7 research outputs found
Visual analytics in FCA-based clustering
Visual analytics is a subdomain of data analysis which combines both human
and machine analytical abilities and is applied mostly in decision-making and
data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was
developed to detect groups of objects with similar properties under similar
conditions. It is used in Social Network Analysis (SNA) and is a basis for
certain types of recommender systems. The problem of triclustering algorithms
is that they do not always produce meaningful clusters. This article describes
a specific triclustering algorithm and a prototype of a visual analytics
platform for working with obtained clusters. This tool is designed as a testing
frameworkis and is intended to help an analyst to grasp the results of
triclustering and recommender algorithms, and to make decisions on
meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on
Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary
Proceedings of the 3rd International Conference on Analysis of Images, Social
Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201
Can FCA-based Recommender System Suggest a Proper Classifier?
The paper briefly introduces multiple classifier systems and describes a new
algorithm, which improves classification accuracy by means of recommendation of
a proper algorithm to an object classification. This recommendation is done
assuming that a classifier is likely to predict the label of the object
correctly if it has correctly classified its neighbors. The process of
assigning a classifier to each object is based on Formal Concept Analysis. We
explain the idea of the algorithm with a toy example and describe our first
experiments with real-world datasets.Comment: 10 pages, 1 figure, 4 tables, ECAI 2014, workshop "What FCA can do
for "Artifficial Intelligence
Improving Article Classification with Edge-Heterogeneous Graph Neural Networks
Classifying research output into context-specific label taxonomies is a
challenging and relevant downstream task, given the volume of existing and
newly published articles. We propose a method to enhance the performance of
article classification by enriching simple Graph Neural Networks (GNN)
pipelines with edge-heterogeneous graph representations. SciBERT is used for
node feature generation to capture higher-order semantics within the articles'
textual metadata. Fully supervised transductive node classification experiments
are conducted on the Open Graph Benchmark (OGB) ogbn-arxiv dataset and the
PubMed diabetes dataset, augmented with additional metadata from Microsoft
Academic Graph (MAG) and PubMed Central, respectively. The results demonstrate
that edge-heterogeneous graphs consistently improve the performance of all GNN
models compared to the edge-homogeneous graphs. The transformed data enable
simple and shallow GNN pipelines to achieve results on par with more complex
architectures. On ogbn-arxiv, we achieve a top-15 result in the OGB competition
with a 2-layer GCN (accuracy 74.61%), being the highest-scoring solution with
sub-1 million parameters. On PubMed, we closely trail SOTA GNN architectures
using a 2-layer GraphSAGE by including additional co-authorship edges in the
graph (accuracy 89.88%). The implementation is available at:
Migration data, Russia, 2003-2013
This Excel file contains annual net migration records for Russian regions by 1-year age groups, from 0 to 80, for the periods 2003-2010 and 2011-2013. The first period is defined by the two Russian Censuses (end of 2002 and end of 2010). The second period is limited by the availability of data. Moreover, there was a significant change in the current migration record in 2011; so, the data for the two periods are barely comparable. <br>There are 78 regions , as the data for Moscow and Leningrad regions are merged with the data for the federal cities of Moscow and St.Petersburg, correspondingly. <br><br>List of data files:<br>IR_0310.csv - inter-regional migration in 2003-2010<br>IN_0310.csv - international migration in 2003-2010<br>IR_1113.csv - inter-regional migration in 2011-2013<br>IN_1113.csv - international migration in 2011-2013 <br
Overview of the DagPap22 Shared Task on Detecting Automatically Generated Scientific Papers
International audienceThis paper provides an overview of the 2022 COLING Scholarly Document Processing workshop shared task on the detection of automatically generated scientific papers. We frame the detection problem as a binary classification task: given an excerpt of text, label it as either human-written or machine-generated. We shared a dataset containing excerpts from human-written papers as well as artificially generated content and suspicious documents collected by Elsevier publishing and editorial teams. As a test set, the participants were provided with a 5x larger corpus of openly accessible human-written as well as generated papers from the same scientific domains of documents. The shared task saw 180 submissions across 14 participating teams and resulted in two published technical reports. We discuss our findings from the shared task in this overview paper
Evaluating approaches to identifying research supporting the United Nations Sustainable Development Goals
The United Nations (UN) Sustainable Development Goals (SDGs) challenge the
global community to build a world where no one is left behind. Recognizing that
research plays a fundamental part in supporting these goals, attempts have been
made to classify research publications according to their relevance in
supporting each of the UN's SDGs. In this paper, we outline the methodology
that we followed when mapping research articles to SDGs and which is adopted by
Times Higher Education in their Social Impact rankings. We compare our solution
with other existing queries and models mapping research papers to SDGs. We also
discuss various aspects in which the methodology can be improved and
generalized to other types of content apart from research articles. The results
presented in this paper are the outcome of the SDG Research Mapping Initiative
that was established as a partnership between the University of Southern
Denmark, the Aurora European Universities Alliance (represented by Vrije
Universiteit Amsterdam), the University of Auckland, and Elsevier to bring
together broad expertise and share best practices on identifying research
contributions to UN's Sustainable Development Goals.Comment: 12 pages, 3 figures, 7 tables, 19 reference