pwc-archive/papers-with-abstracts huggingface dataset:
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique
Authors: Carlos-Emiliano González-Gallardo, Malek Hajjem, Eric SanJuan, Juan-Manuel Torres-Moreno
Date: 2018-09-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Informativeness, speech-recognition, Speech Recognition, Text Summarization

In this paper we propose a new approach to evaluate the informativeness of
transcriptions coming from Automatic Speech Recognition systems. This approach,
based in the notion of informativeness, is focused on the framework of
Automatic Text Summarization performed over these transcriptions. At a first
glance we estimate the informative content of the various automatic
transcriptions, then we explore the capacity of Automatic Text Summarization to
overcome the informative loss. To do this we use an automatic summary
evaluation protocol without reference (based on the informative content), which
computes the divergence between probability distributions of different textual
representations: manual and automatic transcriptions and their summaries. After
a set of evaluations this analysis allowed us to judge both the quality of the
transcriptions in terms of informativeness and to assess the ability of
automatic text summarization to compensate the problems raised during the
transcription phase.
Equivalence of approximation by convolutional neural networks and fully-connected networks
Authors: Philipp Petersen, Felix Voigtlaender
Date: 2018-09-04 00:00:00
Tasks: All, Translation

Convolutional neural networks are the most widely used type of neural networks in applications. In mathematical analysis, however, mostly fully-connected networks are studied. In this paper, we establish a connection between both network architectures. Using this connection, we show that all upper and lower bounds concerning approximation rates of {fully-connected} neural networks for functions $f \in \mathcal{C}$ -- for an arbitrary function class $\mathcal{C}$ -- translate to essentially the same bounds concerning approximation rates of convolutional neural networks for functions $f \in {\mathcal{C}^{equi}}$, with the class ${\mathcal{C}^{equi}}$ consisting of all translation equivariant functions whose first coordinate belongs to $\mathcal{C}$. All presented results consider exclusively the case of convolutional neural networks without any pooling operation and with circular convolutions, i.e., not based on zero-padding.
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera
Authors: Amirafshar Moshtaghpour, José M. Bioucas-Dias, Laurent Jacques
Date: 2018-09-04 00:00:00
Tasks: Compressive Sensing

This paper introduces a single-pixel HyperSpectral (HS) imaging framework
based on Fourier Transform Interferometry (FTI). By combining a space-time
coding of the light illumination with partial interferometric observations of a
collimated light beam (observed by a single pixel), our system benefits from
(i) reduced measurement rate and light-exposure of the observed object compared
to common (Nyquist) FTI imagers, and (ii) high spectral resolution as desirable
in, e.g., Fluorescence Spectroscopy (FS). From the principles of compressive
sensing with multilevel sampling, our method leverages the sparsity "in level"
of FS data, both in the spectral and the spatial domains. This allows us to
optimize the space-time light coding using time-modulated Hadamard patterns. We
confirm the effectiveness of our approach by a few numerical experiments.
Improving generalization of vocal tract feature reconstruction: from augmented acoustic inversion to articulatory feature reconstruction without articulatory data
Authors: Rosanna Turrisi, Raffaele Tavarone, Leonardo Badino
Date: 2018-09-04 00:00:00

We address the problem of reconstructing articulatory movements, given audio
and/or phonetic labels. The scarce availability of multi-speaker articulatory
data makes it difficult to learn a reconstruction that generalizes to new
speakers and across datasets. We first consider the XRMB dataset where audio,
articulatory measurements and phonetic transcriptions are available. We show
that phonetic labels, used as input to deep recurrent neural networks that
reconstruct articulatory features, are in general more helpful than acoustic
features in both matched and mismatched training-testing conditions. In a
second experiment, we test a novel approach that attempts to build articulatory
features from prior articulatory information extracted from phonetic labels.
Such approach recovers vocal tract movements directly from an acoustic-only
dataset without using any articulatory measurement. Results show that
articulatory features generated by this approach can correlate up to 0.59
Pearson product-moment correlation with measured articulatory features.
Faster Balanced Clusterings in High Dimension
Authors: Hu Ding
Date: 2018-09-04 00:00:00
Tasks: Clustering, Constrained Clustering, Vocal Bursts Intensity Prediction

The problem of constrained clustering has attracted significant attention in
the past decades. In this paper, we study the balanced $k$-center, $k$-median,
and $k$-means clustering problems where the size of each cluster is constrained
by the given lower and upper bounds. The problems are motivated by the
applications in processing large-scale data in high dimension. Existing methods
often need to compute complicated matchings (or min cost flows) to satisfy the
balance constraint, and thus suffer from high complexities especially in high
dimension. We develop an effective framework for the three balanced clustering
problems to address this issue, and our method is based on a novel spatial
partition idea in geometry. For the balanced $k$-center clustering, we provide
a $4$-approximation algorithm that improves the existing approximation factors;
for the balanced $k$-median and $k$-means clusterings, our algorithms yield
constant and $(1+\epsilon)$-approximation factors with any $\epsilon>0$. More
importantly, our algorithms achieve linear or nearly linear running times when
$k$ is a constant, and significantly improve the existing ones. Our results can
be easily extended to metric balanced clusterings and the running times are
sub-linear in terms of the complexity of $n$-point metric.
A Neural Network Model for Determining the Success or Failure of High-tech Projects Development: A Case of Pharmaceutical industry
Authors: Hossein Sabzian, Ehsan Kamrani, Seyyed Mostafa Seyyed Hashemi
Date: 2018-09-04 00:00:00

Financing high-tech projects always entails a great deal of risk. The lack of
a systematic method to pinpoint the risk of such projects has been recognized
as one of the most salient barriers for evaluating them. So, in order to
develop a mechanism for evaluating high-tech projects, an Artificial Neural
Network (ANN) has been developed through this study. The structure of this
paper encompasses four parts. The first part deals with introducing paper's
whole body. The second part gives a literature review. The collection process
of risk related variables and the process of developing a Risk Assessment Index
system (RAIS) through Principal Component Analysis (PCA) are those issues that
are discussed in the third part. The fourth part particularly deals with
pharmaceutical industry. Finally, the fifth part has focused on developing an
ANN for pattern recognition of failure or success of high-tech projects.
Analysis of model's results and a final conclusion are also presented in this
part.
Segmentation-free Compositional $n$-gram Embedding
Authors: Geewook Kim, Kazuki Fukui, Hidetoshi Shimodaira
Date: 2018-09-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Representation Learning, Segmentation, Word Embeddings

We propose a new type of representation learning method that models words, phrases and sentences seamlessly. Our method does not depend on word segmentation and any human-annotated resources (e.g., word dictionaries), yet it is very effective for noisy corpora written in unsegmented languages such as Chinese and Japanese. The main idea of our method is to ignore word boundaries completely (i.e., segmentation-free), and construct representations for all character $n$-grams in a raw corpus with embeddings of compositional sub-$n$-grams. Although the idea is simple, our experiments on various benchmarks and real-world datasets show the efficacy of our proposal.
OCNet: Object Context Network for Scene Parsing
Authors: Yuhui Yuan, Lang Huang, Jianyuan Guo, Chao Zhang, Xilin Chen, Jingdong Wang
Date: 2018-09-04 00:00:00
Tasks: Object, Relation, Scene Parsing, Semantic Segmentation

In this paper, we address the semantic segmentation task with a new context aggregation scheme named \emph{object context}, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}. We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff
Bangla License Plate Recognition Using Convolutional Neural Networks (CNN)
Authors: M M Shaifur Rahman, Mst Shamima Nasrin, Moin Mostakim, Md Zahangir Alom
Date: 2018-09-04 00:00:00
Tasks: BIG-bench Machine Learning, License Plate Recognition, Management

In the last few years, the deep learning technique in particular
Convolutional Neural Networks (CNNs) is using massively in the field of
computer vision and machine learning. This deep learning technique provides
state-of-the-art accuracy in different classification, segmentation, and
detection tasks on different benchmarks such as MNIST, CIFAR-10, CIFAR-100,
Microsoft COCO, and ImageNet. However, there are a lot of research has been
conducted for Bangla License plate recognition with traditional machine
learning approaches in last decade. None of them are used to deploy a physical
system for Bangla License Plate Recognition System (BLPRS) due to their poor
recognition accuracy. In this paper, we have implemented CNNs based Bangla
license plate recognition system with better accuracy that can be applied for
different purposes including roadside assistance, automatic parking lot
management system, vehicle license status detection and so on. Along with that,
we have also created and released a very first and standard database for BLPRS.
Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation
Authors: Xinge Zhu, Hui Zhou, Ceyuan Yang, Jianping Shi, Dahua Lin
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation

Due to the expensive and time-consuming annotations (e.g., segmentation) for
real-world images, recent works in computer vision resort to synthetic data.
However, the performance on the real image often drops significantly because of
the domain shift between the synthetic data and the real images. In this
setting, domain adaptation brings an appealing option. The effective approaches
of domain adaptation shape the representations that (1) are discriminative for
the main task and (2) have good generalization capability for domain shift. To
this end, we propose a novel loss function, i.e., Conservative Loss, which
penalizes the extreme good and bad cases while encouraging the moderate
examples. More specifically, it enables the network to learn features that are
discriminative by gradient descent and are invariant to the change of domains
via gradient ascend method. Extensive experiments on synthetic to real
segmentation adaptation show our proposed method achieves state of the art
results. Ablation studies give more insights into properties of the
Conservative Loss. Exploratory experiments and discussion demonstrate that our
Conservative Loss has good flexibility rather than restricting an exact form.
Parity Queries for Binary Classification
Authors: Hye Won Chung, Ji Oon Lee, Do-Yeon Kim, Alfred O. Hero
Date: 2018-09-04 00:00:00
Tasks: Binary Classification, Classification, General Classification

Consider a query-based data acquisition problem that aims to recover the values of $k$ binary variables from parity (XOR) measurements of chosen subsets of the variables. Assume the response model where only a randomly selected subset of the measurements is received. We propose a method for designing a sequence of queries so that the variables can be identified with high probability using as few ($n$) measurements as possible. We define the query difficulty $\bar{d}$ as the average size of the query subsets and the sample complexity $n$ as the minimum number of measurements required to attain a given recovery accuracy. We obtain fundamental trade-offs between recovery accuracy, query difficulty, and sample complexity. In particular, the necessary and sufficient sample complexity required for recovering all $k$ variables with high probability is $n = c_0 \max\{k, (k \log k)/\bar{d}\}$ and the sample complexity for recovering a fixed proportion $(1-\delta)k$ of the variables for $\delta=o(1)$ is $n = c_1\max\{k, (k \log(1/\delta))/\bar{d}\}$, where $c_0, c_1>0$.
Image Reassembly Combining Deep Learning and Shortest Path Problem
Authors: M. -M. Paumard, D. Picard, H. Tabia
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018
Tasks: Deep Learning, Position

This paper addresses the problem of reassembling images from disjointed
fragments. More specifically, given an unordered set of fragments, we aim at
reassembling one or several possibly incomplete images. The main contributions
of this work are: 1) several deep neural architectures to predict the relative
position of image fragments that outperform the previous state of the art; 2)
casting the reassembly problem into the shortest path in a graph problem for
which we provide several construction algorithms depending on available
information; 3) a new dataset of images taken from the Metropolitan Museum of
Art (MET) dedicated to image reassembly for which we provide a clear setup and
a strong baseline.
Perturb and Combine to Identify Influential Spreaders in Real-World Networks
Authors: Antoine J. -P. Tixier, Maria-Evgenia G. Rossi, Fragkiskos D. Malliaros, Jesse Read, Michalis Vazirgiannis
Date: 2018-07-13 00:00:00

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propose the first Perturb and Combine (P&C) procedure for networks. It (1) creates many perturbed versions of a given graph, (2) applies a node scoring function separately to each graph, and (3) combines the results. Experiments conducted on real-world networks of various sizes with the k-core, generalized k-core, and PageRank algorithms reveal that P&C brings substantial improvements. Moreover, this performance boost can be obtained at almost no extra cost through parallelization. Finally, a bias-variance analysis suggests that P&C works mainly by reducing bias, and that therefore, it should be capable of improving the performance of all vertex scoring functions, including stable ones.
MesoNet: a Compact Facial Video Forgery Detection Network
Authors: Darius Afchar, Vincent Nozick, Junichi Yamagishi, Isao Echizen
Date: 2018-09-04 00:00:00
Tasks: DeepFake Detection, Face Swapping, Fake Image Detection, Image Forensics

This paper presents a method to automatically and efficiently detect face
tampering in videos, and particularly focuses on two recent techniques used to
generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional
image forensics techniques are usually not well suited to videos due to the
compression that strongly degrades the data. Thus, this paper follows a deep
learning approach and presents two networks, both with a low number of layers
to focus on the mesoscopic properties of images. We evaluate those fast
networks on both an existing dataset and a dataset we have constituted from
online videos. The tests demonstrate a very successful detection rate with more
than 98% for Deepfake and 95% for Face2Face.
Automated bird sound recognition in realistic settings
Authors: Timos Papadopoulos, Stephen J. Roberts, Katherine J. Willis
Date: 2018-09-04 00:00:00
Tasks: Classification, General Classification

We evaluated the effectiveness of an automated bird sound identification
system in a situation that emulates a realistic, typical application. We
trained classification algorithms on a crowd-sourced collection of bird audio
recording data and restricted our training methods to be completely free of
manual intervention. The approach is hence directly applicable to the analysis
of multiple species collections, with labelling provided by crowd-sourced
collection. We evaluated the performance of the bird sound recognition system
on a realistic number of candidate classes, corresponding to real conditions.
We investigated the use of two canonical classification methods, chosen due to
their widespread use and ease of interpretation, namely a k Nearest Neighbour
(kNN) classifier with histogram-based features and a Support Vector Machine
(SVM) with time-summarisation features. We further investigated the use of a
certainty measure, derived from the output probabilities of the classifiers, to
enhance the interpretability and reliability of the class decisions. Our
results demonstrate that both identification methods achieved similar
performance, but we argue that the use of the kNN classifier offers somewhat
more flexibility. Furthermore, we show that employing an outcome certainty
measure provides a valuable and consistent indicator of the reliability of
classification results. Our use of generic training data and our investigation
of probabilistic classification methodologies that can flexibly address the
variable number of candidate species/classes that are expected to be
encountered in the field, directly contribute to the development of a practical
bird sound identification system with potentially global application. Further,
we show that certainty measures associated with identification outcomes can
significantly contribute to the practical usability of the overall system.
Handwriting styles: benchmarks and evaluation metrics
Authors: Omar Mohammed, Gerard Bailly, Damien Pellier
Date: 2018-09-04 00:00:00
Tasks: Handwriting generation, Temporal Sequences

Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality of different handwriting style methods. This will be done
using deep learning techniques, which have shown remarkable results in
different machine learning tasks, learning classification, regression, and most
relevant to our work, generating temporal sequences. We discuss the challenges
associated with evaluating our methods, which is related to evaluation of
generative models in general. We then propose evaluation metrics, which we find
relevant to this problem, and we discuss how we evaluate the evaluation
metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge,
there is no work done before in generating handwriting (either in terms of
methodology or the performance metrics), our in exploring styles using this
dataset.
Non-monotonic Reasoning in Deductive Argumentation
Authors: Anthony Hunter
Date: 2018-09-04 00:00:00

Argumentation is a non-monotonic process. This reflects the fact that
argumentation involves uncertain information, and so new information can cause
a change in the conclusions drawn. However, the base logic does not need to be
non-monotonic. Indeed, most proposals for structured argumentation use a
monotonic base logic (e.g. some form of modus ponens with a rule-based
language, or classical logic). Nonetheless, there are issues in capturing
defeasible reasoning in argumentation including choice of base logic and
modelling of defeasible knowledge. And there are insights and tools to be
harnessed for research in non-monontonic logics. We consider some of these
issues in this paper.
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images
Authors: Dena Bazazian, Dimosthenis Karatzas, Andrew D. Bagdanov
Date: 2018-09-04 00:00:00
Tasks: Attribute, Dynamic Time Warping, Scene Understanding

Word spotting in natural scene images has many applications in scene understanding and visual assistance. In this paper we propose a technique to create and exploit an intermediate representation of images based on text attributes which are character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.
Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories
Authors: Huanhuan Yu, Menglei Hu, Songcan Chen
Date: 2018-09-04 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn the unlabeled target
domain by transferring the knowledge of the labeled source domain. To date,
most of the existing works focus on the scenario of one source domain and one
target domain (1S1T), and just a few works concern the scenario of multiple
source domains and one target domain (mS1T). While, to the best of our
knowledge, almost no work concerns the scenario of one source domain and
multiple target domains (1SmT), in which these unlabeled target domains may not
necessarily share the same categories, therefore, contrasting to mS1T, 1SmT is
more challenging. Accordingly, for such a new UDA scenario, we propose a UDA
framework through the model parameter adaptation (PA-1SmT). A key ingredient of
PA-1SmT is to transfer knowledge through adaptive learning of a common model
parameter dictionary, which is completely different from existing popular
methods for UDA, such as subspace alignment, distribution matching etc., and
can also be directly used for DA of privacy protection due to the fact that the
knowledge is transferred just via the model parameters rather than data itself.
Finally, our experimental results on three domain adaptation benchmark datasets
demonstrate the superiority of our framework.