pwc-archive/papers-with-abstracts huggingface dataset:
Vehicle Color Recognition using Convolutional Neural Network
Authors: Reza Fuad Rachmadi, I Ketut Eddy Purnama
Date: 2015-10-26 00:00:00
Tasks: General Classification, Vehicle Color Recognition

Vehicle color information is one of the important elements in ITS
(Intelligent Traffic System). In this paper, we present a vehicle color
recognition method using convolutional neural network (CNN). Naturally, CNN is
designed to learn classification method based on shape information, but we
proved that CNN can also learn classification based on color distribution. In
our method, we convert the input image to two different color spaces, HSV and
CIE Lab, and run it to some CNN architecture. The training process follow
procedure introduce by Krizhevsky, that learning rate is decreasing by factor
of 10 after some iterations. To test our method, we use publicly vehicle color
recognition dataset provided by Chen. The results, our model outperform the
original system provide by Chen with 2% higher overall accuracy.
Tracking Words in Chinese Poetry of Tang and Song Dynasties with the China Biographical Database
Authors: Chao-Lin Liu, Kuo-Feng Luo
Date: 2016-11-19 00:00:00
Proceeding: WS 2016 12

Large-scale comparisons between the poetry of Tang and Song dynasties shed
light on how words, collocations, and expressions were used and shared among
the poets. That some words were used only in the Tang poetry and some only in
the Song poetry could lead to interesting research in linguistics. That the
most frequent colors are different in the Tang and Song poetry provides a trace
of the changing social circumstances in the dynasties. Results of the current
work link to research topics of lexicography, semantics, and social
transitions. We discuss our findings and present our algorithms for efficient
comparisons among the poems, which are crucial for completing billion times of
comparisons within acceptable time.
Evaluation of Automatic Video Captioning Using Direct Assessment
Authors: Yvette Graham, George Awad, Alan Smeaton
Date: 2017-10-29 00:00:00
Tasks: Caption Generation, Machine Translation, Translation, Video Captioning

We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics for comparing automatic video captions against a manual caption such as
BLEU and METEOR, drawn from techniques used in evaluating machine translation,
were used in the TRECVid video captioning task in 2016 but these are shown to
have weaknesses. The work presented here brings human assessment into the
evaluation by crowdsourcing how well a caption describes a video. We
automatically degrade the quality of some sample captions which are assessed
manually and from this we are able to rate the quality of the human assessors,
a factor we take into account in the evaluation. Using data from the TRECVid
video-to-text task in 2016, we show how our direct assessment method is
replicable and robust and should scale to where there many caption-generation
techniques to be evaluated.
A Neural-Symbolic Approach to Design of CAPTCHA
Authors: Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, Dapeng Wu
Date: 2017-10-29 00:00:00
Tasks: BIG-bench Machine Learning, Image Captioning, Sentence

CAPTCHAs based on reading text are susceptible to machine-learning-based
attacks due to recent significant advances in deep learning (DL). To address
this, this paper promotes image/visual captioning based CAPTCHAs, which is
robust against machine-learning-based attacks. To develop
image/visual-captioning-based CAPTCHAs, this paper proposes a new image
captioning architecture by exploiting tensor product representations (TPR), a
structured neural-symbolic framework developed in cognitive science over the
past 20 years, with the aim of integrating DL with explicit language structures
and rules. We call it the Tensor Product Generation Network (TPGN). The key
ideas of TPGN are: 1) unsupervised learning of role-unbinding vectors of words
via a TPR-based deep neural network, and 2) integration of TPR with typical DL
architectures including Long Short-Term Memory (LSTM) models. The novelty of
our approach lies in its ability to generate a sentence and extract partial
grammatical structure of the sentence by using role-unbinding vectors, which
are obtained in an unsupervised manner. Experimental results demonstrate the
effectiveness of the proposed approach.
Personalized word representations Carrying Personalized Semantics Learned from Social Network Posts
Authors: Zih-Wei Lin, Tzu-Wei Sung, Hung-Yi Lee, Lin-shan Lee
Date: 2017-10-29 00:00:00
Tasks: Sentence, Sentence Completion

Distributed word representations have been shown to be very useful in various
natural language processing (NLP) application tasks. These word vectors learned
from huge corpora very often carry both semantic and syntactic information of
words. However, it is well known that each individual user has his own language
patterns because of different factors such as interested topics, friend groups,
social activities, wording habits, etc., which may imply some kind of
personalized semantics. With such personalized semantics, the same word may
imply slightly differently for different users. For example, the word
"Cappuccino" may imply "Leisure", "Joy", "Excellent" for a user enjoying
coffee, by only a kind of drink for someone else. Such personalized semantics
of course cannot be carried by the standard universal word vectors trained with
huge corpora produced by many people. In this paper, we propose a framework to
train different personalized word vectors for different users based on the very
successful continuous skip-gram model using the social network data posted by
many individual users. In this framework, universal background word vectors are
first learned from the background corpora, and then adapted by the personalized
corpus for each individual user to learn the personalized word vectors. We use
two application tasks to evaluate the quality of the personalized word vectors
obtained in this way, the user prediction task and the sentence completion
task. These personalized word vectors were shown to carry some personalized
semantics and offer improved performance on these two evaluation tasks.
Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels
Authors: Benjamin J. Meyer, Ben Harwood, Tom Drummond
Date: 2017-05-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, Metric Learning

We present a Gaussian kernel loss function and training algorithm for
convolutional neural networks that can be directly applied to both distance
metric learning and image classification problems. Our method treats all
training features from a deep neural network as Gaussian kernel centres and
computes loss by summing the influence of a feature's nearby centres in the
feature embedding space. Our approach is made scalable by treating it as an
approximate nearest neighbour search problem. We show how to make end-to-end
learning feasible, resulting in a well formed embedding space, in which
semantically related instances are likely to be located near one another,
regardless of whether or not the network was trained on those classes. Our
approach outperforms state-of-the-art deep metric learning approaches on
embedding learning challenges, as well as conventional softmax classification
on several datasets.
Intel RealSense Stereoscopic Depth Cameras
Authors: Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, Achintya Bhowmik
Date: 2017-05-16 00:00:00
Tasks: 3D Reconstruction, Gesture Recognition

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD
imaging systems. We discuss these systems' mode-of-operation, functional
behavior and include models of their expected performance, shortcomings, and
limitations. We provide information about the systems' optical characteristics,
their correlation algorithms, and how these properties can affect different
applications, including 3D reconstruction and gesture recognition. Our
discussion covers the Intel RealSense R200 and the Intel RealSense D400
(formally RS400).
Synthetic Iris Presentation Attack using iDCGAN
Authors: Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, Afzel Noore
Date: 2017-10-29 00:00:00
Tasks: Generative Adversarial Network, Iris Recognition

Reliability and accuracy of iris biometric modality has prompted its
large-scale deployment for critical applications such as border control and
national ID projects. The extensive growth of iris recognition systems has
raised apprehensions about susceptibility of these systems to various attacks.
In the past, researchers have examined the impact of various iris presentation
attacks such as textured contact lenses and print attacks. In this research, we
present a novel presentation attack using deep learning based synthetic iris
generation. Utilizing the generative capability of deep convolutional
generative adversarial networks and iris quality metrics, we propose a new
framework, named as iDCGAN (iris deep convolutional generative adversarial
network) for generating realistic appearing synthetic iris images. We
demonstrate the effect of these synthetically generated iris images as
presentation attack on iris recognition by using a commercial system. The
state-of-the-art presentation attack detection framework, DESIST is utilized to
analyze if it can discriminate these synthetically generated iris images from
real images. The experimental results illustrate that mitigating the proposed
synthetic presentation attack is of paramount importance.
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Authors: Hangyu Mao, Zhibo Gong, Yan Ni, Zhen Xiao
Date: 2017-06-10 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Communication is a critical factor for the big multi-agent world to stay
organized and productive. Typically, most previous multi-agent
"learning-to-communicate" studies try to predefine the communication protocols
or use technologies such as tabular reinforcement learning and evolutionary
algorithm, which can not generalize to changing environment or large collection
of agents.
  In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework
for solving "learning-to-communicate" problem. The ACCNet naturally combines
the powerful actor-critic reinforcement learning technology with deep learning
technology. It can efficiently learn the communication protocols even from
scratch under partially observable environment. We demonstrate that the ACCNet
can achieve better results than several baselines under both continuous and
discrete action space environments. We also analyse the learned protocols and
discuss some design considerations.
A Bayesian Data Augmentation Approach for Learning Deep Models
Authors: Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid
Date: 2017-10-29 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Data Augmentation, General Classification, Generative Adversarial Network

Data augmentation is an essential part of the training process applied to
deep learning models. The motivation is that a robust training process for deep
learning models depends on large annotated datasets, which are expensive to be
acquired, stored and processed. Therefore a reasonable alternative is to be
able to automatically generate new annotated training samples using a process
known as data augmentation. The dominant data augmentation approach in the
field assumes that new training samples can be obtained via random geometric or
appearance transformations applied to annotated training samples, but this is a
strong assumption because it is unclear if this is a reliable generative model
for producing new training samples. In this paper, we provide a novel Bayesian
formulation to data augmentation, where new annotated training points are
treated as missing variables and generated based on the distribution learned
from the training set. For learning, we introduce a theoretically sound
algorithm --- generalised Monte Carlo expectation maximisation, and demonstrate
one possible implementation via an extension of the Generative Adversarial
Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the
better performance of our proposed method compared to the current dominant data
augmentation approach mentioned above --- the results also show that our
approach produces better classification results than similar GAN models.
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Authors: Mo Zhang, Xiang Li, Mengjia Xu, Quanzheng Li
Date: 2017-10-23 00:00:00
Tasks: Cell Segmentation, Classification, General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Reliable cell segmentation and classification from biomedical images is a
crucial step for both scientific research and clinical practice. A major
challenge for more robust segmentation and classification methods is the large
variations in the size, shape and viewpoint of the cells, combining with the
low image quality caused by noise and artifacts. To address this issue, in this
work we propose a learning-based, simultaneous cell segmentation and
classification method based on the deep U-Net structure with deformable
convolution layers. The U-Net architecture for deep learning has been shown to
offer a precise localization for image semantic segmentation. Moreover,
deformable convolution layer enables the free form deformation of the feature
learning process, thus makes the whole network more robust to various cell
morphologies and image settings. The proposed method is tested on microscopic
red blood cell images from patients with sickle cell disease. The results show
that U-Net with deformable convolution achieves the highest accuracy for
segmentation and classification, comparing with original U-Net structure.
A Novel Approach to Artistic Textual Visualization via GAN
Authors: Yichi Ma, Muhan Ma
Date: 2017-10-29 00:00:00
Tasks: Generative Adversarial Network

While the visualization of statistical data tends to a mature technology, the
visualization of textual data is still in its infancy, especially for the
artistic text. Due to the fact that visualization of artistic text is valuable
and attractive in both art and information science, we attempt to realize this
tentative idea in this article. We propose the Generative Adversarial Network
based Artistic Textual Visualization (GAN-ATV) which can create paintings after
analyzing the semantic content of existing poems. Our GAN-ATV consists of two
main sections: natural language analysis section and visual information
synthesis section. In natural language analysis section, we use Bag-of-Word
(BoW) feature descriptors and a two-layer network to mine and analyze the
high-level semantic information from poems. In visual information synthesis
section, we design a cross-modal semantic understanding module and integrate it
with Generative Adversarial Network (GAN) to create paintings, whose content
are corresponding to the original poems. Moreover, in order to train our
GAN-ATV and verify its performance, we establish a cross-modal artistic dataset
named "Cross-Art". In the Cross-Art dataset, there are six topics and each
topic has their corresponding paintings and poems. The experimental results on
Cross-Art dataset are shown in this article.
Vehicle Routing Problem with Vector Profits (VRPVP) with Max-Min Criterion
Authors: Dongoo Lee, Jaemyung Ahn
Date: 2017-10-29 00:00:00

This paper introduces a new routing problem referred to as the vehicle
routing problem with vector profits. Given a network composed of nodes
(depot/sites) and arcs connecting the nodes, the problem determines routes that
depart from the depot, visit sites to collect profits, and return to the depot.
There are multiple stakeholders interested in the mission and each site is
associated with a vector whose k-th element represents the profit value for the
k-th stakeholder. The objective of the problem is to maximize the profit sum
for the least satisfied stakeholder, i.e., the stakeholder with the smallest
total profit value. An approach based on the linear programming relaxation and
column-generation to solve this max-min type routing problem was developed. Two
cases studies - the planetary surface exploration and the Rome tour cases -
were presented to demonstrate the effectiveness of the proposed problem
formulation and solution methodology.
Interpretation of Neural Networks is Fragile
Authors: Amirata Ghorbani, Abubakar Abid, James Zou
Date: 2017-10-29 00:00:00
Tasks: BIG-bench Machine Learning, Feature Importance

In order for machine learning to be deployed and trusted in many
applications, it is crucial to be able to reliably explain why the machine
learning algorithm makes certain predictions. For example, if an algorithm
classifies a given pathology image to be a malignant tumor, then the doctor may
need to know which parts of the image led the algorithm to this classification.
How to interpret black-box predictors is thus an important and active area of
research. A fundamental question is: how much can we trust the interpretation
itself? In this paper, we show that interpretation of deep learning predictions
is extremely fragile in the following sense: two perceptively indistinguishable
inputs with the same predicted label can be assigned very different
interpretations. We systematically characterize the fragility of several
widely-used feature-importance interpretation methods (saliency maps, relevance
propagation, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that
even small random perturbation can change the feature importance and new
systematic perturbations can lead to dramatically different interpretations
without changing the label. We extend these results to show that
interpretations based on exemplars (e.g. influence functions) are similarly
fragile. Our analysis of the geometry of the Hessian matrix gives insight on
why fragility could be a fundamental challenge to the current interpretation
approaches.
Partial Knowledge In Embeddings
Authors: Ramanathan V. Guha
Date: 2017-10-28 00:00:00

Representing domain knowledge is crucial for any task. There has been a wide
range of techniques developed to represent this knowledge, from older logic
based approaches to the more recent deep learning based techniques (i.e.
embeddings). In this paper, we discuss some of these methods, focusing on the
representational expressiveness tradeoffs that are often made. In particular,
we focus on the the ability of various techniques to encode `partial knowledge'
- a key component of successful knowledge systems. We introduce and describe
the concepts of `ensembles of embeddings' and `aggregate embeddings' and
demonstrate how they allow for partial knowledge.
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
Authors: Emad M. Grais, Hagen Wierstorf, Dominic Ward, Mark D. Plumbley
Date: 2017-10-28 00:00:00
Tasks: Audio Source Separation

In deep neural networks with convolutional layers, each layer typically has
fixed-size/single-resolution receptive field (RF). Convolutional layers with a
large RF capture global information from the input features, while layers with
small RF size capture local details with high resolution from the input
features. In this work, we introduce novel deep multi-resolution fully
convolutional neural networks (MR-FCNN), where each layer has different RF
sizes to extract multi-resolution features that capture the global and local
details information from its input features. The proposed MR-FCNN is applied to
separate a target audio source from a mixture of many audio sources.
Experimental results show that using MR-FCNN improves the performance compared
to feedforward deep neural networks (DNNs) and single resolution deep fully
convolutional neural networks (FCNNs) on the audio source separation problem.
Interpretable Deep Learning applied to Plant Stress Phenotyping
Authors: Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar
Date: 2017-10-24 00:00:00
Tasks: Deep Learning, General Classification, Transfer Learning

Availability of an explainable deep learning model that can be applied to
practical real world scenarios and in turn, can consistently, rapidly and
accurately identify specific and minute traits in applicable fields of
biological sciences, is scarce. Here we consider one such real world example
viz., accurate identification, classification and quantification of biotic and
abiotic stresses in crop research and production. Up until now, this has been
predominantly done manually by visual inspection and require specialized
training. However, such techniques are hindered by subjectivity resulting from
inter- and intra-rater cognitive variability. Here, we demonstrate the ability
of a machine learning framework to identify and classify a diverse set of
foliar stresses in the soybean plant with remarkable accuracy. We also present
an explanation mechanism using gradient-weighted class activation mapping that
isolates the visual symptoms used by the model to make predictions. This
unsupervised identification of unique visual symptoms for each stress provides
a quantitative measure of stress severity, allowing for identification,
classification and quantification in one framework. The learnt model appears to
be agnostic to species and make good predictions for other (non-soybean)
species, demonstrating an ability of transfer learning.
Interpretable Apprenticeship Learning with Temporal Logic Specifications
Authors: Daniel Kasenberg, Matthias Scheutz
Date: 2017-10-28 00:00:00
Tasks: Multiobjective Optimization

Recent work has addressed using formulas in linear temporal logic (LTL) as
specifications for agents planning in Markov Decision Processes (MDPs). We
consider the inverse problem: inferring an LTL specification from demonstrated
behavior trajectories in MDPs. We formulate this as a multiobjective
optimization problem, and describe state-based ("what actually happened") and
action-based ("what the agent expected to happen") objective functions based on
a notion of "violation cost". We demonstrate the efficacy of the approach by
employing genetic programming to solve this problem in two simple domains.
Automated Tumor Segmentation and Brain Mapping for the Tumor Area
Authors: Pranay Manocha, Snehal Bhasme, Tanvi Gupta, BK Panigrahi, Tapan K. Gandhi
Date: 2017-10-28 00:00:00
Tasks: Brain Tumor Segmentation, Clustering, Computed Tomography (CT), Diagnostic, Tumor Segmentation

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise
detection of various pathologies. Magnetic Resonance (MR) is more preferred
than Computed Tomography (CT) due to the high resolution in MR images which
help in better detection of neurological conditions. Graphical user interface
(GUI) aided disease detection has become increasingly useful due to the
increasing workload of doctors. In this proposed work, a novel two steps GUI
technique for brain tumor segmentation as well as Brodmann area detec-tion of
the segmented tumor is proposed. A data set of T2 weighted images of 15
patients is used for validating the proposed method. The patient data
incor-porates variations in ethnicities, gender (male and female) and age
(25-50), thus enhancing the authenticity of the proposed method. The tumors
were segmented using Fuzzy C Means Clustering and Brodmann area detection was
done using a known template, mapping each area to the segmented tumor image.
The proposed method was found to be fairly accurate and robust in detecting
tumor.