pwc-archive/papers-with-abstracts huggingface dataset:
Cancer Prognosis Prediction Using Balanced Stratified Sampling
Authors: J S Saleema, N Bhagawathi, S Monica, P Deepa Shenoy, K R Venugopal, L M Patnaik
Date: 2014-03-12 00:00:00
Tasks: General Classification, Prediction, Prognosis

High accuracy in cancer prediction is important to improve the quality of the
treatment and to improve the rate of survivability of patients. As the data
volume is increasing rapidly in the healthcare research, the analytical
challenge exists in double. The use of effective sampling technique in
classification algorithms always yields good prediction accuracy. The SEER
public use cancer database provides various prominent class labels for
prognosis prediction. The main objective of this paper is to find the effect of
sampling techniques in classifying the prognosis variable and propose an ideal
sampling method based on the outcome of the experimentation. In the first phase
of this work the traditional random sampling and stratified sampling techniques
have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested.
Much of the initial time has been focused on performing the pre_processing of
the SEER data set. The classification model for experimentation has been built
using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naive Bayes and K-Nearest
Neighbor. The three prognosis factors survival, stage and metastasis have been
used as class labels for experimental comparisons. The results shows a steady
increase in the prediction accuracy of balanced stratified model as the sample
size increases, but the traditional approach fluctuates before the optimum
results.
Uav Route Planning For Maximum Target Coverage
Authors: Murat Karakaya
Date: 2014-03-12 00:00:00

Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil
operations is getting popular. One of the challenges in effectively tasking
these expensive vehicles is planning the flight routes to monitor the targets.
In this work, we aim to develop an algorithm which produces routing plans for a
limited number of UAVs to cover maximum number of targets considering their
flight range. The proposed solution for this practical optimization problem is
designed by modifying the Max-Min Ant System (MMAS) algorithm. To evaluate the
success of the proposed method, an alternative approach, based on the Nearest
Neighbour (NN) heuristic, has been developed as well. The results showed the
success of the proposed MMAS method by increasing the number of covered targets
compared to the solution based on the NN heuristic.
Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras
Authors: M. Martínez-Zarzuela, M. Pedraza-Hueso, F. J. Díaz-Pernas, D. González-Ortega, M. Antón-Rodríguez
Date: 2014-03-12 00:00:00

This article describes the design and development of a system for remote
indoor 3D monitoring using an undetermined number of Microsoft(R) Kinect
sensors. In the proposed client-server system, the Kinect cameras can be
connected to different computers, addressing this way the hardware limitation
of one sensor per USB controller. The reason behind this limitation is the high
bandwidth needed by the sensor, which becomes also an issue for the distributed
system TCP/IP communications. Since traffic volume is too high, 3D data has to
be compressed before it can be sent over the network. The solution consists in
selfcoding the Kinect data into RGB images and then using a standard multimedia
codec to compress color maps. Information from different sources is collected
into a central client computer, where point clouds are transformed to
reconstruct the scene in 3D. An algorithm is proposed to merge the skeletons
detected locally by each Kinect conveniently, so that monitoring of people is
robust to self and inter-user occlusions. Final skeletons are labeled and
trajectories of every joint can be saved for event reconstruction or further
analysis.
A survey of dimensionality reduction techniques
Authors: C. O. S. Sorzano, J. Vargas, A. Pascual Montano
Date: 2014-03-12 00:00:00
Tasks: Dimensionality Reduction, Survey

Experimental life sciences like biology or chemistry have seen in the recent
decades an explosion of the data available from experiments. Laboratory
instruments become more and more complex and report hundreds or thousands
measurements for a single experiment and therefore the statistical methods face
challenging tasks when dealing with such high dimensional data. However, much
of the data is highly redundant and can be efficiently brought down to a much
smaller number of variables without a significant loss of information. The
mathematical procedures making possible this reduction are called
dimensionality reduction techniques; they have widely been developed by fields
like Statistics or Machine Learning, and are currently a hot research topic. In
this review we categorize the plethora of dimension reduction techniques
available and give the mathematical insight behind them.
Shape-Based Plagiarism Detection for Flowchart Figures in Texts
Authors: Senosy Arrish, Fadhil Noer Afif, Ahmadu Maidorawa, Naomie Salim
Date: 2014-03-12 00:00:00
Tasks: Retrieval

Plagiarism detection is well known phenomenon in the academic arena. Copying
other people is considered as serious offence that needs to be checked. There
are many plagiarism detection systems such as turn-it-in that has been
developed to provide this checks. Most, if not all, discard the figures and
charts before checking for plagiarism. Discarding the figures and charts
results in look holes that people can take advantage. That means people can
plagiarized figures and charts easily without the current plagiarism systems
detecting it. There are very few papers which talks about flowcharts plagiarism
detection. Therefore, there is a need to develop a system that will detect
plagiarism in figures and charts. This paper presents a method for detecting
flow chart figure plagiarism based on shape-based image processing and
multimedia retrieval. The method managed to retrieve flowcharts with ranked
similarity according to different matching sets.
Application of Particle Swarm Optimization to Microwave Tapered Microstrip Lines
Authors: Ezgi Deniz Ulker, Sadik Ulker
Date: 2014-03-12 00:00:00
Tasks: Electrical Engineering, Multiobjective Optimization

Application of metaheuristic algorithms has been of continued interest in the
field of electrical engineering because of their powerful features. In this
work special design is done for a tapered transmission line used for matching
an arbitrary real load to a 50{\Omega} line. The problem at hand is to match
this arbitrary load to 50 {\Omega} line using three section tapered
transmission line with impedances in decreasing order from the load. So the
problem becomes optimizing an equation with three unknowns with various
conditions. The optimized values are obtained using Particle Swarm
Optimization. It can easily be shown that PSO is very strong in solving this
kind of multiobjective optimization problems.
HPS: a hierarchical Persian stemming method
Authors: Ayshe Rashidi, Mina Zolfy Lighvan
Date: 2014-03-12 00:00:00
Tasks: Sentence

In this paper, a novel hierarchical Persian stemming approach based on the
Part-Of-Speech of the word in a sentence is presented. The implemented stemmer
includes hash tables and several deterministic finite automata in its different
levels of hierarchy for removing the prefixes and suffixes of the words. We had
two intentions in using hash tables in our method. The first one is that the
DFA don't support some special words, so hash table can partly solve the
addressed problem. the second goal is to speed up the implemented stemmer with
omitting the time that deterministic finite automata need. Because of the
hierarchical organization, this method is fast and flexible enough. Our
experiments on test sets from Hamshahri collection and security news (istna.ir)
show that our method has the average accuracy of 95.37% which is even improved
in using the method on a test set with common topics.
Polyceptron: A Polyhedral Learning Algorithm
Authors: Naresh Manwani, P. S. Sastry
Date: 2011-07-08 00:00:00

In this paper we propose a new algorithm for learning polyhedral classifiers
which we call as Polyceptron. It is a Perception like algorithm which updates
the parameters only when the current classifier misclassifies any training
data. We give both batch and online version of Polyceptron algorithm. Finally
we give experimental results to show the effectiveness of our approach.
Markov properties for mixed graphs
Authors: Kayvan Sadeghi, Steffen Lauritzen
Date: 2011-09-27 00:00:00

In this paper, we unify the Markov theory of a variety of different types of
graphs used in graphical Markov models by introducing the class of loopless
mixed graphs, and show that all independence models induced by $m$-separation
on such graphs are compositional graphoids. We focus in particular on the
subclass of ribbonless graphs which as special cases include undirected graphs,
bidirected graphs, and directed acyclic graphs, as well as ancestral graphs and
summary graphs. We define maximality of such graphs as well as a pairwise and a
global Markov property. We prove that the global and pairwise Markov properties
of a maximal ribbonless graph are equivalent for any independence model that is
a compositional graphoid.
Counterfactual Estimation and Optimization of Click Metrics for Search Engines
Authors: Lihong Li, Shunbao Chen, Jim Kleban, Ankur Gupta
Date: 2014-03-07 00:00:00
Tasks: Causal Inference, counterfactual, valid

Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different search result page for the same query, but we normally cannot
infer reliably from search log how users would react to the new result page.
Consequently, it appears impossible to accurately estimate online metrics that
depend on user feedback, unless the new engine is run to serve users and
compared with a baseline in an A/B test. This approach, while valid and
successful, is unfortunately expensive and time-consuming. In this paper, we
propose to address this problem using causal inference techniques, under the
contextual-bandit framework. This approach effectively allows one to run
(potentially infinitely) many A/B tests offline from search log, making it
possible to estimate and optimize online metrics quickly and inexpensively.
Focusing on an important component in a commercial search engine, we show how
these ideas can be instantiated and applied, and obtain very promising results
that suggest the wide applicability of these techniques.
Learning Deep Face Representation
Authors: Haoqiang Fan, Zhimin Cao, Yuning Jiang, Qi Yin, Chinchilla Doudou
Date: 2014-03-12 00:00:00
Tasks: Face Recognition

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy-to-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation-efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy ($85.8\%$ on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance ($97.3\%$) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.
The Bursty Dynamics of the Twitter Information Network
Authors: Seth A. Myers, Jure Leskovec
Date: 2014-03-11 00:00:00

In online social media systems users are not only posting, consuming, and
resharing content, but also creating new and destroying existing connections in
the underlying social network. While each of these two types of dynamics has
individually been studied in the past, much less is known about the connection
between the two. How does user information posting and seeking behavior
interact with the evolution of the underlying social network structure?
  Here, we study ways in which network structure reacts to users posting and
sharing content. We examine the complete dynamics of the Twitter information
network, where users post and reshare information while they also create and
destroy connections. We find that the dynamics of network structure can be
characterized by steady rates of change, interrupted by sudden bursts.
Information diffusion in the form of cascades of post re-sharing often creates
such sudden bursts of new connections, which significantly change users' local
network structure. These bursts transform users' networks of followers to
become structurally more cohesive as well as more homogenous in terms of
follower interests. We also explore the effect of the information content on
the dynamics of the network and find evidence that the appearance of new topics
and real-world events can lead to significant changes in edge creations and
deletions. Lastly, we develop a model that quantifies the dynamics of the
network and the occurrence of these bursts as a function of the information
spreading through the network. The model can successfully predict which
information diffusion events will lead to bursts in network dynamics.
Unsupervised Learning of Invariant Representations in Hierarchical Architectures
Authors: Fabio Anselmi, Joel Z. Leibo, Lorenzo Rosasco, Jim Mutch, Andrea Tacchetti, Tomaso Poggio
Date: 2013-11-17 00:00:00
Tasks: Object Recognition, speech-recognition, Speech Recognition

The present phase of Machine Learning is characterized by supervised learning
algorithms relying on large sets of labeled examples ($n \to \infty$). The next
phase is likely to focus on algorithms capable of learning from very few
labeled examples ($n \to 1$), like humans seem able to do. We propose an
approach to this problem and describe the underlying theory, based on the
unsupervised, automatic learning of a ``good'' representation for supervised
learning, characterized by small sample complexity ($n$). We consider the case
of visual object recognition though the theory applies to other domains. The
starting point is the conjecture, proved in specific cases, that image
representations which are invariant to translations, scaling and other
transformations can considerably reduce the sample complexity of learning. We
prove that an invariant and unique (discriminative) signature can be computed
for each image patch, $I$, in terms of empirical distributions of the
dot-products between $I$ and a set of templates stored during unsupervised
learning. A module performing filtering and pooling, like the simple and
complex cells described by Hubel and Wiesel, can compute such estimates.
Hierarchical architectures consisting of this basic Hubel-Wiesel moduli inherit
its properties of invariance, stability, and discriminability while capturing
the compositional organization of the visual world in terms of wholes and
parts. The theory extends existing deep learning convolutional architectures
for image and speech recognition. It also suggests that the main computational
goal of the ventral stream of visual cortex is to provide a hierarchical
representation of new objects/images which is invariant to transformations,
stable, and discriminative for recognition---and that this representation may
be continuously learned in an unsupervised way during development and visual
experience.
Flying Insect Classification with Inexpensive Sensors
Authors: Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, Eamonn Keogh
Date: 2014-03-11 00:00:00
Tasks: Attribute, Classification, General Classification

The ability to use inexpensive, noninvasive sensors to accurately classify
flying insects would have significant implications for entomological research,
and allow for the development of many useful applications in vector control for
both medical and agricultural entomology. Given this, the last sixty years have
seen many research efforts on this task. To date, however, none of this
research has had a lasting impact. In this work, we explain this lack of
progress. We attribute the stagnation on this problem to several factors,
including the use of acoustic sensing devices, the over-reliance on the single
feature of wingbeat frequency, and the attempts to learn complex models with
relatively little data. In contrast, we show that pseudo-acoustic optical
sensors can produce vastly superior data, that we can exploit additional
features, both intrinsic and extrinsic to the insect's flight behavior, and
that a Bayesian classification approach allows us to efficiently learn
classification models that are very robust to over-fitting. We demonstrate our
findings with large scale experiments that dwarf all previous works combined,
as measured by the number of insects and the number of species considered.
Hardness Results for Approximate Pure Horn CNF Formulae Minimization
Authors: Endre Boros, Aritanan Gruber
Date: 2012-04-16 00:00:00

We study the hardness of approximation of clause minimum and literal minimum
representations of pure Horn functions in $n$ Boolean variables. We show that
unless P=NP, it is not possible to approximate in polynomial time the minimum
number of clauses and the minimum number of literals of pure Horn CNF
representations to within a factor of $2^{\log^{1-o(1)} n}$. This is the case
even when the inputs are restricted to pure Horn 3-CNFs with
$O(n^{1+\varepsilon})$ clauses, for some small positive constant $\varepsilon$.
Furthermore, we show that even allowing sub-exponential time computation, it is
still not possible to obtain constant factor approximations for such problems
unless the Exponential Time Hypothesis turns out to be false.
Turing: Then, Now and Still Key
Authors: Kieran Greer
Date: 2014-03-11 00:00:00

This paper looks at Turing's postulations about Artificial Intelligence in
his paper 'Computing Machinery and Intelligence', published in 1950. It notes
how accurate they were and how relevant they still are today. This paper notes
the arguments and mechanisms that he suggested and tries to expand on them
further. The paper however is mostly about describing the essential ingredients
for building an intelligent model and the problems related with that. The
discussion includes recent work by the author himself, who adds his own
thoughts on the matter that come from a purely technical investigation into the
problem. These are personal and quite speculative, but provide an interesting
insight into the mechanisms that might be used for building an intelligent
system.
Cognitive Internet of Things: A New Paradigm beyond Connection
Authors: Qihui Wu, Guoru Ding, Yuhua Xu, Shuo Feng, Zhiyong Du, Jinlong Wang, Keping Long
Date: 2014-03-11 00:00:00
Tasks: Decision Making

Current research on Internet of Things (IoT) mainly focuses on how to enable
general objects to see, hear, and smell the physical world for themselves, and
make them connected to share the observations. In this paper, we argue that
only connected is not enough, beyond that, general objects should have the
capability to learn, think, and understand both physical and social worlds by
themselves. This practical need impels us to develop a new paradigm, named
Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain'
for high-level intelligence. Specifically, we first present a comprehensive
definition for CIoT, primarily inspired by the effectiveness of human
cognition. Then, we propose an operational framework of CIoT, which mainly
characterizes the interactions among five fundamental cognitive tasks:
perception-action cycle, massive data analytics, semantic derivation and
knowledge discovery, intelligent decision-making, and on-demand service
provisioning. Furthermore, we provide a systematic tutorial on key enabling
techniques involved in the cognitive tasks. In addition, we also discuss the
design of proper performance metrics on evaluating the enabling techniques.
Last but not least, we present the research challenges and open issues ahead.
Building on the present work and potentially fruitful future studies, CIoT has
the capability to bridge the physical world (with objects, resources, etc.) and
the social world (with human demand, social behavior, etc.), and enhance smart
resource allocation, automatic network operation, and intelligent service
provisioning.
Transfer Learning across Networks for Collective Classification
Authors: Meng Fang, Jie Yin, Xingquan Zhu
Date: 2014-03-11 00:00:00
Tasks: Classification, General Classification, Transfer Learning

This paper addresses the problem of transferring useful knowledge from a
source network to predict node labels in a newly formed target network. While
existing transfer learning research has primarily focused on vector-based data,
in which the instances are assumed to be independent and identically
distributed, how to effectively transfer knowledge across different information
networks has not been well studied, mainly because networks may have their
distinct node features and link relationships between nodes. In this paper, we
propose a new transfer learning algorithm that attempts to transfer common
latent structure features across the source and target networks. The proposed
algorithm discovers these latent features by constructing label propagation
matrices in the source and target networks, and mapping them into a shared
latent feature space. The latent features capture common structure patterns
shared by two networks, and serve as domain-independent features to be
transferred between networks. Together with domain-dependent node features, we
thereafter propose an iterative classification algorithm that leverages label
correlations to predict node labels in the target network. Experiments on
real-world networks demonstrate that our proposed algorithm can successfully
achieve knowledge transfer between networks to help improve the accuracy of
classifying nodes in the target network.
Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means
Authors: Haijuan Hu, Bing Li, Quansheng Liu
Date: 2014-03-11 00:00:00

We first establish a law of large numbers and a convergence theorem in
distribution to show the rate of convergence of the non-local means filter for
removing Gaussian noise. We then introduce the notion of degree of similarity
to measure the role of similarity for the non-local means filter. Based on the
convergence theorems, we propose a patch-based weighted means filter for
removing impulse noise and its mixture with Gaussian noise by combining the
essential idea of the trilateral filter and that of the non-local means filter.
Our experiments show that our filter is competitive compared to recently
proposed methods.