pwc-archive/papers-with-abstracts huggingface dataset:
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Authors: Colin Lea, Austin Reiter, Rene Vidal, Gregory D. Hager
Date: 2016-02-09 00:00:00
Tasks: Action Classification, Action Recognition, Action Segmentation, Fine-grained Action Recognition, General Classification, Segmentation, Temporal Action Localization

Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition approaches remains low. We propose a model for action segmentation
which combines low-level spatiotemporal features with a high-level segmental
classifier. Our spatiotemporal CNN is comprised of a spatial component that
uses convolutional filters to capture information about objects and their
relationships, and a temporal component that uses large 1D convolutional
filters to capture information about how object relationships change across
time. These features are used in tandem with a semi-Markov model that models
transitions from one action to another. We introduce an efficient constrained
segmental inference algorithm for this model that is orders of magnitude faster
than the current approach. We highlight the effectiveness of our Segmental
Spatiotemporal CNN on cooking and surgical action datasets for which we observe
substantially improved performance relative to recent baseline methods.
Learning to Decode Linear Codes Using Deep Learning
Authors: Eliya Nachmani, Yair Beery, David Burshtein
Date: 2016-07-16 00:00:00
Tasks: Decoder, Deep Learning

A novel deep learning method for improving the belief propagation algorithm
is proposed. The method generalizes the standard belief propagation algorithm
by assigning weights to the edges of the Tanner graph. These edges are then
trained using deep learning techniques. A well-known property of the belief
propagation algorithm is the independence of the performance on the transmitted
codeword. A crucial property of our new method is that our decoder preserved
this property. Furthermore, this property allows us to learn only a single
codeword instead of exponential number of code-words. Improvements over the
belief propagation algorithm are demonstrated for various high density parity
check codes.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
Authors: Jacob Abernethy, Cyrus Anderson, Chengyu Dai, Arya Farahi, Linh Nguyen, Adam Rauh, Eric Schwartz, Wenbo Shen, Guangsha Shi, Jonathan Stroud, Xinyu Tan, Jared Webb, Sheng Yang
Date: 2016-09-30 00:00:00

Recovery from the Flint Water Crisis has been hindered by uncertainty in both
the water testing process and the causes of contamination. In this work, we
develop an ensemble of predictive models to assess the risk of lead
contamination in individual homes and neighborhoods. To train these models, we
utilize a wide range of data sources, including voluntary residential water
tests, historical records, and city infrastructure data. Additionally, we use
our models to identify the most prominent factors that contribute to a high
risk of lead contamination. In this analysis, we find that lead service lines
are not the only factor that is predictive of the risk of lead contamination of
water. These results could be used to guide the long-term recovery efforts in
Flint, minimize the immediate damages, and improve resource-allocation
decisions for similar water infrastructure crises.
Characterization of experts in crowdsourcing platforms
Authors: Amal Ben Rjab, Mouloud Kharoune, Zoltan Miklos, Arnaud Martin
Date: 2016-09-30 00:00:00

Crowdsourcing platforms enable to propose simple human intelligence tasks to
a large number of participants who realise these tasks. The workers often
receive a small amount of money or the platforms include some other incentive
mechanisms, for example they can increase the workers reputation score, if they
complete the tasks correctly. We address the problem of identifying experts
among participants, that is, workers, who tend to answer the questions
correctly. Knowing who are the reliable workers could improve the quality of
knowledge one can extract from responses. As opposed to other works in the
literature, we assume that participants can give partial or incomplete
responses, in case they are not sure that their answers are correct. We model
such partial or incomplete responses with the help of belief functions, and we
derive a measure that characterizes the expertise level of each participant.
This measure is based on precise and exactitude degrees that represent two
parts of the expertise level. The precision degree reflects the reliability
level of the participants and the exactitude degree reflects the knowledge
level of the participants. We also analyze our model through simulation and
demonstrate that our richer model can lead to more reliable identification of
experts.
A deep representation for depth images from synthetic data
Authors: Fabio Maria Carlucci, Paolo Russo, Barbara Caputo
Date: 2016-09-30 00:00:00
Tasks: Colorization, Object Categorization

Convolutional Neural Networks (CNNs) trained on large scale RGB databases
have become the secret sauce in the majority of recent approaches for object
categorization from RGB-D data. Thanks to colorization techniques, these
methods exploit the filters learned from 2D images to extract meaningful
representations in 2.5D. Still, the perceptual signature of these two kind of
images is very different, with the first usually strongly characterized by
textures, and the second mostly by silhouettes of objects. Ideally, one would
like to have two CNNs, one for RGB and one for depth, each trained on a
suitable data collection, able to capture the perceptual properties of each
channel for the task at hand. This has not been possible so far, due to the
lack of a suitable depth database. This paper addresses this issue, proposing
to opt for synthetically generated images rather than collecting by hand a 2.5D
large scale database. While being clearly a proxy for real data, synthetic
images allow to trade quality for quantity, making it possible to generate a
virtually infinite amount of data. We show that the filters learned from such
data collection, using the very same architecture typically used on visual
data, learns very different filters, resulting in depth features (a) able to
better characterize the different facets of depth images, and (b) complementary
with respect to those derived from CNNs pre-trained on 2D datasets. Experiments
on two publicly available databases show the power of our approach.
Training a Feedback Loop for Hand Pose Estimation
Authors: Markus Oberweger, Paul Wohlhart, Vincent Lepetit
Date: 2016-09-30 00:00:00
Proceeding: ICCV 2015 12
Tasks: GPU, Hand Pose Estimation, Pose Estimation

We propose an entirely data-driven approach to estimating the 3D pose of a
hand given a depth image. We show that we can correct the mistakes made by a
Convolutional Neural Network trained to predict an estimate of the 3D pose by
using a feedback loop. The components of this feedback loop are also Deep
Networks, optimized using training data. They remove the need for fitting a 3D
model to the input data, which requires both a carefully designed fitting
function and algorithm. We show that our approach outperforms state-of-the-art
methods, and is efficient as our implementation runs at over 400 fps on a
single GPU.
Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging
Authors: Petros-Pavlos Ypsilantis, Giovanni Montana
Date: 2016-09-28 00:00:00
Tasks: Computed Tomography (CT), Sensitivity

Computed tomography (CT) generates a stack of cross-sectional images covering
a region of the body. The visual assessment of these images for the
identification of potential abnormalities is a challenging and time consuming
task due to the large amount of information that needs to be processed. In this
article we propose a deep artificial neural network architecture, ReCTnet, for
the fully-automated detection of pulmonary nodules in CT scans. The
architecture learns to distinguish nodules and normal structures at the pixel
level and generates three-dimensional probability maps highlighting areas that
are likely to harbour the objects of interest. Convolutional and recurrent
layers are combined to learn expressive image representations exploiting the
spatial dependencies across axial slices. We demonstrate that leveraging
intra-slice dependencies substantially increases the sensitivity to detect
pulmonary nodules without inflating the false positive rate. On the publicly
available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet
reaches a detection sensitivity of 90.5% with an average of 4.5 false positives
per scan. Comparisons with a competing multi-channel convolutional neural
network for multi-slice segmentation and other published methodologies using
the same dataset provide evidence that ReCTnet offers significant performance
gains.
Predicting the consequence of action in digital control state spaces
Authors: Emmanuel Daucé
Date: 2016-09-30 00:00:00

The objective of this dissertation is to shed light on some fundamental
impediments in learning control laws in continuous state spaces. In particular,
if one wants to build artificial devices capable to learn motor tasks the same
way they learn to classify signals and images, one needs to establish control
rules that do not necessitate comparisons between quantities of the surrounding
space. We propose, in that context, to take inspiration from the "end effector
control" principle, as suggested by neuroscience studies, as opposed to the
"displacement control" principle used in the classical control theory.
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Authors: Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi
Date: 2016-09-30 00:00:00
Tasks: General Classification, GPU

Convolutional Neural Networks (CNNs) have gained significant traction in the
field of machine learning, particularly due to their high accuracy in visual
recognition. Recent works have pushed the performance of GPU implementations of
CNNs to significantly improve their classification and training times. With
these improvements, many frameworks have become available for implementing CNNs
on both CPUs and GPUs, with no support for FPGA implementations. In this work
we present a modified version of the popular CNN framework Caffe, with FPGA
support. This allows for classification using CNN models and specialized FPGA
implementations with the flexibility of reprogramming the device when
necessary, seamless memory transactions between host and device, simple-to-use
test benches, and the ability to create pipelined layer implementations. To
validate the framework, we use the Xilinx SDAccel environment to implement an
FPGA-based Winograd convolution engine and show that the FPGA layer can be used
alongside other layers running on a host processor to run several popular CNNs
(AlexNet, GoogleNet, VGG A, Overfeat). The results show that our framework
achieves 50 GFLOPS across 3x3 convolutions in the benchmarks. This is achieved
within a practical framework, which will aid in future development of
FPGA-based CNNs.
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes
Authors: Fotis Kalaganis, Dimitrios A. Adamos, Nikos Laskaris
Date: 2016-09-20 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Feature Engineering

We investigated the possibility of using a machine-learning scheme in
conjunction with commercial wearable EEG-devices for translating listener's
subjective experience of music into scores that can be used for the automated
annotation of music in popular on-demand streaming services. Based on the
established -neuroscientifically sound- concepts of brainwave frequency bands,
activation asymmetry index and cross-frequency-coupling (CFC), we introduce a
Brain Computer Interface (BCI) system that automatically assigns a rating score
to the listened song. Our research operated in two distinct stages: i) a
generic feature engineering stage, in which features from signal-analytics were
ranked and selected based on their ability to associate music induced
perturbations in brainwaves with listener's appraisal of music. ii) a
personalization stage, during which the efficiency of ex- treme learning
machines (ELMs) is exploited so as to translate the derived pat- terns into a
listener's score. Encouraging experimental results, from a pragmatic use of the
system, are presented.
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
Authors: Minjae Lee, Kyuyeon Hwang, Jinhwan Park, Sungwook Choi, Sungho Shin, Wonyong Sung
Date: 2016-09-30 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper, a neural network based real-time speech recognition (SR)
system is developed using an FPGA for very low-power operation. The implemented
system employs two recurrent neural networks (RNNs); one is a
speech-to-character RNN for acoustic modeling (AM) and the other is for
character-level language modeling (LM). The system also employs a statistical
word-level LM to improve the recognition accuracy. The results of the AM, the
character-level LM, and the word-level LM are combined using a fairly simple
N-best search algorithm instead of the hidden Markov model (HMM) based network.
The RNNs are implemented using massively parallel processing elements (PEs) for
low latency and high throughput. The weights are quantized to 6 bits to store
all of them in the on-chip memory of an FPGA. The proposed algorithm is
implemented on a Xilinx XC7Z045, and the system can operate much faster than
real-time.
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach
Authors: J. Jin, Y. Yuan, W. Pan, D. L. T. Pham, C. J. Tomlin, A. Webb, J. Goncalves
Date: 2016-09-30 00:00:00
Tasks: Compressive Sensing

This paper begins with considering the identification of sparse linear
time-invariant networks described by multivariable ARX models. Such models
possess relatively simple structure thus used as a benchmark to promote further
research. With identifiability of the network guaranteed, this paper presents
an identification method that infers both the Boolean structure of the network
and the internal dynamics between nodes. Identification is performed directly
from data without any prior knowledge of the system, including its order. The
proposed method solves the identification problem using Maximum a posteriori
estimation (MAP) but with inseparable penalties for complexity, both in terms
of element (order of nonzero connections) and group sparsity (network
topology). Such an approach is widely applied in Compressive Sensing (CS) and
known as Sparse Bayesian Learning (SBL). We then propose a novel scheme that
combines sparse Bayesian and group sparse Bayesian to efficiently solve the
problem. The resulted algorithm has a similar form of the standard Sparse Group
Lasso (SGL) while with known noise variance, it simplifies to exact re-weighted
SGL. The method and the developed toolbox can be applied to infer networks from
a wide range of fields, including systems biology applications such as
signaling and genetic regulatory networks.
A CNN Cascade for Landmark Guided Semantic Part Segmentation
Authors: Aaron Jackson, Michel Valstar, Georgios Tzimiropoulos
Date: 2016-09-30 00:00:00
Tasks: Pose Estimation, Segmentation

This paper proposes a CNN cascade for semantic part segmentation guided by
pose-specific information encoded in terms of a set of landmarks (or
keypoints). There is large amount of prior work on each of these tasks
separately, yet, to the best of our knowledge, this is the first time in
literature that the interplay between pose estimation and semantic part
segmentation is investigated. To address this limitation of prior work, in this
paper, we propose a CNN cascade of tasks that firstly performs landmark
localisation and then uses this information as input for guiding semantic part
segmentation. We applied our architecture to the problem of facial part
segmentation and report large performance improvement over the standard
unguided network on the most challenging face datasets. Testing code and models
will be published online at http://cs.nott.ac.uk/~psxasj/.
Pano2CAD: Room Layout From A Single Panorama Image
Authors: Jiu Xu, Bjorn Stenger, Tommi Kerola, Tony Tung
Date: 2016-09-29 00:00:00
Tasks: 2D Object Detection, Bayesian Inference, Object, object-detection, Object Detection, Pose Estimation, Surface Normal Estimation

This paper presents a method of estimating the geometry of a room and the 3D
pose of objects from a single 360-degree panorama image. Assuming Manhattan
World geometry, we formulate the task as a Bayesian inference problem in which
we estimate positions and orientations of walls and objects. The method
combines surface normal estimation, 2D object detection and 3D object pose
estimation. Quantitative results are presented on a dataset of synthetically
generated 3D rooms containing objects, as well as on a subset of hand-labeled
images from the public SUN360 dataset.
Turing learning: a metric-free approach to inferring behavior and its application to swarms
Authors: Wei Li, Melvin Gauci, Roderich Gross
Date: 2016-03-15 00:00:00

We propose Turing Learning, a novel system identification method for
inferring the behavior of natural or artificial systems. Turing Learning
simultaneously optimizes two populations of computer programs, one representing
models of the behavior of the system under investigation, and the other
representing classifiers. By observing the behavior of the system as well as
the behaviors produced by the models, two sets of data samples are obtained.
The classifiers are rewarded for discriminating between these two sets, that
is, for correctly categorizing data samples as either genuine or counterfeit.
Conversely, the models are rewarded for 'tricking' the classifiers into
categorizing their data samples as genuine. Unlike other methods for system
identification, Turing Learning does not require predefined metrics to quantify
the difference between the system and its models. We present two case studies
with swarms of simulated robots and prove that the underlying behaviors cannot
be inferred by a metric-based system identification method. By contrast, Turing
Learning infers the behaviors with high accuracy. It also produces a useful
by-product - the classifiers - that can be used to detect abnormal behavior in
the swarm. Moreover, we show that Turing Learning also successfully infers the
behavior of physical robot swarms. The results show that collective behaviors
can be directly inferred from motion trajectories of individuals in the swarm,
which may have significant implications for the study of animal collectives.
Furthermore, Turing Learning could prove useful whenever a behavior is not
easily characterizable using metrics, making it suitable for a wide range of
applications.
Big Data analytics. Three use cases with R, Python and Spark
Authors: Philippe Besse, Brendan Guillouet, Jean-Michel Loubes
Date: 2016-09-30 00:00:00
Tasks: Collaborative Filtering, Management, regression

Management and analysis of big data are systematically associated with a data
distributed architecture in the Hadoop and now Spark frameworks. This article
offers an introduction for statisticians to these technologies by comparing the
performance obtained by the direct use of three reference environments: R,
Python Scikit-learn, Spark MLlib on three public use cases: character
recognition, recommending films, categorizing products. As main result, it
appears that, if Spark is very efficient for data munging and recommendation by
collaborative filtering (non-negative factorization), current implementations
of conventional learning methods (logistic regression, random forests) in MLlib
or SparkML do not ou poorly compete habitual use of these methods (R, Python
Scikit-learn) in an integrated or undistributed architecture
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Authors: Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park
Date: 2016-08-29 00:00:00
Tasks: CPU, General Classification, GPU, object-detection, Object Detection, Real-Time Object Detection, Region Proposal, Scheduling

This paper presents how we can achieve the state-of-the-art accuracy in
multi-category object detection task while minimizing the computational cost by
adapting and combining recent technical innovations. Following the common
pipeline of "CNN feature extraction + region proposal + RoI classification", we
mainly redesign the feature extraction part, since region proposal part is not
computationally expensive and classification part can be efficiently compressed
with common techniques like truncated SVD. Our design principle is "less
channels with more layers" and adoption of some building blocks including
concatenated ReLU, Inception, and HyperNet. The designed network is deep and
thin and trained with the help of batch normalization, residual connections,
and learning rate scheduling based on plateau detection. We obtained solid
results on well-known object detection benchmarks: 83.8% mAP (mean average
precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only
750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA
Titan X GPU. Theoretically, our network requires only 12.3% of the
computational cost compared to ResNet-101, the winner on VOC2012.
Social Computing for Mobile Big Data in Wireless Networks
Authors: Xing Zhang, Zhenglei Yi, Zhi Yan, Geyong Min, Wenbo Wang, Sabita Maharjan, Yan Zhang
Date: 2016-09-30 00:00:00
Tasks: Marketing

Mobile big data contains vast statistical features in various dimensions,
including spatial, temporal, and the underlying social domain. Understanding
and exploiting the features of mobile data from a social network perspective
will be extremely beneficial to wireless networks, from planning, operation,
and maintenance to optimization and marketing. In this paper, we categorize and
analyze the big data collected from real wireless cellular networks. Then, we
study the social characteristics of mobile big data and highlight several
research directions for mobile big data in the social computing areas.
Swarm Intelligence for Multiobjective Optimization of Extraction Process
Authors: T. Ganesan, I. Elamvazuthi, P. Vasant
Date: 2016-09-30 00:00:00
Tasks: Multiobjective Optimization

Multi objective (MO) optimization is an emerging field which is increasingly
being implemented in many industries globally. In this work, the MO
optimization of the extraction process of bioactive compounds from the Gardenia
Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been
applied in conjunction with normal-boundary intersection (NBI) method to solve
this MO problem. The gravitational search algorithm (GSA) and the particle
swarm optimization (PSO) technique were implemented in this work. In addition,
a novel Hopfield-enhanced particle swarm optimization was developed and applied
to the extraction problem. By measuring the levels of dominance, the optimality
of the approximate Pareto frontiers produced by all the algorithms were gauged
and compared. Besides, by measuring the levels of convergence of the frontier,
some understanding regarding the structure of the objective space in terms of
its relation to the level of frontier dominance is uncovered. Detail
comparative studies were conducted on all the algorithms employed and developed
in this work.