pwc-archive/papers-with-abstracts huggingface dataset:
Bicycle-Sharing System Analysis and Trip Prediction
Authors: Jiawei Zhang, Xiao Pan, Moyin Li, Philip S. Yu
Date: 2016-04-03 00:00:00
Tasks: Prediction

Bicycle-sharing systems, which can provide shared bike usage services for the
public, have been launched in many big cities. In bicycle-sharing systems,
people can borrow and return bikes at any stations in the service region very
conveniently. Therefore, bicycle-sharing systems are normally used as a
short-distance trip supplement for private vehicles as well as regular public
transportation. Meanwhile, for stations located at different places in the
service region, the bike usages can be quite skewed and imbalanced. Some
stations have too many incoming bikes and get jammed without enough docks for
upcoming bikes, while some other stations get empty quickly and lack enough
bikes for people to check out. Therefore, inferring the potential destinations
and arriving time of each individual trip beforehand can effectively help the
service providers schedule manual bike re-dispatch in advance. In this paper,
we will study the individual trip prediction problem for bicycle-sharing
systems. To address the problem, we study a real-world bicycle-sharing system
and analyze individuals' bike usage behaviors first. Based on the analysis
results, a new trip destination prediction and trip duration inference model
will be introduced. Experiments conducted on a real-world bicycle-sharing
system demonstrate the effectiveness of the proposed model.
Primal-Dual Active-Set Methods for Isotonic Regression and Trend Filtering
Authors: Zheng Han, Frank E. Curtis
Date: 2015-08-10 00:00:00
Tasks: regression

Isotonic regression (IR) is a non-parametric calibration method used in
supervised learning. For performing large-scale IR, we propose a primal-dual
active-set (PDAS) algorithm which, in contrast to the state-of-the-art Pool
Adjacent Violators (PAV) algorithm, can be parallized and is easily
warm-started thus well-suited in the online settings. We prove that, like the
PAV algorithm, our PDAS algorithm for IR is convergent and has a work
complexity of O(n), though our numerical experiments suggest that our PDAS
algorithm is often faster than PAV. In addition, we propose PDAS variants (with
safeguarding to ensure convergence) for solving related trend filtering (TF)
problems, providing the results of experiments to illustrate their
effectiveness.
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations
Authors: W. Pan, F. Doshi-Velez
Date: 2016-04-03 00:00:00
Tasks: Dimensionality Reduction

Nonnegative matrix factorization (NMF) is a popular dimension reduction
technique that produces interpretable decomposition of the data into parts.
However, this decompostion is not generally identifiable (even up to
permutation and scaling). While other studies have provide criteria under which
NMF is identifiable, we present the first (to our knowledge) characterization
of the non-identifiability of NMF. We describe exactly when and how
non-uniqueness can occur, which has important implications for algorithms to
efficiently discover alternate solutions, if they exist.
Multi-Relational Learning at Scale with ADMM
Authors: Lucas Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme
Date: 2016-04-03 00:00:00
Tasks: Recommendation Systems, Relational Reasoning

Learning from multiple-relational data which contains noise, ambiguities, or
duplicate entities is essential to a wide range of applications such as
statistical inference based on Web Linked Data, recommender systems,
computational biology, and natural language processing. These tasks usually
require working with very large and complex datasets - e.g., the Web graph -
however, current approaches to multi-relational learning are not practical for
such scenarios due to their high computational complexity and poor scalability
on large data.
  In this paper, we propose a novel and scalable approach for multi-relational
factorization based on consensus optimization. Our model, called ConsMRF, is
based on the Alternating Direction Method of Multipliers (ADMM) framework,
which enables us to optimize each target relation using a smaller set of
parameters than the state-of-the-art competitors in this task.
  Due to ADMM's nature, ConsMRF can be easily parallelized which makes it
suitable for large multi-relational data. Experiments on large Web datasets -
derived from DBpedia, Wikipedia and YAGO - show the efficiency and performance
improvement of ConsMRF over strong competitors. In addition, ConsMRF
near-linear scalability indicates great potential to tackle Web-scale problem
sizes.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Authors: Li Shen, Zhouchen Lin, Qingming Huang
Date: 2015-12-18 00:00:00
Tasks: General Classification, Long-tail Learning, Scene Classification

Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.
GAL: A Global-Attributes Assisted Labeling System for Outdoor Scenes
Authors: Yuzhuo Ren, Chen Chen, Shang-Wen Li, C. -C. Jay Kuo
Date: 2016-04-03 00:00:00

An approach that extracts global attributes from outdoor images to facilitate
geometric layout labeling is investigated in this work. The proposed
Global-attributes Assisted Labeling (GAL) system exploits both local features
and global attributes. First, by following a classical method, we use local
features to provide initial labels for all super-pixels. Then, we develop a set
of techniques to extract global attributes from 2D outdoor images. They include
sky lines, ground lines, vanishing lines, etc. Finally, we propose the GAL
system that integrates global attributes in the conditional random field (CRF)
framework to improve initial labels so as to offer a more robust labeling
result. The performance of the proposed GAL system is demonstrated and
benchmarked with several state-of-the-art algorithms against a popular outdoor
scene layout dataset.
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Authors: Tao Kong, Anbang Yao, Yurong Chen, Fuchun Sun
Date: 2016-04-03 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU, Object, object-detection, Object Detection, Region Proposal

Almost all of the current top-performing object detection networks employ
region proposals to guide the search for object instances. State-of-the-art
region proposal methods usually need several thousand proposals to get high
recall, thus hurting the detection efficiency. Although the latest Region
Proposal Network method gets promising detection accuracy with several hundred
proposals, it still struggles in small-size object detection and precise
localization (e.g., large IoU thresholds), mainly due to the coarseness of its
feature maps. In this paper, we present a deep hierarchical network, namely
HyperNet, for handling region proposal generation and object detection jointly.
Our HyperNet is primarily based on an elaborately designed Hyper Feature which
aggregates hierarchical feature maps first and then compresses them into a
uniform space. The Hyper Features well incorporate deep but highly semantic,
intermediate but really complementary, and shallow but naturally
high-resolution features of the image, thus enabling us to construct HyperNet
by sharing them both in generating proposals and detecting objects via an
end-to-end joint training strategy. For the deep VGG16 model, our method
achieves completely leading recall and state-of-the-art object detection
accuracy on PASCAL VOC 2007 and 2012 using only 100 proposals per image. It
runs with a speed of 5 fps (including all steps) on a GPU, thus having the
potential for real-time processing.
Convex block-sparse linear regression with expanders -- provably
Authors: Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher
Date: 2016-03-21 00:00:00
Tasks: regression

Sparse matrices are favorable objects in machine learning and optimization.
When such matrices are used, in place of dense ones, the overall complexity
requirements in optimization can be significantly reduced in practice, both in
terms of space and run-time. Prompted by this observation, we study a convex
optimization scheme for block-sparse recovery from linear measurements. To
obtain linear sketches, we use expander matrices, i.e., sparse matrices
containing only few non-zeros per column. Hitherto, to the best of our
knowledge, such algorithmic solutions have been only studied from a non-convex
perspective. Our aim here is to theoretically characterize the performance of
convex approaches under such setting.
  Our key novelty is the expression of the recovery error in terms of the
model-based norm, while assuring that solution lives in the model. To achieve
this, we show that sparse model-based matrices satisfy a group version of the
null-space property. Our experimental findings on synthetic and real
applications support our claims for faster recovery in the convex setting -- as
opposed to using dense sensing matrices, while showing a competitive recovery
performance.
Channel Equalization Using Multilayer Perceptron Networks
Authors: Saba Baloch, Javed Ali Baloch, Mukhtiar Ali Unar
Date: 2016-04-02 00:00:00

In most digital communication systems, bandwidth limited channel along with
multipath propagation causes ISI (Inter Symbol Interference) to occur. This
phenomenon causes distortion of the given transmitted symbol due to other
transmitted symbols. With the help of equalization ISI can be reduced. This
paper presents a solution to the ISI problem by performing blind equalization
using ANN (Artificial Neural Networks). The simulated network is a multilayer
feedforward Perceptron ANN, which has been trained by utilizing the error
back-propagation algorithm. The weights of the network are updated in
accordance with training of the network. This paper presents a very effective
method for blind channel equalization, being more efficient than the
pre-existing algorithms. The obtained results show a visible reduction in the
noise content.
SAM: Support Vector Machine Based Active Queue Management
Authors: Muhammad Saleh Shah, Asim Imdad Wagan, Mukhtiar Ali Unar
Date: 2016-04-02 00:00:00
Tasks: Management

Recent years have seen an increasing interest in the design of AQM (Active
Queue Management) controllers. The purpose of these controllers is to manage
the network congestion under varying loads, link delays and bandwidth. In this
paper, a new AQM controller is proposed which is trained by using the SVM
(Support Vector Machine) with the RBF (Radial Basis Function) kernal. The
proposed controller is called the support vector based AQM (SAM) controller.
The performance of the proposed controller has been compared with three
conventional AQM controllers, namely the Random Early Detection, Blue and
Proportional Plus Integral Controller. The preliminary simulation studies show
that the performance of the proposed controller is comparable to the
conventional controllers. However, the proposed controller is more efficient in
controlling the queue size than the conventional controllers.
pH Prediction by Artificial Neural Networks for the Drinking Water of the Distribution System of Hyderabad City
Authors: Niaz Ahmed Memon, Mukhtiar Ali Unar, Abdul Khalique Ansari
Date: 2016-04-02 00:00:00

In this research, feedforward ANN (Artificial Neural Network) model is
developed and validated for predicting the pH at 10 different locations of the
distribution system of drinking water of Hyderabad city. The developed model is
MLP (Multilayer Perceptron) with back propagation algorithm.The data for the
training and testing of the model are collected through an experimental
analysis on weekly basis in a routine examination for maintaining the quality
of drinking water in the city. 17 parameters are taken into consideration
including pH. These all parameters are taken as input variables for the model
and then pH is predicted for 03 phases;raw water of river Indus,treated water
in the treatment plants and then treated water in the distribution system of
drinking water. The training and testing results of this model reveal that MLP
neural networks are exceedingly extrapolative for predicting the pH of river
water, untreated and treated water at all locations of the distribution system
of drinking water of Hyderabad city. The optimum input and output weights are
generated with minimum MSE (Mean Square Error) < 5%.Experimental, predicted and
tested values of pH are plotted and the effectiveness of the model is
determined by calculating the coefficient of correlation (R2=0.999) of trained
and tested results.
Image Quality Assessment for Performance Evaluation of Focus Measure Operators
Authors: Farida Memon, Mukhtiar Ali Unar, Sheeraz Memon
Date: 2016-04-02 00:00:00
Tasks: Image Quality Assessment

This paper presents the performance evaluation of eight focus measure
operators namely Image CURV (Curvature), GRAE (Gradient Energy), HISE
(Histogram Entropy), LAPM (Modified Laplacian), LAPV (Variance of Laplacian),
LAPD (Diagonal Laplacian), LAP3 (Laplacian in 3D Window) and WAVS (Sum of
Wavelet Coefficients). Statistical matrics such as MSE (Mean Squared Error),
PNSR (Peak Signal to Noise Ratio), SC (Structural Content), NCC (Normalized
Cross Correlation), MD (Maximum Difference) and NAE (Normalized Absolute Error)
are used to evaluate stated focus measures in this research. . FR (Full
Reference) method of the image quality assessment is utilized in this paper.
Results indicate that LAPD method is comparatively better than other seven
focus operators at typical imaging conditions.
Improving SAT Solvers via Blocked Clause Decomposition
Authors: Jingchao Chen
Date: 2016-04-02 00:00:00
Tasks: Variable Selection

The decision variable selection policy used by the most competitive CDCL
(Conflict-Driven Clause Learning) SAT solvers is either VSIDS (Variable State
Independent Decaying Sum) or its variants such as exponential version EVSIDS.
The common characteristic of VSIDS and its variants is to make use of
statistical information in the solving process, but ignore structure
information of the problem. For this reason, this paper modifies the decision
variable selection policy, and presents a SAT solving technique based on BCD
(Blocked Clause Decomposition). Its basic idea is that a part of decision
variables are selected by VSIDS heuristic, while another part of decision
variables are selected by blocked sets that are obtained by BCD. Compared with
the existing BCD-based technique, our technique is simple, and need not to
reencode CNF formulas. SAT solvers for certified UNSAT track can apply also our
BCD-based technique. Our experiments on application benchmarks demonstrate that
the new variables selection policy based on BCD can increase the performance of
SAT solvers such as abcdSAT. The solver with BCD solved an instance from the
SAT Race 2015 that was not solved by any solver so far. This shows that in some
cases, the heuristic based on structure information is more efficient than that
based on statistical information.
Voronoi Region-Based Adaptive Unsupervised Color Image Segmentation
Authors: R. Hettiarachchi, J. F. Peters
Date: 2016-04-02 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Color image segmentation is a crucial step in many computer vision and
pattern recognition applications. This article introduces an adaptive and
unsupervised clustering approach based on Voronoi regions, which can be applied
to solve the color image segmentation problem. The proposed method performs
region splitting and merging within Voronoi regions of the Dirichlet
Tessellated image (also called a Voronoi diagram) , which improves the
efficiency and the accuracy of the number of clusters and cluster centroids
estimation process. Furthermore, the proposed method uses cluster centroid
proximity to merge proximal clusters in order to find the final number of
clusters and cluster centroids. In contrast to the existing adaptive
unsupervised cluster-based image segmentation algorithms, the proposed method
uses K-means clustering algorithm in place of the Fuzzy C-means algorithm to
find the final segmented image. The proposed method was evaluated on three
different unsupervised image segmentation evaluation benchmarks and its results
were compared with two other adaptive unsupervised cluster-based image
segmentation algorithms. The experimental results reported in this article
confirm that the proposed method outperforms the existing algorithms in terms
of the quality of image segmentation results. Also, the proposed method results
in the lowest average execution time per image compared to the existing methods
reported in this article.
Multilingual Language Processing From Bytes
Authors: Dan Gillick, Cliff Brunk, Oriol Vinyals, Amarnag Subramanya
Date: 2015-12-01 00:00:00
Proceeding: NAACL 2016 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads
text as bytes and outputs span annotations of the form [start, length, label]
where start positions, lengths, and labels are separate entries in our
vocabulary. Because we operate directly on unicode bytes rather than
language-specific words or characters, we can analyze text in many languages
with a single model. Due to the small vocabulary size, these multilingual
models are very compact, but produce results similar to or better than the
state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that
use only the provided training datasets (no external data sources). Our models
are learning "from scratch" in that they do not rely on any elements of the
standard pipeline in Natural Language Processing (including tokenization), and
thus can run in standalone fashion on raw text.
Discriminative Phrase Embedding for Paraphrase Identification
Authors: Wenpeng Yin, Hinrich Schütze
Date: 2016-04-02 00:00:00
Proceeding: HLT 2015 5
Tasks: Paraphrase Identification

This work, concerning paraphrase identification task, on one hand contributes
to expanding deep learning embeddings to include continuous and discontinuous
linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN
to learn the discriminative weights of words and phrases specific to paraphrase
task, so that a weighted sum of embeddings can represent sentences more
effectively. Based on these two innovations we get competitive state-of-the-art
performance on paraphrase identification.
Online Updating of Word Representations for Part-of-Speech Tagging
Authors: Wenpeng Yin, Tobias Schnabel, Hinrich Schütze
Date: 2016-04-02 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Domain Adaptation, Online unsupervised domain adaptation, Part-Of-Speech Tagging, POS, POS Tagging, Unsupervised Domain Adaptation

We propose online unsupervised domain adaptation (DA), which is performed
incrementally as data comes in and is applicable when batch DA is not possible.
In a part-of-speech (POS) tagging evaluation, we find that online unsupervised
DA performs as well as batch DA.
A Formal Calculus for International Relations Computation and Evaluation
Authors: Mohd Anuar Mat Isa, Ramlan Mahmod, Nur Izura Udzir, Jamalul-lail Ab Manan, Audun Jøsang, Ali Dehghan Tanha
Date: 2016-04-02 00:00:00
Tasks: Bayesian Inference, Relation

This publication presents a relation computation or calculus for
international relations using a mathematical modeling. It examined trust for
international relations and its calculus, which related to Bayesian inference,
Dempster-Shafer theory and subjective logic. Based on an observation in the
literature, we found no literature discussing the calculus method for the
international relations. To bridge this research gap, we propose a relation
algebra method for international relations computation. The proposed method
will allow a relation computation which is previously subjective and
incomputable. We also present three international relations as case studies to
demonstrate the proposed method is a real-world scenario. The method will
deliver the relation computation for the international relations that to
support decision makers in a government such as foreign ministry, defense
ministry, presidential or prime minister office. The Department of Defense
(DoD) may use our method to determine a nation that can be identified as a
friendly, neutral or hostile nation.
Overlay Text Extraction From TV News Broadcast
Authors: Raghvendra Kannao, Prithwijit Guha
Date: 2016-04-02 00:00:00
Tasks: Articles, Optical Character Recognition (OCR), Text Detection

The text data present in overlaid bands convey brief descriptions of news
events in broadcast videos. The process of text extraction becomes challenging
as overlay text is presented in widely varying formats and often with animation
effects. We note that existing edge density based methods are well suited for
our application on account of their simplicity and speed of operation. However,
these methods are sensitive to thresholds and have high false positive rates.
In this paper, we present a contrast enhancement based preprocessing stage for
overlay text detection and a parameter free edge density based scheme for
efficient text band detection. The second contribution of this paper is a novel
approach for multiple text region tracking with a formal identification of all
possible detection failure cases. The tracking stage enables us to establish
the temporal presence of text bands and their linking over time. The third
contribution is the adoption of Tesseract OCR for the specific task of overlay
text recognition using web news articles. The proposed approach is tested and
found superior on news videos acquired from three Indian English television
news channels along with benchmark datasets.
Sherlock: Scalable Fact Learning in Images
Authors: Mohamed Elhoseiny, Scott Cohen, Walter Chang, Brian Price, Ahmed Elgammal
Date: 2015-11-16 00:00:00
Tasks: Multiview Learning, Retrieval

We study scalable and uniform understanding of facts in images. Existing
visual recognition systems are typically modeled differently for each fact type
such as objects, actions, and interactions. We propose a setting where all
these facts can be modeled simultaneously with a capacity to understand
unbounded number of facts in a structured way. The training data comes as
structured facts in images, including (1) objects (e.g., $<$boy$>$), (2)
attributes (e.g., $<$boy, tall$>$), (3) actions (e.g., $<$boy, playing$>$), and
(4) interactions (e.g., $<$boy, riding, a horse $>$). Each fact has a semantic
language view (e.g., $<$ boy, playing$>$) and a visual view (an image with this
fact). We show that learning visual facts in a structured way enables not only
a uniform but also generalizable visual understanding. We propose and
investigate recent and strong approaches from the multiview learning literature
and also introduce two learning representation models as potential baselines.
We applied the investigated methods on several datasets that we augmented with
structured facts and a large scale dataset of more than 202,000 facts and
814,000 images. Our experiments show the advantage of relating facts by the
structure by the proposed models compared to the designed baselines on
bidirectional fact retrieval.