pwc-archive/papers-with-abstracts huggingface dataset:
Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks
Authors: Sayyed M. Zahiri, Jinho D. Choi
Date: 2017-08-14 00:00:00
Tasks: Document Classification, General Classification

While there have been significant advances in detecting emotions from speech
and image recognition, emotion detection on text is still under-explored and
remained as an active research field. This paper introduces a corpus for
text-based emotion detection on multiparty dialogue as well as deep neural
models that outperform the existing approaches for document classification. We
first present a new corpus that provides annotation of seven emotions on
consecutive utterances in dialogues extracted from the show, Friends. We then
suggest four types of sequence-based convolutional neural network models with
attention that leverage the sequence information encapsulated in dialogue. Our
best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained
emotions, respectively. Given the difficulty of this task, this is promising.
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity
Authors: Hagit Grushka-Cohen, Oded Sofer, Ofer Biller, Michael Dymshits, Lior Rokach, Bracha Shapira
Date: 2017-08-14 00:00:00
Tasks: Anomaly Detection, Vocal Bursts Intensity Prediction

Data leakage and theft from databases is a dangerous threat to organizations.
Data Security and Data Privacy protection systems (DSDP) monitor data access
and usage to identify leakage or suspicious activities that should be
investigated. Because of the high velocity nature of database systems, such
systems audit only a portion of the vast number of transactions that take
place. Anomalies are investigated by a Security Officer (SO) in order to choose
the proper response. In this paper we investigate the effect of sampling
methods based on the risk the transaction poses and propose a new method for
"combined sampling" for capturing a more varied sample.
A Simple Approach to Learn Polysemous Word Embeddings
Authors: Yifan Sun, Nikhil Rao, Weicong Ding
Date: 2017-07-06 00:00:00
Tasks: Relevance Detection, Representation Learning, Word Embeddings, Word Sense Induction, Word Similarity

Many NLP applications require disambiguating polysemous words. Existing
methods that learn polysemous word vector representations involve first
detecting various senses and optimizing the sense-specific embeddings
separately, which are invariably more involved than single sense learning
methods such as word2vec. Evaluating these methods is also problematic, as
rigorous quantitative evaluations in this space is limited, especially when
compared with single-sense embeddings. In this paper, we propose a simple
method to learn a word representation, given any context. Our method only
requires learning the usual single sense representation, and coefficients that
can be learnt via a single pass over the data. We propose several new test sets
for evaluating word sense induction, relevance detection, and contextual word
similarity, significantly supplementing the currently available tests. Results
on these and other tests show that while our method is embarrassingly simple,
it achieves excellent results when compared to the state of the art models for
unsupervised polysemous word representation learning.
Attributing Hacks
Authors: Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng, Jun Zhou
Date: 2016-11-07 00:00:00
Tasks: Attribute

In this paper we describe an algorithm for estimating the provenance of hacks
on websites. That is, given properties of sites and the temporal occurrence of
attacks, we are able to attribute individual attacks to joint causes and
vulnerabilities, as well as estimating the evolution of these vulnerabilities
over time. Specifically, we use hazard regression with a time-varying additive
hazard function parameterized in a generalized linear form. The activation
coefficients on each feature are continuous-time functions over time. We
formulate the problem of learning these functions as a constrained variational
maximum likelihood estimation problem with total variation penalty and show
that the optimal solution is a 0th order spline (a piecewise constant function)
with a finite number of known knots. This allows the inference problem to be
solved efficiently and at scale by solving a finite dimensional optimization
problem. Extensive experiments on real data sets show that our method
significantly outperforms Cox's proportional hazard model. We also conduct a
case study and verify that the fitted functions are indeed recovering
vulnerable features and real-life events such as the release of code to exploit
these features in hacker blogs.
Learning spectro-temporal features with 3D CNNs for speech emotion recognition
Authors: Jaebok Kim, Khiet P. Truong, Gwenn Englebienne, Vanessa Evers
Date: 2017-08-14 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

In this paper, we propose to use deep 3-dimensional convolutional networks
(3D CNNs) in order to address the challenge of modelling spectro-temporal
dynamics for speech emotion recognition (SER). Compared to a hybrid of
Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our
proposed 3D CNNs simultaneously extract short-term and long-term spectral
features with a moderate number of parameters. We evaluated our proposed and
other state-of-the-art methods in a speaker-independent manner using aggregated
corpora that give a large and diverse set of speakers. We found that 1) shallow
temporal and moderately deep spectral kernels of a homogeneous architecture are
optimal for the task; and 2) our 3D CNNs are more effective for
spectro-temporal feature learning compared to other methods. Finally, we
visualised the feature space obtained with our proposed method using
t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct
clusters of emotions.
Learning Blind Motion Deblurring
Authors: Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf, Hendrik P. A. Lensch
Date: 2017-08-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring

As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the blurry recordings is a highly ill-posed problem as neither
the sharp image nor the motion blur kernel is known. Propagating information
between multiple consecutive blurry observations can help restore the desired
sharp image or video. Solutions for blind deconvolution based on neural
networks rely on a massive amount of ground-truth data which is hard to
acquire. In this work, we propose an efficient approach to produce a
significant amount of realistic training data and introduce a novel recurrent
network architecture to deblur frames taking temporal information into account,
which can efficiently handle arbitrary spatial and temporal input sizes. We
demonstrate the versatility of our approach in a comprehensive comparison on a
number of challening real-world examples.
Learning to Plan Chemical Syntheses
Authors: Marwin H. S. Segler, Mike Preuss, Mark P. Waller
Date: 2017-08-14 00:00:00
Tasks: Retrosynthesis

From medicines to materials, small organic molecules are indispensable for
human well-being. To plan their syntheses, chemists employ a problem solving
technique called retrosynthesis. In retrosynthesis, target molecules are
recursively transformed into increasingly simpler precursor compounds until a
set of readily available starting materials is obtained. Computer-aided
retrosynthesis would be a highly valuable tool, however, past approaches were
slow and provided results of unsatisfactory quality. Here, we employ Monte
Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS
was combined with an expansion policy network that guides the search, and an
"in-scope" filter network to pre-select the most promising retrosynthetic
steps. These deep neural networks were trained on 12 million reactions, which
represents essentially all reactions ever published in organic chemistry. Our
system solves almost twice as many molecules and is 30 times faster in
comparison to the traditional search method based on extracted rules and
hand-coded heuristics. Finally after a 60 year history of computer-aided
synthesis planning, chemists can no longer distinguish between routes generated
by a computer system and real routes taken from the scientific literature. We
anticipate that our method will accelerate drug and materials discovery by
assisting chemists to plan better syntheses faster, and by enabling fully
automated robot synthesis.
Understanding and Visualizing the District of Columbia Capital Bikeshare System Using Data Analysis for Balancing Purposes
Authors: Kiana Roshan Zamir, Ali Shafahi, Ali Haghani
Date: 2017-08-14 00:00:00
Tasks: Management

Bike sharing systems' popularity has consistently been rising during the past
years. Managing and maintaining these emerging systems are indispensable parts
of these systems. Visualizing the current operations can assist in getting a
better grasp on the performance of the system. In this paper, a data mining
approach is used to identify and visualize some important factors related to
bike-share operations and management. To consolidate the data, we cluster
stations that have a similar pickup and drop-off profiles during weekdays and
weekends. We provide the temporal profile of the center of each cluster which
can be used as a simple and practical approach for approximating the number of
pickups and drop-offs of the stations. We also define two indices based on
stations' shortages and surpluses that reflect the degree of balancing aid a
station needs. These indices can help stakeholders improve the quality of the
bike-share user experience in at-least two ways. It can act as a complement to
balancing optimization efforts, and it can identify stations that need
expansion. We mine the District of Columbia's regional bike-share data and
discuss the findings of this data set. We examine the bike-share system during
different quarters of the year and during both peak and non-peak hours.
Findings reflect that on weekdays most of the pickups and drop-offs happen
during the morning and evening peaks whereas on weekends pickups and drop-offs
are spread out throughout the day. We also show that throughout the day, more
than 40% of the stations are relatively self-balanced. Not worrying about these
stations during ordinary days can allow the balancing efforts to focus on a
fewer stations and therefore potentially improve the efficiency of the
balancing optimization models.
Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
Authors: Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu, Min Sun
Date: 2017-05-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Captioning, Sentence, valid

Impressive image captioning results are achieved in domains with plenty of
training image and sentence pairs (e.g., MSCOCO). However, transferring to a
target domain with significant domain shifts but no paired training data
(referred to as cross-domain image captioning) remains largely unexplored. We
propose a novel adversarial training procedure to leverage unpaired data in the
target domain. Two critic networks are introduced to guide the captioner,
namely domain critic and multi-modal critic. The domain critic assesses whether
the generated sentences are indistinguishable from sentences in the target
domain. The multi-modal critic assesses whether an image and its generated
sentence are a valid pair. During training, the critics and captioner act as
adversaries -- captioner aims to generate indistinguishable sentences, whereas
critics aim at distinguishing them. The assessment improves the captioner
through policy gradient updates. During inference, we further propose a novel
critic-based planning method to select high-quality sentences without
additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source
domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k)
as the target domains. Our method consistently performs well on all datasets.
In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after
adaptation. Utilizing critics during inference further gives another 4.5%
boost.
The difference between memory and prediction in linear recurrent networks
Authors: Sarah Marzen
Date: 2017-06-26 00:00:00
Tasks: Prediction

Recurrent networks are trained to memorize their input better, often in the
hopes that such training will increase the ability of the network to predict.
We show that networks designed to memorize input can be arbitrarily bad at
prediction. We also find, for several types of inputs, that one-node networks
optimized for prediction are nearly at upper bounds on predictive capacity
given by Wiener filters, and are roughly equivalent in performance to randomly
generated five-node networks. Our results suggest that maximizing memory
capacity leads to very different networks than maximizing predictive capacity,
and that optimizing recurrent weights can decrease reservoir size by half an
order of magnitude.
Strategy Synthesis in POMDPs via Game-Based Abstractions
Authors: Leonore Winterer, Sebastian Junges, Ralf Wimmer, Nils Jansen, Ufuk Topcu, Joost-Pieter Katoen, Bernd Becker
Date: 2017-08-14 00:00:00
Tasks: Motion Planning

We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Verification and strategy synthesis for POMDPs are, however, computationally intractable in general. We alleviate this difficulty by focusing on planning applications and exploiting typical structural properties of such scenarios; for instance, we assume that the agent has the ability to observe its own position inside an environment. We propose an abstraction refinement framework which turns such a POMDP model into a (fully observable) probabilistic two-player game (PG). For the obtained PGs, efficient verification and synthesis tools allow to determine strategies with optimal safety and performance measures, which approximate optimal schedulers on the POMDP. If the approximation is too coarse to satisfy the given specifications, an refinement scheme improves the computed strategies. As a running example, we use planning problems where an agent moves inside an environment with randomly moving obstacles and restricted observability. We demonstrate that the proposed method advances the state of the art by solving problems several orders-of-magnitude larger than those that can be handled by existing POMDP solvers. Furthermore, this method gives guarantees on safety constraints, which is not supported by the majority of the existing solvers.
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
Authors: Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas Kirste
Date: 2017-07-20 00:00:00
Tasks: Activity Recognition, State Space Models

Bayesian Filtering for plan and activity recognition is challenging for
scenarios that contain many observation equivalent entities (i.e. entities that
produce the same observations). This is due to the combinatorial explosion in
the number of hypotheses that need to be tracked. However, this class of
problems exhibits a certain symmetry that can be exploited for state space
representation and inference. We analyze current state of the art methods and
find that none of them completely fits the requirements arising in this problem
class. We sketch a novel inference algorithm that provides a solution by
incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset
Rewriting Systems, and Computational State Space Models. Two experiments
confirm that this novel algorithm has the potential to perform efficient
probabilistic inference on this problem class.
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Authors: Jie Chen, Haim Avron, Vikas Sindhwani
Date: 2016-08-02 00:00:00

We propose a novel class of kernels to alleviate the high computational cost
of large-scale nonparametric learning with kernel methods. The proposed kernel
is defined based on a hierarchical partitioning of the underlying data domain,
where the Nystr\"om method (a globally low-rank approximation) is married with
a locally lossless approximation in a hierarchical fashion. The kernel
maintains (strict) positive-definiteness. The corresponding kernel matrix
admits a recursively off-diagonal low-rank structure, which allows for fast
linear algebra computations. Suppressing the factor of data dimension, the
memory and arithmetic complexities for training a regression or a classifier
are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively,
where $n$ is the number of training examples and $r$ is the rank on each level
of the hierarchy. Although other randomized approximate kernels entail a
similar complexity, empirical results show that the proposed kernel achieves a
matching performance with a smaller $r$. We demonstrate comprehensive
experiments to show the effective use of the proposed kernel on data sizes up
to the order of millions.
Early Improving Recurrent Elastic Highway Network
Authors: Hyunsin Park, Chang D. Yoo
Date: 2017-08-14 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Language Modeling, Language Modelling

To model time-varying nonlinear temporal dynamics in sequential data, a
recurrent network capable of varying and adjusting the recurrence depth between
input intervals is examined. The recurrence depth is extended by several
intermediate hidden state units, and the weight parameters involved in
determining these units are dynamically calculated. The motivation behind the
paper lies on overcoming a deficiency in Recurrent Highway Networks and
improving their performances which are currently at the forefront of RNNs: 1)
Determining the appropriate number of recurrent depth in RHN for different
tasks is a huge burden and just setting it to a large number is computationally
wasteful with possible repercussion in terms of performance degradation and
high latency. Expanding on the idea of adaptive computation time (ACT), with
the use of an elastic gate in the form of a rectified exponentially decreasing
function taking on as arguments as previous hidden state and input, the
proposed model is able to evaluate the appropriate recurrent depth for each
input. The rectified gating function enables the most significant intermediate
hidden state updates to come early such that significant performance gain is
achieved early. 2) Updating the weights from that of previous intermediate
layer offers a richer representation than the use of shared weights across all
intermediate recurrence layers. The weight update procedure is just an
expansion of the idea underlying hypernetworks. To substantiate the
effectiveness of the proposed network, we conducted three experiments:
regression on synthetic data, human activity recognition, and language modeling
on the Penn Treebank dataset. The proposed networks showed better performance
than other state-of-the-art recurrent networks in all three experiments.
Context-based Normalization of Histological Stains using Deep Convolutional Features
Authors: Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler, Dorit Merhof
Date: 2017-08-14 00:00:00

While human observers are able to cope with variations in color and
appearance of histological stains, digital pathology algorithms commonly
require a well-normalized setting to achieve peak performance, especially when
a limited amount of labeled data is available. This work provides a fully
automated, end-to-end learning-based setup for normalizing histological stains,
which considers the texture context of the tissue. We introduce Feature Aware
Normalization, which extends the framework of batch normalization in
combination with gating elements from Long Short-Term Memory units for
normalization among different spatial regions of interest. By incorporating a
pretrained deep neural network as a feature extractor steering a pixelwise
processing pipeline, we achieve excellent normalization results and ensure a
consistent representation of color and texture. The evaluation comprises a
comparison of color histogram deviations, structural similarity and measures
the color volume obtained by the different methods.
Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning
Authors: Elhocine Boutellaa, Miguel Bordallo López, Samy Ait-Aoudia, Xiaoyi Feng, Abdenour Hadid
Date: 2017-08-14 00:00:00
Tasks: Kinship Verification

Automatic kinship verification using facial images is a relatively new and
challenging research problem in computer vision. It consists in automatically
predicting whether two persons have a biological kin relation by examining
their facial attributes. While most of the existing works extract shallow
handcrafted features from still face images, we approach this problem from
spatio-temporal point of view and explore the use of both shallow texture
features and deep features for characterizing faces. Promising results,
especially those of deep features, are obtained on the benchmark UvA-NEMO Smile
database. Our extensive experiments also show the superiority of using videos
over still images, hence pointing out the important role of facial dynamics in
kinship verification. Furthermore, the fusion of the two types of features
(i.e. shallow spatio-temporal texture features and deep features) shows
significant performance improvements compared to state-of-the-art methods.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Authors: Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison Cottrell
Date: 2017-04-07 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the
current value of a time series based upon its previous values as well as the
current and past values of multiple driving (exogenous) series, has been
studied for decades. Despite the fact that various NARX models have been
developed, few of them can capture the long-term temporal dependencies
appropriately and select the relevant driving series to make predictions. In
this paper, we propose a dual-stage attention-based recurrent neural network
(DA-RNN) to address these two issues. In the first stage, we introduce an input
attention mechanism to adaptively extract relevant driving series (a.k.a.,
input features) at each time step by referring to the previous encoder hidden
state. In the second stage, we use a temporal attention mechanism to select
relevant encoder hidden states across all time steps. With this dual-stage
attention scheme, our model can not only make predictions effectively, but can
also be easily interpreted. Thorough empirical studies based upon the SML 2010
dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can
outperform state-of-the-art methods for time series prediction.
Colorimetric Calibration of a Digital Camera
Authors: Renata Rychtarikova, Pavel Soucek, Dalibor Stys
Date: 2017-08-14 00:00:00

In this paper, we introduce a novel - physico-chemical - approach for
calibration of a digital camera chip. This approach utilizes results of
measurement of incident light spectra of calibration films of different levels
of gray for construction of calibration curve (number of incident photons vs.
image pixel intensity) for each camera pixel. We show spectral characteristics
of such corrected digital raw image files (a primary camera signal) and
demonstrate their suitability for next image processing and analysis.