pwc-archive/papers-with-abstracts huggingface dataset:
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
Authors: Marco Castelluccio, Giovanni Poggi, Carlo Sansone, Luisa Verdoliva
Date: 2015-08-01 00:00:00
Tasks: General Classification

We explore the use of convolutional neural networks for the semantic
classification of remote sensing scenes. Two recently proposed architectures,
CaffeNet and GoogLeNet, are adopted, with three different learning modalities.
Besides conventional training from scratch, we resort to pre-trained networks
that are only fine-tuned on the target data, so as to avoid overfitting
problems and reduce design time. Experiments on two remote sensing datasets,
with markedly different characteristics, testify on the effectiveness and wide
applicability of the proposed solution, which guarantees a significant
performance improvement over all state-of-the-art references.
Turnover Prediction Of Shares using Data Mining techniques : A Case Study
Authors: D. S. Shashaank, V. Sruthi, M. L. S Vijayalakshimi, Jacob Shomona Garcia
Date: 2015-08-01 00:00:00
Tasks: feature selection, General Classification, Navigate

Predicting the turnover of a company in the ever fluctuating Stock market has
always proved to be a precarious situation and most certainly a difficult task
in hand. Data mining is a well-known sphere of Computer Science that aims on
extracting meaningful information from large databases. However, despite the
existence of many algorithms for the purpose of predicting the future trends,
their efficiency is questionable as their predictions suffer from a high error
rate. The objective of this paper is to investigate various classification
algorithms to predict the turnover of different companies based on the Stock
price. The authorized dataset for predicting the turnover was taken from
www.bsc.com and included the stock market values of various companies over the
past 10 years. The algorithms were investigated using the "R" tool. The feature
selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the
Total Turnover of the company was predicted using various classification
algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company
on an everyday basis and hence could help navigate through dubious stock market
trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of stock market attributes was established as well.
Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation
Authors: Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama
Date: 2015-08-01 00:00:00
Tasks: Clustering, Density Estimation, Multi-Task Learning

Log-density gradient estimation is a fundamental statistical problem and
possesses various practical applications such as clustering and measuring
non-Gaussianity. A naive two-step approach of first estimating the density and
then taking its log-gradient is unreliable because an accurate density estimate
does not necessarily lead to an accurate log-density gradient estimate. To cope
with this problem, a method to directly estimate the log-density gradient
without density estimation has been explored, and demonstrated to work much
better than the two-step method. The objective of this paper is to further
improve the performance of this direct method in multi-dimensional cases. Our
idea is to regard the problem of log-density gradient estimation in each
dimension as a task, and apply regularized multi-task learning to the direct
log-density gradient estimator. We experimentally demonstrate the usefulness of
the proposed multi-task method in log-density gradient estimation and
mode-seeking clustering.
The Child is Father of the Man: Foresee the Success at the Early Stage
Authors: Liangyue Li, Hanghang Tong
Date: 2015-04-03 00:00:00

Understanding the dynamic mechanisms that drive the high-impact scientific
work (e.g., research papers, patents) is a long-debated research topic and has
many important implications, ranging from personal career development and
recruitment search, to the jurisdiction of research resources. Recent advances
in characterizing and modeling scientific success have made it possible to
forecast the long-term impact of scientific work, where data mining techniques,
supervised learning in particular, play an essential role. Despite much
progress, several key algorithmic challenges in relation to predicting
long-term scientific impact have largely remained open. In this paper, we
propose a joint predictive model to forecast the long-term scientific impact at
the early stage, which simultaneously addresses a number of these open
challenges, including the scholarly feature design, the non-linearity, the
domain-heterogeneity and dynamics. In particular, we formulate it as a
regularized optimization problem and propose effective and scalable algorithms
to solve it. We perform extensive empirical evaluations on large, real
scholarly data sets to validate the effectiveness and the efficiency of our
method.
Building Hierarchies of Concepts via Crowdsourcing
Authors: Yuyin Sun, Adish Singla, Dieter Fox, Andreas Krause
Date: 2015-04-27 00:00:00

Hierarchies of concepts are useful in many applications from navigation to
organization of objects. Usually, a hierarchy is created in a centralized
manner by employing a group of domain experts, a time-consuming and expensive
process. The experts often design one single hierarchy to best explain the
semantic relationships among the concepts, and ignore the natural uncertainty
that may exist in the process. In this paper, we propose a crowdsourcing system
to build a hierarchy and furthermore capture the underlying uncertainty. Our
system maintains a distribution over possible hierarchies and actively selects
questions to ask using an information gain criterion. We evaluate our
methodology on simulated data and on a set of real world application domains.
Experimental results show that our system is robust to noise, efficient in
picking questions, cost-effective and builds high quality hierarchies.
Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics
Authors: Zenon Colaco, Mohan Sridharan
Date: 2015-08-01 00:00:00
Tasks: Explanation Generation, Logical Reasoning

Robots assisting humans in complex domains have to represent knowledge and
reason at both the sensorimotor level and the social level. The architecture
described in this paper couples the non-monotonic logical reasoning
capabilities of a declarative language with probabilistic belief revision,
enabling robots to represent and reason with qualitative and quantitative
descriptions of knowledge and degrees of belief. Specifically, incomplete
domain knowledge, including information that holds in all but a few exceptional
situations, is represented as a Answer Set Prolog (ASP) program. The answer set
obtained by solving this program is used for inference, planning, and for
jointly explaining (a) unexpected action outcomes due to exogenous actions and
(b) partial scene descriptions extracted from sensor input. For any given task,
each action in the plan contained in the answer set is executed
probabilistically. The subset of the domain relevant to the action is
identified automatically, and observations extracted from sensor inputs perform
incremental Bayesian updates to a belief distribution defined over this domain
subset, with highly probable beliefs being committed to the ASP program. The
architecture's capabilities are illustrated in simulation and on a mobile robot
in the context of a robot waiter operating in the dining room of a restaurant.
Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
Authors: Danny Ho, Luiz Fernando Capretz, Xishi Huang, Jing Ren
Date: 2015-07-31 00:00:00
Tasks: Management

Accurate estimation such as cost estimation, quality estimation and risk
analysis is a major issue in management. We propose a patent pending soft
computing framework to tackle this challenging problem. Our generic framework
is independent of the nature and type of estimation. It consists of neural
network, fuzzy logic, and an algorithmic estimation model. We made use of the
Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function
Point Analysis as the algorithmic models and validated the accuracy of the
Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using
industrial project data. Our model produces more accurate estimation than using
an algorithmic model alone. We also discuss the prototypes of our tools that
implement the NFA Model. We conclude with our roadmap and direction to enrich
the model in tackling different estimation challenges.
On First-Order Model-Based Reasoning
Authors: Maria Paola Bonacina, Ulrich Furbach, Viorica Sofronie-Stokkermans
Date: 2015-02-09 00:00:00
Tasks: model

Reasoning semantically in first-order logic is notoriously a challenge. This paper surveys a selection of semantically-guided or model-based methods that aim at meeting aspects of this challenge. For first-order logic we touch upon resolution-based methods, tableaux-based methods, DPLL-inspired methods, and we give a preview of a new method called SGGS, for Semantically-Guided Goal-Sensitive reasoning. For first-order theories we highlight hierarchical and locality-based methods, concluding with the recent Model-Constructing satisfiability calculus.
A Minimal Architecture for General Cognition
Authors: Michael S. Gashler, Zachariah Kindle, Michael R. Smith
Date: 2015-07-31 00:00:00

A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.
Resourceful Contextual Bandits
Authors: Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
Date: 2014-02-27 00:00:00
Tasks: Multi-Armed Bandits

We study contextual bandits with ancillary constraints on resources, which
are common in real-world applications such as choosing ads or dynamic pricing
of items. We design the first algorithm for solving these problems that handles
constrained resources other than time, and improves over a trivial reduction to
the non-contextual case. We consider very general settings for both contextual
bandits (arbitrary policy sets, e.g. Dudik et al. (UAI'11)) and bandits with
resource constraints (bandits with knapsacks, Badanidiyuru et al. (FOCS'13)),
and prove a regret guarantee with near-optimal statistical properties.
SnowWatch: Snow Monitoring through Acquisition and Analysis of User-Generated Content
Authors: Roman Fedorov, Piero Fraternali, Chiara Pasini, Marco Tagliasacchi
Date: 2015-07-31 00:00:00

We present a system for complementing snow phenomena monitoring with virtual
measurements extracted from public visual content. The proposed system
integrates an automatic acquisition and analysis of photographs and webcam
images depicting Alpine mountains. In particular, the technical demonstration
consists in a web portal that interfaces the whole system with the population.
It acts as an entertaining photo-sharing social web site, acquiring at the same
time visual content necessary for environmental monitoring.
Efficient and robust calibration of the Heston option pricing model for American options using an improved Cuckoo Search Algorithm
Authors: Stefan Haring, Ronald Hochreiter
Date: 2015-07-31 00:00:00

In this paper an improved Cuckoo Search Algorithm is developed to allow for
an efficient and robust calibration of the Heston option pricing model for
American options. Calibration of stochastic volatility models like the Heston
is significantly harder than classical option pricing models as more parameters
have to be estimated. The difficult task of calibrating one of these models to
American Put options data is the main objective of this paper. Numerical
results are shown to substantiate the suitability of the chosen method to
tackle this problem.
Exploiting Binary Floating-Point Representations for Constraint Propagation: The Complete Unabridged Version
Authors: Roberto Bagnara, Matthieu Carlier, Roberta Gori, Arnaud Gotlieb
Date: 2013-08-18 00:00:00

Floating-point computations are quickly finding their way in the design of
safety- and mission-critical systems, despite the fact that designing
floating-point algorithms is significantly more difficult than designing
integer algorithms. For this reason, verification and validation of
floating-point computations is a hot research topic. An important verification
technique, especially in some industrial sectors, is testing. However,
generating test data for floating-point intensive programs proved to be a
challenging problem. Existing approaches usually resort to random or
search-based test data generation, but without symbolic reasoning it is almost
impossible to generate test inputs that execute complex paths controlled by
floating-point computations. Moreover, as constraint solvers over the reals or
the rationals do not natively support the handling of rounding errors, the need
arises for efficient constraint solvers over floating-point domains. In this
paper, we present and fully justify improved algorithms for the propagation of
arithmetic IEEE 754 binary floating-point constraints. The key point of these
algorithms is a generalization of an idea by B. Marre and C. Michel that
exploits a property of the representation of floating-point numbers.
Spin Glass Models of Syntax and Language Evolution
Authors: Karthik Siva, Jim Tao, Matilde Marcolli
Date: 2015-07-31 00:00:00

Using the SSWL database of syntactic parameters of world languages, and the
MIT Media Lab data on language interactions, we construct a spin glass model of
language evolution. We treat binary syntactic parameters as spin states, with
languages as vertices of a graph, and assigned interaction energies along the
edges. We study a rough model of syntax evolution, under the assumption that a
strong interaction energy tends to cause parameters to align, as in the case of
ferromagnetic materials. We also study how the spin glass model needs to be
modified to account for entailment relations between syntactic parameters. This
modification leads naturally to a generalization of Potts models with external
magnetic field, which consists of a coupling at the vertices of an Ising model
and a Potts model with q=3, that have the same edge interactions. We describe
the results of simulations of the dynamics of these models, in different
temperature and energy regimes. We discuss the linguistic interpretation of the
parameters of the physical model.
A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning
Authors: Jiachen Yanga, Zhiyong Dinga, Fei Guoa, Huogen Wanga, Nick Hughesb
Date: 2015-07-31 00:00:00

In this paper, we investigate the problem of optimization multivariate
performance measures, and propose a novel algorithm for it. Different from
traditional machine learning methods which optimize simple loss functions to
learn prediction function, the problem studied in this paper is how to learn
effective hyper-predictor for a tuple of data points, so that a complex loss
function corresponding to a multivariate performance measure can be minimized.
We propose to present the tuple of data points to a tuple of sparse codes via a
dictionary, and then apply a linear function to compare a sparse code against a
give candidate class label. To learn the dictionary, sparse codes, and
parameter of the linear function, we propose a joint optimization problem. In
this problem, the both the reconstruction error and sparsity of sparse code,
and the upper bound of the complex loss function are minimized. Moreover, the
upper bound of the loss function is approximated by the sparse codes and the
linear function parameter. To optimize this problem, we develop an iterative
algorithm based on descent gradient methods to learn the sparse codes and
hyper-predictor parameter alternately. Experiment results on some benchmark
data sets show the advantage of the proposed methods over other
state-of-the-art algorithms.
Image Super-Resolution Using Deep Convolutional Networks
Authors: Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang
Date: 2014-12-31 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

We propose a deep learning method for single image super-resolution (SR). Our
method directly learns an end-to-end mapping between the low/high-resolution
images. The mapping is represented as a deep convolutional neural network (CNN)
that takes the low-resolution image as the input and outputs the
high-resolution one. We further show that traditional sparse-coding-based SR
methods can also be viewed as a deep convolutional network. But unlike
traditional methods that handle each component separately, our method jointly
optimizes all layers. Our deep CNN has a lightweight structure, yet
demonstrates state-of-the-art restoration quality, and achieves fast speed for
practical on-line usage. We explore different network structures and parameter
settings to achieve trade-offs between performance and speed. Moreover, we
extend our network to cope with three color channels simultaneously, and show
better overall reconstruction quality.
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
Authors: Ohad Shamir
Date: 2015-07-31 00:00:00

We study the convergence properties of the VR-PCA algorithm introduced by
\cite{shamir2015stochastic} for fast computation of leading singular vectors.
We prove several new results, including a formal analysis of a block version of
the algorithm, and convergence from random initialization. We also make a few
observations of independent interest, such as how pre-initializing with just a
single exact power iteration can significantly improve the runtime of
stochastic methods, and what are the convexity and non-convexity properties of
the underlying optimization problem.
Dense v.s. Sparse: A Comparative Study of Sampling Analysis in Scene Classification of High-Resolution Remote Sensing Imagery
Authors: Jingwen Hu, Gui-Song Xia, Fan Hu, Liangpei Zhang
Date: 2015-02-04 00:00:00
Tasks: Classification, General Classification, Scene Classification, Topic Models

Scene classification is a key problem in the interpretation of
high-resolution remote sensing imagery. Many state-of-the-art methods, e.g.
bag-of-visual-words model and its variants, the topic models as well as deep
learning-based approaches, share similar procedures: patch sampling, feature
description/learning and classification. Patch sampling is the first and a key
procedure which has a great influence on the results. In the literature, many
different sampling strategies have been used, {e.g. dense sampling, random
sampling, keypoint-based sampling and saliency-based sampling, etc. However, it
is still not clear which sampling strategy is suitable for the scene
classification of high-resolution remote sensing images. In this paper, we
comparatively study the effects of different sampling strategies under the
scenario of scene classification of high-resolution remote sensing images. We
divide the existing sampling methods into two types: dense sampling and sparse
sampling, the later of which includes random sampling, keypoint-based sampling
and various saliency-based sampling proposed recently. In order to compare
their performances, we rely on a standard bag-of-visual-words model to
construct our testing scheme, owing to their simplicity, robustness and
efficiency. The experimental results on two commonly used datasets show that
dense sampling has the best performance among all the strategies but with high
spatial and computational complexity, random sampling gives better or
comparable results than other sparse sampling methods, like the sophisticated
multi-scale key-point operators and the saliency-based methods which are
intensively studied and commonly used recently.
Multimodal Multipart Learning for Action Recognition in Depth Videos
Authors: Amir Shahroudy, Gang Wang, Tian-Tsong Ng, Qingxiong Yang
Date: 2015-07-31 00:00:00
Tasks: Action Recognition, feature selection, Multimodal Activity Recognition, Temporal Action Localization

The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes the structured sparsity to model each action as a combination of
multimodal features from a sparse set of body parts. To represent dynamics and
appearance of parts, we employ a heterogeneous set of depth and skeleton based
features. The proper structure of multimodal multipart features are formulated
into the learning framework via the proposed hierarchical mixed norm, to
regularize the structured features of each part and to apply sparsity between
them, in favor of a group feature selection. Our experimental results expose
the effectiveness of the proposed learning method in which it outperforms other
methods in all three tested datasets while saturating one of them by achieving
perfect accuracy.
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification
Authors: Fa Wu, Peijun Hu, Dexing Kong
Date: 2015-07-31 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

This paper presents a new version of Dropout called Split Dropout (sDropout)
and rotational convolution techniques to improve CNNs' performance on image
classification. The widely used standard Dropout has advantage of preventing
deep neural networks from overfitting by randomly dropping units during
training. Our sDropout randomly splits the data into two subsets and keeps both
rather than discards one subset. We also introduce two rotational convolution
techniques, i.e. rotate-pooling convolution (RPC) and flip-rotate-pooling
convolution (FRPC) to boost CNNs' performance on the robustness for rotation
transformation. These two techniques encode rotation invariance into the
network without adding extra parameters. Experimental evaluations on
ImageNet2012 classification task demonstrate that sDropout not only enhances
the performance but also converges faster. Additionally, RPC and FRPC make CNNs
more robust for rotation transformations. Overall, FRPC together with sDropout
bring $1.18\%$ (model of Zeiler and Fergus~\cite{zeiler2013visualizing},
10-view, top-1) accuracy increase in ImageNet 2012 classification task compared
to the original network.
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
Authors: Ohad Shamir
Date: 2015-07-31 00:00:00

We consider the closely related problems of bandit convex optimization with
two-point feedback, and zero-order stochastic convex optimization with two
function evaluations per round. We provide a simple algorithm and analysis
which is optimal for convex Lipschitz functions. This improves on
\cite{dujww13}, which only provides an optimal result for smooth functions;
Moreover, the algorithm and analysis are simpler, and readily extend to
non-Euclidean problems. The algorithm is based on a small but surprisingly
powerful modification of the gradient estimator.