pwc-archive/papers-with-abstracts huggingface dataset:
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
Authors: Siyang Song, Shuimei Zhang, Björn Schuller, Linlin Shen, Michel Valstar
Date: 2018-05-03 00:00:00
Tasks: Quantization, Speaker Verification, Text-Independent Speaker Verification

The performance of speaker-related systems usually degrades heavily in
practical applications largely due to the presence of background noise. To
improve the robustness of such systems in unknown noisy environments, this
paper proposes a simple pre-processing method called Noise Invariant Frame
Selection (NIFS). Based on several noisy constraints, it selects noise
invariant frames from utterances to represent speakers. Experiments conducted
on the TIMIT database showed that the NIFS can significantly improve the
performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal
Background Model (GMM-UBM) and i-vector-based speaker verification systems in
different unknown noisy environments with different SNRs, in comparison to
their baselines. Meanwhile, the proposed NIFS-based speaker verification
systems achieves similar performance when we change the constraints
(hyper-parameters) or features, which indicates that it is robust and easy to
reproduce. Since NIFS is designed as a general algorithm, it could be further
applied to other similar tasks.
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback
Authors: Carolin Lawrence, Stefan Riezler
Date: 2018-05-03 00:00:00
Proceeding: ACL 2018 7
Tasks: counterfactual, Semantic Parsing

Counterfactual learning from human bandit feedback describes a scenario where
user feedback on the quality of outputs of a historic system is logged and used
to improve a target system. We show how to apply this learning framework to
neural semantic parsing. From a machine learning perspective, the key challenge
lies in a proper reweighting of the estimator so as to avoid known degeneracies
in counterfactual learning, while still being applicable to stochastic gradient
optimization. To conduct experiments with human users, we devise an easy-to-use
interface to collect human feedback on semantic parses. Our work is the first
to show that semantic parsers can be improved significantly by counterfactual
learning from logged human feedback data.
Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints
Authors: Jan Křetínský, Guillermo A. Pérez, Jean-François Raskin
Date: 2018-04-24 00:00:00

We formalize the problem of maximizing the mean-payoff value with high
probability while satisfying a parity objective in a Markov decision process
(MDP) with unknown probabilistic transition function and unknown reward
function. Assuming the support of the unknown transition function and a lower
bound on the minimal transition probability are known in advance, we show that
in MDPs consisting of a single end component, two combinations of guarantees on
the parity and mean-payoff objectives can be achieved depending on how much
memory one is willing to use. (i) For all $\epsilon$ and $\gamma$ we can
construct an online-learning finite-memory strategy that almost-surely
satisfies the parity objective and which achieves an $\epsilon$-optimal mean
payoff with probability at least $1 - \gamma$. (ii) Alternatively, for all
$\epsilon$ and $\gamma$ there exists an online-learning infinite-memory
strategy that satisfies the parity objective surely and which achieves an
$\epsilon$-optimal mean payoff with probability at least $1 - \gamma$. We
extend the above results to MDPs consisting of more than one end component in a
natural way. Finally, we show that the aforementioned guarantees are tight,
i.e. there are MDPs for which stronger combinations of the guarantees cannot be
ensured.
An Asymptotically Optimal Strategy for Constrained Multi-armed Bandit Problems
Authors: Hyeong Soo Chang
Date: 2018-05-03 00:00:00

For the stochastic multi-armed bandit (MAB) problem from a constrained model
that generalizes the classical one, we show that an asymptotic optimality is
achievable by a simple strategy extended from the $\epsilon_t$-greedy strategy.
We provide a finite-time lower bound on the probability of correct selection of
an optimal near-feasible arm that holds for all time steps. Under some
conditions, the bound approaches one as time $t$ goes to infinity. A particular
example sequence of $\{\epsilon_t\}$ having the asymptotic convergence rate in
the order of $(1-\frac{1}{t})^4$ that holds from a sufficiently large $t$ is
also discussed.
Image Based Camera Localization: an Overview
Authors: Yihong Wu, Fulin Tang, Heping Li
Date: 2016-10-12 00:00:00
Tasks: Autonomous Driving, Camera Localization

Recently, virtual reality, augmented reality, robotics, autonomous driving et
al attract much attention of both academic and industrial community, in which
image based camera localization is a key task. However, there has not been a
complete review on image-based camera localization. It is urgent to map this
topic to help people enter the field quickly. In this paper, an overview of
image based camera localization is presented. A new and complete kind of
classifications for image based camera localization is provided and the related
techniques are introduced. Trends for the future development are also
discussed. It will be useful to not only researchers but also engineers and
other people interested.
audEERING's approach to the One-Minute-Gradual Emotion Challenge
Authors: Andreas Triantafyllopoulos, Hesam Sagha, Florian Eyben, Björn Schuller
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition

This paper describes audEERING's submissions as well as additional
evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We
provide the results for audio and video processing on subject (in)dependent
evaluations. On the provided Development set, we achieved 0.343 Concordance
Correlation Coefficient (CCC) for arousal (from audio) and .401 for valence
(from video).
Semantic segmentation of mFISH images using convolutional networks
Authors: Esteban Pardo, José Mário T Morgado, Norberto Malpica
Date: 2018-05-03 00:00:00
Tasks: General Classification, Segmentation, Semantic Segmentation

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to
detect major chromosomal alterations using fluorescent probes and imaging
techniques. Manual interpretation of mFISH images is a time consuming step that
can be automated using machine learning; in previous works, pixel or patch wise
classification was employed, overlooking spatial information which can help
identify chromosomes. In this work, we propose a fully convolutional semantic
segmentation network for the interpretation of mFISH images, which uses both
spatial and spectral information to classify each pixel in an end-to-end
fashion. The semantic segmentation network developed was tested on samples
extracted from a public dataset using cross validation. Despite having no
labeling information of the image it was tested on our algorithm yielded an
average correct classification ratio (CCR) of 87.41%. Previously, this level of
accuracy was only achieved with state of the art algorithms when classifying
pixels from the same image in which the classifier has been trained. These
results provide evidence that fully convolutional semantic segmentation
networks may be employed in the computer aided diagnosis of genetic diseases
with improved performance over the current methods of image analysis.
CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service
Authors: Marco Lippi, Przemyslaw Palka, Giuseppe Contissa, Francesca Lagioia, Hans-Wolfgang Micklitz, Giovanni Sartor, Paolo Torroni
Date: 2018-05-03 00:00:00
Tasks: BIG-bench Machine Learning, Unfairness Detection

Terms of service of on-line platforms too often contain clauses that are
potentially unfair to the consumer. We present an experimental study where
machine learning is employed to automatically detect such potentially unfair
clauses. Results show that the proposed system could provide a valuable tool
for lawyers and consumers alike.
Disentangling Language and Knowledge in Task-Oriented Dialogs
Authors: Dinesh Raghu, Nikhil Gupta, Mausam
Date: 2018-05-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Disentanglement, Language Modeling, Language Modelling

The Knowledge Base (KB) used for real-world applications, such as booking a
movie or restaurant reservation, keeps changing over time. End-to-end neural
networks trained for these task-oriented dialogs are expected to be immune to
any changes in the KB. However, existing approaches breakdown when asked to
handle such changes. We propose an encoder-decoder architecture (BoSsNet) with
a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled
learning of the response's language model and its knowledge incorporation.
Consequently, the KB can be modified with new knowledge without a drop in
interpretability. We find that BoSsNet outperforms state-of-the-art models,
with considerable improvements (> 10\%) on bAbI OOV test sets and other
human-human datasets. We also systematically modify existing datasets to
measure disentanglement and show BoSsNet to be robust to KB modifications.
The Algorithm Selection Competitions 2015 and 2017
Authors: Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
Date: 2018-05-03 00:00:00

The algorithm selection problem is to choose the most suitable algorithm for
solving a given problem instance. It leverages the complementarity between
different approaches that is present in many areas of AI. We report on the
state of the art in algorithm selection, as defined by the Algorithm Selection
competitions in 2015 and 2017. The results of these competitions show how the
state of the art improved over the years. We show that although performance in
some cases is very good, there is still room for improvement in other cases.
Finally, we provide insights into why some scenarios are hard, and pose
challenges to the community on how to advance the current state of the art.
Found Graph Data and Planted Vertex Covers
Authors: Austin R. Benson, Jon Kleinberg
Date: 2018-05-03 00:00:00
Proceeding: NeurIPS 2018 12

A typical way in which network data is recorded is to measure all the
interactions among a specified set of core nodes; this produces a graph
containing this core together with a potentially larger set of fringe nodes
that have links to the core. Interactions between pairs of nodes in the fringe,
however, are not recorded by this process, and hence not present in the
resulting graph data. For example, a phone service provider may only have
records of calls in which at least one of the participants is a customer; this
can include calls between a customer and a non-customer, but not between pairs
of non-customers.
  Knowledge of which nodes belong to the core is an important piece of metadata
that is crucial for interpreting the network dataset. But in many cases, this
metadata is not available, either because it has been lost due to difficulties
in data provenance, or because the network consists of found data obtained in
settings such as counter-surveillance. This leads to a natural algorithmic
problem, namely the recovery of the core set. Since the core set forms a vertex
cover of the graph, we essentially have a planted vertex cover problem, but
with an arbitrary underlying graph. We develop a theoretical framework for
analyzing this planted vertex cover problem, based on results in the theory of
fixed-parameter tractability, together with algorithms for recovering the core.
Our algorithms are fast, simple to implement, and out-perform several methods
based on network core-periphery structure on various real-world datasets.
Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
Authors: Songwen Pei, Fuwu Tang, Yanfei Ji, Jing Fan, Zhong Ning
Date: 2018-04-27 00:00:00
Tasks: Autonomous Driving, Deep Learning, Traffic Sign Detection, Traffic Sign Recognition

Autonomous driving is becoming a future practical lifestyle greatly driven by
deep learning. Specifically, an effective traffic sign detection by deep
learning plays a critical role for it. However, different countries have
different sets of traffic signs, making localized traffic sign recognition
model training a tedious and daunting task. To address the issues of taking
amount of time to compute complicate algorithm and low ratio of detecting
blurred and sub-pixel images of localized traffic signs, we propose Multi-Scale
Deconvolution Networks (MDN), which flexibly combines multi-scale convolutional
neural network with deconvolution sub-network, leading to efficient and
reliable localized traffic sign recognition model training. It is demonstrated
that the proposed MDN is effective compared with classical algorithms on the
benchmarks of the localized traffic sign, such as Chinese Traffic Sign Dataset
(CTSD), and the German Traffic Sign Benchmarks (GTSRB).
Label Embedding with Partial Heterogeneous Contexts
Authors: Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan
Date: 2018-05-03 00:00:00
Tasks: Descriptive, image-classification, Image Classification

Label embedding plays an important role in many real-world applications. To
enhance the label relatedness captured by the embeddings, multiple contexts can
be adopted. However, these contexts are heterogeneous and often partially
observed in practical tasks, imposing significant challenges to capture the
overall relatedness among labels. In this paper, we propose a general Partial
Heterogeneous Context Label Embedding (PHCLE) framework to address these
challenges. Categorizing heterogeneous contexts into two groups, relational
context and descriptive context, we design tailor-made matrix factorization
formula to effectively exploit the label relatedness in each context. With a
shared embedding principle across heterogeneous contexts, the label relatedness
is selectively aligned in a shared space. Due to our elegant formulation, PHCLE
overcomes the partial context problem and can nicely incorporate more contexts,
which both cannot be tackled with existing multi-context label embedding
methods. An effective alternative optimization algorithm is further derived to
solve the sparse matrix factorization problem. Experimental results demonstrate
that the label embeddings obtained with PHCLE achieve superb performance in
image classification task and exhibit good interpretability in the downstream
label similarity analysis and image understanding task.
BirdNet: a 3D Object Detection Framework from LiDAR information
Authors: Jorge Beltran, Carlos Guindel, Francisco Miguel Moreno, Daniel Cruzado, Fernando Garcia, Arturo de la Escalera
Date: 2018-05-03 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Object, object-detection, Object Detection

Understanding driving situations regardless the conditions of the traffic
scene is a cornerstone on the path towards autonomous vehicles; however,
despite common sensor setups already include complementary devices such as
LiDAR or radar, most of the research on perception systems has traditionally
focused on computer vision. We present a LiDAR-based 3D object detection
pipeline entailing three stages. First, laser information is projected into a
novel cell encoding for bird's eye view projection. Later, both object location
on the plane and its heading are estimated through a convolutional neural
network originally designed for image processing. Finally, 3D oriented
detections are computed in a post-processing phase. Experiments on KITTI
dataset show that the proposed framework achieves state-of-the-art results
among comparable methods. Further tests with different LiDAR sensors in real
scenarios assess the multi-device capabilities of the approach.
Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths
Authors: Markus Schröder, Jörn Hees, Ansgar Bernardi, Daniel Ewert, Peter Klotz, Steffen Stadtmüller
Date: 2018-05-03 00:00:00

Within the Semantic Web community, SPARQL is one of the predominant languages
to query and update RDF knowledge. However, the complexity of SPARQL, the
underlying graph structure and various encodings are common sources of
confusion for Semantic Web novices.
  In this paper we present a general purpose approach to convert any given
SPARQL endpoint into a simple to use REST API. To lower the initial hurdle, we
represent the underlying graph as an interlinked view of nested JSON objects
that can be traversed by the API path.
Optimization of computational budget for power system risk assessment
Authors: Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick Panciatici
Date: 2018-05-03 00:00:00

We address the problem of maintaining high voltage power transmission
networks in security at all time, namely anticipating exceeding of thermal
limit for eventual single line disconnection (whatever its cause may be) by
running slow, but accurate, physical grid simulators. New conceptual frameworks
are calling for a probabilistic risk-based security criterion. However, these
approaches suffer from high requirements in terms of tractability. Here, we
propose a new method to assess the risk. This method uses both machine learning
techniques (artificial neural networks) and more standard simulators based on
physical laws. More specifically we train neural networks to estimate the
overall dangerousness of a grid state. A classical benchmark problem (manpower
118 buses test case) is used to show the strengths of the proposed method.
Anticipating contingengies in power grids using fast neural net screening
Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici
Date: 2018-05-03 00:00:00
Tasks: All

We address the problem of maintaining high voltage power transmission
networks in security at all time. This requires that power flowing through all
lines remain below a certain nominal thermal limit above which lines might
melt, break or cause other damages. Current practices include enforcing the
deterministic "N-1" reliability criterion, namely anticipating exceeding of
thermal limit for any eventual single line disconnection (whatever its cause
may be) by running a slow, but accurate, physical grid simulator. New
conceptual frameworks are calling for a probabilistic risk based security
criterion and are in need of new methods to assess the risk. To tackle this
difficult assessment, we address in this paper the problem of rapidly ranking
higher order contingencies including all pairs of line disconnections, to
better prioritize simulations. We present a novel method based on neural
networks, which ranks "N-1" and "N-2" contingencies in decreasing order of
presumed severity. We demonstrate on a classical benchmark problem that the
residual risk of contingencies decreases dramatically compared to considering
solely all "N-1" cases, at no additional computational cost. We evaluate that
our method scales up to power grids of the size of the French high voltage
power grid (over 1000 power lines).
SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing
Authors: Qun Liu, Suman Kumar, Vijay Mago
Date: 2018-05-03 00:00:00
Tasks: Cloud Computing

World wide road traffic fatality and accident rates are high, and this is
true even in technologically advanced countries like the USA. Despite the
advances in Intelligent Transportation Systems, safe transportation routing
i.e., finding safest routes is largely an overlooked paradigm. In recent years,
large amount of traffic data has been produced by people, Internet of Vehicles
and Internet of Things (IoT). Also, thanks to advances in cloud computing and
proliferation of mobile communication technologies, it is now possible to
perform analysis on vast amount of generated data (crowd sourced) and deliver
the result back to users in real time. This paper proposes SafeRNet, a safe
route computation framework which takes advantage of these technologies to
analyze streaming traffic data and historical data to effectively infer safe
routes and deliver them back to users in real time. SafeRNet utilizes Bayesian
network to formulate safe route model. Furthermore, a case study is presented
to demonstrate the effectiveness of our approach using real traffic data.
SafeRNet intends to improve drivers safety in a modern technology rich
transportation system.
Memory Attention Networks for Skeleton-based Action Recognition
Authors: Chunyu Xie, Ce Li, Baochang Zhang, Chen Chen, Jungong Han, Changqing Zou, Jianzhuang Liu
Date: 2018-04-23 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based action recognition task is entangled with complex
spatio-temporal variations of skeleton joints, and remains challenging for
Recurrent Neural Networks (RNNs). In this work, we propose a
temporal-then-spatial recalibration scheme to alleviate such complex
variations, resulting in an end-to-end Memory Attention Networks (MANs) which
consist of a Temporal Attention Recalibration Module (TARM) and a
Spatio-Temporal Convolution Module (STCM). Specifically, the TARM is deployed
in a residual learning module that employs a novel attention learning network
to recalibrate the temporal attention of frames in a skeleton sequence. The
STCM treats the attention calibrated skeleton joint sequences as images and
leverages the Convolution Neural Networks (CNNs) to further model the spatial
and temporal information of skeleton data. These two modules (TARM and STCM)
seamlessly form a single network architecture that can be trained in an
end-to-end fashion. MANs significantly boost the performance of skeleton-based
action recognition and achieve the best results on four challenging benchmark
datasets: NTU RGB+D, HDM05, SYSU-3D and UT-Kinect.
Superpixel-guided Two-view Deterministic Geometric Model Fitting
Authors: Guobao Xiao, Hanzi Wang, Yan Yan, David Suter
Date: 2018-05-03 00:00:00
Tasks: model, Model Selection, Superpixels, Vocal Bursts Valence Prediction

Geometric model fitting is a fundamental research topic in computer vision
and it aims to fit and segment multiple-structure data. In this paper, we
propose a novel superpixel-guided two-view geometric model fitting method
(called SDF), which can obtain reliable and consistent results for real images.
Specifically, SDF includes three main parts: a deterministic sampling
algorithm, a model hypothesis updating strategy and a novel model selection
algorithm. The proposed deterministic sampling algorithm generates a set of
initial model hypotheses according to the prior information of superpixels.
Then the proposed updating strategy further improves the quality of model
hypotheses. After that, by analyzing the properties of the updated model
hypotheses, the proposed model selection algorithm extends the conventional
"fit-and-remove" framework to estimate model instances in multiple-structure
data. The three parts are tightly coupled to boost the performance of SDF in
both speed and accuracy, and SDF has the deterministic nature. Experimental
results show that the proposed SDF has significant advantages over several
state-of-the-art fitting methods when it is applied to real images with
single-structure and multiple-structure data.