pwc-archive/papers-with-abstracts huggingface dataset:
AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models
Authors: Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Date: 2018-06-26 00:00:00
Tasks: feature selection

The interpretation of defect models heavily relies on software metrics that
are used to construct them. However, such software metrics are often correlated
to defect models. Prior work often uses feature selection techniques to remove
correlated metrics in order to improve the performance of defect models. Yet,
the interpretation of defect models may be misleading if feature selection
techniques produce subsets of inconsistent and correlated metrics. In this
paper, we investigate the consistency and correlation of the subsets of metrics
that are produced by nine commonly-used feature selection techniques. Through a
case study of 13 publicly-available defect datasets, we find that feature
selection techniques produce inconsistent subsets of metrics and do not
mitigate correlated metrics, suggesting that feature selection techniques
should not be used and correlation analyses must be applied when the goal is
model interpretation. Since correlation analyses often involve manual selection
of metrics by a domain expert, we introduce AutoSpearman, an automated metric
selection approach based on correlation analyses. Our evaluation indicates that
AutoSpearman yields the highest consistency of subsets of metrics among
training samples and mitigates correlated metrics, while impacting model
performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when
interpreting defect models, we recommend future studies use AutoSpearman in
lieu of commonly-used feature selection techniques.
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
Authors: Qijie Zhao, Tao Sheng, Yongtao Wang, Feng Ni, Ling Cai
Date: 2018-06-26 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

The ability to detect small objects and the speed of the object detector are
very important for the application of autonomous driving, and in this paper, we
propose an effective yet efficient one-stage detector, which gained the second
place in the Road Object Detection competition of CVPR2018 workshop - Workshop
of Autonomous Driving(WAD). The proposed detector inherits the architecture of
SSD and introduces a novel Comprehensive Feature Enhancement(CFE) module into
it. Experimental results on this competition dataset as well as the MSCOCO
dataset demonstrate that the proposed detector (named CFENet) performs much
better than the original SSD and the state-of-the-art method RefineDet
especially for small objects, while keeping high efficiency close to the
original SSD. Specifically, the single scale version of the proposed detector
can run at the speed of 21 fps, while the multi-scale version with larger input
size achieves the mAP 29.69, ranking second on the leaderboard
On an Immuno-inspired Distributed, Embodied Action-Evolution cum Selection Algorithm
Authors: Tushar Semwal, Divya D Kulkarni, Shivashankar B. Nair
Date: 2018-06-26 00:00:00

Traditional Evolutionary Robotics (ER) employs evolutionary techniques to
search for a single monolithic controller which can aid a robot to learn a
desired task. These techniques suffer from bootstrap and deception issues when
the tasks are complex for a single controller to learn. Behaviour-decomposition
techniques have been used to divide a task into multiple subtasks and evolve
separate subcontrollers for each subtask. However, these subcontrollers and the
associated subcontroller arbitrator(s) are all evolved off-line. A distributed,
fully embodied and evolutionary version of such approaches will greatly aid
online learning and help reduce the reality gap. In this paper, we propose an
immunology-inspired embodied action-evolution cum selection algorithm that can
cater to distributed ER. This algorithm evolves different subcontrollers for
different portions of the search space in a distributed manner just as
antibodies are evolved and primed for different antigens in the antigenic
space. Experimentation on a collective of real robots embodied with the
algorithm showed that a repertoire of antibody-like subcontrollers was created,
evolved and shared on-the-fly to cope up with different environmental
conditions. In addition, instead of the conventionally used approach of
broadcasting for sharing, we present an Intelligent Packet Migration scheme
that reduces energy consumption.
Causal Inference for Early Detection of Pathogenic Social Media Accounts
Authors: Hamidreza Alvari, Paulo Shakarian
Date: 2018-06-26 00:00:00
Tasks: Causal Inference, Community Detection, General Classification

Pathogenic social media accounts such as terrorist supporters exploit
communities of supporters for conducting attacks on social media. Early
detection of PSM accounts is crucial as they are likely to be key users in
making a harmful message "viral". This paper overviews my recent doctoral work
on utilizing causal inference to identify PSM accounts within a short time
frame around their activity. The proposed scheme (1) assigns time-decay
causality scores to users, (2) applies a community detection-based algorithm to
group of users sharing similar causality scores and finally (3) deploys a
classification algorithm to classify accounts. Unlike existing techniques that
require network structure, cascade path, or content, our scheme relies solely
on action log of users.
Theory of Machine Networks: A Case Study
Authors: Rooz Mahdavian, Richard Diehl Martinez
Date: 2018-06-26 00:00:00

We propose a simplification of the Theory-of-Mind Network architecture, which
focuses on modeling complex, deterministic machines as a proxy for modeling
nondeterministic, conscious entities. We then validate this architecture in the
context of understanding engines, which, we argue, meet the required internal
and external complexity to yield meaningful abstractions.
Understanding Dropout as an Optimization Trick
Authors: Sangchul Hahn, Heeyoul Choi
Date: 2018-06-26 00:00:00
Tasks: image-classification, Image Classification

As one of standard approaches to train deep neural networks, dropout has been applied to regularize large models to avoid overfitting, and the improvement in performance by dropout has been explained as avoiding co-adaptation between nodes. However, when correlations between nodes are compared after training the networks with or without dropout, one question arises if co-adaptation avoidance explains the dropout effect completely. In this paper, we propose an additional explanation of why dropout works and propose a new technique to design better activation functions. First, we show that dropout can be explained as an optimization technique to push the input towards the saturation area of nonlinear activation function by accelerating gradient information flowing even in the saturation area in backpropagation. Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area. Then, input to the activation function can climb onto the saturation area which makes the network more robust because the model converges on a flat region. Experiment results support our explanation of dropout and confirm that the proposed GAAF technique improves image classification performance with expected properties.
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Authors: Johan Dahlin, Adrian Wills, Brett Ninness
Date: 2018-06-26 00:00:00

Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for
sampling from target distributions which are not easy to evaluate point-wise.
However, pmMH requires good proposal distributions to sample efficiently from
the target, which can be problematic to construct in practice. This is
especially a problem for high-dimensional targets when the standard random-walk
proposal is inefficient. We extend pmMH to allow for constructing the proposal
based on information from multiple past iterations. As a consequence,
quasi-Newton (qN) methods can be employed to form proposals which utilize
gradient information to guide the Markov chain to areas of high probability and
to construct approximations of the local curvature to scale step sizes. The
proposed method is demonstrated on several problems which indicate that qN
proposals can perform better than other common Hessian-based proposals.
On the Implicit Bias of Dropout
Authors: Poorya Mianjy, Raman Arora, Rene Vidal
Date: 2018-06-26 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Learning

Algorithmic approaches endow deep learning systems with implicit bias that
helps them generalize even in over-parametrized settings. In this paper, we
focus on understanding such a bias induced in learning through dropout, a
popular technique to avoid overfitting in deep learning. For single
hidden-layer linear neural networks, we show that dropout tends to make the
norm of incoming/outgoing weight vectors of all the hidden nodes equal. In
addition, we provide a complete characterization of the optimization landscape
induced by dropout.
Cross-position Activity Recognition with Stratified Transfer Learning
Authors: Yiqiang Chen, Jindong Wang, Meiyu Huang, Han Yu
Date: 2018-06-26 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Position, Transfer Learning

Human activity recognition aims to recognize the activities of daily living
by utilizing the sensors on different body parts. However, when the labeled
data from a certain body position (i.e. target domain) is missing, how to
leverage the data from other positions (i.e. source domain) to help learn the
activity labels of this position? When there are several source domains
available, it is often difficult to select the most similar source domain to
the target domain. With the selected source domain, we need to perform accurate
knowledge transfer between domains. Existing methods only learn the global
distance between domains while ignoring the local property. In this paper, we
propose a \textit{Stratified Transfer Learning} (STL) framework to perform both
source domain selection and knowledge transfer. STL is based on our proposed
\textit{Stratified} distance to capture the local property of domains. STL
consists of two components: Stratified Domain Selection (STL-SDS) can select
the most similar source domain to the target domain; Stratified Activity
Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive
experiments on three public activity recognition datasets demonstrate the
superiority of STL. Furthermore, we extensively investigate the performance of
transfer learning across different degrees of similarities and activity levels
between domains. We also discuss the potential applications of STL in other
fields of pervasive computing for future research.
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
Authors: Zhengxing Chen, Chris Amato, Truong-Huy Nguyen, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr
Date: 2018-06-26 00:00:00
Tasks: Card Games

Deck building is a crucial component in playing Collectible Card Games
(CCGs). The goal of deck building is to choose a fixed-sized subset of cards
from a large card pool, so that they work well together in-game against
specific opponents. Existing methods either lack flexibility to adapt to
different opponents or require large computational resources, still making them
unsuitable for any real-time or large-scale application. We propose a new deck
recommendation system, named Q-DeckRec, which learns a deck search policy
during a training phase and uses it to solve deck building problem instances.
Our experimental results demonstrate Q-DeckRec requires less computational
resources to build winning-effective decks after a training phase compared to
several baseline methods.
Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency
Authors: Zheming Zhou, Zhiqiang Sui, Odest Chadwicke Jenkins
Date: 2018-06-26 00:00:00
Tasks: Object Localization, Transparent objects

In order to fully function in human environments, robot perception will need
to account for the uncertainty caused by translucent materials. Translucency
poses several open challenges in the form of transparent objects (e.g.,
drinking glasses), refractive media (e.g., water), and diffuse partial
occlusions (e.g., objects behind stained glass panels). This paper presents
Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object
poses in the presence of translucency using plenoptic (light-field)
observations. We propose a new depth descriptor, the Depth Likelihood Volume
(DLV), and its use within a Monte Carlo object localization algorithm. We
present results of localizing and manipulating objects with translucent
materials and objects occluded by layers of translucency. Our PMCL
implementation uses observations from a Lytro first generation light field
camera to allow a Michigan Progress Fetch robot to perform grasping.
Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Authors: Puyang Wang, Vishal M. Patel, Ilker Hacihaliloglu
Date: 2018-06-26 00:00:00
Tasks: General Classification, Segmentation

Various imaging artifacts, low signal-to-noise ratio, and bone surfaces
appearing several millimeters in thickness have hindered the success of
ultrasound (US) guided computer assisted orthopedic surgery procedures. In this
work, a multi-feature guided convolutional neural network (CNN) architecture is
proposed for simultaneous enhancement, segmentation, and classification of bone
surfaces from US data. The proposed CNN consists of two main parts: a
pre-enhancing net, that takes the concatenation of B-mode US scan and three
filtered image features for the enhancement of bone surfaces, and a modified
U-net with a classification layer. The proposed method was validated on 650 in
vivo US scans collected using two US machines, by scanning knee, femur, distal
radius and tibia bones. Validation, against expert annotation, achieved
statistically significant improvements in segmentation of bone surfaces
compared to state-of-the-art.
Deep Generative Models with Learnable Knowledge Constraints
Authors: Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, Eric Xing
Date: 2018-06-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Generation, Reinforcement Learning, Reinforcement Learning (RL), Sentence

The broad set of deep generative models (DGMs) has achieved remarkable
advances. However, it is often difficult to incorporate rich structured domain
knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a
principled framework to impose structured constraints on probabilistic models,
but has limited applicability to the diverse DGMs that can lack a Bayesian
formulation or even explicit density evaluation. PR also requires constraints
to be fully specified a priori, which is impractical or suboptimal for complex
knowledge with learnable uncertain parts. In this paper, we establish
mathematical correspondence between PR and reinforcement learning (RL), and,
based on the connection, expand PR to learn constraints as the extrinsic reward
in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is
flexible to adapt arbitrary constraints with the model jointly. Experiments on
human image generation and templated sentence generation show models with
learned knowledge constraints by our algorithm greatly improve over base
generative models.
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
Authors: Yichen Zhou, Giles Hooker
Date: 2018-06-26 00:00:00
Tasks: regression

This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge as the number of trees grows. We also demonstrate a central limit theorem for this limit, allowing a characterization of uncertainty for predictions. A simulation study and real world examples provide support for both the predictive accuracy of the model and its limiting behavior.
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
Authors: Xingchao Peng, Ben Usman, Kuniaki Saito, Neela Kaushik, Judy Hoffman, Kate Saenko
Date: 2018-06-26 00:00:00
Tasks: Classification, Diversity, Domain Adaptation, General Classification, Object, object-detection, Object Detection, Object Recognition

Unsupervised transfer of object recognition models from synthetic to real
data is an important problem with many potential applications. The challenge is
how to "adapt" a model trained on simulated images so that it performs well on
real-world data without any additional supervision. Unfortunately, current
benchmarks for this problem are limited in size and task diversity. In this
paper, we present a new large-scale benchmark called Syn2Real, which consists
of a synthetic domain rendered from 3D object models and two real-image domains
containing the same object categories. We define three related tasks on this
benchmark: closed-set object classification, open-set object classification,
and object detection. Our evaluation of multiple state-of-the-art methods
reveals a large gap in adaptation performance between the easier closed-set
classification task and the more difficult open-set and detection tasks. We
conclude that developing adaptation methods that work well across all three
tasks presents a significant future challenge for syn2real domain transfer.
A Practical Incremental Learning Framework For Sparse Entity Extraction
Authors: Hussein S. Al-Olimat, Steven Gustafson, Jason Mackay, Krishnaprasad Thirunarayan, Amit Sheth
Date: 2018-06-26 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, Entity Extraction using GAN, Incremental Learning

This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation. We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback. This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy. We evaluate our framework on three publicly available datasets and show that it drastically reduces the cost of sparse entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores respectively. Moreover, the method exhibited robust performance across all datasets.
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
Authors: Eunhee Kang, Hyun Jung Koo, Dong Hyun Yang, Joon Bum Seo, Jong Chul Ye
Date: 2018-06-26 00:00:00
Tasks: Denoising, Diagnostic

In coronary CT angiography, a series of CT images are taken at different
levels of radiation dose during the examination. Although this reduces the
total radiation dose, the image quality during the low-dose phases is
significantly degraded. To address this problem, here we propose a novel
semi-supervised learning technique that can remove the noises of the CT images
obtained in the low-dose phases by learning from the CT images in the routine
dose phases. Although a supervised learning approach is not possible due to the
differences in the underlying heart structure in two phases, the images in the
two phases are closely related so that we propose a cycle-consistent
adversarial denoising network to learn the non-degenerate mapping between the
low and high dose cardiac phases. Experimental results showed that the proposed
method effectively reduces the noise in the low-dose CT image while the
preserving detailed texture and edge information. Moreover, thanks to the
cyclic consistency and identity loss, the proposed network does not create any
artificial features that are not present in the input images. Visual grading
and quality evaluation also confirm that the proposed method provides
significant improvement in diagnostic quality.
A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations
Authors: Xi Sheryl Zhang, Dandi Chen, Yongjun Zhu, Chao Che, Chang Su, Sendong Zhao, Xu Min, Fei Wang
Date: 2018-06-26 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

This paper presents details of our winning solutions to the task IV of NIPS
2017 Competition Track entitled Classifying Clinically Actionable Genetic
Mutations. The machine learning task aims to classify genetic mutations based
on text evidence from clinical literature with promising performance. We
develop a novel multi-view machine learning framework with ensemble
classification models to solve the problem. During the Challenge, feature
combinations derived from three views including document view, entity text
view, and entity name view, which complements each other, are comprehensively
explored. As the final solution, we submitted an ensemble of nine basic
gradient boosting models which shows the best performance in the evaluation.
The approach scores 0.5506 and 0.6694 in terms of logarithmic loss on a fixed
split in stage-1 testing phase and 5-fold cross validation respectively, which
also makes us ranked as a top-1 team out of more than 1,300 solutions in NIPS
2017 Competition Track IV.
Computational Analysis of Insurance Complaints: GEICO Case Study
Authors: Amir Karami, Noelle M. Pendergraft
Date: 2018-06-26 00:00:00

The online environment has provided a great opportunity for insurance
policyholders to share their complaints with respect to different services.
These complaints can reveal valuable information for insurance companies who
seek to improve their services; however, analyzing a huge number of online
complaints is a complicated task for human and must involve computational
methods to create an efficient process. This research proposes a computational
approach to characterize the major topics of a large number of online
complaints. Our approach is based on using the topic modeling approach to
disclose the latent semantic of complaints. The proposed approach deployed on
thousands of GEICO negative reviews. Analyzing 1,371 GEICO complaints indicates
that there are 30 major complains in four categories: (1) customer service, (2)
insurance coverage, paperwork, policy, and reports, (3) legal issues, and (4)
costs, estimates, and payments. This research approach can be used in other
applications to explore a large number of reviews.
Evotype: Towards the Evolution of Type Stencils
Authors: Tiago Martins, João Correia, Ernesto Costa, Penousal Machado
Date: 2018-06-26 00:00:00
Tasks: Vocal Bursts Type Prediction

Typefaces are an essential resource employed by graphic designers. The
increasing demand for innovative type design work increases the need for good
technological means to assist the designer in the creation of a typeface. We
present an evolutionary computation approach for the generation of type
stencils to draw coherent glyphs for different characters. The proposed system
employs a Genetic Algorithm to evolve populations of type stencils. The
evaluation of each candidate stencil uses a hill climbing algorithm to search
the best configurations to draw the target glyphs. We study the interplay
between legibility, coherence and expressiveness, and show how our framework
can be used in practice.