pwc-archive/papers-with-abstracts huggingface dataset:
Numerical Coding of Nominal Data
Authors: Zenon Gniazdowski, Michal Grabowski
Date: 2016-01-08 00:00:00
Tasks: Attribute, Classification, General Classification

In this paper, a novel approach for coding nominal data is proposed. For the
given nominal data, a rank in a form of complex number is assigned. The
proposed method does not lose any information about the attribute and brings
other properties previously unknown. The approach based on these knew
properties can been used for classification. The analyzed example shows that
classification with the use of coded nominal data or both numerical as well as
coded nominal data is more effective than the classification, which uses only
numerical data.
An Application of the Generalized Rectangular Fuzzy Model to Critical Thinking Assessment
Authors: Igor Ya. Subbotin, Michael Gr. Voskoglou
Date: 2016-01-08 00:00:00

The authors apply the Generalized Rectangular Model to assessing critical
thinking skills and its relations with their language competency.
Visualizing and Understanding Neural Models in NLP
Authors: Jiwei Li, Xinlei Chen, Eduard Hovy, Dan Jurafsky
Date: 2015-06-02 00:00:00
Proceeding: NAACL 2016 6
Tasks: Negation, Sentence

While neural networks have been successfully applied to many NLP tasks the
resulting vector-based models are very difficult to interpret. For example it's
not clear how they achieve {\em compositionality}, building sentence meaning
from the meanings of words and phrases. In this paper we describe four
strategies for visualizing compositionality in neural models for NLP, inspired
by similar work in computer vision. We first plot unit values to visualize
compositionality of negation, intensification, and concessive clauses, allow us
to see well-known markedness asymmetries in negation. We then introduce three
simple and straightforward methods for visualizing a unit's {\em salience}, the
amount it contributes to the final composed meaning: (1) gradient
back-propagation, (2) the variance of a token from the average word node, (3)
LSTM-style gates that measure information flow. We test our methods on
sentiment using simple recurrent nets and LSTMs. Our general-purpose methods
may have wide applications for understanding compositionality and other
semantic properties of deep networks , and also shed light on why LSTMs
outperform simple recurrent nets,
Profile Likelihood Biclustering
Authors: Cheryl J. Flynn, Patrick O. Perry
Date: 2012-06-29 00:00:00
Tasks: Clustering

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.
Nonparametric semi-supervised learning of class proportions
Authors: Shantanu Jain, Martha White, Michael W. Trosset, Predrag Radivojac
Date: 2016-01-08 00:00:00
Tasks: Density Estimation

The problem of developing binary classifiers from positive and unlabeled data
is often encountered in machine learning. A common requirement in this setting
is to approximate posterior probabilities of positive and negative classes for
a previously unseen data point. This problem can be decomposed into two steps:
(i) the development of accurate predictors that discriminate between positive
and unlabeled data, and (ii) the accurate estimation of the prior probabilities
of positive and negative examples. In this work we primarily focus on the
latter subproblem. We study nonparametric class prior estimation and formulate
this problem as an estimation of mixing proportions in two-component mixture
models, given a sample from one of the components and another sample from the
mixture itself. We show that estimation of mixing proportions is generally
ill-defined and propose a canonical form to obtain identifiability while
maintaining the flexibility to model any distribution. We use insights from
this theory to elucidate the optimization surface of the class priors and
propose an algorithm for estimating them. To address the problems of
high-dimensional density estimation, we provide practical transformations to
low-dimensional spaces that preserve class priors. Finally, we demonstrate the
efficacy of our method on univariate and multivariate data.
GELATO and SAGE: An Integrated Framework for MS Annotation
Authors: Khalifeh AlJadda, Rene Ranzinger, Melody Porterfield, Brent Weatherly, Mohammed Korayem, John A. Miller, Khaled Rasheed, Krys J. Kochut, William S. York
Date: 2015-12-28 00:00:00

Several algorithms and tools have been developed to (semi) automate the
process of glycan identification by interpreting Mass Spectrometric data.
However, each has limitations when annotating MSn data with thousands of MS
spectra using uncurated public databases. Moreover, the existing tools are not
designed to manage MSn data where n > 2. We propose a novel software package to
automate the annotation of tandem MS data. This software consists of two major
components. The first, is a free, semi-automated MSn data interpreter called
the Glycomic Elucidation and Annotation Tool (GELATO). This tool extends and
automates the functionality of existing open source projects, namely,
GlycoWorkbench (GWB) and GlycomeDB. The second is a machine learning model
called Smart Anotation Enhancement Graph (SAGE), which learns the behavior of
glycoanalysts to select annotations generated by GELATO that emulate human
interpretation of the spectra.
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Authors: Christina Heinze, Brian McWilliams, Nicolai Meinshausen
Date: 2015-06-08 00:00:00
Tasks: Distributed Optimization

We present DUAL-LOCO, a communication-efficient algorithm for distributed
statistical estimation. DUAL-LOCO assumes that the data is distributed
according to the features rather than the samples. It requires only a single
round of communication where low-dimensional random projections are used to
approximate the dependences between features available to different workers. We
show that DUAL-LOCO has bounded approximation error which only depends weakly
on the number of workers. We compare DUAL-LOCO against a state-of-the-art
distributed optimization method on a variety of real world datasets and show
that it obtains better speedups while retaining good accuracy.
A Predictive Model using the Markov Property
Authors: Robert A. Murphy
Date: 2016-01-08 00:00:00
Tasks: model

Given a data set of numerical values which are sampled from some unknown
probability distribution, we will show how to check if the data set exhibits
the Markov property and we will show how to use the Markov property to predict
future values from the same distribution, with probability 1.
Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
Authors: Adeyinka K. Akanbi, Muthoni Masinde
Date: 2016-01-08 00:00:00

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous
devices would be able to connect and communicate with each other seamlessly,
irrespective of the domain. Semantic representation of data through detailed
standardized annotation has shown to improve the integration of the
interconnected heterogeneous devices. However, the semantic representation of
these heterogeneous data sources for environmental monitoring systems is not
yet well supported. To achieve the maximum benefits of IoT for drought
forecasting, a dedicated semantic middleware solution is required. This
research proposes a middleware that semantically represents and integrates
heterogeneous data sources with indigenous knowledge based on a unified
ontology for an accurate IoT-based drought early warning system (DEWS).
Toward a Robust Diversity-Based Model to Detect Changes of Context
Authors: Sylvain Castagnos, Amaury L 'Huillier, Anne Boyer
Date: 2016-01-08 00:00:00
Tasks: Diversity, Privacy Preserving, Recommendation Systems

Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.
Regret Guarantees for Item-Item Collaborative Filtering
Authors: Guy Bresler, Devavrat Shah, Luis F. Voloch
Date: 2015-07-20 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

There is much empirical evidence that item-item collaborative filtering works
well in practice. Motivated to understand this, we provide a framework to
design and analyze various recommendation algorithms. The setup amounts to
online binary matrix completion, where at each time a random user requests a
recommendation and the algorithm chooses an entry to reveal in the user's row.
The goal is to minimize regret, or equivalently to maximize the number of +1
entries revealed at any time. We analyze an item-item collaborative filtering
algorithm that can achieve fundamentally better performance compared to
user-user collaborative filtering. The algorithm achieves good "cold-start"
performance (appropriately defined) by quickly making good recommendations to
new users about whom there is little information.
A Novel Performance Evaluation Methodology for Single-Target Trackers
Authors: Matej Kristan, Jiri Matas, Ales Leonardis, Tomas Vojir, Roman Pflugfelder, Gustavo Fernandez, Georg Nebehay, Fatih Porikli, Luka Cehovin
Date: 2015-03-04 00:00:00
Tasks: Clustering

This paper addresses the problem of single-target tracker performance
evaluation. We consider the performance measures, the dataset and the
evaluation system to be the most important components of tracker evaluation and
propose requirements for each of them. The requirements are the basis of a new
evaluation methodology that aims at a simple and easily interpretable tracker
comparison. The ranking-based methodology addresses tracker equivalence in
terms of statistical significance and practical differences. A fully-annotated
dataset with per-frame annotations with several visual attributes is
introduced. The diversity of its visual properties is maximized in a novel way
by clustering a large number of videos according to their visual attributes.
This makes it the most sophistically constructed and annotated dataset to date.
A multi-platform evaluation system allowing easy integration of third-party
trackers is presented as well. The proposed evaluation methodology was tested
on the VOT2014 challenge on the new dataset and 38 trackers, making it the
largest benchmark to date. Most of the tested trackers are indeed
state-of-the-art since they outperform the standard baselines, resulting in a
highly-challenging benchmark. An exhaustive analysis of the dataset from the
perspective of tracking difficulty is carried out. To facilitate tracker
comparison a new performance visualization technique is proposed.
Learning Precise Spike Train to Spike Train Transformations in Multilayer Feedforward Neuronal Networks
Authors: Arunava Banerjee
Date: 2014-12-13 00:00:00

We derive a synaptic weight update rule for learning temporally precise spike
train to spike train transformations in multilayer feedforward networks of
spiking neurons. The framework, aimed at seamlessly generalizing error
backpropagation to the deterministic spiking neuron setting, is based strictly
on spike timing and avoids invoking concepts pertaining to spike rates or
probabilistic models of spiking. The derivation is founded on two innovations.
First, an error functional is proposed that compares the spike train emitted by
the output neuron of the network to the desired spike train by way of their
putative impact on a virtual postsynaptic neuron. This formulation sidesteps
the need for spike alignment and leads to closed form solutions for all
quantities of interest. Second, virtual assignment of weights to spikes rather
than synapses enables a perturbation analysis of individual spike times and
synaptic weights of the output as well as all intermediate neurons in the
network, which yields the gradients of the error functional with respect to the
said entities. Learning proceeds via a gradient descent mechanism that
leverages these quantities. Simulation experiments demonstrate the efficacy of
the proposed learning framework. The experiments also highlight asymmetries
between synapses on excitatory and inhibitory neurons.
Deep convolutional acoustic word embeddings using word-pair side information
Authors: Herman Kamper, Weiran Wang, Karen Livescu
Date: 2015-10-05 00:00:00
Tasks: speech-recognition, Speech Recognition, Word Embeddings

Recent studies have been revisiting whole words as the basic modelling unit
in speech recognition and query applications, instead of phonetic units. Such
whole-word segmental systems rely on a function that maps a variable-length
speech segment to a vector in a fixed-dimensional space; the resulting acoustic
word embeddings need to allow for accurate discrimination between different
word types, directly in the embedding space. We compare several old and new
approaches in a word discrimination task. Our best approach uses side
information in the form of known word pairs to train a Siamese convolutional
neural network (CNN): a pair of tied networks that take two speech segments as
input and produce their embeddings, trained with a hinge loss that separates
same-word pairs and different-word pairs by some margin. A word classifier CNN
performs similarly, but requires much stronger supervision. Both types of CNNs
yield large improvements over the best previously published results on the word
discrimination task.
Research Project: Text Engineering Tool for Ontological Scientometry
Authors: Rustam Tagiew
Date: 2016-01-08 00:00:00
Tasks: Relationship Extraction (Distant Supervised)

The number of scientific papers grows exponentially in many disciplines. The
share of online available papers grows as well. At the same time, the period of
time for a paper to loose at chance to be cited anymore shortens. The decay of
the citing rate shows similarity to ultradiffusional processes as for other
online contents in social networks. The distribution of papers per author shows
similarity to the distribution of posts per user in social networks. The rate
of uncited papers for online available papers grows while some papers 'go
viral' in terms of being cited. Summarized, the practice of scientific
publishing moves towards the domain of social networks. The goal of this
project is to create a text engineering tool, which can semi-automatically
categorize a paper according to its type of contribution and extract
relationships between them into an ontological database. Semi-automatic
categorization means that the mistakes made by automatic pre-categorization and
relationship-extraction will be corrected through a wikipedia-like front-end by
volunteers from general public. This tool should not only help researchers and
the general public to find relevant supplementary material and peers faster,
but also provide more information for research funding agencies.
Visual Script and Language Identification
Authors: Anguelos Nicolaou, Andrew Bagdanov, Lluis Gomez-Bigorda, Dimosthenis Karatzas
Date: 2016-01-08 00:00:00
Tasks: Language Identification

In this paper we introduce a script identification method based on
hand-crafted texture features and an artificial neural network. The proposed
pipeline achieves near state-of-the-art performance for script identification
of video-text and state-of-the-art performance on visual language
identification of handwritten text. More than using the deep network as a
classifier, the use of its intermediary activations as a learned metric
demonstrates remarkable results and allows the use of discriminative models on
unknown classes. Comparative experiments in video-text and text in the wild
datasets provide insights on the internals of the proposed deep network.
Facial age estimation using BSIF and LBP
Authors: Salah Eddine Bekhouche, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed, Abdenour Hadid, Azeddine Benlamoudi
Date: 2016-01-08 00:00:00
Tasks: Age Estimation, regression

Human face aging is irreversible process causing changes in human face
characteristics such us hair whitening, muscles drop and wrinkles. Due to the
importance of human face aging in biometrics systems, age estimation became an
attractive area for researchers. This paper presents a novel method to estimate
the age from face images, using binarized statistical image features (BSIF) and
local binary patterns (LBP)histograms as features performed by support vector
regression (SVR) and kernel ridge regression (KRR). We applied our method on
FG-NET and PAL datasets. Our proposed method has shown superiority to that of
the state-of-the-art methods when using the whole PAL database.
Automated Management of Pothole related Disasters Using Image Processing and Geotagging
Authors: Madhura Katageri, Manisha Mandal, Mansi Gandhi, Navin Koregaonkar, Prof. Sharmila Sengupta
Date: 2016-01-08 00:00:00
Tasks: Edge Detection, Management

Potholes though seem inconsequential, may cause accidents resulting in loss
of human life. In this paper, we present an automated system to efficiently
manage the potholes in a ward by deploying geotagging and image processing
techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target
pothole regions in the 2D images using edge detection and morphological image
processing operations. A method is developed to accurately estimate the
dimensions of the potholes from their images, analyze their area and depth,
estimate the quantity of filling material required and therefore enabling
pothole attendance on a priority basis. This will further enable the government
official to have a fully automated system for effectively managing pothole
related disasters.
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
Authors: Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, Raquel Urtasun, Andreas Moshovos
Date: 2015-11-17 00:00:00

This work investigates how using reduced precision data in Convolutional
Neural Networks (CNNs) affects network accuracy during classification. More
specifically, this study considers networks where each layer may use different
precision data. Our key result is the observation that the tolerance of CNNs to
reduced precision data not only varies across networks, a well established
observation, but also within networks. Tuning precision per layer is appealing
as it could enable energy and performance improvements. In this paper we study
how error tolerance across layers varies and propose a method for finding a low
precision configuration for a network while maintaining high accuracy. A
diverse set of CNNs is analyzed showing that compared to a conventional
implementation using a 32-bit floating-point representation for all layers, and
with less than 1% loss in relative accuracy, the data footprint required by
these networks can be reduced by an average of 74% and up to 92%.
FireCaffe: near-linear acceleration of deep neural network training on compute clusters
Authors: Forrest N. Iandola, Khalid Ashraf, Matthew W. Moskewicz, Kurt Keutzer
Date: 2015-10-31 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU, Image Classification

Long training times for high-accuracy deep neural networks (DNNs) impede
research into new DNN architectures and slow the development of high-accuracy
DNNs. In this paper we present FireCaffe, which successfully scales deep neural
network training across a cluster of GPUs. We also present a number of best
practices to aid in comparing advancements in methods for scaling and
accelerating the training of deep neural networks. The speed and scalability of
distributed algorithms is almost always limited by the overhead of
communicating between servers; DNN training is not an exception to this rule.
Therefore, the key consideration here is to reduce communication overhead
wherever possible, while not degrading the accuracy of the DNN models that we
train. Our approach has three key pillars. First, we select network hardware
that achieves high bandwidth between GPU servers -- Infiniband or Cray
interconnects are ideal for this. Second, we consider a number of communication
algorithms, and we find that reduction trees are more efficient and scalable
than the traditional parameter server approach. Third, we optionally increase
the batch size to reduce the total quantity of communication during DNN
training, and we identify hyperparameters that allow us to reproduce the
small-batch accuracy while training with large batch sizes. When training
GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup,
respectively, when training on a cluster of 128 GPUs.
Wikiometrics: A Wikipedia Based Ranking System
Authors: Gilad Katz, Lior Rokach
Date: 2016-01-06 00:00:00

We present a new concept - Wikiometrics - the derivation of metrics and
indicators from Wikipedia. Wikipedia provides an accurate representation of the
real world due to its size, structure, editing policy and popularity. We
demonstrate an innovative mining methodology, where different elements of
Wikipedia - content, structure, editorial actions and reader reviews - are used
to rank items in a manner which is by no means inferior to rankings produced by
experts or other methods. We test our proposed method by applying it to two
real-world ranking problems: top world universities and academic journals. Our
proposed ranking methods were compared to leading and widely accepted
benchmarks, and were found to be extremely correlative but with the advantage
of the data being publically available.