pwc-archive/papers-with-abstracts huggingface dataset:
Automated Lane Detection in Crowds using Proximity Graphs
Authors: Stijn Heldens, Claudio Martella, Nelly Litvak, Maarten van Steen
Date: 2017-07-06 00:00:00
Tasks: Lane Detection

Studying the behavior of crowds is vital for understanding and predicting
human interactions in public areas. Research has shown that, under certain
conditions, large groups of people can form collective behavior patterns: local
interactions between individuals results in global movements patterns. To
detect these patterns in a crowd, we assume each person is carrying an on-body
device that acts a local proximity sensor, e.g., smartphone or bluetooth badge,
and represent the texture of the crowd as a proximity graph. Our goal is
extract information about crowds from these proximity graphs. In this work, we
focus on one particular type of pattern: lane formation. We present a formal
definition of a lane, proposed a simple probabilistic model that simulates
lanes moving through a stationary crowd, and present an automated
lane-detection method. Our preliminary results show that our method is able to
detect lanes of different shapes and sizes. We see our work as an initial step
towards rich pattern recognition using proximity graphs.
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)
Authors: Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu
Date: 2017-06-03 00:00:00
Tasks: Android Malware Detection, Malware Detection

It is well-known that Android malware constantly evolves so as to evade
detection. This causes the entire malware population to be non-stationary.
Contrary to this fact, most of the prior works on Machine Learning based
Android malware detection have assumed that the distribution of the observed
malware characteristics (i.e., features) does not change over time. In this
work, we address the problem of malware population drift and propose a novel
online learning based framework to detect malware, named CASANDRA
(Contextaware, Adaptive and Scalable ANDRoid mAlware detector). In order to
perform accurate detection, a novel graph kernel that facilitates capturing
apps' security-sensitive behaviors along with their context information from
dependency graphs is proposed. Besides being accurate and scalable, CASANDRA
has specific advantages: i) being adaptive to the evolution in malware features
over time ii) explaining the significant features that led to an app's
classification as being malicious or benign. In a large-scale comparative
analysis, CASANDRA outperforms two state-of-the-art techniques on a benchmark
dataset achieving 99.23% F-measure. When evaluated with more than 87,000 apps
collected in-the-wild, CASANDRA achieves 89.92% accuracy, outperforming
existing techniques by more than 25% in their typical batch learning setting
and more than 7% when they are continuously retained, while maintaining
comparable efficiency.
A Conditional Variational Framework for Dialog Generation
Authors: Xiaoyu Shen, Hui Su, Yan-ran Li, Wenjie Li, Shuzi Niu, Yang Zhao, Akiko Aizawa, Guoping Long
Date: 2017-04-30 00:00:00
Proceeding: ACL 2017 7
Tasks: Attribute, Open-Domain Dialog, Response Generation

Deep latent variable models have been shown to facilitate the response
generation for open-domain dialog systems. However, these latent variables are
highly randomized, leading to uncontrollable generated responses. In this
paper, we propose a framework allowing conditional response generation based on
specific attributes. These attributes can be either manually assigned or
automatically detected. Moreover, the dialog states for both speakers are
modeled separately in order to reflect personal features. We validate this
framework on two different scenarios, where the attribute refers to genericness
and sentiment states respectively. The experiment result testified the
potential of our model, where meaningful responses can be generated in
accordance with the specified attributes.
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Authors: Tao Kong, Fuchun Sun, Anbang Yao, Huaping Liu, Ming Lu, Yurong Chen
Date: 2017-07-06 00:00:00
Proceeding: CVPR 2017 7
Tasks: GPU, Object, object-detection, Object Detection, Object Localization

We present RON, an efficient and effective framework for generic object
detection. Our motivation is to smartly associate the best of the region-based
(e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully
convolutional architecture, RON mainly focuses on two fundamental problems: (a)
multi-scale object localization and (b) negative sample mining. To address (a),
we design the reverse connection, which enables the network to detect objects
on multi-levels of CNNs. To deal with (b), we propose the objectness prior to
significantly reduce the searching space of objects. We optimize the reverse
connection, objectness prior and object detector jointly by a multi-task loss
function, thus RON can directly predict final detection results from all
locations of various feature maps. Extensive experiments on the challenging
PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the
competitive performance of RON. Specifically, with VGG-16 and low resolution
384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on
PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger
and more difficult, as demonstrated by the results on the MS COCO dataset. With
1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster
than the Faster R-CNN counterpart.
Logics and practices of transparency and opacity in real-world applications of public sector machine learning
Authors: Michael Veale
Date: 2017-06-19 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

Machine learning systems are increasingly used to support public sector
decision-making across a variety of sectors. Given concerns around
accountability in these domains, and amidst accusations of intentional or
unintentional bias, there have been increased calls for transparency of these
technologies. Few, however, have considered how logics and practices concerning
transparency have been understood by those involved in the machine learning
systems already being piloted and deployed in public bodies today. This short
paper distils insights about transparency on the ground from interviews with 27
such actors, largely public servants and relevant contractors, across 5 OECD
countries. Considering transparency and opacity in relation to trust and
buy-in, better decision-making, and the avoidance of gaming, it seeks to
provide useful insights for those hoping to develop socio-technical approaches
to transparency that might be useful to practitioners on-the-ground.
  An extended, archival version of this paper is available as Veale M., Van
Kleek M., & Binns R. (2018). `Fairness and accountability design needs for
algorithmic support in high-stakes public sector decision-making' Proceedings
of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18),
http://doi.org/10.1145/3173574.3174014.
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
Authors: Pinxin Long, Wenxi Liu, Jia Pan
Date: 2016-09-22 00:00:00
Tasks: Collision Avoidance

High-speed, low-latency obstacle avoidance that is insensitive to sensor
noise is essential for enabling multiple decentralized robots to function
reliably in cluttered and dynamic environments. While other distributed
multi-agent collision avoidance systems exist, these systems require online
geometric optimization where tedious parameter tuning and perfect sensing are
necessary.
  We present a novel end-to-end framework to generate reactive collision
avoidance policy for efficient distributed multi-agent navigation. Our method
formulates an agent's navigation strategy as a deep neural network mapping from
the observed noisy sensor measurements to the agent's steering commands in
terms of movement velocity. We train the network on a large number of frames of
collision avoidance data collected by repeatedly running a multi-agent
simulator with different parameter settings. We validate the learned deep
neural network policy in a set of simulated and real scenarios with noisy
measurements and demonstrate that our method is able to generate a robust
navigation strategy that is insensitive to imperfect sensing and works reliably
in all situations. We also show that our method can be well generalized to
scenarios that do not appear in our training data, including scenes with static
obstacles and agents with different sizes. Videos are available at
https://sites.google.com/view/deepmaca.
An Embedded Deep Learning based Word Prediction
Authors: Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Date: 2017-07-06 00:00:00
Tasks: Deep Learning, Language Modeling, Language Modelling, Machine Translation, Prediction, Translation

Recent developments in deep learning with application to language modeling
have led to success in tasks of text processing, summarizing and machine
translation. However, deploying huge language models for mobile device such as
on-device keyboards poses computation as a bottle-neck due to their puny
computation capacities. In this work we propose an embedded deep learning based
word prediction method that optimizes run-time memory and also provides a real
time prediction environment. Our model size is 7.40MB and has average
prediction time of 6.47 ms. We improve over the existing methods for word
prediction in terms of key stroke savings and word prediction rate.
Persistence Diagrams with Linear Machine Learning Models
Authors: Ippei Obayashi, Yasuaki Hiraoka
Date: 2017-06-30 00:00:00
Tasks: BIG-bench Machine Learning

Persistence diagrams have been widely recognized as a compact descriptor for
characterizing multiscale topological features in data. When many datasets are
available, statistical features embedded in those persistence diagrams can be
extracted by applying machine learnings. In particular, the ability for
explicitly analyzing the inverse in the original data space from those
statistical features of persistence diagrams is significantly important for
practical applications. In this paper, we propose a unified method for the
inverse analysis by combining linear machine learning models with persistence
images. The method is applied to point clouds and cubical sets, showing the
ability of the statistical inverse analysis and its advantages.
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
Authors: Jing Zhang, Wanqing Li, Philip Ogunbona, Dong Xu
Date: 2017-05-11 00:00:00
Tasks: Transfer Learning

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly.
Convergence Analysis of Optimization Algorithms
Authors: HyoungSeok Kim, JiHoon Kang, WooMyoung Park, SukHyun Ko, YoonHo Cho, DaeSung Yu, YoungSook Song, JungWon Choi
Date: 2017-07-06 00:00:00

The regret bound of an optimization algorithms is one of the basic criteria
for evaluating the performance of the given algorithm. By inspecting the
differences between the regret bounds of traditional algorithms and adaptive
one, we provide a guide for choosing an optimizer with respect to the given
data set and the loss function. For analysis, we assume that the loss function
is convex and its gradient is Lipschitz continuous.
An HTM based cortical algorithm for detection of seismic waves
Authors: Ruggero Micheletto, Ahyi Kim
Date: 2017-07-06 00:00:00
Tasks: CPU

Recognizing seismic waves immediately is very important for the realization
of efficient disaster prevention. Generally these systems consist of a network
of seismic detectors that send real time data to a central server. The server
elaborates the data and attempts to recognize the first signs of an earthquake.
The current problem with this approach is that it is subject to false alarms. A
critical trade-off exists between sensitivity of the system and error rate. To
overcame this problems, an artificial neural network based intelligent learning
systems can be used. However, conventional supervised ANN systems are difficult
to train, CPU intensive and prone to false alarms. To surpass these problems,
here we attempt to use a next-generation unsupervised cortical algorithm HTM.
This novel approach does not learn particular waveforms, but adapts to
continuously fed data reaching the ability to discriminate between normality
(seismic sensor background noise in no-earthquake conditions) and anomaly
(sensor response to a jitter or an earthquake). Main goal of this study is test
the ability of the HTM algorithm to be used to signal earthquakes automatically
in a feasible disaster prevention system. We describe the methodology used and
give the first qualitative assessments of the recognition ability of the
system. Our preliminary results show that the cortical algorithm used is very
robust to noise and that can successfully recognize synthetic earthquake-like
signals efficiently and reliably.
Graph Learning from Data under Structural and Laplacian Constraints
Authors: Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
Date: 2016-11-16 00:00:00
Tasks: Computational Efficiency, Graph Learning, parameter estimation

Graphs are fundamental mathematical structures used in various fields to
represent data, signals and processes. In this paper, we propose a novel
framework for learning/estimating graphs from data. The proposed framework
includes (i) formulation of various graph learning problems, (ii) their
probabilistic interpretations and (iii) associated algorithms. Specifically,
graph learning problems are posed as estimation of graph Laplacian matrices
from some observed data under given structural constraints (e.g., graph
connectivity and sparsity level). From a probabilistic perspective, the
problems of interest correspond to maximum a posteriori (MAP) parameter
estimation of Gaussian-Markov random field (GMRF) models, whose precision
(inverse covariance) is a graph Laplacian matrix. For the proposed graph
learning problems, specialized algorithms are developed by incorporating the
graph Laplacian and structural constraints. The experimental results
demonstrate that the proposed algorithms outperform the current
state-of-the-art methods in terms of accuracy and computational efficiency.
Rapid focus map surveying for whole slide imaging with continues sample motion
Authors: Jun Liao, Yutong Jiang, Zichao Bian, Bahareh Mahrou, Aparna Nambiar, Alexander W. Magsam, Kaikai Guo, Yong Ku Cho, Guoan Zheng
Date: 2017-07-06 00:00:00

Whole slide imaging (WSI) has recently been cleared for primary diagnosis in
the US. A critical challenge of WSI is to perform accurate focusing in high
speed. Traditional systems create a focus map prior to scanning. For each focus
point on the map, sample needs to be static in the x-y plane and axial scanning
is needed to maximize the contrast. Here we report a novel focus map surveying
method for WSI. The reported method requires no axial scanning, no additional
camera and lens, works for stained and transparent samples, and allows
continuous sample motion in the surveying process. It can be used for both
brightfield and fluorescence WSI. By using a 20X, 0.75 NA objective lens, we
demonstrate a mean focusing error of ~0.08 microns in the static mode and ~0.17
microns in the continuous motion mode. The reported method may provide a
turnkey solution for most existing WSI systems for its simplicity, robustness,
accuracy, and high-speed. It may also standardize the imaging performance of
WSI systems for digital pathology and find other applications in high-content
microscopy such as DNA sequencing and time-lapse live-cell imaging.
$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds
Authors: Ning Xu, Jian Hong, Timothy C. G. Fisher
Date: 2017-05-20 00:00:00
Tasks: Model Selection

In this paper, we introduce a new concept of stability for cross-validation,
called the $\left( \beta, \varpi \right)$-stability, and use it as a new
perspective to build the general theory for cross-validation. The $\left(
\beta, \varpi \right)$-stability mathematically connects the generalization
ability and the stability of the cross-validated model via the Rademacher
complexity. Our result reveals mathematically the effect of cross-validation
from two sides: on one hand, cross-validation picks the model with the best
empirical generalization ability by validating all the alternatives on test
sets; on the other hand, cross-validation may compromise the stability of the
model selection by causing subsampling error. Moreover, the difference between
training and test errors in q\textsuperscript{th} round, sometimes referred to
as the generalization error, might be autocorrelated on q. Guided by the ideas
above, the $\left( \beta, \varpi \right)$-stability help us derivd a new class
of Rademacher bounds, referred to as the one-round/convoluted Rademacher
bounds, for the stability of cross-validation in both the i.i.d.\ and
non-i.i.d.\ cases. For both light-tail and heavy-tail losses, the new bounds
quantify the stability of the one-round/average test error of the
cross-validated model in terms of its one-round/average training error, the
sample sizes $n$, number of folds $K$, the tail property of the loss (encoded
as Orlicz-$\Psi_\nu$ norms) and the Rademacher complexity of the model class
$\Lambda$. The new class of bounds not only quantitatively reveals the
stability of the generalization ability of the cross-validated model, it also
shows empirically the optimal choice for number of folds $K$, at which the
upper bound of the one-round/average test error is lowest, or, to put it in
another way, where the test error is most stable.
SUNNY-CP and the MiniZinc Challenge
Authors: Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
Date: 2017-06-26 00:00:00

In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)
A Data Science Approach to Understanding Residential Water Contamination in Flint
Authors: Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob Abernethy, Eric Schwartz
Date: 2017-07-05 00:00:00

When the residents of Flint learned that lead had contaminated their water
system, the local government made water-testing kits available to them free of
charge. The city government published the results of these tests, creating a
valuable dataset that is key to understanding the causes and extent of the lead
contamination event in Flint. This is the nation's largest dataset on lead in a
municipal water system.
  In this paper, we predict the lead contamination for each household's water
supply, and we study several related aspects of Flint's water troubles, many of
which generalize well beyond this one city. For example, we show that elevated
lead risks can be (weakly) predicted from observable home attributes. Then we
explore the factors associated with elevated lead. These risk assessments were
developed in part via a crowd sourced prediction challenge at the University of
Michigan. To inform Flint residents of these assessments, they have been
incorporated into a web and mobile application funded by \texttt{Google.org}.
We also explore questions of self-selection in the residential testing program,
examining which factors are linked to when and how frequently residents
voluntarily sample their water.
A Deep Network with Visual Text Composition Behavior
Authors: Hongyu Guo
Date: 2017-07-05 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification, Sentence, text-classification, Text Classification

While natural languages are compositional, how state-of-the-art neural models
achieve compositionality is still unclear. We propose a deep network, which not
only achieves competitive accuracy for text classification, but also exhibits
compositional behavior. That is, while creating hierarchical representations of
a piece of text, such as a sentence, the lower layers of the network distribute
their layer-specific attention weights to individual words. In contrast, the
higher layers compose meaningful phrases and clauses, whose lengths increase as
the networks get deeper until fully composing the sentence.
Agent based simulation of the evolution of society as an alternate maximization problem
Authors: Amartya Sanyal, Sanjana Garg, Asim Unmesh
Date: 2017-07-05 00:00:00

Understanding the evolution of human society, as a complex adaptive system,
is a task that has been looked upon from various angles. In this paper, we
simulate an agent-based model with a high enough population tractably. To do
this, we characterize an entity called \textit{society}, which helps us reduce
the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We
propose a very realistic setting, where we design a joint alternate
maximization step algorithm to maximize a certain \textit{fitness} function,
which we believe simulates the way societies develop. Our key contributions
include (i) proposing a novel protocol for simulating the evolution of a
society with cheap, non-optimal joint alternate maximization steps (ii)
providing a framework for carrying out experiments that adhere to this
joint-optimization simulation framework (iii) carrying out experiments to show
that it makes sense empirically (iv) providing an alternate justification for
the use of \textit{society} in the simulations.