pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Algebraic Solution to the Perspective-Three-Point Problem
Authors: Tong Ke, Stergios Roumeliotis
Date: 2017-01-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Position

In this work, we present an algebraic solution to the classical
perspective-3-point (P3P) problem for determining the position and attitude of
a camera from observations of three known reference points. In contrast to
previous approaches, we first directly determine the camera's attitude by
employing the corresponding geometric constraints to formulate a system of
trigonometric equations. This is then efficiently solved, following an
algebraic approach, to determine the unknown rotation matrix and subsequently
the camera's position. As compared to recent alternatives, our method avoids
computing unnecessary (and potentially numerically unstable) intermediate
results, and thus achieves higher numerical accuracy and robustness at a lower
computational cost. These benefits are validated through extensive Monte-Carlo
simulations for both nominal and close-to-singular geometric configurations.
Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
Authors: Danielle Mowery, Craig Bryan, Mike Conway
Date: 2017-01-28 00:00:00
Tasks: General Classification

The utility of Twitter data as a medium to support population-level mental
health monitoring is not well understood. In an effort to better understand the
predictive power of supervised machine learning classifiers and the influence
of feature sets for efficiently classifying depression-related tweets on a
large-scale, we conducted two feature study experiments. In the first
experiment, we assessed the contribution of feature groups such as lexical
information (e.g., unigrams) and emotions (e.g., strongly negative) using a
feature ablation study. In the second experiment, we determined the percentile
of top ranked features that produced the optimal classification performance by
applying a three-step feature elimination approach. In the first experiment, we
observed that lexical features are critical for identifying depressive
symptoms, specifically for depressed mood (-35 points) and for disturbed sleep
(-43 points). In the second experiment, we observed that the optimal F1-score
performance of top ranked features in percentiles variably ranged across
classes e.g., fatigue or loss of energy (5th percentile, 288 features) to
depressed mood (55th percentile, 3,168 features) suggesting there is no
consistent count of features for predicting depressive-related tweets. We
conclude that simple lexical features and reduced feature sets can produce
comparable results to larger feature sets.
Sampling Without Time: Recovering Echoes of Light via Temporal Phase Retrieval
Authors: Ayush Bhandari, Aurelien Bourquard, Ramesh Raskar
Date: 2017-01-27 00:00:00
Tasks: Retrieval

This paper considers the problem of sampling and reconstruction of a
continuous-time sparse signal without assuming the knowledge of the sampling
instants or the sampling rate. This topic has its roots in the problem of
recovering multiple echoes of light from its low-pass filtered and
auto-correlated, time-domain measurements. Our work is closely related to the
topic of sparse phase retrieval and in this context, we discuss the advantage
of phase-free measurements. While this problem is ill-posed, cues based on
physical constraints allow for its appropriate regularization. We validate our
theory with experiments based on customized, optical time-of-flight imaging
sensors. What singles out our approach is that our sensing method allows for
temporal phase retrieval as opposed to the usual case of spatial phase
retrieval. Preliminary experiments and results demonstrate a compelling
capability of our phase-retrieval based imaging device.
Adversarial Evaluation of Dialogue Models
Authors: Anjuli Kannan, Oriol Vinyals
Date: 2017-01-27 00:00:00
Tasks: Decoder

The recent application of RNN encoder-decoder models has resulted in
substantial progress in fully data-driven dialogue systems, but evaluation
remains a challenge. An adversarial loss could be a way to directly evaluate
the extent to which generated dialogue responses sound like they came from a
human. This could reduce the need for human evaluation, while more directly
evaluating on a generative task. In this work, we investigate this idea by
training an RNN to discriminate a dialogue model's samples from human-generated
samples. Although we find some evidence this setup could be viable, we also
note that many issues remain in its practical application. We discuss both
aspects and conclude that future work is warranted.
Incremental Maintenance Of Association Rules Under Support Threshold Change
Authors: Mohamed Anis Bach Tobji, Mohamed Salah Gouider
Date: 2017-01-27 00:00:00

Maintenance of association rules is an interesting problem. Several
incremental maintenance algorithms were proposed since the work of (Cheung et
al, 1996). The majority of these algorithms maintain rule bases assuming that
support threshold doesn't change. In this paper, we present incremental
maintenance algorithm under support threshold change. This solution allows user
to maintain its rule base under any support threshold.
Comparative Study Of Data Mining Query Languages
Authors: Mohamed Anis Bach Tobji
Date: 2017-01-27 00:00:00

Since formulation of Inductive Database (IDB) problem, several Data Mining
(DM) languages have been proposed, confirming that KDD process could be
supported via inductive queries (IQ) answering. This paper reviews the existing
DM languages. We are presenting important primitives of the DM language and
classifying our languages according to primitives' satisfaction. In addition,
we presented languages' syntaxes and tried to apply each one to a database
sample to test a set of KDD operations. This study allows us to highlight
languages capabilities and limits, which is very useful for future work and
perspectives.
Bayesian Learning of Consumer Preferences for Residential Demand Response
Authors: Mikhail V. Goubko, Sergey O. Kuznetsov, Alexey A. Neznanov, Dmitry I. Ignatov
Date: 2017-01-27 00:00:00
Tasks: Recommendation Systems

In coming years residential consumers will face real-time electricity tariffs
with energy prices varying day to day, and effective energy saving will require
automation - a recommender system, which learns consumer's preferences from her
actions. A consumer chooses a scenario of home appliance use to balance her
comfort level and the energy bill. We propose a Bayesian learning algorithm to
estimate the comfort level function from the history of appliance use. In
numeric experiments with datasets generated from a simulation model of a
consumer interacting with small home appliances the algorithm outperforms
popular regression analysis tools. Our approach can be extended to control an
air heating and conditioning system, which is responsible for up to half of a
household's energy bill.
Robust Semi-supervised Least Squares Classification by Implicit Constraints
Authors: Jesse H. Krijthe, Marco Loog
Date: 2015-12-27 00:00:00
Tasks: Classification, General Classification

We introduce the implicitly constrained least squares (ICLS) classifier, a
novel semi-supervised version of the least squares classifier. This classifier
minimizes the squared loss on the labeled data among the set of parameters
implied by all possible labelings of the unlabeled data. Unlike other
discriminative semi-supervised methods, this approach does not introduce
explicit additional assumptions into the objective function, but leverages
implicit assumptions already present in the choice of the supervised least
squares classifier. This method can be formulated as a quadratic programming
problem and its solution can be found using a simple gradient descent
procedure. We prove that, in a limited 1-dimensional setting, this approach
never leads to performance worse than the supervised classifier. Experimental
results show that also in the general multidimensional case performance
improvements can be expected, both in terms of the squared loss that is
intrinsic to the classifier, as well as in terms of the expected classification
error.
Cascading Denoising Auto-Encoder as a Deep Directed Generative Model
Authors: Dong-Hyun Lee
Date: 2015-11-23 00:00:00
Tasks: Denoising

Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE)
become gener-ative models as a density estimator. However,in practice, the
framework suffers from a mixingproblem in the MCMC sampling process and
nodirect method to estimate the test log-likelihood.We consider a directed
model with an stochas-tic identity mapping (simple corruption pro-cess) as an
inference model and a DAE as agenerative model. By cascading these mod-els, we
propose Cascading Denoising Auto-Encoders(CDAE) which can generate samples
ofdata distribution from tractable prior distributionunder the assumption that
probabilistic distribu-tion of corrupted data approaches tractable
priordistribution as the level of corruption increases.This work tries to
answer two questions. On theone hand, can deep directed models be success-fully
trained without intractable posterior infer-ence and difficult optimization of
very deep neu-ral networks in inference and generative mod-els? These are
unavoidable when recent suc-cessful directed model like VAE (Kingma &Welling,
2014) is trained on complex dataset likereal images. On the other hand, can
DAEs getclean samples of data distribution from heavilycorrupted samples which
can be considered oftractable prior distribution far from data mani-fold?
so-called global denoising scheme.Our results show positive responses of
thesequestions and this work can provide fairly simpleframework for generative
models of very com-plex dataset.
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model
Authors: Dipaloke Saha, Md Saddam Hossain, Md. Saiful Islam, Sabir Ismail
Date: 2017-01-27 00:00:00
Tasks: Clustering, Language Modeling, Language Modelling, POS, POS Tagging, Recommendation Systems, text-classification, Text Classification, Word Sense Disambiguation

In this paper, we describe a research method that generates Bangla word
clusters on the basis of relating to meaning in language and contextual
similarity. The importance of word clustering is in parts of speech (POS)
tagging, word sense disambiguation, text classification, recommender system,
spell checker, grammar checker, knowledge discover and for many others Natural
Language Processing (NLP) applications. In the history of word clustering,
English and some other languages have already implemented some methods on word
clustering efficiently. But due to lack of the resources, word clustering in
Bangla has not been still implemented efficiently. Presently, its
implementation is in the beginning stage. In some research of word clustering
in English based on preceding and next five words of a key word they found an
efficient result. Now, we are trying to implement the tri-gram, 4-gram and
5-gram model of word clustering for Bangla to observe which one is the best
among them. We have started our research with quite a large corpus of
approximate 1 lakh Bangla words. We are using a machine learning technique in
this research. We will generate word clusters and analyze the clusters by
testing some different threshold values.
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Authors: Victor Campos, Brendan Jou, Xavier Giro-i-Nieto
Date: 2016-04-12 00:00:00
Tasks: Sentiment Analysis, Visual Sentiment Prediction

Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolutional Neural Networks (CNNs), a now de facto computational
machine learning tool particularly in the area of Computer Vision, can be
specifically applied to the task of visual sentiment prediction. We accomplish
this through fine-tuning experiments using a state-of-the-art CNN and via
rigorous architecture analysis, we present several modifications that lead to
accuracy improvements over prior art on a dataset of images from a popular
social media platform. We additionally present visualizations of local patterns
that the network learned to associate with image sentiment for insight into how
visual positivity (or negativity) is perceived by the model.
Statistical power and prediction accuracy in multisite resting-state fMRI connectivity
Authors: Christian Dansereau, Yassine Benhajali, Celine Risterucci, Emilio Merlo Pich, Pierre Orban, Douglas Arnold, Pierre Bellec
Date: 2016-07-12 00:00:00

Connectivity studies using resting-state functional magnetic resonance
imaging are increasingly pooling data acquired at multiple sites. While this
may allow investigators to speed up recruitment or increase sample size,
multisite studies also potentially introduce systematic biases in connectivity
measures across sites. In this work, we measure the inter-site effect in
connectivity and its impact on our ability to detect individual and group
differences. Our study was based on real, as opposed to simulated, multisite
fMRI datasets collected in N=345 young, healthy subjects across 8 scanning
sites with 3T scanners and heterogeneous scanning protocols, drawn from the
1000 functional connectome project. We first empirically show that typical
functional networks were reliably found at the group level in all sites, and
that the amplitude of the inter-site effects was small to moderate, with a
Cohen's effect size below 0.5 on average across brain connections. We then
implemented a series of Monte-Carlo simulations, based on real data, to
evaluate the impact of the multisite effects on detection power in statistical
tests comparing two groups (with and without the effect) using a general linear
model, as well as on the prediction of group labels with a support-vector
machine. As a reference, we also implemented the same simulations with fMRI
data collected at a single site using an identical sample size. Simulations
revealed that using data from heterogeneous sites only slightly decreased our
ability to detect changes compared to a monosite study with the GLM, and had a
greater impact on prediction accuracy. Taken together, our results support the
feasibility of multisite studies in rs-fMRI provided the sample size is large
enough.
Organic Computing in the Spotlight
Authors: Sven Tomforde, Bernhard Sick, Christian Müller-Schloer
Date: 2017-01-27 00:00:00

Organic Computing is an initiative in the field of systems engineering that
proposed to make use of concepts such as self-adaptation and self-organisation
to increase the robustness of technical systems. Based on the observation that
traditional design and operation concepts reach their limits, transferring more
autonomy to the systems themselves should result in a reduction of complexity
for users, administrators, and developers. However, there seems to be a need
for an updated definition of the term "Organic Computing", of desired
properties of technical, organic systems, and the objectives of the Organic
Computing initiative. With this article, we will address these points.
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
Authors: Björn Ross, Michael Rist, Guillermo Carbonell, Benjamin Cabrera, Nils Kurowsky, Michael Wojatzki
Date: 2017-01-27 00:00:00
Tasks: Hate Speech Detection

Some users of social media are spreading racist, sexist, and otherwise
hateful content. For the purpose of training a hate speech detection system,
the reliability of the annotations is crucial, but there is no universally
agreed-upon definition. We collected potentially hateful messages and asked two
groups of internet users to determine whether they were hate speech or not,
whether they should be banned or not and to rate their degree of offensiveness.
One of the groups was shown a definition prior to completing the survey. We
aimed to assess whether hate speech can be annotated reliably, and the extent
to which existing definitions are in accordance with subjective ratings. Our
results indicate that showing users a definition caused them to partially align
their own opinion with the definition but did not improve reliability, which
was very low overall. We conclude that the presence of hate speech should
perhaps not be considered a binary yes-or-no decision, and raters need more
detailed instructions for the annotation.
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
Authors: Apratim Bhattacharyya, Jilles Vreeken
Date: 2017-01-27 00:00:00

Discovering the key structure of a database is one of the main goals of data
mining. In pattern set mining we do so by discovering a small set of patterns
that together describe the data well. The richer the class of patterns we
consider, and the more powerful our description language, the better we will be
able to summarise the data. In this paper we propose \ourmethod, a novel greedy
MDL-based method for summarising sequential data using rich patterns that are
allowed to interleave. Experiments show \ourmethod is orders of magnitude
faster than the state of the art, results in better models, as well as
discovers meaningful semantics in the form patterns that identify multiple
choices of values.
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
Authors: Adam Charles, Dong Yin, Christopher Rozell
Date: 2016-05-26 00:00:00

Recurrent neural networks (RNNs) have drawn interest from machine learning
researchers because of their effectiveness at preserving past inputs for
time-varying data processing tasks. To understand the success and limitations
of RNNs, it is critical that we advance our analysis of their fundamental
memory properties. We focus on echo state networks (ESNs), which are RNNs with
simple memoryless nodes and random connectivity. In most existing analyses, the
short-term memory (STM) capacity results conclude that the ESN network size
must scale linearly with the input size for unstructured inputs. The main
contribution of this paper is to provide general results characterizing the STM
capacity for linear ESNs with multidimensional input streams when the inputs
have common low-dimensional structure: sparsity in a basis or significant
statistical dependence between inputs. In both cases, we show that the number
of nodes in the network must scale linearly with the information rate and
poly-logarithmically with the ambient input dimension. The analysis relies on
advanced applications of random matrix theory and results in explicit
non-asymptotic bounds on the recovery error. Taken together, this analysis
provides a significant step forward in our understanding of the STM properties
in RNNs.
Emotion Recognition From Speech With Recurrent Neural Networks
Authors: Vladimir Chernykh, Pavel Prikhodko
Date: 2017-01-27 00:00:00
Tasks: Emotion Recognition

In this paper the task of emotion recognition from speech is considered.
Proposed approach uses deep recurrent neural network trained on a sequence of
acoustic features calculated over small speech intervals. At the same time
special probabilistic-nature CTC loss function allows to consider long
utterances containing both emotional and neutral parts. The effectiveness of
such an approach is shown in two ways. Firstly, the comparison with recent
advances in this field is carried out. Secondly, human performance on the same
task is measured. Both criteria show the high quality of the proposed method.
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes
Authors: Franz J. Király, Zhaozhi Qian
Date: 2017-01-27 00:00:00
Tasks: Computational Efficiency, Low-Rank Matrix Completion, Matrix Completion, Prediction

Prediction and modelling of competitive sports outcomes has received much
recent attention, especially from the Bayesian statistics and machine learning
communities. In the real world setting of outcome prediction, the seminal
\'{E}l\H{o} update still remains, after more than 50 years, a valuable baseline
which is difficult to improve upon, though in its original form it is a
heuristic and not a proper statistical "model". Mathematically, the \'{E}l\H{o}
rating system is very closely related to the Bradley-Terry models, which are
usually used in an explanatory fashion rather than in a predictive supervised
or on-line learning setting.
  Exploiting this close link between these two model classes and some newly
observed similarities, we propose a new supervised learning framework with
close similarities to logistic regression, low-rank matrix completion and
neural networks. Building on it, we formulate a class of structured log-odds
models, unifying the desirable properties found in the above: supervised
probabilistic prediction of scores and wins/draws/losses, batch/epoch and
on-line learning, as well as the possibility to incorporate features in the
prediction, without having to sacrifice simplicity, parsimony of the
Bradley-Terry models, or computational efficiency of \'{E}l\H{o}'s original
approach.
  We validate the structured log-odds modelling approach in synthetic
experiments and English Premier League outcomes, where the added expressivity
yields the best predictions reported in the state-of-art, close to the quality
of contemporary betting odds.
A Comparative Study on Different Types of Approaches to Bengali document Categorization
Authors: Md. Saiful Islam, Fazla Elahi Md Jubayer, Syed Ikhtiar Ahmed
Date: 2017-01-27 00:00:00
Tasks: Classification, feature selection, General Classification

Document categorization is a technique where the category of a document is
determined. In this paper three well-known supervised learning techniques which
are Support Vector Machine(SVM), Na\"ive Bayes(NB) and Stochastic Gradient
Descent(SGD) compared for Bengali document categorization. Besides classifier,
classification also depends on how feature is selected from dataset. For
analyzing those classifier performances on predicting a document against twelve
categories several feature selection techniques are also applied in this
article namely Chi square distribution, normalized TFIDF (term
frequency-inverse document frequency) with word analyzer. So, we attempt to
explore the efficiency of those three-classification algorithms by using two
different feature selection techniques in this article.