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"Topic": models, code, and papers

Falls Prediction in eldery people using Gated Recurrent Units

Aug 02, 2019
Marcin Radzio, Maciej Wielgosz, Matej Mertik

Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this work, we present Gated Recurrent Unit-based neural networks models designed for predicting falls (syncope). The cardiovascular systems signals used in the study come from Gravitational Physiology, Aging and Medicine Research Unit, Institute of Physiology, Medical University of Graz. We used two of the collected signals, heart rate, and mean blood pressure. By using bidirectional GRU model, it was possible to predict the syncope occurrence approximately ten minutes before the manual marker.

* short concept paper 

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Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments

Apr 13, 2019
Jerrold Soh Tsin Howe, Lim How Khang, Ian Ernst Chai

This paper conducts a comparative study on the performance of various machine learning (``ML'') approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.

* Accepted to the 1st Workshop on Natural Legal Language Processing (co-located with NAACL2019) 

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On Evaluation of Embodied Navigation Agents

Jul 18, 2018
Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.

* Report of a working group on empirical methodology in navigation research. Authors are listed in alphabetical order 

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Deploying Deep Neural Networks in the Embedded Space

Jun 22, 2018
Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.

* Accepted at MobiSys18: 2nd International Workshop on Embedded and Mobile Deep Learning (EMDL) 2018 

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Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data

May 17, 2018
Chan Woo Lee, Kyu Ye Song, Jihoon Jeong, Woo Yong Choi

Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.

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Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms

Feb 18, 2018
Kento Nozawa, Masanari Kimura, Atsunori Kanemura

Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respect to graph centrality measures that characterize diverse graphs, through systematic experiments with four node embedding algorithms, four or five graph centralities, and six datasets. Experimental results give insights into the properties of node embedding algorithms, which can be a basis for further research on this topic.

* Under review at ICLR 2018 workshop track 

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Joint Cuts and Matching of Partitions in One Graph

Nov 27, 2017
Tianshu Yu, Junchi Yan, Jieyi Zhao, Baoxin Li

As two fundamental problems, graph cuts and graph matching have been investigated over decades, resulting in vast literature in these two topics respectively. However the way of jointly applying and solving graph cuts and matching receives few attention. In this paper, we first formalize the problem of simultaneously cutting a graph into two partitions i.e. graph cuts and establishing their correspondence i.e. graph matching. Then we develop an optimization algorithm by updating matching and cutting alternatively, provided with theoretical analysis. The efficacy of our algorithm is verified on both synthetic dataset and real-world images containing similar regions or structures.

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Tracking Words in Chinese Poetry of Tang and Song Dynasties with the China Biographical Database

Oct 29, 2017
Chao-Lin Liu, Kuo-Feng Luo

Large-scale comparisons between the poetry of Tang and Song dynasties shed light on how words, collocations, and expressions were used and shared among the poets. That some words were used only in the Tang poetry and some only in the Song poetry could lead to interesting research in linguistics. That the most frequent colors are different in the Tang and Song poetry provides a trace of the changing social circumstances in the dynasties. Results of the current work link to research topics of lexicography, semantics, and social transitions. We discuss our findings and present our algorithms for efficient comparisons among the poems, which are crucial for completing billion times of comparisons within acceptable time.

* 9 pages, 3 figures, Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH), 26th International Conference on Computational Linguistics (COLING) 

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Authorship Verification - An Approach based on Random Forest

Jul 29, 2016
Promita Maitra, Souvick Ghosh, Dipankar Das

Authorship attribution, being an important problem in many areas in-cluding information retrieval, computational linguistics, law and journalism etc., has been identified as a subject of increasingly research interest in the re-cent years. In case of Author Identification task in PAN at CLEF 2015, the main focus was given on cross-genre and cross-topic author verification tasks. We have used several word-based and style-based features to identify the dif-ferences between the known and unknown problems of one given set and label the unknown ones accordingly using a Random Forest based classifier.

* 9 pages in Working Notes Papers of the CLEF 2015 

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Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey

Dec 10, 2015
Li Wang, Dennis Sng

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of different research topics: object detection, object tracking, face recognition, image classification and scene labeling.

* 8 pages, 18 figures 

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