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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

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Realizability of BPMN 2.0 Choreography using CSP#

Realizability of BPMN 2.0 Choreography using CSP#

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IEEE International Conference on Computer Science and Educational Informatization (CSEI)

作者： Wu RuiQiong Duan Qing Dai Fei School of Software Yunnan university Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781728123097

Service-oriented computing is a new computing paradigm that uses services to support the rapid development of distributed software systems. The paradigm of software development is built software systems by reusing and combining existing concurrent and distributed services. In order to accomplish a task together, these services must coordinate their execution through interacting with each other. Interactions among a set of services involved in a new system are described from a global point of view using choreography specification languages. The realizability indicates whether peers can be generated from a choreography so that they will behave exactly as formalized in its specification. This paper uses CSP# to analysis the realizability of BPMN2.0 choreography. The main research contents are as follows: 1) Encoding BMPN2.0 choreography in CSP#. 2) The mapping of CSP # choreography to peers. 3) The realizability under synchronous communication and bounded asynchronous communication with CSP#.

关键词： Logic gates Task analysis Analytical models Asynchronous communication Encoding Standards

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NDRIDC: NMF-based deep representation algorithm for incomplete data clustering

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Knowledge-Based Systems 2025年 324卷

作者： Dexian Wang Zonglin Li Sha Yang Tianrui Ren Pengfei Zhang Ping Deng Tianrui Li Chengdu University of Traditional Chinese Medicine Chengdu 611137 China Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China School of Computer and Software Engineering Xihua University Chengdu 610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu 611756 China

Incomplete data clustering is a hot research topic in the field of data mining. Many methods based on incomplete clustering have two issues: (1) their models do not have non-negative constraints; (2) their model building strategies do not consider combining traditional machine learning and deep learning. To address these issues, we proposed the NMF-based deep representation algorithm for incomplete data clustering (NDRIDC). First, the element-based objective function is constructed based on the known elements in the incomplete data matrix with matrix factorization principle. Then the NDRIDC deep network is constructed based on the element update gradient and nonlinear function with non-negative value domain. The learning of the NDRIDC network enables the optimization of the objective function and the filling of missing values. Further, based on the same learning framework, the NDRIDC network is employed to perform low-dimensional representation of the filled matrix. Finally, extensive experiments on regular and large-scale datasets show that our proposed algorithm has good performance.

关键词： Clustering Deep learning Incomplete data Non-negative matrix factorization

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Predict the Next Location From Trajectory Based on Spatiotemporal Sequence

Predict the Next Location From Trajectory Based on Spatiotem...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Jun Zeng Haoran Tang Yingbo Wu Ling Liu Sachio Hirokawa School of Big Data and Software Engineering Chongqing University China Research Institute for Information Technology Kyushu University Fukuoka Japan

The achievement of wireless communication technology of mobile devices has been witnessed, which produces a large number of trajectory data of mobile users. Processing and analyzing these trajectories could obtain users' movement patterns and behavior rules, leading to provide better location-based services such as point-of-interest recommendation and location prediction. However, enormous volumes of GPS trajectory with high frequency will pose challenges in storage, transmission and computation. Recently, with the rise of social networking sites, more and more users tend to share their geographic locations in real time, thus forming check-in sequences. Hence, this paper proposes a model called SSTLP for predicting next location from trajectory based on spatiotemporal sequence. Firstly, construct location transition probability model by capturing the change of locations in historical trajectory. Secondly, compute distance possibilities of locations by the combination of normal distribution and cosine similarity and then the next location could be figured out. Experiments on real-world data set demonstrate that the proposed model outperforms traditional prediction algorithms.

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SkeletonNet: A topology-preserving solution for learning mesh reconstruction of object surfaces from RGB images

arXiv

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arXiv 2020年

作者： Tang, Jiapeng Han, Xiaoguang Tan, Mingkui Tong, Xin Jia, Kui The School of Electronic Information Engineering South China University of Technology The Pazhou Lab Guangzhou China The Chinese University of Hong Kong Shenzhen and Shenzhen Research Institute of Big Data Shenzhen China The School of Software Engineering South China University of Technology Guangzhou China Microsoft Research Asia Beijing China

This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. Existing methods achieve varying degrees of success by using different surface representations. However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topological structures in their learning frameworks. To this end, we propose to learn and use the topology-preserved, skeletal shape representation to assist the downstream task of object surface reconstruction from RGB images. Technically, we propose the novel SkeletonNet design that learns a volumetric representation of a skeleton via a bridged learning of a skeletal point set, where we use parallel decoders each responsible for the learning of points on 1D skeletal curves and 2D skeletal sheets, as well as an efficient module of globally guided subvolume synthesis for a refined, high-resolution skeletal volume;we present a differentiable Point2Voxel layer to make SkeletonNet end-to-end and trainable. With the learned skeletal volumes, we propose two models, the Skeleton-Based Graph Convolutional Neural Network (SkeGCNN) and the Skeleton-Regularized Deep Implicit Surface Network (SkeDISN), which respectively build upon and improve over the existing frameworks of explicit mesh deformation and implicit field learning for the downstream surface reconstruction task. We conduct thorough experiments that verify the efficacy of our proposed SkeletonNet. SkeGCNN and SkeDISN outperform existing methods as well, and they have their own merits when measured by different metrics. Additional results in generalized task settings further demonstrate the usefulness of our proposed methods. We have made our implementation code publicly available at https://***/tangjiapeng/SkeletonNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Musculoskeletal system

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A novel measure to quantify the robustness of social network under the virus attacks 6th

A novel measure to quantify the robustness of social network...

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6th International Symposium on Security and Privacy in Social Networks and big data, SocialSec 2020

作者： Song, Bo Jing, Zhengjun Jay Guo, Y. Liu, Ren Ping Zhou, Qian School of Modern Posts and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China University of Technology Sydney SydneyNSW2007 Australia Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu University of Technology Changzhou213001 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

ISBN: (纸本)9789811590306

Combining the spread velocity, the epidemic threshold and the infection scale at steady state, a new network robust measure with respect to the virus attacks is proposed in this paper. Through examples, we show that spread velocity plays an important role on the network robustness. By using the SI and SIS epidemic model, we analyze the robustness of homogeneous networks. The results show that the irregularity in node degrees decreases the robustness of the networks. Moreover, the simulation results show that the network becomes more fragile as the average degree grows in both homogeneous and heterogeneous networks. © Springer Nature Singapore Pte Ltd 2020.

关键词： Heterogeneous networks

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Forward engineering completeness for software by using requirements validation framework 31

Forward engineering completeness for software by using requi...

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31st International Conference on software engineering and Knowledge engineering, SEKE 2019

作者： Iqbal, Nayyar Sang, Jun Gao, Min Hu, Haibo Xiang, Hong Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Computer Science University of Agriculture Faisalabad Faisalabad38000 Pakistan

ISBN: (纸本)1891706489

In software development environment, software companies usually ignore the user requirements validation process in requirement gathering phase, which results in large number of modifications being required in the software maintenance phase to fulfill the customer requirements. Identification of accurate requirements from user stories and determining the effectiveness of work deliverable of software industry has always been a challenging task. In this paper, a new measurement approach for forward engineering completeness for software was introduced by using requirements validation framework. The forward engineering completeness for software was measured in two steps. In the first step, software component structure was developed in order to find the functional and nonfunctional requirements rejected by the customers in the requirement validation framework. In the second step, completeness of software from component-based development was determined in which the following parameters, such as functional, non-functional completeness attributes, were considered in the measurement process, and the unadopted attributes of the reuse code were also considered. Quality level for the attributes were assigned based upon the valuation of interior quality of the source code. Therefore, it resulted in the reduction of development time required for the software and the cost required for the software development was also reduced. A case study was incorporated in this research to explain the measurement process of forward engineering completeness. If the forward engineering code is satisfying the quality standards, then the code is in the completeness form. The attributes of code that negates to be used were considered as unadopted attributes. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Computer software reusability

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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HI-Net: Hyperdense inception 3D UNet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Qamar, Saqib Ahmad, Parvez Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block. We use hyper dense connections among factorized convolutional layers to extract more contexual information, with the help of features reusability. We use a dice loss function to cope with class imbalances. We validate the proposed architecture on the multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset. Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively. © 2020, CC BY.

关键词： Convolution

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Forecasting of Credit Card Default Based on Incremental Random Forest

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Xiang Gao Junhao Wen Cheng Zhang College of Computer Science Chongqing University Chongqing China Chongqing Branch of China Minsheng Bank Chongqing China College of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.

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