pwc-archive/papers-with-abstracts huggingface dataset:
Studying the impact of negotiation environments on negotiation teams' performance
Authors: Victor Sanchez-Anguix, Vicente Julian, Vicente Botti, Ana Garcia-Fornes
Date: 2016-04-16 00:00:00

In this article we study the impact of the negotiation environment on the
performance of several intra-team strategies (team dynamics) for agent-based
negotiation teams that negotiate with an opponent. An agent-based negotiation
team is a group of agents that joins together as a party because they share
common interests in the negotiation at hand. It is experimentally shown how
negotiation environment conditions like the deadline of both parties, the
concession speed of the opponent, similarity among team members, and team size
affect performance metrics like the minimum utility of team members, the
average utility of team members, and the number of negotiation rounds. Our goal
is identifying which intra-team strategies work better in different
environmental conditions in order to provide useful knowledge for team members
to select appropriate intra-team strategies according to environmental
conditions.
Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders
Authors: Victor Sanchez-Anguix, Reyhan Aydogan, Vicente Julian, Catholijn Jonker
Date: 2016-04-16 00:00:00

Under some circumstances, a group of individuals may need to negotiate
together as a negotiation team against another party. Unlike bilateral
negotiation between two individuals, this type of negotiations entails to adopt
an intra-team strategy for negotiation teams in order to make team decisions
and accordingly negotiate with the opponent. It is crucial to be able to
negotiate successfully with heterogeneous opponents since opponents'
negotiation strategy and behavior may vary in an open environment. While one
opponent might collaborate and concede over time, another may not be inclined
to concede. This paper analyzes the performance of recently proposed intra-team
strategies for negotiation teams against different categories of opponents:
competitors, matchers, and conceders. Furthermore, it provides an extension of
the negotiation tool Genius for negotiation teams in bilateral settings.
Consequently, this work facilitates research in negotiation teams.
Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions
Authors: Victor Sanchez-Anguix, Soledad Valero, Vicente Julian, Vicente Botti, Ana Garcia-Fornes
Date: 2016-04-16 00:00:00

Ambient Intelligence aims to offer personalized services and easier ways of
interaction between people and systems. Since several users and systems may
coexist in these environments, it is quite possible that entities with opposing
preferences need to cooperate to reach their respective goals. Automated
negotiation is pointed as one of the mechanisms that may provide a solution to
this kind of problems. In this article, a multi-issue bilateral bargaining
model for Ambient Intelligence domains is presented where it is assumed that
agents have computational bounded resources and do not know their opponents'
preferences. The main goal of this work is to provide negotiation models that
obtain efficient agreements while maintaining the computational cost low. A
niching genetic algorithm is used before the negotiation process to sample
one's own utility function (self-sampling). During the negotiation process,
genetic operators are applied over the opponent's and one's own offers in order
to sample new offers that are interesting for both parties. Results show that
the proposed model is capable of outperforming similarity heuristics which only
sample before the negotiation process and of obtaining similar results to
similarity heuristics which have access to all of the possible offers.
Reaching Unanimous Agreements Within Agent-Based Negotiation Teams With Linear and Monotonic Utility Functions
Authors: Victor Sanchez-Anguix, Vicente Julian, Vicente Botti, Ana Garcia-Fornes
Date: 2016-04-16 00:00:00

In this article, an agent-based negotiation model for negotiation teams that
negotiate a deal with an opponent is presented. Agent-based negotiation teams
are groups of agents that join together as a single negotiation party because
they share an interest that is related to the negotiation process. The model
relies on a trusted mediator that coordinates and helps team members in the
decisions that they have to take during the negotiation process: which offer is
sent to the opponent, and whether the offers received from the opponent are
accepted. The main strength of the proposed negotiation model is the fact that
it guarantees unanimity within team decisions since decisions report a utility
to team members that is greater than or equal to their aspiration levels at
each negotiation round. This work analyzes how unanimous decisions are taken
within the team and the robustness of the model against different types of
manipulations. An empirical evaluation is also performed to study the impact of
the different parameters of the model.
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Authors: Victor Sanchez-Anguix, Vicente Julian, Vicente Botti, Ana Garcia-Fornes
Date: 2016-04-16 00:00:00

An agent-based negotiation team is a group of interdependent agents that join
together as a single negotiation party due to their shared interests in the
negotiation at hand. The reasons to employ an agent-based negotiation team may
vary: (i) more computation and parallelization capabilities, (ii) unite agents
with different expertise and skills whose joint work makes it possible to
tackle complex negotiation domains, (iii) the necessity to represent different
stakeholders or different preferences in the same party (e.g., organizations,
countries, and married couple). The topic of agent-based negotiation teams has
been recently introduced in multi-agent research. Therefore, it is necessary to
identify good practices, challenges, and related research that may help in
advancing the state-of-the-art in agent-based negotiation teams. For that
reason, in this article we review the tasks to be carried out by agent-based
negotiation teams. Each task is analyzed and related with current advances in
different research areas. The analysis aims to identify special challenges that
may arise due to the particularities of agent-based negotiation teams.
Unanimously acceptable agreements for negotiation teams in unpredictable domains
Authors: Victor Sanchez-Anguix, Reyhan Aydogan, Vicente Julian, Catholijn Jonker
Date: 2016-04-16 00:00:00
Tasks: Decision Making

A negotiation team is a set of agents with common and possibly also
conflicting preferences that forms one of the parties of a negotiation. A
negotiation team is involved in two decision making processes simultaneously, a
negotiation with the opponents, and an intra-team process to decide on the
moves to make in the negotiation. This article focuses on negotiation team
decision making for circumstances that require unanimity of team decisions.
Existing agent-based approaches only guarantee unanimity in teams negotiating
in domains exclusively composed of predictable and compatible issues. This
article presents a model for negotiation teams that guarantees unanimous team
decisions in domains consisting of predictable and compatible, and also
unpredictable issues. Moreover, the article explores the influence of using
opponent, and team member models in the proposing strategies that team members
use. Experimental results show that the team benefits if team members employ
Bayesian learning to model their teammates' preferences.
Automatic Segmentation of Dynamic Objects from an Image Pair
Authors: Sri Raghu Malireddi, Shanmuganathan Raman
Date: 2016-04-16 00:00:00
Tasks: Segmentation

Automatic segmentation of objects from a single image is a challenging
problem which generally requires training on large number of images. We
consider the problem of automatically segmenting only the dynamic objects from
a given pair of images of a scene captured from different positions. We exploit
dense correspondences along with saliency measures in order to first localize
the interest points on the dynamic objects from the two images. We propose a
novel approach based on techniques from computational geometry in order to
automatically segment the dynamic objects from both the images using a top-down
segmentation strategy. We discuss how the proposed approach is unique in
novelty compared to other state-of-the-art segmentation algorithms. We show
that the proposed approach for segmentation is efficient in handling large
motions and is able to achieve very good segmentation of the objects for
different scenes. We analyse the results with respect to the manually marked
ground truth segmentation masks created using our own dataset and provide key
observations in order to improve the work in future.
An artificial intelligence tool for heterogeneous team formation in the classroom
Authors: Juan M. Alberola, Elena Del Val, Victor Sanchez-Anguix, Alberto Palomares, Maria Dolores Teruel
Date: 2016-04-16 00:00:00

Nowadays, there is increasing interest in the development of teamwork skills
in the educational context. This growing interest is motivated by its
pedagogical effectiveness and the fact that, in labour contexts, enterprises
organize their employees in teams to carry out complex projects. Despite its
crucial importance in the classroom and industry, there is a lack of support
for the team formation process. Not only do many factors influence team
performance, but the problem becomes exponentially costly if teams are to be
optimized. In this article, we propose a tool whose aim it is to cover such a
gap. It combines artificial intelligence techniques such as coalition structure
generation, Bayesian learning, and Belbin's role theory to facilitate the
generation of working groups in an educational context. This tool improves
current state of the art proposals in three ways: i) it takes into account the
feedback of other teammates in order to establish the most predominant role of
a student instead of self-perception questionnaires; ii) it handles uncertainty
with regard to each student's predominant team role; iii) it is iterative since
it considers information from several interactions in order to improve the
estimation of role assignments. We tested the performance of the proposed tool
in an experiment involving students that took part in three different team
activities. The experiments suggest that the proposed tool is able to improve
different teamwork aspects such as team dynamics and student satisfaction.
Phone-based Metric as a Predictor for Basic Personality Traits
Authors: Bjarke Mønsted, Anders Mollgaard, Joachim Mathiesen
Date: 2016-04-16 00:00:00
Tasks: Dimensionality Reduction

Basic personality traits are typically assessed through questionnaires. Here
we consider phone-based metrics as a way to asses personality traits. We use
data from smartphones with custom data-collection software distributed to 730
individuals. The data includes information about location, physical motion,
face-to-face contacts, online social network friends, text messages and calls.
The data is further complemented by questionnaire-based data on basic
personality traits. From the phone-based metrics, we define a set of behavioral
variables, which we use in a prediction of basic personality traits. We find
that predominantly, the Big Five personality traits extraversion and, to some
degree, neuroticism are strongly expressed in our data. As an alternative to
the Big Five, we investigate whether other linear combinations of the 44
questions underlying the Big Five Inventory are more predictable. In a tertile
classification problem, basic dimensionality reduction techniques, such as
independent component analysis, increase the predictability relative to the
baseline from $11\%$ to $23\%$. Finally, from a supervised linear classifier,
we were able to further improve this predictability to $33\%$. In all cases,
the most predictable projections had an overweight of the questions related to
extraversion and neuroticism. In addition, our findings indicate that the score
system underlying the Big Five Inventory disregards a part of the information
available in the 44 questions.
Dropping Convexity for Faster Semi-definite Optimization
Authors: Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi
Date: 2015-09-14 00:00:00

We study the minimization of a convex function $f(X)$ over the set of
$n\times n$ positive semi-definite matrices, but when the problem is recast as
$\min_U g(U) := f(UU^\top)$, with $U \in \mathbb{R}^{n \times r}$ and $r \leq
n$. We study the performance of gradient descent on $g$---which we refer to as
Factored Gradient Descent (FGD)---under standard assumptions on the original
function $f$.
  We provide a rule for selecting the step size and, with this choice, show
that the local convergence rate of FGD mirrors that of standard gradient
descent on the original $f$: i.e., after $k$ steps, the error is $O(1/k)$ for
smooth $f$, and exponentially small in $k$ when $f$ is (restricted) strongly
convex. In addition, we provide a procedure to initialize FGD for (restricted)
strongly convex objectives and when one only has access to $f$ via a
first-order oracle; for several problem instances, such proper initialization
leads to global convergence guarantees.
  FGD and similar procedures are widely used in practice for problems that can
be posed as matrix factorization. To the best of our knowledge, this is the
first paper to provide precise convergence rate guarantees for general convex
functions under standard convex assumptions.
Anatomy-Aware Measurement of Segmentation Accuracy
Authors: Hamid. R. Tizhoosh, Ahmed A. Othman
Date: 2016-04-16 00:00:00
Tasks: Anatomy, Segmentation

Quantifying the accuracy of segmentation and manual delineation of organs,
tissue types and tumors in medical images is a necessary measurement that
suffers from multiple problems. One major shortcoming of all accuracy measures
is that they neglect the anatomical significance or relevance of different
zones within a given segment. Hence, existing accuracy metrics measure the
overlap of a given segment with a ground-truth without any anatomical
discrimination inside the segment. For instance, if we understand the rectal
wall or urethral sphincter as anatomical zones, then current accuracy measures
ignore their significance when they are applied to assess the quality of the
prostate gland segments. In this paper, we propose an anatomy-aware measurement
scheme for segmentation accuracy of medical images. The idea is to create a
``master gold'' based on a consensus shape containing not just the outline of
the segment but also the outlines of the internal zones if existent or
relevant. To apply this new approach to accuracy measurement, we introduce the
anatomy-aware extensions of both Dice coefficient and Jaccard index and
investigate their effect using 500 synthetic prostate ultrasound images with 20
different segments for each image. We show that through anatomy-sensitive
calculation of segmentation accuracy, namely by considering relevant anatomical
zones, not only the measurement of individual users can change but also the
ranking of users' segmentation skills may require reordering.
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
Authors: Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber
Date: 2016-04-16 00:00:00
Proceeding: WS 2016 6
Tasks: Decoder, Sentence

We demonstrate that an attention-based encoder-decoder model can be used for
sentence-level grammatical error identification for the Automated Evaluation of
Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder
models can be used for the generation of corrections, in addition to error
identification, which is of interest for certain end-user applications. We show
that a character-based encoder-decoder model is particularly effective,
outperforming other results on the AESW Shared Task on its own, and showing
gains over a word-based counterpart. Our final model--a combination of three
character-based encoder-decoder models, one word-based encoder-decoder model,
and a sentence-level CNN--is the highest performing system on the AESW 2016
binary prediction Shared Task.
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
Authors: Xinran Liu, Hamid. R. Tizhoosh, Jonathan Kofman
Date: 2016-04-16 00:00:00
Tasks: Content-Based Image Retrieval, General Classification, Image Retrieval, Medical Image Retrieval, Retrieval

Content-based image retrieval (CBIR) in large medical image archives is a
challenging and necessary task. Generally, different feature extraction methods
are used to assign expressive and invariant features to each image such that
the search for similar images comes down to feature classification and/or
matching. The present work introduces a new image retrieval method for medical
applications that employs a convolutional neural network (CNN) with recently
introduced Radon barcodes. We combine neural codes for global classification
with Radon barcodes for the final retrieval. We also examine image search based
on regions of interest (ROI) matching after image retrieval. The IRMA dataset
with more than 14,000 x-rays images is used to evaluate the performance of our
method. Experimental results show that our approach is superior to many
published works.
Radon Features and Barcodes for Medical Image Retrieval via SVM
Authors: Shujin Zhu, H. R. Tizhoosh
Date: 2016-04-16 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Medical Image Retrieval, Retrieval, TAG

For more than two decades, research has been performed on content-based image
retrieval (CBIR). By combining Radon projections and the support vector
machines (SVM), a content-based medical image retrieval method is presented in
this work. The proposed approach employs the normalized Radon projections with
corresponding image category labels to build an SVM classifier, and the Radon
barcode database which encodes every image in a binary format is also generated
simultaneously to tag all images. To retrieve similar images when a query image
is given, Radon projections and the barcode of the query image are generated.
Subsequently, the k-nearest neighbor search method is applied to find the
images with minimum Hamming distance of the Radon barcode within the same class
predicted by the trained SVM classifier that uses Radon features. The
performance of the proposed method is validated by using the IRMA 2009 dataset
with 14,410 x-ray images in 57 categories. The results demonstrate that our
method has the capacity to retrieve similar responses for the correctly
identified query image and even for those mistakenly classified by SVM. The
approach further is very fast and has low memory requirement.
Evolutionary Projection Selection for Radon Barcodes
Authors: Hamid. R. Tizhoosh, Shahryar Rahnamayan
Date: 2016-04-16 00:00:00

Recently, Radon transformation has been used to generate barcodes for tagging
medical images. The under-sampled image is projected in certain directions, and
each projection is binarized using a local threshold. The concatenation of the
thresholded projections creates a barcode that can be used for tagging or
annotating medical images. A small number of equidistant projections, e.g., 4
or 8, is generally used to generate short barcodes. However, due to the diverse
nature of digital images, and since we are only working with a small number of
projections (to keep the barcode short), taking equidistant projections may not
be the best course of action. In this paper, we proposed to find $n$ optimal
projections, whereas $n\!<\!180$, in order to increase the expressiveness of
Radon barcodes. We show examples for the exhaustive search for the simple case
when we attempt to find 4 best projections out of 16 equidistant projections
and compare it with the evolutionary approach in order to establish the benefit
of the latter when operating on a small population size as in the case of
micro-DE. We randomly selected 10 different classes from IRMA dataset (14,400
x-ray images in 58 classes) and further randomly selected 5 images per class
for our tests.
Bags of Local Convolutional Features for Scalable Instance Search
Authors: Eva Mohedano, Amaia Salvador, Kevin McGuinness, Ferran Marques, Noel E. O'Connor, Xavier Giro-i-Nieto
Date: 2016-04-15 00:00:00
Tasks: Instance Search, Reranking, Retrieval

This work proposes a simple instance retrieval pipeline based on encoding the
convolutional features of CNN using the bag of words aggregation scheme (BoW).
Assigning each local array of activations in a convolutional layer to a visual
word produces an \textit{assignment map}, a compact representation that relates
regions of an image with a visual word. We use the assignment map for fast
spatial reranking, obtaining object localizations that are used for query
expansion. We demonstrate the suitability of the BoW representation based on
local CNN features for instance retrieval, achieving competitive performance on
the Oxford and Paris buildings benchmarks. We show that our proposed system for
CNN feature aggregation with BoW outperforms state-of-the-art techniques using
sum pooling at a subset of the challenging TRECVid INS benchmark.
ModelWizard: Toward Interactive Model Construction
Authors: Dylan Hutchison
Date: 2016-04-15 00:00:00
Tasks: model

Data scientists engage in model construction to discover machine learning
models that well explain a dataset, in terms of predictiveness,
understandability and generalization across domains. Questions such as "what if
we model common cause Z" and "what if Y's dependence on X reverses" inspire
many candidate models to consider and compare, yet current tools emphasize
constructing a final model all at once.
  To more naturally reflect exploration when debating numerous models, we
propose an interactive model construction framework grounded in composable
operations. Primitive operations capture core steps refining data and model
that, when verified, form an inductive basis to prove model validity. Derived,
composite operations enable advanced model families, both generic and
specialized, abstracted away from low-level details.
  We prototype our envisioned framework in ModelWizard, a domain-specific
language embedded in F# to construct Tabular models. We enumerate language
design and demonstrate its use through several applications, emphasizing how
language may facilitate creation of complex models. To future engineers
designing data science languages and tools, we offer ModelWizard's design as a
new model construction paradigm, speeding discovery of our universe's
structure.
Convex Biclustering
Authors: Eric C. Chi, Genevera I. Allen, Richard G. Baraniuk
Date: 2014-08-05 00:00:00
Tasks: Collaborative Filtering

In the biclustering problem, we seek to simultaneously group observations and
features. While biclustering has applications in a wide array of domains,
ranging from text mining to collaborative filtering, the problem of identifying
structure in high dimensional genomic data motivates this work. In this
context, biclustering enables us to identify subsets of genes that are
co-expressed only within a subset of experimental conditions. We present a
convex formulation of the biclustering problem that possesses a unique global
minimizer and an iterative algorithm, COBRA, that is guaranteed to identify it.
Our approach generates an entire solution path of possible biclusters as a
single tuning parameter is varied. We also show how to reduce the problem of
selecting this tuning parameter to solving a trivial modification of the convex
biclustering problem. The key contributions of our work are its simplicity,
interpretability, and algorithmic guarantees - features that arguably are
lacking in the current alternative algorithms. We demonstrate the advantages of
our approach, which includes stably and reproducibly identifying biclusterings,
on simulated and real microarray data.