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20th International Symposium on Communications and Information Technologies (ISCIT)

作者： Honda, Yoshimichi Kawamura, Arata Kyoto Sangyo Univ Div Frontier Informat Kyoto Japan

ISBN: (纸本)9781665449588

To enjoy fishing indoors, we study a hardware-type fishing simulator that employs a real fishing rod. In this paper, as the first step of our research, we develop a pull force acquisition system and a winding system that consists of a motor, reel, and controller. The pull force acquisition system obtains an actual fish pull force. The time variation of the pull force represents the pull pattern of the fish. We show that the winding system can reproduce a pull pattern similar to the original pull pattern obtained at the pull force acquisition system. A lot of pull pattern of fish has to be acquired to represent a specific pull pattern to the fish species. It is inefficient to obtain them with fieldwork. We use a variational autoencoder (VAE) to generate multiple pull patterns similar to the original pull pattern. Here we assume that the fish species-specific pull pattern maintains its rough shape of movements. Simulation results showed that VAE generated multiple pull patterns roughly maintaining the original shape.

关键词： fishing fishing-simulator pull of a fish real fishing rod variational autoencoder

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Future Network Traffic Matrix Synthesis and Estimation Based on Deep Generative Models 30

Future Network Traffic Matrix Synthesis and Estimation Based...

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30th International Conference on Computer Communications and Networks (ICCCN)

作者： Kakkavas, Grigorios Kalntis, Michail Karyotis, Vasileios Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Iroon Polytech 9 Athens 15780 Greece Ionian Univ Dept Informat Tsirigoti Sq 7 Corfu 49132 Greece

ISBN: (纸本)9780738113302

Traffic matrices (TMs) contain information that is essential for network management, traffic engineering, and anomaly detection. However, constructing a TM through direct traffic measurements has a high administrative and computational cost. A more feasible approach is to estimate the TM from the easily obtainable link load measurements. In this paper, we address the issue of traffic matrix estimation (TME) from link loads using a deep generative model - namely, a variational autoencoder (VAE) - to solve the respective ill-posed inverse problem. In particular, we train the VAE with historical data (previously observed TMs) and we leverage the trained decoder to transform TME into a minimization problem in the latent space, which in turn can be solved by employing a gradient-based optimizer. Furthermore, the trained decoder can be used for traffic matrix synthesis, i.e., for generating synthetic TM examples that have "similar" properties to the samples of the training set. Finally, we explore the incremental optimization of the sequence of objectives constructed from the sequence of decoders that we obtain at different stages of the VAE training. The performance of the proposed methods is evaluated using a publicly available dataset of actual traffic matrices recorded in a real backbone network.

关键词： Network monitoring network tomography traffic matrix estimation deep learning variational autoencoder

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PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105682-105682页

作者： Abir, Farhan Fuad Alyafei, Khalid Chowdhury, Muhammad E. H. Khandakar, Amith Ahmed, Rashid Hossain, Muhammad Maqsud Mahmud, Sakib Rahman, Ashiqur Abbas, Tareq O. Zughaier, Susu M. Naji, Khalid Kamal Univ Dhaka Dept Elect & Elect Engn Dhaka 1000 Bangladesh Qatar Univ Coll Engn Dept Mech & Ind Engn Doha 2713 Qatar Qatar Univ Dept Elect Engn Doha 2713 Qatar Qatar Univ Biomed Res Ctr Doha 2713 Qatar North South Univ NSU Genome Res Inst NGRI Dhaka 1229 Bangladesh Tohoku Univ Inst Multidisciplinary Res Adv Mat Sendai Japan Surg Dept Urol Div Sidra Med Doha 26999 Qatar Qatar Univ Coll Med Dept Basic Med Sci QU Hlth Doha 2713 Qatar Qatar Univ Coll Engn Doha 2713 Qatar

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory variational autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

关键词： Presymptomatic COVID-19 Smartwatch Resting heart rate Anomaly detection Long short -term memory variational autoencoder

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VAPER: A deep learning model for explainable probabilistic regression

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期63卷

作者： Jung, Seungwon Noh, Yoona Moon, Jaeuk Hwang, Eenjun Korea Univ Sch Elect Engn Seoul 02841 South Korea

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the pro-posed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.

关键词： Probabilistic regression Explainable artificial intelligence Layer -wise relevance propagation variational autoencoder

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A review of molecular representation in the age of machine learning

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2022年第5期12卷 e1603-e1603页

作者： Wigh, Daniel S. Goodman, Jonathan M. Lapkin, Alexei A. Univ Cambridge Dept Chem Engn & Biotechnol Cambridge CB3 0AS England Univ Cambridge Yusuf Hamied Dept Chem Cambridge England

Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine-readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature-based, and computer-learned representations. Three of the most significant representations are simplified molecular-input line-entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatics

关键词： chemoinformatics fingerprints machine learning molecular representation variational autoencoder

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Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm

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SENSORS 2022年第24期22卷 9943-9943页

作者： Chen, Ting Chen, Youjing Li, Hao Gao, Tao Tu, Huizhao Li, Siyu Changan Univ Sch Informat Engn Xian 710064 Peoples R China Tongji Univ Coll Transportat Engn Key Lab Rd Traff Engn Minist Educ Shanghai 201804 Peoples R China

With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.

关键词： self-driving vehicles latent states variational autoencoder deep reinforcement learning

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Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 125卷 102251-102251页

作者： Munoz-Ramirez, Veronica Kmetzsch, Virgilio Forbes, Florence Meoni, Sara Moro, Elena Dojat, Michel Univ Grenoble Alpes INRIA Grenoble INP CNRSLJK F-38000 Grenoble France Univ Grenoble Alpes Inserm U1216 CHU Grenoble Alpes Grenoble Inst Neurosci F-38000 Grenoble France CHU Grenoble Alpes Div Neurol F-38000 Grenoble France

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n = 56) and tested them on those of de novo PD patients (n = 129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.

关键词： Machine learning Unsupervised learning variational autoencoder Neurodegenerative disease Parkinson's disease

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Protecting gender and identity with disentangled speech representations 22

Protecting gender and identity with disentangled speech repr...

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Interspeech Conference

作者： Stoidis, Dimitrios Cavallaro, Andrea Queen Mary Univ London Ctr Intelligent Sensing London England

ISBN: (纸本)9781713836902

Besides its linguistic content, our speech is rich in biometric information that can be inferred by classifiers. Learning privacy-preserving representations for speech signals enables downstream tasks without sharing unnecessary, private information about an individual. In this paper, we show that protecting gender information in speech is more effective than modelling speaker-identity information only when generating a nonsensitive representation of speech. Our method relies on reconstructing speech by decoding linguistic content along with gender information using a variational autoencoder. Specifically, we exploit disentangled representation learning to encode information about different attributes into separate subspaces that can be factorised independently. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers, namely gender and identity, in a privacyprotecting setting. Experiments on the LibriSpeech dataset show that gender recognition and speaker verification can be reduced to a random guess, protecting against classification-based attacks.

关键词： privacy soft biometrics disentangled representation learning variational autoencoder

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ECG Data Modeling and Analyzing via Deep Representation Learning and Nonparametric Hidden Markov Models 21

ECG Data Modeling and Analyzing via Deep Representation Lear...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Zhu, Jiaojiao Fan, Wentao Huaqiao Univ Dept Comp Sci & Technol Xiamen Fujian Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Xiamen Fujian Peoples R China

ISBN: (纸本)9781450380379

In modern clinical medicine, electrocardiogram (ECG) is a common diagnosis technique of cardiovascular diseases. The purpose of this paper is to propose a novel model-based clustering approach for analyzing ECG data. Our approach is composed of two modules: representation learning and ECG data clustering. In the module of representation learning, a deep generative model referred to as the hyperspherical variational recurrent autoencoder (HVRAE) is developed to extract the representation of observed ECG data, based on the variational autoencoder (VAE) with long short-term memory (LSTM) networks. In the module of ECG data clustering, we develop a nonparametric hidden Markov model (NHMM) based on Dirichlet process in which the number of hidden states is inferred automatically during the learning process. Moreover, the emission density of each hidden state of our NHMM follows a mixture of von Mises-Fisher (VMF) distributions which have better capability for modeling ECG representations than other commonly used distributions (such as the Gaussian distribution). To learn the proposed VMF-based NHMM, we theoretically develop an effective learning algorithm based on variational Bayes. The merits of our model-based clustering approach for analyzing ECG data are verified through experiments on publicly available ECG data sets.

关键词： ECG data clustering representation learning variational autoencoder HMM variational Bayes

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Statistical Modeling of Pulmonary Vasculatures with Topological Priors in CT Volumes 4th

Statistical Modeling of Pulmonary Vasculatures with Topologi...

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4th Int Workshop on Interpretabil of Machine Intelligence in Med Image Comp (iMIMIC) / 1st Int Workshop on Topol Data Analysis and Its Applicat for Med Data (TDA4MedicalData) at 24th Int Conf on Med Image Comp and Comp Assisted Intervent (MICCAI)

作者： Saeki, Yuki Saito, Atsushi Cousty, Jean Kenmochi, Yukiko Shimizu, Akinobu Tokyo Univ Agr & Technol Tokyo Japan Univ Gustave Eiffel CNRS ESIEE Paris LIGM Marne La Vallee France

ISBN: (纸本)9783030874445;9783030874438

A statistical appearance model of blood vessels based on variational autoencoder (VAE) is well adapted to image intensity variations. However, images reconstructed with such a statistical model may have topological defects, such as loss of bifurcation and creation of undesired hole. In order to build a 3D anatomical model of blood vessels, we incorporate topological prior into the statistical modeling. Qualitative and quantitative results on 2567 real CT volume patches and on 10000 artificial ones show the efficiency of the proposed framework.

关键词： Statistical model Topological data analysis variational autoencoder Vasculature

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