pwc-archive/papers-with-abstracts huggingface dataset:
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
Authors: Rodrigo F. Araújo, Alexandre Ribeiro, Iury V. Bessa, Lucas C. Cordeiro, João E. C. Filho
Date: 2017-08-14 00:00:00
Tasks: global-optimization

We describe and evaluate a novel optimization-based off-line path planning
algorithm for mobile robots based on the Counterexample-Guided Inductive
Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples
generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories
(SMT) solvers, in order to guide the optimization process and to ensure global
optimization. This paper marks the first application of CEGIO for planning
mobile robot path. In particular, CEGIO has been successfully applied to obtain
optimal two-dimensional paths for autonomous mobile robots using off-the-shelf
SAT and SMT solvers.
DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters
Authors: You-Luen Lee, Da-Cheng Juan, Xuan-An Tseng, Yu-Ting Chen, Shih-Chieh Chang
Date: 2017-08-14 00:00:00

When will a server fail catastrophically in an industrial datacenter? Is it
possible to forecast these failures so preventive actions can be taken to
increase the reliability of a datacenter? To answer these questions, we have
studied what are probably the largest, publicly available datacenter traces,
containing more than 104 million events from 12,500 machines. Among these
samples, we observe and categorize three types of machine failures, all of
which are catastrophic and may lead to information loss, or even worse,
reliability degradation of a datacenter. We further propose a two-stage
framework-DC-Prophet-based on One-Class Support Vector Machine and Random
Forest. DC-Prophet extracts surprising patterns and accurately predicts the
next failure of a machine. Experimental results show that DC-Prophet achieves
an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88
(out of 1). On average, DC-Prophet outperforms other classical machine learning
methods by 39.45% in F3-score.
Style2Vec: Representation Learning for Fashion Items from Style Sets
Authors: Hanbit Lee, Jinseok Seol, Sang-goo Lee
Date: 2017-08-14 00:00:00
Tasks: Image Classification, Recommendation Systems, Representation Learning, Stochastic Optimization, Word Embeddings

With the rapid growth of online fashion market, demand for effective fashion
recommendation systems has never been greater. In fashion recommendation, the
ability to find items that goes well with a few other items based on style is
more important than picking a single item based on the user's entire purchase
history. Since the same user may have purchased dress suits in one month and
casual denims in another, it is impossible to learn the latent style features
of those items using only the user ratings. If we were able to represent the
style features of fashion items in a reasonable way, we will be able to
recommend new items that conform to some small subset of pre-purchased items
that make up a coherent style set. We propose Style2Vec, a vector
representation model for fashion items. Based on the intuition of
distributional semantics used in word embeddings, Style2Vec learns the
representation of a fashion item using other items in matching outfits as
context. Two different convolutional neural networks are trained to maximize
the probability of item co-occurrences. For evaluation, a fashion analogy test
is conducted to show that the resulting representation connotes diverse fashion
related semantics like shapes, colors, patterns and even latent styles. We also
perform style classification using Style2Vec features and show that our method
outperforms other baselines.
What matters in a transferable neural network model for relation classification in the biomedical domain?
Authors: Sunil Kumar Sahu, Ashish Anand
Date: 2017-08-11 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Relation, Relation Classification, Transfer Learning

Lack of sufficient labeled data often limits the applicability of advanced
machine learning algorithms to real life problems. However efficient use of
Transfer Learning (TL) has been shown to be very useful across domains. TL
utilizes valuable knowledge learned in one task (source task), where sufficient
data is available, to the task of interest (target task). In biomedical and
clinical domain, it is quite common that lack of sufficient training data do
not allow to fully exploit machine learning models. In this work, we present
two unified recurrent neural models leading to three transfer learning
frameworks for relation classification tasks. We systematically investigate
effectiveness of the proposed frameworks in transferring the knowledge under
multiple aspects related to source and target tasks, such as, similarity or
relatedness between source and target tasks, and size of training data for
source task. Our empirical results show that the proposed frameworks in general
improve the model performance, however these improvements do depend on aspects
related to source and target tasks. This dependence then finally determine the
choice of a particular TL framework.
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Authors: Chen Zhou, Jiaolong Yang, Chunshui Zhao, Gang Hua
Date: 2017-08-14 00:00:00
Tasks: Self-Driving Cars, Visual Odometry

Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Authors: Feiyun Zhu, Jun Guo, Zheng Xu, Peng Liao, Junzhou Huang
Date: 2017-08-14 00:00:00
Tasks: Clustering, Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Due to the popularity of smartphones and wearable devices nowadays, mobile
health (mHealth) technologies are promising to bring positive and wide impacts
on people's health. State-of-the-art decision-making methods for mHealth rely
on some ideal assumptions. Those methods either assume that the users are
completely homogenous or completely heterogeneous. However, in reality, a user
might be similar with some, but not all, users. In this paper, we propose a
novel group-driven reinforcement learning method for the mHealth. We aim to
understand how to share information among similar users to better convert the
limited user information into sharper learned RL policies. Specifically, we
employ the K-means clustering method to group users based on their trajectory
information similarity and learn a shared RL policy for each group. Extensive
experiment results have shown that our method can achieve clear gains over the
state-of-the-art RL methods for mHealth.
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
Authors: Qi Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang, Bin Liu, Nenghai Yu
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Computational Efficiency, Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results caused by occlusion. Our framework achieves computational
efficiency by sharing features and using ROI-Pooling to obtain individual
features for each target. Some online learned target-specific CNN layers are
used for adapting the appearance model for each target. In the framework, we
introduce spatial-temporal attention mechanism (STAM) to handle the drift
caused by occlusion and interaction among targets. The visibility map of the
target is learned and used for inferring the spatial attention map. The spatial
attention map is then applied to weight the features. Besides, the occlusion
status can be estimated from the visibility map, which controls the online
updating process via weighted loss on training samples with different occlusion
statuses in different frames. It can be considered as temporal attention
mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on
challenging MOT15 and MOT16 benchmark dataset respectively.
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier
Authors: Prathusha Kameswara Sarma, Bill Sethares
Date: 2017-08-14 00:00:00
Tasks: Learning Word Embeddings, Sentiment Analysis, Word Embeddings

Word embeddings are representations of individual words of a text document in
a vector space and they are often use- ful for performing natural language pro-
cessing tasks. Current state of the art al- gorithms for learning word
embeddings learn vector representations from large corpora of text documents in
an unsu- pervised fashion. This paper introduces SWESA (Supervised Word
Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via
word embeddings. SWESA leverages document label infor- mation to learn vector
representations of words from a modest corpus of text doc- uments by solving an
optimization prob- lem that minimizes a cost function with respect to both word
embeddings as well as classification accuracy. Analysis re- veals that SWESA
provides an efficient way of estimating the dimension of the word embeddings
that are to be learned. Experiments on several real world data sets show that
SWESA has superior per- formance when compared to previously suggested
approaches to word embeddings and sentiment analysis tasks.
Data Sets: Word Embeddings Learned from Tweets and General Data
Authors: Quanzhi Li, Sameena Shah, Xiaomo Liu, Armineh Nourbakhsh
Date: 2017-08-14 00:00:00
Tasks: Articles, Sentiment Analysis, Topic Classification, Word Embeddings

A word embedding is a low-dimensional, dense and real- valued vector
representation of a word. Word embeddings have been used in many NLP tasks.
They are usually gener- ated from a large text corpus. The embedding of a word
cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and
have unique lexical and semantic features that are different from other types
of text. Therefore, it is necessary to have word embeddings learned
specifically from tweets. In this paper, we present ten word embedding data
sets. In addition to the data sets learned from just tweet data, we also built
embedding sets from the general data and the combination of tweets with the
general data. The general data consist of news articles, Wikipedia data and
other web data. These ten embedding models were learned from about 400 million
tweets and 7 billion words from the general text. In this paper, we also
present two experiments demonstrating how to use the data sets in some NLP
tasks, such as tweet sentiment analysis and tweet topic classification tasks.
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
Authors: Yan Yan, Wentao Guo, Meng Zhao, Jinghe Hu, Weipeng P. Yan
Date: 2017-08-14 00:00:00
Tasks: Recommendation Systems

With the transition from people's traditional `brick-and-mortar' shopping to
online mobile shopping patterns in web 2.0 $\mathit{era}$, the recommender
system plays a critical role in E-Commerce and E-Retails. This is especially
true when designing this system for more than $\mathbf{236~million}$ daily
active users. Ranking strategy, the key module of the recommender system, needs
to be precise, accurate, and responsive for estimating customers' intents. We
propose a dynamic ranking paradigm, named as DNN-MAB, that is composed of a
pairwise deep neural network (DNN) $\mathit{pre}$-ranker connecting a revised
multi-armed bandit (MAB) dynamic $\mathit{post}$-ranker. By taking into account
of explicit and implicit user feedbacks such as impressions, clicks,
conversions, etc. DNN-MAB is able to adjust DNN $\mathit{pre}$-ranking scores
to assist customers locating items they are interested in most so that they can
converge quickly and frequently. To the best of our knowledge, frameworks like
DNN-MAB have not been discussed in the previous literature to either E-Commerce
or machine learning audiences. In practice, DNN-MAB has been deployed to
production and it easily outperforms against other state-of-the-art models by
significantly lifting the gross merchandise volume (GMV) which is the objective
metrics at JD.
Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution
Authors: Dimitrios Mallis, Thomas Sgouros, Nikolaos Mitianoudis
Date: 2017-08-14 00:00:00
Tasks: Audio Source Separation

Audio source separation is the task of isolating sound sources that are
active simultaneously in a room captured by a set of microphones. Convolutive
audio source separation of equal number of sources and microphones has a number
of shortcomings including the complexity of frequency-domain ICA, the
permutation ambiguity and the problem's scalabity with increasing number of
sensors. In this paper, the authors propose a multiple-microphone audio source
separation algorithm based on a previous work of Mitianoudis and Davies (2003).
Complex FastICA is substituted by Robust ICA increasing robustness and
performance. Permutation ambiguity is solved using two methodologies. The first
is using the Likelihood Ration Jump solution, which is now modified to decrease
computational complexity in the case of multiple microphones. The application
of the MuSIC algorithm, as a preprocessing step to the previous solution, forms
a second methodology with promising results.
Encouraging LSTMs to Anticipate Actions Very Early
Authors: Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson
Date: 2017-03-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Anticipation, Autonomous Navigation

In contrast to the widely studied problem of recognizing an action given a
complete sequence, action anticipation aims to identify the action from only
partially available videos. As such, it is therefore key to the success of
computer vision applications requiring to react as early as possible, such as
autonomous navigation. In this paper, we propose a new action anticipation
method that achieves high prediction accuracy even in the presence of a very
small percentage of a video sequence. To this end, we develop a multi-stage
LSTM architecture that leverages context-aware and action-aware features, and
introduce a novel loss function that encourages the model to predict the
correct class as early as possible. Our experiments on standard benchmark
datasets evidence the benefits of our approach; We outperform the
state-of-the-art action anticipation methods for early prediction by a relative
increase in accuracy of 22.0% on JHMDB-21, 14.0% on UT-Interaction and 49.9% on
UCF-101.
Information-gain computation
Authors: Anthony Di Franco
Date: 2017-07-05 00:00:00

Despite large incentives, ecorrectness in software remains an elusive goal.
Declarative programming techniques, where algorithms are derived from a
specification of the desired behavior, offer hope to address this problem,
since there is a combinatorial reduction in complexity in programming in terms
of specifications instead of algorithms, and arbitrary desired properties can
be expressed and enforced in specifications directly. However, limitations on
performance have prevented programming with declarative specifications from
becoming a mainstream technique for general-purpose programming. To address the
performance bottleneck in deriving an algorithm from a specification, I propose
information-gain computation, a framework where an adaptive evaluation strategy
is used to efficiently perform a search which derives algorithms that provide
information about a query most directly. Within this framework, opportunities
to compress the search space present themselves, which suggest that
information-theoretic bounds on the performance of such a system might be
articulated and a system designed to achieve them. In a preliminary empirical
study of adaptive evaluation for a simple test program, the evaluation strategy
adapts successfully to evaluate a query efficiently.
A Simple and Realistic Pedestrian Model for Crowd Simulation and Application
Authors: Wonho Kang, Youngnam Han
Date: 2017-08-10 00:00:00

The simulation of pedestrian crowd that reflects reality is a major challenge for researches. Several crowd simulation models have been proposed such as cellular automata model, agent-based model, fluid dynamic model, etc. It is important to note that agent-based model is able, over others approaches, to provide a natural description of the system and then to capture complex human behaviors. In this paper, we propose a multi-agent simulation model in which pedestrian positions are updated at discrete time intervals. It takes into account the major normal conditions of a simple pedestrian situated in a crowd such as preferences, realistic perception of environment, etc. Our objective is to simulate the pedestrian crowd realistically towards a simulation of believable pedestrian behaviors. Typical pedestrian phenomena, including the unidirectional and bidirectional movement in a corridor as well as the flow through bottleneck, are simulated. The conducted simulations show that our model is able to produce realistic pedestrian behaviors. The obtained fundamental diagram and flow rate at bottleneck agree very well with classic conclusions and empirical study results. It is hoped that the idea of this study may be helpful in promoting the modeling and simulation of pedestrian crowd in a simple way.
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
Authors: Kun Li, Joel W. Burdick
Date: 2017-07-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a new method for inverse reinforcement learning in
large-scale and high-dimensional state spaces. To avoid solving the
computationally expensive reinforcement learning problems in reward learning,
we propose a function approximation method to ensure that the Bellman
Optimality Equation always holds, and then estimate a function to maximize the
likelihood of the observed motion. The time complexity of the proposed method
is linearly proportional to the cardinality of the action set, thus it can
handle large state spaces efficiently. We test the proposed method in a
simulated environment, and show that it is more accurate than existing methods
and significantly better in scalability. We also show that the proposed method
can extend many existing methods to high-dimensional state spaces. We then
apply the method to evaluating the effect of rehabilitative stimulations on
patients with spinal cord injuries based on the observed patient motions.
Ontological Multidimensional Data Models and Contextual Data Qality
Authors: Leopoldo Bertossi, Mostafa Milani
Date: 2017-04-01 00:00:00

Data quality assessment and data cleaning are context-dependent activities.
Motivated by this observation, we propose the Ontological Multidimensional Data
Model (OMD model), which can be used to model and represent contexts as
logic-based ontologies. The data under assessment is mapped into the context,
for additional analysis, processing, and quality data extraction. The resulting
contexts allow for the representation of dimensions, and multidimensional data
quality assessment becomes possible. At the core of a multidimensional context
we include a generalized multidimensional data model and a Datalog+/- ontology
with provably good properties in terms of query answering. These main
components are used to represent dimension hierarchies, dimensional
constraints, dimensional rules, and define predicates for quality data
specification. Query answering relies upon and triggers navigation through
dimension hierarchies, and becomes the basic tool for the extraction of quality
data. The OMD model is interesting per se, beyond applications to data quality.
It allows for a logic-based, and computationally tractable representation of
multidimensional data, extending previous multidimensional data models with
additional expressive power and functionalities.
Lattice Long Short-Term Memory for Human Action Recognition
Authors: Lin Sun, Kui Jia, Kevin Chen, Dit Yan Yeung, Bertram E. Shi, Silvio Savarese
Date: 2017-08-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Human actions captured in video sequences are three-dimensional signals
characterizing visual appearance and motion dynamics. To learn action patterns,
existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and
RNNs). CNN based methods are effective in learning spatial appearances, but are
limited in modeling long-term motion dynamics. RNNs, especially Long Short-Term
Memory (LSTM), are able to learn temporal motion dynamics. However, naively
applying RNNs to video sequences in a convolutional manner implicitly assumes
that motions in videos are stationary across different spatial locations. This
assumption is valid for short-term motions but invalid when the duration of the
motion is long.
  In this work, we propose Lattice-LSTM (L2STM), which extends LSTM by learning
independent hidden state transitions of memory cells for individual spatial
locations. This method effectively enhances the ability to model dynamics
across time and addresses the non-stationary issue of long-term motion dynamics
without significantly increasing the model complexity. Additionally, we
introduce a novel multi-modal training procedure for training our network.
Unlike traditional two-stream architectures which use RGB and optical flow
information as input, our two-stream model leverages both modalities to jointly
train both input gates and both forget gates in the network rather than
treating the two streams as separate entities with no information about the
other. We apply this end-to-end system to benchmark datasets (UCF-101 and
HMDB-51) of human action recognition. Experiments show that on both datasets,
our proposed method outperforms all existing ones that are based on LSTM and/or
CNNs of similar model complexities.
Interstitial Content Detection
Authors: Elizabeth Lucas
Date: 2017-08-13 00:00:00
Tasks: BIG-bench Machine Learning

Interstitial content is online content which grays out, or otherwise obscures
the main page content. In this technical report, we discuss exploratory
research into detecting the presence of interstitial content in web pages. We
discuss the use of computer vision techniques to detect interstitials, and the
potential use of these techniques to provide a labelled dataset for machine
learning.