pwc-archive/papers-with-abstracts huggingface dataset:
How good is good enough? Re-evaluating the bar for energy disaggregation
Authors: Nipun Batra, Rishi Baijal, Amarjeet Singh, Kamin Whitehouse
Date: 2015-10-26 00:00:00

Since the early 1980s, the research community has developed ever more
sophisticated algorithms for the problem of energy disaggregation, but despite
decades of research, there is still a dearth of applications with demonstrated
value. In this work, we explore a question that is highly pertinent to this
research community: how good does energy disaggregation need to be in order to
infer characteristics of a household? We present novel techniques that use
unsupervised energy disaggregation to predict both household occupancy and
static properties of the household such as size of the home and number of
occupants. Results show that basic disaggregation approaches performs up to 30%
better at occupancy estimation than using aggregate power data alone, and are
up to 10% better at estimating static household characteristics. These results
show that even rudimentary energy disaggregation techniques are sufficient for
improved inference of household characteristics. To conclude, we re-evaluate
the bar set by the community for energy disaggregation accuracy and try to
answer the question "how good is good enough?"
Pan-Tilt Camera and PIR Sensor Fusion Based Moving Object Detection for Mobile Security Robots
Authors: YongChol Sin, MyongSong Choe, GyongIl Ryang
Date: 2015-10-26 00:00:00
Tasks: Mobile Security, Moving Object Detection, Object, object-detection, Object Detection, Sensor Fusion

One of fundamental issues for security robots is to detect and track people
in the surroundings. The main problems of this task are real-time constraints,
a changing background, varying illumination conditions and a non-rigid shape of
the person to be tracked. In this paper, we propose a solution for tracking
with a pan-tilt camera and a passive infrared range (PIR) sensor to detect the
moving object based on consecutive frame difference. The proposed method is
excellent in real-time performance because it requires only a little memory and
computation. Experiment results show that this method can detect the moving
object such as human efficiently and accurately in non-stationary and complex
indoor environment.
Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation
Authors: Nipun Batra, Amarjeet Singh, Kamin Whitehouse
Date: 2015-10-26 00:00:00

In this paper, we investigate whether "big-data" is more valuable than
"precise" data for the problem of energy disaggregation: the process of
breaking down aggregate energy usage on a per-appliance basis. Existing
techniques for disaggregation rely on energy metering at a resolution of 1
minute or higher, but most power meters today only provide a reading once per
month, and at most once every 15 minutes. In this paper, we propose a new
technique called Neighbourhood NILM that leverages data from 'neighbouring'
homes to disaggregate energy given only a single energy reading per month. The
key intuition behind our approach is that 'similar' homes have 'similar' energy
consumption on a per-appliance basis. Neighbourhood NILM matches every home
with a set of 'neighbours' that have direct submetering infrastructure, i.e.
power meters on individual circuits or loads. Many such homes already exist.
Then, it estimates the appliance-level energy consumption of the target home to
be the average of its K neighbours. We evaluate this approach using 25 homes
and results show that our approach gives comparable or better disaggregation in
comparison to state-of-the-art accuracy reported in the literature that depend
on manual model training, high frequency power metering, or both. Results show
that Neighbourhood NILM can achieve 83% and 79% accuracy disaggregating fridge
and heating/cooling loads, compared to 74% and 73% for a technique called FHMM.
Furthermore, it achieves up to 64% accuracy on washing machine, dryer,
dishwasher, and lighting loads, which is higher than previously reported
results. Many existing techniques are not able to disaggregate these loads at
all. These results indicate a potentially substantial advantage to installing
submetering infrastructure in a select few homes rather than installing new
high-frequency smart metering infrastructure in all homes.
How to merge three different methods for information filtering ?
Authors: Jean-Valère Cossu, Ludovic Bonnefoy, Xavier Bost, Marc El Bèze
Date: 2015-10-26 00:00:00
Tasks: Marketing

Twitter is now a gold marketing tool for entities concerned with online
reputation. To automatically monitor online reputation of entities , systems
have to deal with ambiguous entity names, polarity detection and topic
detection. We propose three approaches to tackle the first issue: monitoring
Twitter in order to find relevant tweets about a given entity. Evaluated within
the framework of the RepLab-2013 Filtering task, each of them has been shown
competitive with state-of-the-art approaches. Mainly we investigate on how much
merging strategies may impact performances on a filtering task according to the
evaluation measure.
Mining User Opinions in Mobile App Reviews: A Keyword-based Approach
Authors: Phong Minh Vu, Tam The Nguyen, Hung Viet Pham, Tung Thanh Nguyen
Date: 2015-05-18 00:00:00

User reviews of mobile apps often contain complaints or suggestions which are
valuable for app developers to improve user experience and satisfaction.
However, due to the large volume and noisy-nature of those reviews, manually
analyzing them for useful opinions is inherently challenging. To address this
problem, we propose MARK, a keyword-based framework for semi-automated review
analysis. MARK allows an analyst describing his interests in one or some mobile
apps by a set of keywords. It then finds and lists the reviews most relevant to
those keywords for further analysis. It can also draw the trends over time of
those keywords and detect their sudden changes, which might indicate the
occurrences of serious issues. To help analysts describe their interests more
effectively, MARK can automatically extract keywords from raw reviews and rank
them by their associations with negative reviews. In addition, based on a
vector-based semantic representation of keywords, MARK can divide a large set
of keywords into more cohesive subsets, or suggest keywords similar to the
selected ones.
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
Authors: S. Hussain Raza, Ahmad Humayun, Matthias Grundmann, David Anderson, Irfan Essa
Date: 2015-10-25 00:00:00

We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.
Geometric Context from Videos
Authors: S. Hussain Raza, Matthias Grundmann, Irfan Essa
Date: 2015-10-25 00:00:00
Proceeding: CVPR 2013 6
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

We present a novel algorithm for estimating the broad 3D geometric structure
of outdoor video scenes. Leveraging spatio-temporal video segmentation, we
decompose a dynamic scene captured by a video into geometric classes, based on
predictions made by region-classifiers that are trained on appearance and
motion features. By examining the homogeneity of the prediction, we combine
predictions across multiple segmentation hierarchy levels alleviating the need
to determine the granularity a priori. We built a novel, extensive dataset on
geometric context of video to evaluate our method, consisting of over 100
ground-truth annotated outdoor videos with over 20,000 frames. To further scale
beyond this dataset, we propose a semi-supervised learning framework to expand
the pool of labeled data with high confidence predictions obtained from
unlabeled data. Our system produces an accurate prediction of geometric context
of video achieving 96% accuracy across main geometric classes.
Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries
Authors: S. Hussain Raza, Omar Javed, Aveek Das, Harpreet Sawhney, Hui Cheng, Irfan Essa
Date: 2015-10-25 00:00:00
Tasks: Camera Pose Estimation, Depth Estimation, Pose Estimation

We present an algorithm to estimate depth in dynamic video scenes. We propose
to learn and infer depth in videos from appearance, motion, occlusion
boundaries, and geometric context of the scene. Using our method, depth can be
estimated from unconstrained videos with no requirement of camera pose
estimation, and with significant background/foreground motions. We start by
decomposing a video into spatio-temporal regions. For each spatio-temporal
region, we learn the relationship of depth to visual appearance, motion, and
geometric classes. Then we infer the depth information of new scenes using
piecewise planar parametrization estimated within a Markov random field (MRF)
framework by combining appearance to depth learned mappings and occlusion
boundary guided smoothness constraints. Subsequently, we perform temporal
smoothing to obtain temporally consistent depth maps. To evaluate our depth
estimation algorithm, we provide a novel dataset with ground truth depth for
outdoor video scenes. We present a thorough evaluation of our algorithm on our
new dataset and the publicly available Make3d static image dataset.
Safe Control under Uncertainty
Authors: Dorsa Sadigh, Ashish Kapoor
Date: 2015-10-25 00:00:00
Tasks: Autonomous Vehicles

Controller synthesis for hybrid systems that satisfy temporal specifications
expressing various system properties is a challenging problem that has drawn
the attention of many researchers. However, making the assumption that such
temporal properties are deterministic is far from the reality. For example,
many of the properties the controller has to satisfy are learned through
machine learning techniques based on sensor input data. In this paper, we
propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an
expressive language to define the stochastic properties, and enforce
probabilistic guarantees on them. We further show how to synthesize safe
controllers using this logic for cyber-physical systems under the assumption
that the stochastic properties are based on a set of Gaussian random variables.
One of the key distinguishing features of PrSTL is that the encoded logic is
adaptive and changes as the system encounters additional data and updates its
beliefs about the latent random variables that define the safety properties. We
demonstrate our approach by synthesizing safe controllers under the PrSTL
specifications for multiple case studies including control of quadrotors and
autonomous vehicles in dynamic environments.
A Framework for Distributed Deep Learning Layer Design in Python
Authors: Clay McLeod
Date: 2015-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

In this paper, a framework for testing Deep Neural Network (DNN) design in
Python is presented. First, big data, machine learning (ML), and Artificial
Neural Networks (ANNs) are discussed to familiarize the reader with the
importance of such a system. Next, the benefits and detriments of implementing
such a system in Python are presented. Lastly, the specifics of the system are
explained, and some experimental results are presented to prove the
effectiveness of the system.
Deep learning with Elastic Averaging SGD
Authors: Sixin Zhang, Anna Choromanska, Yann Lecun
Date: 2014-12-20 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Deep Learning, image-classification, Image Classification, Stochastic Optimization

We study the problem of stochastic optimization for deep learning in the
parallel computing environment under communication constraints. A new algorithm
is proposed in this setting where the communication and coordination of work
among concurrent processes (local workers), is based on an elastic force which
links the parameters they compute with a center variable stored by the
parameter server (master). The algorithm enables the local workers to perform
more exploration, i.e. the algorithm allows the local variables to fluctuate
further from the center variable by reducing the amount of communication
between local workers and the master. We empirically demonstrate that in the
deep learning setting, due to the existence of many local optima, allowing more
exploration can lead to the improved performance. We propose synchronous and
asynchronous variants of the new algorithm. We provide the stability analysis
of the asynchronous variant in the round-robin scheme and compare it with the
more common parallelized method ADMM. We show that the stability of EASGD is
guaranteed when a simple stability condition is satisfied, which is not the
case for ADMM. We additionally propose the momentum-based version of our
algorithm that can be applied in both synchronous and asynchronous settings.
Asynchronous variant of the algorithm is applied to train convolutional neural
networks for image classification on the CIFAR and ImageNet datasets.
Experiments demonstrate that the new algorithm accelerates the training of deep
architectures compared to DOWNPOUR and other common baseline approaches and
furthermore is very communication efficient.
Seam Puckering Objective Evaluation Method for Sewing Process
Authors: Raluca Brad, Eugen HĂloiu, Remus Brad
Date: 2015-10-25 00:00:00
Tasks: General Classification

The paper presents an automated method for the assessment and classification
of puckering defects detected during the preproduction control stage of the
sewing machine or product inspection. In this respect, we have presented the
possible causes and remedies of the wrinkle nonconformities. Subjective factors
related to the control environment and operators during the seams evaluation
can be reduced using an automated system whose operation is based on image
processing. Our implementation involves spectral image analysis using Fourier
transform and an unsupervised neural network, the Kohonen Map, employed to
classify material specimens, the input images, into five discrete degrees of
quality, from grade 5 (best) to grade 1 (the worst).
Random Projections through multiple optical scattering: Approximating kernels at the speed of light
Authors: Alaa Saade, Francesco Caltagirone, Igor Carron, Laurent Daudet, Angélique Drémeau, Sylvain Gigan, Florent Krzakala
Date: 2015-10-22 00:00:00

Random projections have proven extremely useful in many signal processing and
machine learning applications. However, they often require either to store a
very large random matrix, or to use a different, structured matrix to reduce
the computational and memory costs. Here, we overcome this difficulty by
proposing an analog, optical device, that performs the random projections
literally at the speed of light without having to store any matrix in memory.
This is achieved using the physical properties of multiple coherent scattering
of coherent light in random media. We use this device on a simple task of
classification with a kernel machine, and we show that, on the MNIST database,
the experimental results closely match the theoretical performance of the
corresponding kernel. This framework can help make kernel methods practical for
applications that have large training sets and/or require real-time prediction.
We discuss possible extensions of the method in terms of a class of kernels,
speed, memory consumption and different problems.
Defect Detection Techniques for Airbag Production Sewing Stages
Authors: Raluca Brad, Lavinia Barac, Remus Brad
Date: 2015-10-25 00:00:00
Tasks: Defect Detection, valid

Airbags are subject to strict quality control in order to ensure passengers
safety. The quality of fabric and sewing thread influence the final product and
therefore, sewing defects must be early and accurately detected, in order to
remove the item from production. Airbag seams assembly can take various forms,
using linear and circle primitives, with threads of different colors and length
densities, creating lockstitch or double threads chainstitch. The paper
presents a framework for the automatic detection of defects occurring during
the airbag sewing stage. Types of defects as skipped stitch, missed stitch or
superimposed seam for lockstitch and two threads chainstitch are detected and
marked. Using image processing methods, the proposed framework follows the
seams path and determines if a color pattern of the considered stitches is
valid.
On End-to-End Program Generation from User Intention by Deep Neural Networks
Authors: Lili Mou, Rui Men, Ge Li, Lu Zhang, Zhi Jin
Date: 2015-10-25 00:00:00

This paper envisions an end-to-end program generation scenario using
recurrent neural networks (RNNs): Users can express their intention in natural
language; an RNN then automatically generates corresponding code in a
characterby-by-character fashion. We demonstrate its feasibility through a case
study and empirical analysis. To fully make such technique useful in practice,
we also point out several cross-disciplinary challenges, including modeling
user intention, providing datasets, improving model architectures, etc.
Although much long-term research shall be addressed in this new field, we
believe end-to-end program generation would become a reality in future decades,
and we are looking forward to its practice.
Vehicle Speed Prediction using Deep Learning
Authors: Joe Lemieux, Yuan Ma
Date: 2015-10-25 00:00:00
Tasks: Deep Learning, global-optimization, Prediction

Global optimization of the energy consumption of dual power source vehicles
such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in
fuel cell electric vehicles requires knowledge of the complete route
characteristics at the beginning of the trip. One of the main characteristics
is the vehicle speed profile across the route. The profile will translate
directly into energy requirements for a given vehicle. However, the vehicle
speed that a given driver chooses will vary from driver to driver and from time
to time, and may be slower, equal to, or faster than the average traffic flow.
If the specific driver speed profile can be predicted, the energy usage can be
optimized across the route chosen. The purpose of this paper is to research the
application of Deep Learning techniques to this problem to identify at the
beginning of a drive cycle the driver specific vehicle speed profile for an
individual driver repeated drive cycle, which can be used in an optimization
algorithm to minimize the amount of fossil fuel energy used during the trip.
Structured Memory for Neural Turing Machines
Authors: Wei Zhang, Yang Yu, Bo-Wen Zhou
Date: 2015-10-14 00:00:00

Neural Turing Machines (NTM) contain memory component that simulates "working
memory" in the brain to store and retrieve information to ease simple
algorithms learning. So far, only linearly organized memory is proposed, and
during experiments, we observed that the model does not always converge, and
overfits easily when handling certain tasks. We think memory component is key
to some faulty behaviors of NTM, and better organization of memory component
could help fight those problems. In this paper, we propose several different
structures of memory for NTM, and we proved in experiments that two of our
proposed structured-memory NTMs could lead to better convergence, in term of
speed and prediction accuracy on copy task and associative recall task as in
(Graves et al. 2014).
Statistical Parsing by Machine Learning from a Classical Arabic Treebank
Authors: Kais Dukes
Date: 2015-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Constituency Parsing

Research into statistical parsing for English has enjoyed over a decade of
successful results. However, adapting these models to other languages has met
with difficulties. Previous comparative work has shown that Modern Arabic is
one of the most difficult languages to parse due to rich morphology and free
word order. Classical Arabic is the ancient form of Arabic, and is understudied
in computational linguistics, relative to its worldwide reach as the language
of the Quran. The thesis is based on seven publications that make significant
contributions to knowledge relating to annotating and parsing Classical Arabic.
  A central argument of this thesis is that using a hybrid representation
closely aligned to traditional grammar leads to improved parsing for Arabic. To
test this hypothesis, two approaches are compared. As a reference, a pure
dependency parser is adapted using graph transformations, resulting in an
87.47% F1-score. This is compared to an integrated parsing model with an
F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is
better suited to Classical Arabic.
Computational models of attention
Authors: Laurent Itti, Ali Borji
Date: 2015-10-24 00:00:00

This chapter reviews recent computational models of visual attention. We
begin with models for the bottom-up or stimulus-driven guidance of attention to
salient visual items, which we examine in seven different broad categories. We
then examine more complex models which address the top-down or goal-oriented
guidance of attention towards items that are more relevant to the task at hand.
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Authors: Emanuele Frandi, Ricardo Nanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens
Date: 2015-10-24 00:00:00

Frank-Wolfe (FW) algorithms have been often proposed over the last few years
as efficient solvers for a variety of optimization problems arising in the
field of Machine Learning. The ability to work with cheap projection-free
iterations and the incremental nature of the method make FW a very effective
choice for many large-scale problems where computing a sparse model is
desirable.
  In this paper, we present a high-performance implementation of the FW method
tailored to solve large-scale Lasso regression problems, based on a randomized
iteration, and prove that the convergence guarantees of the standard FW method
are preserved in the stochastic setting. We show experimentally that our
algorithm outperforms several existing state of the art methods, including the
Coordinate Descent algorithm by Friedman et al. (one of the fastest known Lasso
solvers), on several benchmark datasets with a very large number of features,
without sacrificing the accuracy of the model. Our results illustrate that the
algorithm is able to generate the complete regularization path on problems of
size up to four million variables in less than one minute.