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"Topic": models, code, and papers

Biometric security technology

Feb 23, 2022
Marcos Faundez-Zanuy

This paper presents an overview of the main topics related to biometric security technology, with the main purpose to provide a primer on this subject. Biometrics can offer greater security and convenience than traditional methods for people recognition. Even if we do not want to replace a classic method (password or handheld token) by a biometric one, for sure, we are potential users of these systems, which will even be mandatory for new passport models. For this reason, to be familiarized with the possibilities of biometric security technology is useful.

* IEEE Aerospace and Electronic Systems Magazine, vol. 21, no. 6, pp. 15-26, June 2006 
* 13 pages 

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An evaluation of data augmentation methods for sound scene geotagging

Oct 09, 2021
Helen L. Bear, Veronica Morfi, Emmanouil Benetos

Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the recording was captured would be of use to many. In this paper we explore a series of common audio data augmentation methods to evaluate which best improves the accuracy of audio geotagging classifiers. Our work improves on the state-of-the-art city geotagging method by 23% in terms of classification accuracy.

* Presented at Interspeech 2021 

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Conformal testing in a binary model situation

Apr 05, 2021
Vladimir Vovk

Conformal testing is a way of testing the IID assumption based on conformal prediction. The topic of this note is computational evaluation of the performance of conformal testing in a model situation in which IID binary observations generated from a Bernoulli distribution are followed by IID binary observations generated from another Bernoulli distribution, with the parameters of the distributions and changepoint unknown. Existing conformal test martingales can be used for this task and work well in simple cases, but their efficiency can be improved greatly.

* 8 pages, 5 figures 

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TweetCOVID: A System for Analyzing Public Sentiments and Discussions about COVID-19 via Twitter Activities

Mar 02, 2021
Jolin Shaynn-Ly Kwan, Kwan Hui Lim

The COVID-19 pandemic has created widespread health and economical impacts, affecting millions around the world. To better understand these impacts, we present the TweetCOVID system that offers the capability to understand the public reactions to the COVID-19 pandemic in terms of their sentiments, emotions, topics of interest and controversial discussions, over a range of time periods and locations, using public tweets. We also present three example use cases that illustrates the usefulness of our proposed TweetCOVID system.

* Accepted to the 26th International Conference on Intelligent User Interfaces (IUI'21), Demo Track 

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Identifying Editor Roles in Argumentative Writing from Student Revision Histories

Sep 03, 2019
Tazin Afrin, Diane Litman

We present a method for identifying editor roles from students' revision behaviors during argumentative writing. We first develop a method for applying a topic modeling algorithm to identify a set of editor roles from a vocabulary capturing three aspects of student revision behaviors: operation, purpose, and position. We validate the identified roles by showing that modeling the editor roles that students take when revising a paper not only accounts for the variance in revision purposes in our data, but also relates to writing improvement.

* In: Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science, vol 11626. Springer, Cham 

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Representation Learning for Spatial Graphs

Dec 22, 2018
Zheng Wang, Ce Ju, Gao Cong, Cheng Long

Recently, the topic of graph representation learning has received plenty of attention. Existing approaches usually focus on structural properties only and thus they are not sufficient for those spatial graphs where the nodes are associated with some spatial information. In this paper, we present the first deep learning approach called s2vec for learning spatial graph representations, which is based on denoising autoencoders framework (DAF). We evaluate the learned representations on real datasets and the results verified the effectiveness of s2vec when used for spatial clustering.

* 4 pages, 1 figure, conference 

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Face Recognition: From Traditional to Deep Learning Methods

Oct 31, 2018
Daniel Sáez Trigueros, Li Meng, Margaret Hartnett

Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and up-to-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, feature-based and hybrid methods) and deep learning methods.


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Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

Apr 09, 2018
Isabelle Augenstein, Sebastian Ruder, Anders Søgaard

We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for topic-based sentiment analysis.

* To appear at NAACL 2018 (long) 

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Sentiment analysis of twitter data

Dec 16, 2017
Hamid Bagheri, Md Johirul Islam

Social networks are the main resources to gather information about people's opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. In this technical paper, we show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiments for tweets are significantly high which clearly shows the limitations of the current works.

* 5 pages 

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Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia

Dec 02, 2015
Derwin Suhartono

One task that is included in managing documents is how to find substantial information inside. Topic modeling is a technique that has been developed to produce document representation in form of keywords. The keywords will be used in the indexing process and document retrieval as needed by users. In this research, we will discuss specifically about Probabilistic Latent Semantic Analysis (PLSA). It will cover PLSA mechanism which involves Expectation Maximization (EM) as the training algorithm, how to conduct testing, and obtain the accuracy result.

* 17 pages, 6 figures, 3 tables, Technical Report Program Studi Doktor Ilmu Komputer Universitas Indonesia 

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