pwc-archive/papers-with-abstracts huggingface dataset:
Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search
Authors: Ya Jing, Chenyang Si, Jun-Bo Wang, Wei Wang, Liang Wang, Tieniu Tan
Date: 2018-09-22 00:00:00
Tasks: Person Search, Sentence, Text based Person Search

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric.
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach
Authors: Ravi Kiran Raman, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney
Date: 2018-09-22 00:00:00
Tasks: Epidemiology

Large-scale computational experiments, often running over weeks and over
large datasets, are used extensively in fields such as epidemiology,
meteorology, computational biology, and healthcare to understand phenomena, and
design high-stakes policies affecting everyday health and economy. For
instance, the OpenMalaria framework is a computationally-intensive simulation
used by various non-governmental and governmental agencies to understand
malarial disease spread and effectiveness of intervention strategies, and
subsequently design healthcare policies. Given that such shared results form
the basis of inferences drawn, technological solutions designed, and day-to-day
policies drafted, it is essential that the computations are validated and
trusted. In particular, in a multi-agent environment involving several
independent computing agents, a notion of trust in results generated by peers
is critical in facilitating transparency, accountability, and collaboration.
Using a novel combination of distributed validation of atomic computation
blocks and a blockchain-based immutable audits mechanism, this work proposes a
universal framework for distributed trust in computations. In particular we
address the scalaibility problem by reducing the storage and communication
costs using a lossy compression scheme. This framework guarantees not only
verifiability of final results, but also the validity of local computations,
and its cost-benefit tradeoffs are studied using a synthetic example of
training a neural network.
Pachinko Prediction: A Bayesian method for event prediction from social media data
Authors: Jonathan Tuke, Andrew Nguyen, Mehwish Nasim, Drew Mellor, Asanga Wickramasinghe, Nigel Bean, Lewis Mitchell
Date: 2018-09-22 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

The combination of large open data sources with machine learning approaches
presents a potentially powerful way to predict events such as protest or social
unrest. However, accounting for uncertainty in such models, particularly when
using diverse, unstructured datasets such as social media, is essential to
guarantee the appropriate use of such methods. Here we develop a Bayesian
method for predicting social unrest events in Australia using social media
data. This method uses machine learning methods to classify individual postings
to social media as being relevant, and an empirical Bayesian approach to
calculate posterior event probabilities. We use the method to predict events in
Australian cities over a period in 2017/18.
Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
Authors: Jingchi Jiang, Huanzheng Wang, Jing Xie, Xitong Guo, Yi Guan, Qiubin Yu
Date: 2018-09-22 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

The representation of knowledge based on first-order logic captures the
richness of natural language and supports multiple probabilistic inference
models. Although symbolic representation enables quantitative reasoning with
statistical probability, it is difficult to utilize with machine learning
models as they perform numerical operations. In contrast, knowledge embedding
(i.e., high-dimensional and continuous vectors) is a feasible approach to
complex reasoning that can not only retain the semantic information of
knowledge but also establish the quantifiable relationship among them. In this
paper, we propose recursive neural knowledge network (RNKN), which combines
medical knowledge based on first-order logic with recursive neural network for
multi-disease diagnosis. After RNKN is efficiently trained from manually
annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented
knowledge embeddings and weight matrixes are learned. Experimental results
verify that the diagnostic accuracy of RNKN is superior to that of some
classical machine learning models and Markov logic network (MLN). The results
also demonstrate that the more explicit the evidence extracted from CEMRs is,
the better is the performance achieved. RNKN gradually exhibits the
interpretation of knowledge embeddings as the number of training epochs
increases.
Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
Authors: Md. Abu Bakr Siddique, Rezoana Bente Arif, Mohammad Mahmudur Rahman Khan, Zahidun Ashrafi
Date: 2018-09-22 00:00:00
Tasks: Clustering

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.
Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals
Authors: Kuan Tung, Po-Kang Liu, Yu-Chuan Chuang, Sheng-Hui Wang, An-Yeu Wu
Date: 2018-09-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

As the result of the growing importance of the Human Computer Interface
system, understanding human's emotion states has become a consequential ability
for the computer. This paper aims to improve the performance of emotion
recognition by conducting the complexity analysis of physiological signals.
Based on AMIGOS dataset, we extracted several entropy-domain features such as
Refined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-Scale
Permutation Entropy (RCMPE) from ECG and GSR signals, and Multivariate
Multi-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (MMPE)
from EEG, respectively. The statistical results show that RCMSE in GSR has a
dominating performance in arousal, while RCMPE in GSR would be the excellent
feature in valence. Furthermore, we selected XGBoost model to predict emotion
and get 68% accuracy in arousal and 84% in valence.
Active image restoration
Authors: Rongrong Xie, Shengfeng Deng, Weibing Deng, Armen E. Allahverdyan
Date: 2018-09-22 00:00:00
Tasks: Image Restoration

We study active restoration of noise-corrupted images generated via the Gibbs
probability of an Ising ferromagnet in external magnetic field. Ferromagnetism
accounts for the prior expectation of data smoothness, i.e. a positive
correlation between neighbouring pixels (Ising spins), while the magnetic field
refers to the bias. The restoration is actively supervised by requesting the
true values of certain pixels after a noisy observation. This additional
information improves restoration of other pixels. The optimal strategy of
active inference is not known for realistic (two-dimensional) images. We
determine this strategy for the mean-field version of the model and show that
it amounts to supervising the values of spins (pixels) that do not agree with
the sign of the average magnetization. The strategy leads to a transparent
analytical expression for the minimal Bayesian risk, and shows that there is a
maximal number of pixels beyond of which the supervision is useless. We show
numerically that this strategy applies for two-dimensional images away from the
critical regime. Within this regime the strategy is outperformed by its local
(adaptive) version, which supervises pixels that do not agree with their
Bayesian estimate. We show on transparent examples how active supervising can
be essential in recovering noise-corrupted images and advocate for a wider
usage of active methods in image restoration.
RPNet: an End-to-End Network for Relative Camera Pose Estimation
Authors: Sovann En, Alexis Lechervy, Frédéric Jurie
Date: 2018-09-22 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation, Translation

This paper addresses the task of relative camera pose estimation from raw
image pixels, by means of deep neural networks. The proposed RPNet network
takes pairs of images as input and directly infers the relative poses, without
the need of camera intrinsic/extrinsic. While state-of-the-art systems based on
SIFT + RANSAC, are able to recover the translation vector only up to scale,
RPNet is trained to produce the full translation vector, in an end-to-end way.
Experimental results on the Cambridge Landmark dataset show very promising
results regarding the recovery of the full translation vector. They also show
that RPNet produces more accurate and more stable results than traditional
approaches, especially for hard images (repetitive textures, textureless
images, etc). To the best of our knowledge, RPNet is the first attempt to
recover full translation vectors in relative pose estimation.
Variational Collaborative Learning for User Probabilistic Representation
Authors: Kenan Cui, Xu Chen, Jiangchao Yao, Ya zhang
Date: 2018-09-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering (CF) has been successfully employed by many modern
recommender systems. Conventional CF-based methods use the user-item
interaction data as the sole information source to recommend items to users.
However, CF-based methods are known for suffering from cold start problems and
data sparsity problems. Hybrid models that utilize auxiliary information on top
of interaction data have increasingly gained attention. A few "collaborative
learning"-based models, which tightly bridges two heterogeneous learners
through mutual regularization, are recently proposed for the hybrid
recommendation. However, the "collaboration" in the existing methods are
actually asynchronous due to the alternative optimization of the two learners.
Leveraging the recent advances in variational autoencoder~(VAE), we here
propose a model consisting of two streams of mutual linked VAEs, named
variational collaborative model (VCM). Unlike the mutual regularization used in
previous works where two learners are optimized asynchronously, VCM enables a
synchronous collaborative learning mechanism. Besides, the two stream VAEs
setup allows VCM to fully leverages the Bayesian probabilistic representations
in collaborative learning. Extensive experiments on three real-life datasets
have shown that VCM outperforms several state-of-art methods.
Relating Zipf's law to textual information
Authors: Weibing Deng, Armen E. Allahverdyan
Date: 2018-09-22 00:00:00

Zipf's law is the main regularity of quantitative linguistics. Despite of
many works devoted to foundations of this law, it is still unclear whether it
is only a statistical regularity, or it has deeper relations with
information-carrying structures of the text. This question relates to that of
distinguishing a meaningful text (written in an unknown system) from a
meaningless set of symbols that mimics statistical features of a text. Here we
contribute to resolving these questions by comparing features of the first half
of a text (from the beginning to the middle) to its second half. This
comparison can uncover hidden effects, because the halves have the same values
of many parameters (style, genre, author's vocabulary {\it etc}). In all
studied texts we saw that for the first half Zipf's law applies from smaller
ranks than in the second half, i.e. the law applies better to the first half.
Also, words that hold Zipf's law in the first half are distributed more
homogeneously over the text. These features do allow to distinguish a
meaningful text from a random sequence of words. Our findings correlate with a
number of textual characteristics that hold in most cases we studied: the first
half is lexically richer, has longer and less repetitive words, more and
shorter sentences, more punctuation signs and more paragraphs. These
differences between the halves indicate on a higher hierarchic level of text
organization that so far went unnoticed in text linguistics. They relate the
validity of Zipf's law to textual information. A complete description of this
effect requires new models, though one existing model can account for some of
its aspects.
Geometric Multi-Model Fitting by Deep Reinforcement Learning
Authors: Zongliang Zhang, Hongbin Zeng, Jonathan Li, Yiping Chen, Chenhui Yang, Cheng Wang
Date: 2018-09-22 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

This paper deals with the geometric multi-model fitting from noisy,
unstructured point set data (e.g., laser scanned point clouds). We formulate
multi-model fitting problem as a sequential decision making process. We then
use a deep reinforcement learning algorithm to learn the optimal decisions
towards the best fitting result. In this paper, we have compared our method
against the state-of-the-art on simulated data. The results demonstrated that
our approach significantly reduced the number of fitting iterations.
The Privacy Policy Landscape After the GDPR
Authors: Thomas Linden, Rishabh Khandelwal, Hamza Harkous, Kassem Fawaz
Date: 2018-09-22 00:00:00
Tasks: Specificity

The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. A year after it went into effect, we study its impact on the landscape of privacy policies online. We conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering their pre-GDPR and the post-GDPR versions. The results of our tests and analyses suggest that the GDPR has been a catalyst for a major overhaul of the privacy policies inside and outside the EU. This overhaul of the policies, manifesting in extensive textual changes, especially for the EU-based websites, comes at mixed benefits to the users. While the privacy policies have become considerably longer, our user study with 470 participants on Amazon MTurk indicates a significant improvement in the visual representation of privacy policies from the users' perspective for the EU websites. We further develop a new workflow for the automated assessment of requirements in privacy policies. Using this workflow, we show that privacy policies cover more data practices and are more consistent with seven compliance requirements post the GDPR. We also assess how transparent the organizations are with their privacy practices by performing specificity analysis. In this analysis, we find evidence for positive changes triggered by the GDPR, with the specificity level improving on average. Still, we find the landscape of privacy policies to be in a transitional phase; many policies still do not meet several key GDPR requirements or their improved coverage comes with reduced specificity.
Understanding Fake Faces
Authors: Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka
Date: 2018-09-22 00:00:00
Tasks: Face Recognition, Face Verification

Face recognition research is one of the most active topics in computer vision
(CV), and deep neural networks (DNN) are now filling the gap between
human-level and computer-driven performance levels in face verification
algorithms. However, although the performance gap appears to be narrowing in
terms of accuracy-based expectations, a curious question has arisen;
specifically, "Face understanding of AI is really close to that of human?" In
the present study, in an effort to confirm the brain-driven concept, we conduct
image-based detection, classification, and generation using an in-house created
fake face database. This database has two configurations: (i) false positive
face detections produced using both the Viola Jones (VJ) method and
convolutional neural networks (CNN), and (ii) simulacra that have fundamental
characteristics that resemble faces but are completely artificial. The results
show a level of suggestive knowledge that indicates the continuing existence of
a gap between the capabilities of recent vision-based face recognition
algorithms and human-level performance. On a positive note, however, we have
obtained knowledge that will advance the progress of face-understanding models.
Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processing
Authors: Junfeng Jiang, Jiahao Li
Date: 2018-09-22 00:00:00

In this paper, we design an integrated algorithm to evaluate the sentiment of
Chinese market. Firstly, with the help of the web browser automation, we crawl
a lot of news and comments from several influential financial websites
automatically. Secondly, we use techniques of Natural Language Processing(NLP)
under Chinese context, including tokenization, Word2vec word embedding and
semantic database WordNet, to compute Senti-scores of these news and comments,
and then construct the sentimental factor. Here, we build a finance-specific
sentimental lexicon so that the sentimental factor can reflect the sentiment of
financial market but not the general sentiments as happiness, sadness, etc.
Thirdly, we also implement an adjustment of the standard sentimental factor.
Our experimental performance shows that there is a significant correlation
between our standard sentimental factor and the Chinese market, and the
adjusted factor is even more informative, having a stronger correlation with
the Chinese market. Therefore, our sentimental factors can be important
references when making investment decisions. Especially during the Chinese
market crash in 2015, the Pearson correlation coefficient of adjusted
sentimental factor with SSE is 0.5844, which suggests that our model can
provide a solid guidance, especially in the special period when the market is
influenced greatly by public sentiment.
A Byte-sized Approach to Named Entity Recognition
Authors: Emily Sheng, Prem Natarajan
Date: 2018-09-22 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In biomedical literature, it is common for entity boundaries to not align
with word boundaries. Therefore, effective identification of entity spans
requires approaches capable of considering tokens that are smaller than words.
We introduce a novel, subword approach for named entity recognition (NER) that
uses byte-pair encodings (BPE) in combination with convolutional and recurrent
neural networks to produce byte-level tags of entities. We present experimental
results on several standard biomedical datasets, namely the BioCreative VI
Bio-ID, JNLPBA, and GENETAG datasets. We demonstrate competitive performance
while bypassing the specialized domain expertise needed to create biomedical
text tokenization rules.
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions
Authors: Meera Hahn, Nataniel Ruiz, Jean-Baptiste Alayrac, Ivan Laptev, James M. Rehg
Date: 2018-09-22 00:00:00
Tasks: Object, Object Recognition

Automatic generation of textual video descriptions that are time-aligned with
video content is a long-standing goal in computer vision. The task is
challenging due to the difficulty of bridging the semantic gap between the
visual and natural language domains. This paper addresses the task of
automatically generating an alignment between a set of instructions and a first
person video demonstrating an activity. The sparse descriptions and ambiguity
of written instructions create significant alignment challenges. The key to our
approach is the use of egocentric cues to generate a concise set of action
proposals, which are then matched to recipe steps using object recognition and
computational linguistic techniques. We obtain promising results on both the
Extended GTEA Gaze+ dataset and the Bristol Egocentric Object Interactions
Dataset.
Galaxy morphology prediction using capsule networks
Authors: Reza Katebi, Yadi Zhou, Ryan Chornock, Razvan Bunescu
Date: 2018-09-22 00:00:00
Tasks: General Classification, Morphology classification, Prediction

Understanding morphological types of galaxies is a key parameter for studying
their formation and evolution. Neural networks that have been used previously
for galaxy morphology classification have some disadvantages, such as not being
invariant under rotation. In this work, we studied the performance of Capsule
Network, a recently introduced neural network architecture that is rotationally
invariant and spatially aware, on the task of galaxy morphology classification.
We designed two evaluation scenarios based on the answers from the question
tree in the Galaxy Zoo project. In the first scenario, we used Capsule Network
for regression and predicted probabilities for all of the questions. In the
second scenario, we chose the answer to the first morphology question that had
the highest user agreement as the class of the object and trained a Capsule
Network classifier, where we also reconstructed galaxy images. We achieved
promising results in both of these scenarios. Automated approaches such as the
one introduced here will greatly decrease the workload of astronomers and will
play a critical role in the upcoming large sky surveys.
Focus On What's Important: Self-Attention Model for Human Pose Estimation
Authors: Guanxiong Sun, Chengqin Ye, Kuanquan Wang
Date: 2018-09-22 00:00:00
Tasks: Pose Estimation, Self-Learning

Human pose estimation is an essential yet challenging task in computer
vision. One of the reasons for this difficulty is that there are many redundant
regions in the images. In this work, we proposed a convolutional network
architecture combined with the novel attention model. We named it attention
convolutional neural network (ACNN). ACNN learns to focus on specific regions
of different input features. It's a multi-stage architecture. Early stages
filtrate the "nothing-regions", such as background and redundant body parts.
And then, they submit the important regions which contain the joints of the
human body to the following stages to get a more accurate result. What's more,
it does not require extra manual annotations and self-learning is one of our
intentions. We separately trained the network because the attention learning
task and the pose estimation task are not independent. State-of-the-art
performance is obtained on the MPII benchmarks.