pwc-archive/papers-with-abstracts huggingface dataset:
Wind models and cross-site interpolation for the refugee reception islands in Greece
Authors: Harris V. Georgiou
Date: 2017-07-25 00:00:00

In this study, the wind data series from five locations in Aegean Sea
islands, the most active `hotspots' in terms of refugee influx during the
Oct/2015 - Jan/2016 period, are investigated. The analysis of the
three-per-site data series includes standard statistical analysis and
parametric distributions, auto-correlation analysis, cross-correlation analysis
between the sites, as well as various ARMA models for estimating the
feasibility and accuracy of such spatio-temporal linear regressors for
predictive analytics. Strong correlations are detected across specific sites
and appropriately trained ARMA(7,5) models achieve 1-day look-ahead error
(RMSE) of less than 1.9 km/h on average wind speed. The results show that such
data-driven statistical approaches are extremely useful in identifying
unexpected and sometimes counter-intuitive associations between the available
spatial data nodes, which is very important when designing corresponding models
for short-term forecasting of sea condition, especially average wave height and
direction, which is in fact what defines the associated weather risk of
crossing these passages in refugee influx patterns.
Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Authors: Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
Date: 2016-12-14 00:00:00
Tasks: Decision Making, Question Answering, Sentence, Visual Question Answering, Visual Question Answering (VQA)

Deep models are the defacto standard in visual decision models due to their
impressive performance on a wide array of visual tasks. However, they are
frequently seen as opaque and are unable to explain their decisions. In
contrast, humans can justify their decisions with natural language and point to
the evidence in the visual world which led to their decisions. We postulate
that deep models can do this as well and propose our Pointing and Justification
(PJ-X) model which can justify its decision with a sentence and point to the
evidence by introspecting its decision and explanation process using an
attention mechanism. Unfortunately there is no dataset available with reference
explanations for visual decision making. We thus collect two datasets in two
domains where it is interesting and challenging to explain decisions. First, we
extend the visual question answering task to not only provide an answer but
also a natural language explanation for the answer. Second, we focus on
explaining human activities which is traditionally more challenging than object
classification. We extensively evaluate our PJ-X model, both on the
justification and pointing tasks, by comparing it to prior models and ablations
using both automatic and human evaluations.
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
Authors: Pedro Herruzo, Laura Portell, Alberto Soto, Beatriz Remeseiro
Date: 2017-07-25 00:00:00

First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos
Authors: Chunchao Guo, Jian-Huang Lai, Xiaohua Xie
Date: 2017-07-25 00:00:00
Tasks: Graph Matching, Object, Object Localization, Semantic Segmentation, Superpixels

We present a novel method of integrating motion and appearance cues for
foreground object segmentation in unconstrained videos. Unlike conventional
methods encoding motion and appearance patterns individually, our method puts
particular emphasis on their mutual assistance. Specifically, we propose using
an interactively constrained encoding (ICE) scheme to incorporate motion and
appearance patterns into a graph that leads to a spatiotemporal energy
optimization. The reason of utilizing ICE is that both motion and appearance
cues for the same target share underlying correlative structure, thus can be
exploited in a deeply collaborative manner. We perform ICE not only in the
initialization but also in the refinement stage of a two-layer framework for
object segmentation. This scheme allows our method to consistently capture
structural patterns about object perceptions throughout the whole framework.
Our method can be operated on superpixels instead of raw pixels to reduce the
number of graph nodes by two orders of magnitude. Moreover, we propose to
partially explore the multi-object localization problem with inter-occlusion by
weighted bipartite graph matching. Comprehensive experiments on three benchmark
datasets (i.e., SegTrack, MOViCS, and GaTech) demonstrate the effectiveness of
our approach compared with extensive state-of-the-art methods.
Thread Reconstruction in Conversational Data using Neural Coherence Models
Authors: Dat Tien Nguyen, Shafiq Joty, Basma El Amel Boussaha, Maarten de Rijke
Date: 2017-07-24 00:00:00

Discussion forums are an important source of information. They are often used
to answer specific questions a user might have and to discover more about a
topic of interest. Discussions in these forums may evolve in intricate ways,
making it difficult for users to follow the flow of ideas. We propose a novel
approach for automatically identifying the underlying thread structure of a
forum discussion. Our approach is based on a neural model that computes
coherence scores of possible reconstructions and then selects the highest
scoring, i.e., the most coherent one. Preliminary experiments demonstrate
promising results outperforming a number of strong baseline methods.
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
Authors: Po-Yao Huang, Ye Yuan, Zhenzhong Lan, Lu Jiang, Alexander G. Hauptmann
Date: 2017-07-05 00:00:00
Tasks: Attribute, General Classification, Representation Learning, Video Classification, Video Understanding

We report on CMU Informedia Lab's system used in Google's YouTube 8 Million
Video Understanding Challenge. In this multi-label video classification task,
our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the
official test set. We attribute the good performance to three components: 1)
Refined video representation learning with residual links and hypercolumns 2)
Latent concept mining which captures interactions among concepts. 3) Learning
with temporal segments and weighted multi-model ensemble. We conduct
experiments to validate and analyze the contribution of our models. We also
share some unsuccessful trials leveraging conventional approaches such as
recurrent neural networks for video representation learning for this
large-scale video dataset. All the codes to reproduce our results are publicly
available at https://github.com/Martini09/informedia-yt8m-release.
Towards Semantic Query Segmentation
Authors: Ajinkya Kale, Thrivikrama Taula, Sanjika Hewavitharana, Amit Srivastava
Date: 2017-07-25 00:00:00
Tasks: Information Retrieval, Retrieval, Segmentation

Query Segmentation is one of the critical components for understanding users'
search intent in Information Retrieval tasks. It involves grouping tokens in
the search query into meaningful phrases which help downstream tasks like
search relevance and query understanding. In this paper, we propose a novel
approach to segment user queries using distributed query embeddings. Our key
contribution is a supervised approach to the segmentation task using
low-dimensional feature vectors for queries, getting rid of traditional hand
tuned and heuristic NLP features which are quite expensive.
  We benchmark on a 50,000 human-annotated web search engine query corpus
achieving comparable accuracy to state-of-the-art techniques. The advantage of
our technique is its fast and does not use external knowledge-base like
Wikipedia for score boosting. This helps us generalize our approach to other
domains like eCommerce without any fine-tuning. We demonstrate the
effectiveness of this method on another 50,000 human-annotated eCommerce query
corpus from eBay search logs. Our approach is easy to implement and generalizes
well across different search domains proving the power of low-dimensional
embeddings in query segmentation task, opening up a new direction of research
for this problem.
Linear Discriminant Generative Adversarial Networks
Authors: Zhun Sun, Mete Ozay, Takayuki Okatani
Date: 2017-07-25 00:00:00

We develop a novel method for training of GANs for unsupervised and class
conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
discriminator of an LD-GAN is trained to maximize the linear separability
between distributions of hidden representations of generated and targeted
samples, while the generator is updated based on the decision hyper-planes
computed by performing LDA over the hidden representations. LD-GAN provides a
concrete metric of separation capacity for the discriminator, and we
experimentally show that it is possible to stabilize the training of LD-GAN
simply by calibrating the update frequencies between generators and
discriminators in the unsupervised case, without employment of normalization
methods and constraints on weights. In the class conditional generation tasks,
the proposed method shows improved training stability together with better
generalization performance compared to WGAN that employs an auxiliary
classifier.
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs
Authors: Zhun Sun, Mete Ozay, Takayuki Okatani
Date: 2017-07-25 00:00:00
Tasks: object-detection, Object Detection, Object Recognition

In this work, we address the problem of improvement of robustness of feature
representations learned using convolutional neural networks (CNNs) to image
deformation. We argue that higher moment statistics of feature distributions
could be shifted due to image deformations, and the shift leads to degrade of
performance and cannot be reduced by ordinary normalization methods as observed
in experimental analyses. In order to attenuate this effect, we apply
additional non-linearity in CNNs by combining power functions with learnable
parameters into convolution operation. In the experiments, we observe that CNNs
which employ the proposed method obtain remarkable boost in both the
generalization performance and the robustness under various types of
deformations using large scale benchmark datasets. For instance, a model
equipped with the proposed method obtains 3.3\% performance boost in mAP on
Pascal Voc object detection task using deformed images, compared to the
reference model, while both models provide the same performance using original
images. To the best of our knowledge, this is the first work that studies
robustness of deep features learned using CNNs to a wide range of deformations
for object recognition and detection.
Multiple-Kernel Local-Patch Descriptor
Authors: Arun Mukundan, Giorgos Tolias, Ondrej Chum
Date: 2017-07-25 00:00:00
Tasks: Position

We propose a multiple-kernel local-patch descriptor based on efficient match
kernels of patch gradients. It combines two parametrizations of gradient
position and direction, each parametrization provides robustness to a different
type of patch miss-registration: polar parametrization for noise in the patch
dominant orientation detection, Cartesian for imprecise location of the feature
point. Even though handcrafted, the proposed method consistently outperforms
the state-of-the-art methods on two local patch benchmarks.
Group-wise Deep Co-saliency Detection
Authors: Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, Fei Wu
Date: 2017-07-24 00:00:00
Tasks: Co-Salient Object Detection, Object Discovery, Representation Learning, Saliency Detection

In this paper, we propose an end-to-end group-wise deep co-saliency detection
approach to address the co-salient object discovery problem based on the fully
convolutional network (FCN) with group input and group output. The proposed
approach captures the group-wise interaction information for group images by
learning a semantics-aware image representation based on a convolutional neural
network, which adaptively learns the group-wise features for co-saliency
detection. Furthermore, the proposed approach discovers the collaborative and
interactive relationships between group-wise feature representation and
single-image individual feature representation, and model this in a
collaborative learning framework. Finally, we set up a unified end-to-end deep
learning scheme to jointly optimize the process of group-wise feature
representation learning and the collaborative learning, leading to more
reliable and robust co-saliency detection results. Experimental results
demonstrate the effectiveness of our approach in comparison with the
state-of-the-art approaches.
Detecting Semantic Parts on Partially Occluded Objects
Authors: Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, Alan Yuille
Date: 2017-07-25 00:00:00
Tasks: Clustering, Semantic Part Detection

In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection
Authors: Lina Wei, Fangfang Wang, Xi Li, Fei Wu, Jun Xiao
Date: 2017-07-25 00:00:00
Tasks: Saliency Detection, Video Saliency Detection

As an important and challenging problem in computer vision, video saliency
detection is typically cast as a spatiotemporal context modeling problem over
consecutive frames. As a result, a key issue in video saliency detection is how
to effectively capture the intrinsical properties of atomic video structures as
well as their associated contextual interactions along the spatial and temporal
dimensions. Motivated by this observation, we propose a graph-theoretic video
saliency detection approach based on adaptive video structure discovery, which
is carried out within a spatiotemporal atomic graph. Through graph-based
manifold propagation, the proposed approach is capable of effectively modeling
the semantically contextual interactions among atomic video structures for
saliency detection while preserving spatial smoothness and temporal
consistency. Experiments demonstrate the effectiveness of the proposed approach
over several benchmark datasets.
Deep Learning For Smile Recognition
Authors: Patrick O. Glauner
Date: 2016-01-30 00:00:00
Tasks: CPU, Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER), GPU, Model Selection, Smile Recognition

Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) database, significantly outperforming existing approaches based on
hand-crafted features with accuracies ranging from 65.55% to 79.67%. The
novelty of this approach includes a comprehensive model selection of the
architecture parameters, allowing to find an appropriate architecture for each
expression such as smile. This is feasible because all experiments were run on
a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations
on a CPU.
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Authors: Patrick O. Glauner, Andre Boechat, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni, Diogo Duarte
Date: 2016-02-26 00:00:00
Tasks: Small Data Image Classification

Non-technical losses (NTL) such as electricity theft cause significant harm
to our economies, as in some countries they may range up to 40% of the total
electricity distributed. Detecting NTLs requires costly on-site inspections.
Accurate prediction of NTLs for customers using machine learning is therefore
crucial. To date, related research largely ignore that the two classes of
regular and non-regular customers are highly imbalanced, that NTL proportions
may change and mostly consider small data sets, often not allowing to deploy
the results in production. In this paper, we present a comprehensive approach
to assess three NTL detection models for different NTL proportions in large
real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and
Support Vector Machine. This work has resulted in appreciable results that are
about to be deployed in a leading industry solution. We believe that the
considerations and observations made in this contribution are necessary for
future smart meter research in order to report their effectiveness on
imbalanced and large real world data sets.
A Survey on Multi-Task Learning
Authors: Yu Zhang, Qiang Yang
Date: 2017-07-25 00:00:00
Tasks: Active Learning, Clustering, Dimensionality Reduction, Multi-Task Learning, MULTI-VIEW LEARNING, Reinforcement Learning, Survey

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Authors: Patrick Glauner, Jorge Meira, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni, Diogo Duarte
Date: 2016-07-04 00:00:00
Tasks: Time Series, Time Series Analysis

Electricity theft is a major problem around the world in both developed and
developing countries and may range up to 40% of the total electricity
distributed. More generally, electricity theft belongs to non-technical losses
(NTL), which are losses that occur during the distribution of electricity in
power grids. In this paper, we build features from the neighborhood of
customers. We first split the area in which the customers are located into
grids of different sizes. For each grid cell we then compute the proportion of
inspected customers and the proportion of NTL found among the inspected
customers. We then analyze the distributions of features generated and show why
they are useful to predict NTL. In addition, we compute features from the
consumption time series of customers. We also use master data features of
customers, such as their customer class and voltage of their connection. We
compute these features for a Big Data base of 31M meter readings, 700K
customers and 400K inspection results. We then use these features to train four
machine learning algorithms that are particularly suitable for Big Data sets
because of their parallelizable structure: logistic regression, k-nearest
neighbors, linear support vector machine and random forest. Using the
neighborhood features instead of only analyzing the time series has resulted in
appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
This work can therefore be deployed to a wide range of different regions around
the world.
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Authors: Patrick Glauner, Angelo Migliosi, Jorge Meira, Petko Valtchev, Radu State, Franck Bettinger
Date: 2017-02-13 00:00:00
Tasks: BIG-bench Machine Learning

Non-technical losses (NTL) occur during the distribution of electricity in
power grids and include, but are not limited to, electricity theft and faulty
meters. In emerging countries, they may range up to 40% of the total
electricity distributed. In order to detect NTLs, machine learning methods are
used that learn irregular consumption patterns from customer data and
inspection results. The Big Data paradigm followed in modern machine learning
reflects the desire of deriving better conclusions from simply analyzing more
data, without the necessity of looking at theory and models. However, the
sample of inspected customers may be biased, i.e. it does not represent the
population of all customers. As a consequence, machine learning models trained
on these inspection results are biased as well and therefore lead to unreliable
predictions of whether customers cause NTL or not. In machine learning, this
issue is called covariate shift and has not been addressed in the literature on
NTL detection yet. In this work, we present a novel framework for quantifying
and visualizing covariate shift. We apply it to a commercial data set from
Brazil that consists of 3.6M customers and 820K inspection results. We show
that some features have a stronger covariate shift than others, making
predictions less reliable. In particular, previous inspections were focused on
certain neighborhoods or customer classes and that they were not sufficiently
spread among the population of customers. This framework is about to be
deployed in a commercial product for NTL detection.