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arXiv 2021年

作者： Lu, Xiangyong Ota, Kaoru Dong, Mianxiong Yu, Chen Jin, Hai Graduate School of Information Sciences Tohoku University Sendai Japan Serviecs Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Information and Electronic Engineering Muroran Institute of Technology Hokkaido Muroran Japan

Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce the local carbon emissions. In order to cut the carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people’s trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people’s trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on the real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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FSTR: Funds Skewness Aware Transaction Routing for Payment Channel Networks

FSTR: Funds Skewness Aware Transaction Routing for Payment C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Siyi Lin Jingjing Zhang Weigang Wu School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Lab. of Big Data Analysis and Processing China MoE Key Lab. of Machine Intelligence and Advanced Computing China

ISBN: (数字)9781728158099

ISBN: (纸本)9781728158105

Payment channel is an effective and popular technique to improve the scalability and throughput of blockchains by transferring transactions from on-chain to off-chain. Multiple payment channels can constitute a payment network and realize transaction execution via multi-hop paths. How to find a feasible and efficient transaction path, i.e., transaction routing, is a key issue in payment channel networks, and different solutions have been proposed. However, the problem of funds skewness, which may cause routing failures, has been largely ignored in existing routing algorithms. In this work, we design FSTR, a routing algorithm that attempts to route transactions using a funds skewness based path selection scheme so as to reduce funds skewness and increase transaction success probability. To evaluate the performance of FSTR, we conduct experiments using the real-world dataset of Ripple. The experiment results show that FSTR outperforms existing routing algorithms, in terms of success ratio, delay, and overhead.

关键词： Routing Delays Throughput Scalability Spread spectrum communication Mice

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Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Meta Mask Correction for Nuclei Segmentation in Histopatholo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen Xi'An Jiao Tong University National Engineering Lab for Big Data Analytics Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'An Jiao Tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.RandD Xi'An Jiao Tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of xi'An Jiaotong University Shaanxi Xi'an710061 China

ISBN: (纸本)9781665401265

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021 IEEE.

关键词： Deep learning

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Multi-Scale Convolution-Transformer Fusion Network for Endoscopic Image Segmentation

Multi-Scale Convolution-Transformer Fusion Network for Endos...

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IEEE International Symposium on Biomedical Imaging

作者： Baosheng Zou Zongguang Zhou Ying Han Kang Li Guotai Wang West China Biomedical Big Data Center Sichuan University Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of General Surgery Division of Gastrointestinal Surgery West China Hospital of Sichuan University Chengdu China Institute of Digestive Surgery & State Key Laboratory of Biotherapy Sichuan University Chengdu China West China Medical Simulation Center Sichuan University West China Hospital Chengdu China Shanghai AI Lab Shanghai China

Automatic semantic segmentation of endoscopic images is an essential part of computer-assisted intervention surgery. Recently, Convolutional Neural Networks (CNNs) have been widely applied to endoscopic image segmentation, but their performance is still limited due to the weak ability to capture global long-range dependencies. This paper proposes a model that combines CNN and Transformer to deal with this problem, and it is named as Multi-scale Convolution-Transformer Fusion Network (MCTFNet) and consists of three components: 1) Multiple-parallel Multi-scale Transformer Convolution (MMTC) modules in parallel branches to extract Multi-scale information, 2) Multi-scale Information Fusion (MIF) module that fuses parallel branch information to allow interaction between different resolutions and 3) High-resolution Information Processing (HIP) module to keep high-resolution features in the image and avoid loss of details. We verified our method on HeiSurF dataset, and the results show that our method achieved an average Dice of 80.07%, which outperformed state-of-the-art CNNs including HRNet (79.93%) and Deeplabv3 (78.34%). It also outperformed several networks designed for medical image segmentation.

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Deep learning-based computational method for soft matter dynamics: Deep Onsager-Machlup method

arXiv

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arXiv 2023年

作者： Li, Zhihao Zou, Boyi Wang, Haiqin Su, Jian Wang, Dong Xu, Xinpeng Department of Civil Engineering Shantou University 243 Daxue Road Guangdong Shantou515063 China Department of Physics MATEC Key Lab Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Technion – Israel Institute of Technology Haifa32000 Israel

A deep learning-based computational method has been proposed for soft matter dynamics – the deep Onsager-Machlup method (DOMM), which combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle – Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method constructed using variational formulations of physics models to solve static problems in physics as discussed in our former work [Wang et al, Soft Matter, 2022, 18, 6015-6031]. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics. © 2023, CC BY-NC-ND.

关键词： Computational methods

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small datasets Performance Improvement – A Neuroevolution Approach

Multiclass Diabetic Retinopathy Classification of Eye Fundus...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： José Luis Vázquez Noguera Julio César Mello-Román Diego P. Pinto-Roa Santiago Gomez Jordan Ayala Diego Aquino-Brítez Pedro Esteban Gardel-Sotomayor Miguel García-Torres Jacques Facon Veronica E. Castillo Benítez Ingrid Castro Matto Pastor Pérez-Estigarribia Facultad Politécnica Universidad Nacional de Asunción San Lorenzo Paraguay Department of Computer Engineer Universidad Americana Asunción Paraguay Facultad de Ciencias y Tecnología Universidad Católica Nuestra Señora de la Asunción Hernandarías Paraguay Data Science and Big Data Lab Universidad Pablo de Olavide Seville Spain Department of Computer and Electronics Universidade Federal do Espírito Santo São Mateus Brazil Department of Ophthalmology Facultad de Ciencias Mé dicas Hospital de Clí nicas Universidad Nacional de Asunción San Lorenzo Paraguay Facultad de Medicina Universidad Sudamericana Pedro Juan Caballero Paraguay

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set.

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Game-Based Student Response System Applied to a Multidisciplinary Teaching Context 12th

Game-Based Student Response System Applied to a Multidiscipl...

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International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th International Conference on European Transnational Education, ICEUTE 2019

作者： Amaro-Mellado, José Lázaro Antón, Daniel Pérez-Suárez, Macarena Martínez-Álvarez, Francisco Department of Graphic Engineering University of Seville Seville Spain Department of Graphic Expression and Building Engineering University of Seville Seville Spain The Creative and Virtual Technologies Research Laboratory School of Architecture Design and the Built Environment Nottingham Trent University Nottingham United Kingdom Department of Applied Economy III University of Seville Seville Spain Data Science & Big Data Lab Pablo de Olavide University SevilleES-41013 Spain

ISBN: (纸本)9783030200046

The prevailing need to promote the application of active methodologies to develop the teaching-learning process in an effective way led to this work. The aim was to evidence the efficiency of an Information and Communication Technologies (ICT) resource for the generation of a collaborative and multidisciplinary context in the university teaching activity within dissimilar fields of knowledge. A Game-based Student Response System (GSRS) was implemented as a common tool for different teachers of non-related fields of knowledge with a view to enhance critical thinking and engagement in the students. The outcomes showed that this methodology increased the involvement and reduced part of the degree of abstraction that complex contents entail. Thus, the environment varied significantly with respect to the traditional relationship between students and teachers, since the latter are in charge of generating student-centred experiences based on interaction. As a result, feedback information was gathered in order to rearrange contents and consider novel strategies. In addition, this work eased the acquisition of knowledge, competence development and the achievement of teaching objectives by the students. © 2020, Springer Nature Switzerland AG.

关键词： Students

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Dual ResGCN for balanced scene graph generation

arXiv

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arXiv 2020年

作者： Zhang, Jingyi Zhang, Yong Wu, Baoyuan Fan, Yanbo Shen, Fumin Shen, Heng Tao Center for Future Media University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 Hong Kong Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen518172 China Tencent AI Lab Shenzhen519000 China

Visual scene graph generation is a challenging task. Previous works have achieved great progress, but most of them do not explicitly consider the class imbalance issue in scene graph generation. Models learned without considering the class imbalance tend to predict the majority classes, which leads to a good performance on trivial frequent predicates, but poor performance on informative infrequent predicates. However, predicates of minority classes often carry more semantic and precise information (e.g., ‘on’ v.s ‘parked on’). To alleviate the influence of the class imbalance, we propose a novel model, dubbed dual ResGCN, which consists of an object residual graph convolutional network and a relation residual graph convolutional network. The two networks are complementary to each other. The former captures object-level context information, i.e., the connections among objects. We propose a novel ResGCN that enhances object features in a cross attention manner. Besides, we stack multiple contextual coefficients to alleviate the imbalance issue and enrich the prediction diversity. The latter is carefully designed to explicitly capture relation-level context information i.e., the connections among relations. We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue. Extensive evaluations of three tasks are performed on the large-scale database VG to demonstrate the superiority of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis

Towards Interpretability and Personalization: A Predictive F...

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IEEE International Conference on data Mining (ICDM)

作者： Yang Li Xianli Zhang Buyue Qian Zeyu Gao Chong Guan Yefeng Zheng Hansen Zheng Fenglang Wu Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China Tencent Jarvis Lab Shenzhen China The First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665423991

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify ‘truly similar patients’ (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors’ importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.

关键词： Precision medicine Conferences Time series analysis Supervised learning Semantics Machine learning Predictive models

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