7,460 research outputs found
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
A Compositional Treatment of Polysemous Arguments in Categorial Grammar
We discuss an extension of the standard logical rules (functional application
and abstraction) in Categorial Grammar (CG), in order to deal with some
specific cases of polysemy. We borrow from Generative Lexicon theory which
proposes the mechanism of {\em coercion}, next to a rich nominal lexical
semantic structure called {\em qualia structure}.
In a previous paper we introduced coercion into the framework of {\em
sign-based} Categorial Grammar and investigated its impact on traditional
Fregean compositionality. In this paper we will elaborate on this idea, mostly
working towards the introduction of a new semantic dimension. Where in current
versions of sign-based Categorial Grammar only two representations are derived:
a prosodic one (form) and a logical one (modelling), here we introduce also a
more detaled representation of the lexical semantics. This extra knowledge will
serve to account for linguistic phenomena like {\em metonymy\/}.Comment: LaTeX file, 19 pages, uses pubsmacs, pubsbib, pubsarticle, leqn
Revisiting the Core Ontology and Problem in Requirements Engineering
In their seminal paper in the ACM Transactions on Software Engineering and
Methodology, Zave and Jackson established a core ontology for Requirements
Engineering (RE) and used it to formulate the "requirements problem", thereby
defining what it means to successfully complete RE. Given that stakeholders of
the system-to-be communicate the information needed to perform RE, we show that
Zave and Jackson's ontology is incomplete. It does not cover all types of basic
concerns that the stakeholders communicate. These include beliefs, desires,
intentions, and attitudes. In response, we propose a core ontology that covers
these concerns and is grounded in sound conceptual foundations resting on a
foundational ontology. The new core ontology for RE leads to a new formulation
of the requirements problem that extends Zave and Jackson's formulation. We
thereby establish new standards for what minimum information should be
represented in RE languages and new criteria for determining whether RE has
been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International
Requirements Engineering Conference, 2008 (RE'08). Best paper awar
Revisiting the Core Ontology and Problem in Requirements Engineering
In their seminal paper in the ACM Transactions on Software Engineering and
Methodology, Zave and Jackson established a core ontology for Requirements
Engineering (RE) and used it to formulate the "requirements problem", thereby
defining what it means to successfully complete RE. Given that stakeholders of
the system-to-be communicate the information needed to perform RE, we show that
Zave and Jackson's ontology is incomplete. It does not cover all types of basic
concerns that the stakeholders communicate. These include beliefs, desires,
intentions, and attitudes. In response, we propose a core ontology that covers
these concerns and is grounded in sound conceptual foundations resting on a
foundational ontology. The new core ontology for RE leads to a new formulation
of the requirements problem that extends Zave and Jackson's formulation. We
thereby establish new standards for what minimum information should be
represented in RE languages and new criteria for determining whether RE has
been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International
Requirements Engineering Conference, 2008 (RE'08). Best paper awar
Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully
Characterizing videos, audience and advertising in Youtube channels for kids
Online video services, messaging systems, games and social media services are
tremendously popular among young people and children in many countries. Most of
the digital services offered on the internet are advertising funded, which
makes advertising ubiquitous in children's everyday life. To understand the
impact of advertising-based digital services on children, we study the
collective behavior of users of YouTube for kids channels and present the
demographics of a large number of users. We collected data from 12,848 videos
from 17 channels in US and UK and 24 channels in Brazil. The channels in
English have been viewed more than 37 billion times. We also collected more
than 14 million comments made by users. Based on a combination of text-analysis
and face recognition tools, we show the presence of racial and gender biases in
our large sample of users. We also identify children actively using YouTube,
although the minimum age for using the service is 13 years in most countries.
We provide comparisons of user behavior among the three countries, which
represent large user populations in the global North and the global South
ConvMixerSeg: Weakly Supervised Semantic Segmentation for CT Liver Images
The predictive power of modern deep learning approaches is posed to revolutionize the medical imaging field, however, their usefulness and applicability are severely limited by the lack of well annotated data. Liver segmentation in CT images is an application that could benefit particularly well from less data hungry methods and potentially lead to better liver volume estimation and tumor detection.
To this end, we propose a new semantic segmentation model called ConvMixerSeg and experimentally show that it outperforms an FCN with a ResNet-50 backbone when trained to segment livers on a subset of the Liver Tumor Segmentation Benchmark data set (LiTS). We have further developed a novel Class Activation Map (CAM) based method to train semantic segmentation models with image level labels without adding parameters. The proposed CAM method includes a Neighborhood Correlation Enforcement module using Gaussian smoothing that reduces part domination and prediction noise. Additionally, our experiments show that the proposed CAM method outperforms the original CAM method for both classification and segmentation with high statistical significance given the same ConvMixerSeg backbone
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