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"Topic": models, code, and papers

Spirometry-based airways disease simulation and recognition using Machine Learning approaches

Nov 08, 2021
Riccardo Dio, André Galligo, Angelos Mantzaflaris, Benjamin Mauroy

The purpose of this study is to provide means to physicians for automated and fast recognition of airways diseases. In this work, we mainly focus on measures that can be easily recorded using a spirometer. The signals used in this framework are simulated using the linear bi-compartment model of the lungs. This allows us to simulate ventilation under the hypothesis of ventilation at rest (tidal breathing). By changing the resistive and elastic parameters, data samples are realized simulating healthy, fibrosis and asthma breathing. On this synthetic data, different machine learning models are tested and their performance is assessed. All but the Naive bias classifier show accuracy of at least 99%. This represents a proof of concept that Machine Learning can accurately differentiate diseases based on manufactured spirometry data. This paves the way for further developments on the topic, notably testing the model on real data.

* LION15 - Learning and Intelligent Optimization Conference, Jun 2021, Athens, Greece 

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Automated Remote Sensing Forest Inventory Using Satellite Imagery

Nov 07, 2021
Abduragim Shtanchaev, Artur Bille, Olga Sutyrina, Sara Elelimy

For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.

* 15 pages, 11 figures, 71th International Astronautical Congress (IAC) - The CyberSpace Edition 

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Automated Remote Sensing Forest Inventory Using Satelite Imagery

Oct 16, 2021
Abduragim Shtanchaev, Artur Bille, Olga Sutyrina, Sara Elelimy

For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.

* 15 pages, 11 figures, 71th International Astronautical Congress (IAC) - The CyberSpace Edition 

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Podcast Metadata and Content: Episode Relevance andAttractiveness in Ad Hoc Search

Aug 25, 2021
Ben Carterette, Rosie Jones, Gareth F. Jones, Maria Eskevich, Sravana Reddy, Ann Clifton, Yongze Yu, Jussi Karlgren, Ian Soboroff

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.


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Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

Jul 28, 2021
Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability.

* 21 pages, 3 figures 

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Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

Jun 05, 2021
Zhixuan Chu, Stephen L. Rathbun, Sheng Li

Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders. Recently, the growing availability of networked observational data offers a new opportunity to deal with the issue of hidden confounders. Unlike networked data in traditional graph learning tasks, such as node classification and link detection, the networked data under the causal inference problem has its particularity, i.e., imbalanced network structure. In this paper, we propose a Graph Infomax Adversarial Learning (GIAL) model for treatment effect estimation, which makes full use of the network structure to capture more information by recognizing the imbalance in network structure. We evaluate the performance of our GIAL model on two benchmark datasets, and the results demonstrate superiority over the state-of-the-art methods.

* Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14--18, 2021, Virtual Event, Singapore 

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Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary

Apr 29, 2021
Sibo Zhang, Jiahong Yuan, Miao Liao, Liangjun Zhang

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.


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Label Leakage and Protection in Two-party Split Learning

Feb 17, 2021
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang

In vertical federated learning, two-party split learning has become an important topic and has found many applications in real business scenarios. However, how to prevent the participants' ground-truth labels from possible leakage is not well studied. In this paper, we consider answering this question in an imbalanced binary classification setting, a common case in online business applications. We first show that, norm attack, a simple method that uses the norm of the communicated gradients between the parties, can largely reveal the ground-truth labels from the participants. We then discuss several protection techniques to mitigate this issue. Among them, we have designed a principled approach that directly maximizes the worst-case error of label detection. This is proved to be more effective in countering norm attack and beyond. We experimentally demonstrate the competitiveness of our proposed method compared to several other baselines.


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Graph Neural Networks: Taxonomy, Advances and Trends

Jan 18, 2021
Yu Zhou, Haixia Zheng, Xin Huang

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.

* 42 pages, 7 figures 

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