pwc-archive/papers-with-abstracts huggingface dataset:
OpenTripPlanner, OpenStreetMap, General Transit Feed Specification: Tools for Disaster Relief and Recovery
Authors: Chelcie Narboneta, Kardi Teknomo
Date: 2016-09-06 00:00:00

Open Trip Planner was identified as the most promising open source
multi-modal trip planning software. Open Street Map, which provides mapping
data to Open Trip Planner, is one of the most well-known open source
international repository of geographic data. General Transit Feed
Specification, which provides transportation data to Open Trip Planner, has
been the standard for describing transit systems and platform for numerous
applications. Together, when used to implement an instance of Open Trip
Planner, these software has been helping in traffic decongestion all over the
world by assisting commuters to shift from using private transportation modes
to public ones. Their potential however goes beyond providing multi-modal
public transportation routes. This paper aims to first discuss the researchers'
experience in implementing a public transportation route planner for the
purpose of traffic decongestion.The researchers would examine the prospective
of using the system for disaster preparedness and recovery and concrete ways on
how to realize them.
Q-Learning with Basic Emotions
Authors: Wilfredo Badoy Jr., Kardi Teknomo
Date: 2016-09-06 00:00:00
Tasks: Q-Learning

Q-learning is a simple and powerful tool in solving dynamic problems where
environments are unknown. It uses a balance of exploration and exploitation to
find an optimal solution to the problem. In this paper, we propose using four
basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent.
Simulations show that the proposed affective agent requires lesser number of
steps to find the optimal path. We found when affective agent finds the optimal
path, the ratio between exploration to exploitation gradually decreases,
indicating lower total step count in the long run
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
Authors: Adria Ruiz, Ognjen Rudovic, Xavier Binefa, Maja Pantic
Date: 2016-09-06 00:00:00
Tasks: Temporal Sequences

In this paper, we address the Multi-Instance-Learning (MIL) problem when bag
labels are naturally represented as ordinal variables (Multi--Instance--Ordinal
Regression). Moreover, we consider the case where bags are temporal sequences
of ordinal instances. To model this, we propose the novel Multi-Instance
Dynamic Ordinal Random Fields (MI-DORF). In this model, we treat
instance-labels inside the bag as latent ordinal states. The MIL assumption is
modelled by incorporating a high-order cardinality potential relating bag and
instance-labels,into the energy function. We show the benefits of the proposed
approach on the task of weakly-supervised pain intensity estimation from the
UNBC Shoulder-Pain Database. In our experiments, the proposed approach
significantly outperforms alternative non-ordinal methods that either ignore
the MIL assumption, or do not model dynamic information in target data.
A Probabilistic Modeling Approach to Hearing Loss Compensation
Authors: Thijs van de Laar, Bert de Vries
Date: 2016-02-03 00:00:00

Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the
impairment of each specific patient. The lack of a fundamental HA fitting
theory is a strong contributing factor to an unsatisfying sound experience for
about 20% of hearing aid patients. This paper proposes a probabilistic modeling
approach to the design of HA algorithms. The proposed method relies on a
generative probabilistic model for the hearing loss problem and provides for
automated inference of the corresponding (1) signal processing algorithm, (2)
the fitting solution as well as a principled (3) performance evaluation metric.
All three tasks are realized as message passing algorithms in a factor graph
representation of the generative model, which in principle allows for fast
implementation on hearing aid or mobile device hardware. The methods are
theoretically worked out and simulated with a custom-built factor graph toolbox
for a specific hearing loss model.
Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model
Authors: Kardi Teknomo
Date: 2016-09-06 00:00:00

Microscopic pedestrian studies consider detailed interaction of pedestrians
to control their movement in pedestrian traffic flow. The tools to collect the
microscopic data and to analyze microscopic pedestrian flow are still very much
in its infancy. The microscopic pedestrian flow characteristics need to be
understood. Manual, semi manual and automatic image processing data collection
systems were developed. It was found that the microscopic speed resemble a
normal distribution with a mean of 1.38 m/second and standard deviation of 0.37
m/second. The acceleration distribution also bear a resemblance to the normal
distribution with an average of 0.68 m/ square second. A physical based
microscopic pedestrian simulation model was also developed. Both Microscopic
Video Data Collection and Microscopic Pedestrian Simulation Model generate a
database called NTXY database. The formulations of the flow performance or
microscopic pedestrian characteristics are explained. Sensitivity of the
simulation and relationship between the flow performances are described.
Validation of the simulation using real world data is then explained through
the comparison between average instantaneous speed distributions of the real
world data with the result of the simulations. The simulation model is then
applied for some experiments on a hypothetical situation to gain more
understanding of pedestrian behavior in one way and two way situations, to know
the behavior of the system if the number of elderly pedestrian increases and to
evaluate a policy of lane-like segregation toward pedestrian crossing and
inspects the performance of the crossing. It was revealed that the microscopic
pedestrian studies have been successfully applied to give more understanding to
the behavior of microscopic pedestrians flow, predict the theoretical and
practical situation and evaluate some design policies before its
implementation.
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
Authors: Bing Liu, Ian Lane
Date: 2016-09-06 00:00:00
Proceeding: WS 2016 9
Tasks: Benchmarking, Intent Detection, Language Modeling, Language Modelling, slot-filling, Slot Filling, Spoken Language Understanding

Speaker intent detection and semantic slot filling are two critical tasks in
spoken language understanding (SLU) for dialogue systems. In this paper, we
describe a recurrent neural network (RNN) model that jointly performs intent
detection, slot filling, and language modeling. The neural network model keeps
updating the intent estimation as word in the transcribed utterance arrives and
uses it as contextual features in the joint model. Evaluation of the language
model and online SLU model is made on the ATIS benchmarking data set. On
language modeling task, our joint model achieves 11.8% relative reduction on
perplexity comparing to the independent training language model. On SLU tasks,
our joint model outperforms the independent task training model by 22.3% on
intent detection error rate, with slight degradation on slot filling F1 score.
The joint model also shows advantageous performance in the realistic ASR
settings with noisy speech input.
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
Authors: Battista Biggio, Giorgio Fumera, Gian Luca Marcialis, Fabio Roli
Date: 2016-09-06 00:00:00

Prior work has shown that multibiometric systems are vulnerable to
presentation attacks, assuming that their matching score distribution is
identical to that of genuine users, without fabricating any fake trait. We have
recently shown that this assumption is not representative of current
fingerprint and face presentation attacks, leading one to overestimate the
vulnerability of multibiometric systems, and to design less effective fusion
rules. In this paper, we overcome these limitations by proposing a statistical
meta-model of face and fingerprint presentation attacks that characterizes a
wider family of fake score distributions, including distributions of known and,
potentially, unknown attacks. This allows us to perform a thorough security
evaluation of multibiometric systems against presentation attacks, quantifying
how their vulnerability may vary also under attacks that are different from
those considered during design, through an uncertainty analysis. We empirically
show that our approach can reliably predict the performance of multibiometric
systems even under never-before-seen face and fingerprint presentation attacks,
and that the secure fusion rules designed using our approach can exhibit an
improved trade-off between the performance in the absence and in the presence
of attack. We finally argue that our method can be extended to other biometrics
besides faces and fingerprints.
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Authors: Bing Liu, Ian Lane
Date: 2016-09-06 00:00:00
Tasks: Decoder, intent-classification, Intent Classification, Intent Detection, Slot Filling, Translation

Attention-based encoder-decoder neural network models have recently shown
promising results in machine translation and speech recognition. In this work,
we propose an attention-based neural network model for joint intent detection
and slot filling, both of which are critical steps for many speech
understanding and dialog systems. Unlike in machine translation and speech
recognition, alignment is explicit in slot filling. We explore different
strategies in incorporating this alignment information to the encoder-decoder
framework. Learning from the attention mechanism in encoder-decoder model, we
further propose introducing attention to the alignment-based RNN models. Such
attentions provide additional information to the intent classification and slot
label prediction. Our independent task models achieve state-of-the-art intent
detection error rate and slot filling F1 score on the benchmark ATIS task. Our
joint training model further obtains 0.56% absolute (23.8% relative) error
reduction on intent detection and 0.23% absolute gain on slot filling over the
independent task models.
Features Fusion for Classification of Logos
Authors: N. Vinay Kumar, Pratheek, V. Vijaya Kantha, K. N. Govindaraju, D. S. Guru
Date: 2016-09-06 00:00:00
Tasks: Classification, General Classification

In this paper, a logo classification system based on the appearance of logo
images is proposed. The proposed classification system makes use of global
characteristics of logo images for classification. Color, texture, and shape of
a logo wholly describe the global characteristics of logo images. The various
combinations of these characteristics are used for classification. The
combination contains only with single feature or with fusion of two features or
fusion of all three features considered at a time respectively. Further, the
system categorizes the logo image into: a logo image with fully text or with
fully symbols or containing both symbols and texts.. The K-Nearest Neighbour
(K-NN) classifier is used for classification. Due to the lack of color logo
image dataset in the literature, the same is created consisting 5044 color logo
images. Finally, the performance of the classification system is evaluated
through accuracy, precision, recall and F-measure computed from the confusion
matrix. The experimental results show that the most promising results are
obtained for fusion of features.
Delaunay Triangulation on Skeleton of Flowers for Classification
Authors: Y H Sharath Kumar, N. Vinay Kumar, D. S. Guru
Date: 2016-09-06 00:00:00
Tasks: Classification, General Classification

In this work, we propose a Triangle based approach to classify flower images.
Initially, flowers are segmented using whorl based region merging segmentation.
Skeleton of a flower is obtained from the segmented flower using a skeleton
pruning method. The Delaunay triangulation is obtained from the endpoints and
junction points detected on the skeleton. The length and angle features are
extracted from the obtained Delaunay triangles and then are aggregated to
represent in the form of interval-valued type data. A suitable classifier has
been explored for the purpose of classification. To corroborate the efficacy of
the proposed method, an experiment is conducted on our own data set of 30
classes of flowers, containing 3000 samples.
A deep learning model for estimating story points
Authors: Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, Tim Menzies
Date: 2016-09-02 00:00:00
Tasks: Deep Learning, Feature Engineering, model, Prediction

Although there has been substantial research in software analytics for effort
estimation in traditional software projects, little work has been done for
estimation in agile projects, especially estimating user stories or issues.
Story points are the most common unit of measure used for estimating the effort
involved in implementing a user story or resolving an issue. In this paper, we
offer for the \emph{first} time a comprehensive dataset for story points-based
estimation that contains 23,313 issues from 16 open source projects. We also
propose a prediction model for estimating story points based on a novel
combination of two powerful deep learning architectures: long short-term memory
and recurrent highway network. Our prediction system is \emph{end-to-end}
trainable from raw input data to prediction outcomes without any manual feature
engineering. An empirical evaluation demonstrates that our approach
consistently outperforms three common effort estimation baselines and two
alternatives in both Mean Absolute Error and the Standardized Accuracy.
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution
Authors: Hang Yang, Zhongbo Zhang, Yujing Guan
Date: 2016-09-06 00:00:00
Tasks: Deblurring, Denoising, Image Deconvolution, Image Restoration, parameter estimation

Image deconvolution is still to be a challenging ill-posed problem for
recovering a clear image from a given blurry image, when the point spread
function is known. Although competitive deconvolution methods are numerically
impressive and approach theoretical limits, they are becoming more complex,
making analysis, and implementation difficult. Furthermore, accurate estimation
of the regularization parameter is not easy for successfully solving image
deconvolution problems. In this paper, we develop an effective approach for
image restoration based on one explicit image filter - guided filter. By
applying the decouple of denoising and deblurring techniques to the
deconvolution model, we reduce the optimization complexity and achieve a simple
but effective algorithm to automatically compute the parameter in each
iteration, which is based on Morozov's discrepancy principle. Experimental
results demonstrate that the proposed algorithm outperforms many
state-of-the-art deconvolution methods in terms of both ISNR and visual
quality.
Object Specific Deep Learning Feature and Its Application to Face Detection
Authors: Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu
Date: 2016-09-06 00:00:00
Tasks: Face Detection, Object

We present a method for discovering and exploiting object specific deep
learning features and use face detection as a case study. Motivated by the
observation that certain convolutional channels of a Convolutional Neural
Network (CNN) exhibit object specific responses, we seek to discover and
exploit the convolutional channels of a CNN in which neurons are activated by
the presence of specific objects in the input image. A method for explicitly
fine-tuning a pre-trained CNN to induce an object specific channel (OSC) and
systematically identifying it for the human face object has been developed.
Based on the basic OSC features, we introduce a multi-resolution approach to
constructing robust face heatmaps for fast face detection in unconstrained
settings. We show that multi-resolution OSC can be used to develop state of the
art face detectors which have the advantage of being simple and compact.
Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction
Authors: András Bódis-Szomorú, Hayko Riemenschneider, Luc van Gool
Date: 2016-09-05 00:00:00

Airborne acquisition and on-road mobile mapping provide complementary 3D
information of an urban landscape: the former acquires roof structures, ground,
and vegetation at a large scale, but lacks the facade and street-side details,
while the latter is incomplete for higher floors and often totally misses out
on pedestrian-only areas or undriven districts. In this work, we introduce an
approach that efficiently unifies a detailed street-side Structure-from-Motion
(SfM) or Multi-View Stereo (MVS) point cloud and a coarser but more complete
point cloud from airborne acquisition in a joint surface mesh. We propose a
point cloud blending and a volumetric fusion based on ray casting across a 3D
tetrahedralization (3DT), extended with data reduction techniques to handle
large datasets. To the best of our knowledge, we are the first to adopt a 3DT
approach for airborne/street-side data fusion. Our pipeline exploits typical
characteristics of airborne and ground data, and produces a seamless,
watertight mesh that is both complete and detailed. Experiments on 3D urban
data from multiple sources and different data densities show the effectiveness
and benefits of our approach.
Vision-based Engagement Detection in Virtual Reality
Authors: Ghassem Tofighi, Kaamraan Raahemifar, Maria Frank, Haisong Gu
Date: 2016-09-05 00:00:00
Tasks: Gesture Recognition

User engagement modeling for manipulating actions in vision-based interfaces
is one of the most important case studies of user mental state detection. In a
Virtual Reality environment that employs camera sensors to recognize human
activities, we have to know when user intends to perform an action and when
not. Without a proper algorithm for recognizing engagement status, any kind of
activities could be interpreted as manipulating actions, called "Midas Touch"
problem. Baseline approach for solving this problem is activating gesture
recognition system using some focus gestures such as waiving or raising hand.
However, a desirable natural user interface should be able to understand user's
mental status automatically. In this paper, a novel multi-modal model for
engagement detection, DAIA, is presented. using DAIA, the spectrum of mental
status for performing an action is quantized in a finite number of engagement
states. For this purpose, a Finite State Transducer (FST) is designed. This
engagement framework shows how to integrate multi-modal information from user
biometric data streams such as 2D and 3D imaging. FST is employed to make the
state transition smoothly using combination of several boolean expressions. Our
FST true detection rate is 92.3% in total for four different states. Results
also show FST can segment user hand gestures more robustly.
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
Authors: Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic
Date: 2016-08-16 00:00:00

We address the task of simultaneous feature fusion and modeling of discrete
ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling
approach. In particular, we introduce GP encoders to project multiple observed
features onto a latent space, while GP decoders are responsible for
reconstructing the original features. Inference is performed in a novel
variational framework, where the recovered latent representations are further
constrained by the ordinal output labels. In this way, we seamlessly integrate
the ordinal structure in the learned manifold, while attaining robust fusion of
the input features. We demonstrate the representation abilities of our model on
benchmark datasets from machine learning and affect analysis. We further
evaluate the model on the tasks of feature fusion and joint ordinal prediction
of facial action units. Our experiments demonstrate the benefits of the
proposed approach compared to the state of the art.
UnrealCV: Connecting Computer Vision to Unreal Engine
Authors: Weichao Qiu, Alan Yuille
Date: 2016-09-05 00:00:00
Tasks: Navigate, Physical Simulations

Computer graphics can not only generate synthetic images and ground truth but
it also offers the possibility of constructing virtual worlds in which: (i) an
agent can perceive, navigate, and take actions guided by AI algorithms, (ii)
properties of the worlds can be modified (e.g., material and reflectance),
(iii) physical simulations can be performed, and (iv) algorithms can be learnt
and evaluated. But creating realistic virtual worlds is not easy. The game
industry, however, has spent a lot of effort creating 3D worlds, which a player
can interact with. So researchers can build on these resources to create
virtual worlds, provided we can access and modify the internal data structures
of the games. To enable this we created an open-source plugin UnrealCV
(http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We
show two applications: (i) a proof of concept image dataset, and (ii) linking
Caffe with the virtual world to test deep network algorithms.
PMI Matrix Approximations with Applications to Neural Language Modeling
Authors: Oren Melamud, Ido Dagan, Jacob Goldberger
Date: 2016-09-05 00:00:00
Tasks: Language Modeling, Language Modelling

The negative sampling (NEG) objective function, used in word2vec, is a
simplification of the Noise Contrastive Estimation (NCE) method. NEG was found
to be highly effective in learning continuous word representations. However,
unlike NCE, it was considered inapplicable for the purpose of learning the
parameters of a language model. In this study, we refute this assertion by
providing a principled derivation for NEG-based language modeling, founded on a
novel analysis of a low-dimensional approximation of the matrix of pointwise
mutual information between the contexts and the predicted words. The obtained
language modeling is closely related to NCE language models but is based on a
simplified objective function. We thus provide a unified formulation for two
main language processing tasks, namely word embedding and language modeling,
based on the NEG objective function. Experimental results on two popular
language modeling benchmarks show comparable perplexity results, with a small
advantage to NEG over NCE.
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Authors: Afonso Menegola, Michel Fornaciali, Ramon Pires, Sandra Avila, Eduardo Valle
Date: 2016-09-05 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

Deep learning is the current bet for image classification. Its greed for huge
amounts of annotated data limits its usage in medical imaging context. In this
scenario transfer learning appears as a prominent solution. In this report we
aim to clarify how transfer learning schemes may influence classification
results. We are particularly focused in the automated melanoma screening
problem, a case of medical imaging in which transfer learning is still not
widely used. We explored transfer with and without fine-tuning, sequential
transfers and usage of pre-trained models in general and specific datasets.
Although some issues remain open, our findings may drive future researches.