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International Conference on Software Maintenance (ICSM)

作者： Zengyang Li Qinyi Yu Peng Liang Ran Mo Chen Yang School of Computer Science & Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Computer Science Wuhan University Wuhan China IBO Technology (Shenzhen) Co. Ltd. Shenzhen China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Defect technical debt (defect debt) refers to known deferred bugs that have not been fixed. The interest of a defect debt item (i.e., bug) is the extra effort needed to fix the bug due to the delay of fixing. It is important to measure defect debt interest in a software system in order to be aware of how much the interest of certain bugs is and which bugs should be fixed first. Furthermore, it is valuable to understand the features of the bugs of high interest or no interest, so as to facilitate the identification of those kinds of bugs. In this work, we proposed three pairs of measures for quantifying defect debt interest at three granularities (i.e., lines of code, source file, and package) of software changes, and conducted an exploratory case study on 13,438 bugs collected from 59 non-trivial Apache open source software projects written mainly in Java. We have the following findings. (1) Each bug, with an average of 224 days delay of fixing, has interest of 660 to 845 lines of code, 0.57 to 1.05 source files, and 0.29 to 0.35 package on average in need for understanding or modifying when fixing the bug. (2) The average interest of a bug shows overall increasing trends over delayed time of bug fixing and bug priority levels from the lowest to highest. (3) Around 30% of the bugs under study did not incur interest at the granularity of lines of code, which means that the involved source files in the bug-fixing commits of such bugs were not modified at all during the delayed time of bug fixing. (4) The average code change size of bug-fixing commits of a bug without interest is much smaller than that of a bug with (high) interest; for a bug without interest at the granularity of lines of code, the source files involved in the bug-fixing commits of the bug were alternately modified by different committers much less frequently than those of the bug with (high) interest.

关键词： Software maintenance computer bugs Software systems Market research Delays Software measurement Open source software

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MSCFNet: A lightweight network with multi-scale context fusion for real-time semantic segmentation

arXiv

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arXiv 2021年

作者： Gao, Guangwei Xu, Guoan Yu, Yi Xie, Jin Yang, Jian Yue, Dong Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China

In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoderdecoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multiscale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

arXiv

引用

arXiv 2021年

作者： Chen, Yuntian Luo, Yingtao Liu, Qiang Xu, Hao Zhang, Dongxiao Intelligent Energy Laboratory Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Computer Science University of Washington SeattleWA United States Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China BIC-ESAT ERE and SKLTCS College of Engineering Peking University Beijing China School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses. However, the PDEs of many real-world problems are uncertain, which calls for PDE discovery. We propose the symbolic genetic algorithm (SGA-PDE) to discover open-form PDEs directly from data without prior knowledge about the equation structure. SGA-PDE focuses on the representation and optimization of PDE. Firstly, SGA-PDE uses symbolic mathematics to realize the flexible representation of any given PDE, transforms a PDE into a forest, and converts each function term into a binary tree. Secondly, SGA-PDE adopts a specially designed genetic algorithm to efficiently optimize the binary trees by iteratively updating the tree topology and node attributes. The SGA-PDE is gradient-free, which is a desirable characteristic in PDE discovery since it is difficult to obtain the gradient between the PDE loss and the PDE structure. In the experiment, SGA-PDE not only successfully discovered nonlinear Burgers' equation, Korteweg-de Vries (KdV) equation, and Chafee-Infante equation, but also handled PDEs with fractional structure and compound functions that cannot be solved by conventional PDE discovery methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Genetic algorithms

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Mobility-Aware Dynamic Taxi Ridesharing

Mobility-Aware Dynamic Taxi Ridesharing

引用

International Conference on Data Engineering

作者： Zhidan Liu Zengyang Gong Jiangzhou Li Kaishun Wu Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University P. R. China College of Computer Science and Software Engineering Shenzhen University P R. China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Taxi ridesharing becomes promising and attractive because of the wide availability of taxis in a city and tremendous benefits of ridesharing, e.g., alleviating traffic congestion and reducing energy consumption. Existing taxi ridesharing schemes, however, are not efficient and practical, due to they simply match ride requests and taxis based on partial trip information and omit the offline passengers, who hail a taxi at roadside with no explicit requests to the system. In this paper, we consider the mobility-aware taxi ridesharing problem, and present mT- Share to address these limitations. mT-Share fully exploits the mobility information of ride requests and taxis to achieve efficient indexing of taxis/requests and better passenger-taxi matching, while still satisfying the constraints on passengers' deadlines and taxis' capacities. Specifically, mT-Share indexes taxis and ride requests with both geographical information and travel directions, and supports the shortest path based routing and probabilistic routing to serve both online and offline ride requests. Extensive experiments with a large real-world taxi dataset demonstrate the efficiency and effectiveness of mT-Share, which can response each ride request in milliseconds and with a moderate detour cost. Compared to state-of-the-art methods, mT-Share serves 42% and 62% more ride requests in peak and non-peak hours, respectively.

关键词： Public transportation Schedules Routing Urban areas Trajectory Roads Indexing

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STADB: A self-thresholding attention guided ADB network for person re-identification

arXiv

引用

arXiv 2020年

作者： Jiang, Bo Wang, Sheng Wang, Xiao Zheng, Aihua Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology of Anhui University Hefei230601 China School of Artificial Intelligence Anhui University Hefei230601 China Peng Cheng Laboratory Shenzhen China

Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on person image representation and re-identification task via feature erasing. However, BDB drops the features randomly which may lead to sub-optimal results. In this paper, we propose a novel Self-Thresholding attention guided Adaptive DropBlock network (STADB) for person re-ID which can adaptively erase the most discriminative regions. Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map. Then, the input features and self-thresholding attention guided drop mask are multiplied to generate the dropped feature maps. In addition, STADB utilizes the spatial and channel attention to learn a better feature map and iteratively trains the feature dropping module for person re-ID. Experiments on several benchmark datasets demonstrate that the proposed STADB outperforms many other related methods for person re-ID. The source code of this paper is released at: https://***/wangxiao5791509/STADB_ReID. Copyright © 2020, The Authors. All rights reserved.

关键词： Drops

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Convolutional Attention Model For Restaurant Recommendation With Multi-View Visual Features

Convolutional Attention Model For Restaurant Recommendation ...

引用

IEEE International Conference on Image Processing

作者： Haihua Luo Xiaoyan Zhang Guibing Guoy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen University Shenzhen China Software College Northeastern University Shenyang China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Current recommendation systems usually use image as a type of side information to enhance recommendation systems. However, there are few models to analyze and apply the effectiveness of different categories of images. In order to effectively combine multi-category image information in the recommendation system, we propose a novel deep network model with different categories of images as side information for recommendation, in which we use convolutional attention to analyze the importance of images within different image categories. The convolutional layers of the attention module are used to evaluate the user's visual preferences for images in different categories. The prediction model uses the cosine similarity of user factors and restaurant factors incorporating visual information. Visual features of the restaurants are extracted from multi-category images by a pre-trained neural network. We apply our model to two real-world restaurant recommendation data sets. Experimental results show that the performance of our model is better than models without or with only one category visual information.

关键词： Visualization Predictive models Feature extraction Computational modeling Analytical models Data models Mathematical model

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The XMM-SERVS X-ray eXtended Galaxy Cluster (XVXGC) catalog

arXiv

引用

arXiv 2024年

作者： Xu, Weiwei Jiang, Linhua Li, Ran Luo, Bin Brandt, W. Nielsen Zhang, Chaoli Erben, Thomas Peking University Beijing100871 China Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy School of Physics Peking University Beijing100871 China School of Astronomy and Space Science University of Chinese Academy of Science Beijing100049 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210093 China Department of Astronomy and Astrophysics The Pennsylvania State University 525 Davey Lab University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University 104 Davey Lab University ParkPA16802 United States College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Universität Bonn Auf dem Hügel 71 Bonn53121 Germany

Context. To explain the well-known tension between cosmological parameter constraints obtained from the primary cosmic microwave background (CMB) and those drawn from galaxy cluster samples, we propose a possible explanation for the incompleteness of detected clusters are higher than estimated, i.e., certain types of galaxy groups or clusters have been overlooked in previous works. Aims. We aim to search for galaxy groups and clusters with particularly extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) data, based on a dedicated source detection and characterization algorithm that is optimized for extended sources. Methods. Our state-of-the-art algorithm is composed of wavelet filtering, source detection, and characterization. We make a visual inspection of the optical image, and spatial distribution of galaxies within the same redshift layer to confirm the existence of clusters and estimate the cluster redshift with the spectroscopic and photometric redshifts of galaxies. The growth curve analysis is used to characterize the detections. Results. We report a catalog of extended X-ray galaxy clusters detected from the XMM-SERVS data, named the XMMSERVS X-ray eXtended Galaxy Cluster (XVXGC) catalog. It includes 141 cluster candidates. Specifically, there are 52 clusters previously identified as clusters with the intra-cluster medium (ICM) emission (class 3), 37 ones previously known as optical or infrared clusters but detected as X-ray clusters for the first time (class 2), and 52 identified as clusters for the first time (class 1). Compared with the class 3 sample, the "class 1 + class 2" sample is systematically fainter, and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.1-2.4] keV band for "class 1 + class 2" and class 3 sample is 2.336×10-14erg/s/cm2and 3.163×10-14erg/s/cm2, respectively. The me

关键词： Galaxies

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NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems

arXiv

引用

arXiv 2021年

作者： Mu, Xidong Liu, Yuanwei Guo, Li Lin, Jiaru Hanzo, Lajos The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Electronics and Computer Science The University of Southampton SouthamptonSO17 1BJ United Kingdom

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, a beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where a cluster-based NOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the pro

关键词： Iterative methods

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DeepAM: Deep semantic address representation for address matching 3rd

DeepAM: Deep semantic address representation for address mat...

引用

3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

作者： Shan, Shuangli Li, Zhixu Qiang, Yang Liu, An Xu, Jiajie Chen, Zhigang Institute of Artificial Intelligence School of Computer Science and Technology Soochow University Suzhou China Neusoft Corporation Shenyang China IFLYTEK Research Suzhou China King Abdullah University of Science and Technology Jeddah Saudi Arabia State Key Laboratory of Cognitive Intelligence iFLYTEK Hefei China

ISBN: (纸本)9783030260712

Address matching is a crucial task in various location-based businesses like take-out services and express delivery, which aims at identifying addresses referring to the same location in address databases. It is a challenging one due to various possible ways to express the address of a location, especially in Chinese. Traditional address matching approaches relying on string similarities and learning matching rules to identify addresses referring to the same location, could hardly solve the cases with redundant, incomplete or unusual expression of addresses. In this paper, we propose to map every address into a fixed-size vector in the same vector space using state-of-the-art deep sentence representation techniques and then measure the semantic similarity between addresses in this vector space. The attention mechanism is also applied to the model to highlight important features of addresses in their semantic representations. Last but not least, we novelly propose to get rich contexts for addresses from the web through web search engines, which could strongly enrich the semantic meaning of addresses that could be learned. Our empirical study conducted on two real-world address datasets demonstrates that our approach greatly improves both precision (up to 5%) and recall (up to 8%) of the state-of-the-art existing methods. © 2019, Springer Nature Switzerland AG.

关键词： Location

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Learning inductive attention guidance for partially supervised pancreatic ductal adenocarcinoma prediction

arXiv

引用

arXiv 2021年

作者： Wang, Yan Tang, Peng Zhou, Yuyin Shen, Wei Fishman, Elliot K. Yuille, Alan L. Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Department of Computer Science The Johns Hopkins University BaltimoreMD21218 United States The Department of Computer Science The Johns Hopkins University BaltimoreMD21218 United States MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China The Department of Radiology The Johns Hopkins University School of Medicine BaltimoreMD21287 United States

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxel-level classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background;For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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