-art
results for edge detection. Keeping our focus in mind, we fuse the high, mid
and low-level features in that specific order, which differs from many other
approaches. It uses the tensor with the highest-levelled features as the
starting point to combine it layer-by-layer with features of a lower
abstraction level until it reaches the lowest level. We train this network on a
modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a
refined PASCAL-val dataset reaching an excellent performance and an Optimal
Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500
dataset we reach state-of-the-art results for edge-detection with an ODS of
0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path
Refinement CN
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