pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Recommender Systems: A Systematic Literature Review
Authors: Erion Çano, Maurizio Morisio
Date: 2019-01-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Systematic Literature Review

Recommender systems are software tools used to generate and provide
suggestions for items and other entities to the users by exploiting various
strategies. Hybrid recommender systems combine two or more recommendation
strategies in different ways to benefit from their complementary advantages.
This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review
work completely focused in hybrid recommenders. We address the most relevant
problems considered and present the associated data mining and recommendation
techniques used to overcome them. We also explore the hybridization classes
each hybrid recommender belongs to, the application domains, the evaluation
process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a
weighted way. Also cold-start and data sparsity are the two traditional and top
problems being addressed in 23 and 22 studies each, while movies and movie
datasets are still widely used by most of the authors. As most of the studies
are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical
challenge. Besides this, newer challenges were also identified such as
responding to the variation of user context, evolving user tastes or providing
cross-domain recommendations. Being a hot topic, hybrid recommenders represent
a good basis with which to respond accordingly by exploring newer opportunities
such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc.
Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication
Authors: Emanuele Pesce, Giovanni Montana
Date: 2019-01-12 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance in small-scale systems. Our experimental results show that the proposed method achieves superior performance in scenarios with up to six agents. We illustrate how different communication patterns can emerge on six different tasks of increasing complexity. Furthermore, we study the effects of corrupting the communication channel, provide a visualisation of the time-varying memory content as the underlying task is being solved and validate the building blocks of the proposed memory device through ablation studies.
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks
Authors: Harshvardhan Tibrewal, Sravan Patchala, Manjesh K. Hanawal, Sumit J. Darak
Date: 2019-01-12 00:00:00
Tasks: Multi-Armed Bandits

We consider an ad hoc network where multiple users access the same set of channels. The channel characteristics are unknown and could be different for each user (heterogeneous). No controller is available to coordinate channel selections by the users, and if multiple users select the same channel, they collide and none of them receive any rate (or reward). For such a completely decentralized network we develop algorithms that aim to achieve optimal network throughput. Due to lack of any direct communication between the users, we allow each user to exchange information by transmitting in a specific pattern and sense such transmissions from others. However, such transmissions and sensing for information exchange do not add to network throughput. For the wideband sensing and narrowband sensing scenarios, we first develop explore-and-commit algorithms that converge to near-optimal allocation with high probability in a small number of rounds. Building on this, we develop an algorithm that gives logarithmic regret, even when the number of users changes with time. We validate our claims through extensive experiments and show that our algorithms perform significantly better than the state-of-the-art CSM-MAB, dE3 and dE3-TS algorithms.
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering
Authors: Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Lixin Su, Xue-Qi Cheng
Date: 2019-01-12 00:00:00
Tasks: Open-Domain Question Answering, Position, Question Answering, Reading Comprehension

This paper is concerned with open-domain question answering (i.e., OpenQA).
Recently, some works have viewed this problem as a reading comprehension (RC)
task, and directly applied successful RC models to it. However, the
performances of such models are not so good as that in the RC task. In our
opinion, the perspective of RC ignores three characteristics in OpenQA task: 1)
many paragraphs without the answer span are included in the data collection; 2)
multiple answer spans may exist within one given paragraph; 3) the end position
of an answer span is dependent with the start position. In this paper, we first
propose a new probabilistic formulation of OpenQA, based on a three-level
hierarchical structure, i.e.,~the question level, the paragraph level and the
answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed
to capture each probability. HAS-QA has the ability to tackle the above three
problems, and experiments on public OpenQA datasets show that it significantly
outperforms traditional RC baselines and recent OpenQA baselines.
HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
Authors: Cheng Sun, Chi-Wei Hsiao, Min Sun, Hwann-Tzong Chen
Date: 2019-01-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Room Layouts From A Single RGB Panorama, Data Augmentation

We present a new approach to the problem of estimating the 3D room layout
from a single panoramic image. We represent room layout as three 1D vectors
that encode, at each image column, the boundary positions of floor-wall and
ceiling-wall, and the existence of wall-wall boundary. The proposed network,
HorizonNet, trained for predicting 1D layout, outperforms previous
state-of-the-art approaches. The designed post-processing procedure for
recovering 3D room layouts from 1D predictions can automatically infer the room
shape with low computation cost - it takes less than 20ms for a panorama image
while prior works might need dozens of seconds. We also propose Pano Stretch
Data Augmentation, which can diversify panorama data and be applied to other
panorama-related learning tasks. Due to the limited data available for
non-cuboid layout, we relabel 65 general layout from the current dataset for
finetuning. Our approach shows good performance on general layouts by
qualitative results and cross-validation.
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
Authors: Harshita Seth, Pulkit Kumar, Muktabh Mayank Srivastava
Date: 2019-01-12 00:00:00
Tasks: General Classification, imbalanced classification, Keyword Spotting, Transfer Learning

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords
in recorded conversations, when a small number of instances of keywords are
available in training data. Unlike the more common Keyword Spotting, where an
algorithm needs to detect lone keywords or short phrases like "Alexa",
"Cortana", "Hi Alexa!", "Whatsup Octavia?" etc. in speech, CSKS needs to filter
out embedded words from a continuous flow of speech, ie. spot "Anna" and
"github" in "I know a developer named Anna who can look into this github
issue." Apart from the issue of limited training data availability, CSKS is an
extremely imbalanced classification problem. We address the limitations of
simple keyword spotting baselines for both aforementioned challenges by using a
novel combination of loss functions (Prototypical networks' loss and metric
loss) and transfer learning. Our method improves F1 score by over 10%.
What comes next? Extractive summarization by next-sentence prediction
Authors: Jingyun Liu, Jackie C. K. Cheung, Annie Louis
Date: 2019-01-12 00:00:00
Tasks: Extractive Summarization, Informativeness, Prediction, Sentence

Existing approaches to automatic summarization assume that a length limit for
the summary is given, and view content selection as an optimization problem to
maximize informativeness and minimize redundancy within this budget. This
framework ignores the fact that human-written summaries have rich internal
structure which can be exploited to train a summarization system. We present
NEXTSUM, a novel approach to summarization based on a model that predicts the
next sentence to include in the summary using not only the source article, but
also the summary produced so far. We show that such a model successfully
captures summary-specific discourse moves, and leads to better content
selection performance, in addition to automatically predicting how long the
target summary should be. We perform experiments on the New York Times
Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model
summarization baselines by significant margins. We also show that the lengths
of summaries produced by our system correlates with the lengths of the
human-written gold standards.
Deep-learning-based identification of odontogenic keratocysts in hematoxylin- and eosin-stained jaw cyst specimens
Authors: Kei Sakamoto, Kei-ichi Morita, Tohru Ikeda, Kou Kayamori
Date: 2019-01-12 00:00:00
Tasks: Binary Classification, General Classification

The aim of this study was to develop a digital histopathology system for
identifying odontogenic keratocysts in hematoxylin- and eosin-stained tissue
specimens of jaw cysts. Approximately 5000 microscopy images with 400$\times$
magnification were obtained from 199 odontogenic keratocysts, 208 dentigerous
cysts, and 55 radicular cysts. A proportion of these images were used to make
training patches, which were annotated as belonging to one of the following
three classes: keratocysts, non-keratocysts, and stroma. The patches for the
cysts contained the complete lining epithelium, with the cyst cavity being
present on the upper side. The convolutional neural network (CNN) VGG16 was
finetuned to this dataset. The trained CNN could recognize the basal cell
palisading pattern, which is the definitive criterion for diagnosing
keratocysts. Some of the remaining images were scanned and analyzed by the
trained CNN, whose output was then used to train another CNN for binary
classification (keratocyst or not). The area under the receiver operating
characteristics curve for the entire algorithm was 0.997 for the test dataset.
Thus, the proposed patch classification strategy is usable for automated
keratocyst diagnosis. However, further optimization must be performed to make
it suitable for practical use.
One-view occlusion detection for stereo matching with a fully connected CRF model
Authors: Mikhail G. Mozerov, Joost Van de Weijer
Date: 2019-01-12 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

In this paper, we extend the standard belief propagation (BP) sequential
technique proposed in the tree-reweighted sequential method to the fully
connected CRF models with the geodesic distance affinity. The proposed method
has been applied to the stereo matching problem. Also a new approach to the BP
marginal solution is proposed that we call one-view occlusion detection (OVOD).
In contrast to the standard winner takes all (WTA) estimation, the proposed
OVOD solution allows to find occluded regions in the disparity map and
simultaneously improve the matching result. As a result we can perform only one
energy minimization process and avoid the cost calculation for the second view
and the left-right check procedure. We show that the OVOD approach considerably
improves results for cost augmentation and energy minimization techniques in
comparison with the standard one-view affinity space implementation. We apply
our method to the Middlebury data set and reach state-of-the-art especially for
median, average and mean squared error metrics.
Summarization and Visualization of Large Volumes of Broadcast Video Data
Authors: Kumar Abhishek, Ashok Yogi
Date: 2019-01-12 00:00:00

Over the past few years, there has been an astounding growth in the number of
news channels as well as the amount of broadcast news video data. As a result,
it is imperative that automated methods need to be developed in order to
effectively summarize and store this voluminous data. Format detection of news
videos plays an important role in news video analysis. Our problem involves
building a robust and versatile news format detector, which identifies the
different band elements in a news frame. Probabilistic progressive Hough
transform has been used for the detection of band edges. The detected bands are
classified as natural images, computer generated graphics (non-text) and text
bands. A contrast based text detector has been used to identify the text
regions from news frames. Two classifers have been trained and evaluated for
the labeling of the detected bands as natural or artificial - Support Vector
Machine (SVM) Classifer with RBF kernel, and Extreme Learning Machine (ELM)
classifier. The classifiers have been trained on a dataset of 6000 images (3000
images of each class). The ELM classifier reports a balanced accuracy of
77.38%, while the SVM classifier outperforms it with a balanced accuracy of
96.5% using 10-fold cross-validation. The detected bands which have been
fragmented due to the presence of gradients in the image have been merged using
a three-tier hierarchical reasoning model. The bands were detected with a
Jaccard Index of 0.8138, when compared to manually marked ground truth data. We
have also presented an extensive literature review of previous work done
towards news videos format detection, element band classification, and
associative reasoning.
Enhancing Explainability of Neural Networks through Architecture Constraints
Authors: Zebin Yang, Aijun Zhang, Agus Sudjianto
Date: 2019-01-12 00:00:00
Tasks: Prediction

Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. The neural networks are known to possess good prediction performance, but lack of sufficient model interpretability. In this paper, we propose to enhance the explainability of neural networks through the following architecture constraints: a) sparse additive subnetworks; b) projection pursuit with orthogonality constraint; and c) smooth function approximation. It leads to an explainable neural network (xNN) with the superior balance between prediction performance and model interpretability. We derive the necessary and sufficient identifiability conditions for the proposed xNN model. The multiple parameters are simultaneously estimated by a modified mini-batch gradient descent method based on the backpropagation algorithm for calculating the derivatives and the Cayley transform for preserving the projection orthogonality. Through simulation study under six different scenarios, we compare the proposed method to several benchmarks including least absolute shrinkage and selection operator, support vector machine, random forest, extreme learning machine, and multi-layer perceptron. It is shown that the proposed xNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved interpretability. Finally, a real data example is employed as a showcase application.
Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks
Authors: Furkan Gursoy, Ahmet Onur Durahim
Date: 2019-01-12 00:00:00
Tasks: Representation Learning

Diffusion reach probability between two nodes on a network is defined as the
probability of a cascade originating from one node reaching to another node. An
infinite number of cascades would enable calculation of true diffusion reach
probabilities between any two nodes. However, there exists only a finite number
of cascades and one usually has access only to a small portion of all available
cascades. In this work, we addressed the problem of estimating diffusion reach
probabilities given only a limited number of cascades and partial information
about underlying network structure. Our proposed strategy employs node
representation learning to generate and feed node embeddings into machine
learning algorithms to create models that predict diffusion reach
probabilities. We provide experimental analysis using synthetically generated
cascades on two real-world social networks. Results show that proposed method
is superior to using values calculated from available cascades when the portion
of cascades is small.
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
Authors: Xi Chen, Donglian Qi, Jianxin Shen
Date: 2019-01-12 00:00:00
Tasks: Portrait Segmentation, Segmentation, Semantic Segmentation, Vocal Bursts Intensity Prediction

Compared with other semantic segmentation tasks, portrait segmentation
requires both higher precision and faster inference speed. However, this
problem has not been well studied in previous works. In this paper, we propose
a lightweight network architecture, called Boundary-Aware Network (BANet) which
selectively extracts detail information in boundary area to make high-quality
segmentation output with real-time( >25FPS) speed. In addition, we design a new
loss function called refine loss which supervises the network with image level
gradient information. Our model is able to produce finer segmentation results
which has richer details than annotations.
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Authors: Huangxun Chen, Chenyu Huang, Qianyi Huang, Qian Zhang, Wei Wang
Date: 2019-01-12 00:00:00
Tasks: Classification, General Classification

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.
ALiPy: Active Learning in Python
Authors: Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang
Date: 2019-01-12 00:00:00
Tasks: Active Learning

Supervised machine learning methods usually require a large set of labeled
examples for model training. However, in many real applications, there are
plentiful unlabeled data but limited labeled data; and the acquisition of
labels is costly. Active learning (AL) reduces the labeling cost by iteratively
selecting the most valuable data to query their labels from the annotator. This
article introduces a Python toobox ALiPy for active learning. ALiPy provides a
module based implementation of active learning framework, which allows users to
conveniently evaluate, compare and analyze the performance of active learning
methods. In the toolbox, multiple options are available for each component of
the learning framework, including data process, active selection, label query,
results visualization, etc. In addition to the implementations of more than 20
state-of-the-art active learning algorithms, ALiPy also supports users to
easily configure and implement their own approaches under different active
learning settings, such as AL for multi-label data, AL with noisy annotators,
AL with different costs and so on. The toolbox is well-documented and
open-source on Github, and can be easily installed through PyPI.
3D Human Pose Machines with Self-supervised Learning
Authors: Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, Pengxu Wei
Date: 2019-01-12 00:00:00
Proceeding: arXiv.org 2019 1
Tasks: 3D Human Pose Estimation, Pose Estimation, Self-Supervised Learning

Driven by recent computer vision and robotic applications, recovering 3D
human poses has become increasingly important and attracted growing interests.
In fact, completing this task is quite challenging due to the diverse
appearances, viewpoints, occlusions and inherently geometric ambiguities inside
monocular images. Most of the existing methods focus on designing some
elaborate priors /constraints to directly regress 3D human poses based on the
corresponding 2D human pose-aware features or 2D pose predictions. However, due
to the insufficient 3D pose data for training and the domain gap between 2D
space and 3D space, these methods have limited scalabilities for all practical
scenarios (e.g., outdoor scene). Attempt to address this issue, this paper
proposes a simple yet effective self-supervised correction mechanism to learn
all intrinsic structures of human poses from abundant images. Specifically, the
proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose
transformation and 3D-to-2D pose projection, to serve as a bridge between 3D
and 2D human poses in a type of "free" self-supervision for accurate 3D human
pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate
3D poses by transforming the pose representation from the 2D domain to the 3D
domain under the sequence-dependent temporal context, while the 3D-to-2D pose
projection contributes to refining the intermediate 3D poses by maintaining
geometric consistency between the 2D projections of 3D poses and the estimated
2D poses. We further apply our self-supervised correction mechanism to develop
a 3D human pose machine, which jointly integrates the 2D spatial relationship,
temporal smoothness of predictions and 3D geometric knowledge. Extensive
evaluations demonstrate the superior performance and efficiency of our
framework over all the compared competing methods.
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
Authors: Yu Liu, Lingqiao Liu, Hamid Rezatofighi, Thanh-Toan Do, Qinfeng Shi, Ian Reid
Date: 2019-01-12 00:00:00
Tasks: object-detection, Object Detection

As the post-processing step for object detection, non-maximum suppression
(GreedyNMS) is widely used in most of the detectors for many years. It is
efficient and accurate for sparse scenes, but suffers an inevitable trade-off
between precision and recall in crowded scenes. To overcome this drawback, we
propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship
network that is based on deep learning is learned to predict if two overlapping
proposal boxes contain two objects or zero/one object, which can handle
multiple overlapping objects effectively. Through neatly coupling with
GreedyNMS without losing efficiency, consistent improvements have been achieved
in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In
addition, Pairwise-NMS can be integrated into any learning based detectors
(Both of Faster-RCNN and DPM detectors are tested in this paper), thus building
a bridge between GreedyNMS and end-to-end learning detectors.