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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xiaojuan Yang Jiaxing Shang Linjiang Zheng Dajiang Liu Shu Fu Baohua Qiang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665416597

The influence maximization (IM) problem, which aims to find $k$ most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic since trust-levels among different individuals usually exhibit high diversity. As a result, an influenced individual may choose not to further propagate the information to his neighbors. Motivated by the observation, in this paper we propose a new accepted influence maximization (AIM) problem where the influenced individuals are further divided into two subgroups, i.e., accepted and active, where only active individuals will continue to propagate the information. We prove this problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with ( $1-1/e-\epsilon$ ) approximation guarantee. Considering the low computational efficiency of the greedy algorithm, we further propose a scalable path-based algorithm ALDAG. We conduct experiments on real datasets and the results demonstrate the effectiveness and efficiency of our method.

关键词： Greedy algorithms Data privacy Social networking (online) Computational modeling Heuristic algorithms Linear programming Approximation algorithms

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Quality Assurance for Automated Commit Message Generation

Quality Assurance for Automated Commit Message Generation

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Bei Wang Meng Yan Zhongxin Liu Ling Xu Xin Xia Xiaohong Zhang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Monash University Australia

ISBN: (纸本)9781728196305;9781728196312

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

关键词： Quality assurance Filtering Conferences Collaborative filtering Semantics Writing Filtering algorithms

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Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning Visualization Semantics Neural networks Data visualization software Task analysis

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Multi-Label Out-of-Distribution Detection Via Exploiting Sparsity and Co-Occurrence of Labels

SSRN

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SSRN 2022年

作者： Wang, Lei Huang, Sheng Huangfu, Luwen Liu, Bo Zhang, Xiaohong School of Big Data and Software Engineering Chongqing University Chonqqing 400044 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing 400044 China Fowler College of Business Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States JD Finance America Corporation Mountain ViewCA94043 United States

Although Out-Of-Distribution (OOD) detection has been extensively studied, the OOD detection under multi-label settings, which is closer to the real world, is still in its infancy. The pioneer work ignores some unique properties of multi-label images, such as the sparsity and co-occurrence of labels. Here, we empirically observe that these properties readily distinguish OOD and in-distribution data. Motivated by this observation, we propose a novel multi-label OOD detection approach named Sparse Label Co-occurrence Scoring (SLCS) to exploit the sparsity and co-occurrence information of labels. SLCS follows conventions and deems the logits outputted by the penultimate layer of the trained multi-label image classification model as the prediction confidences of a sample to categories in the training label set. A logit sparse filtering process is employed to filter out the low-confidence logits for avoiding the interference of low-confidence predictions while preserving the high-confidence logits to obtain the label sparsity. Then, the label co-occurrence pairs are counted for each sample based on its predicted categories and the label co-occurrence matrix constructed on the training set. Finally, the preserved logits are weighted by the label co-occurrence information and accumulated to produce the OOD detection score for each sample. Extensive experimental results on three well-known multi-label image datasets demonstrate the discriminating power of SLCS, which achieves greatly improved performances compared with the only multi-label OOD detection approach - JointEnergry and the state-of-the-art single-label OOD detection approaches. The performance improvements of SLCS over JointEnergy in FPR95 are 12.85%, 12.41%, and 9.50% on MS-COCO, VOC 2012, and NUS-WIDE datasets *** Out-Of-Distribution (OOD) detection has been extensively studied, the OOD detection under multi-label settings, which is closer to the real world, is still in its infancy. The pione

关键词： Image classification

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Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next Point-of-Interest Recommendation

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Expert Systems with Applications 2025年 284卷

作者： Zhang, Qing Huang, Xiao Zhang, Qi Zhou, Wei Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe Chongqing400044 China College of Computer and Information Science Southwest University Chongqing400715 China

Some studies based on the joint modeling of users’ long- and short-term preferences have achieved significant performance improvements in the next POI prediction task. However, these methods fail to fully explore the potential behavioral commonalities within the long- and short-term visit sequences, thus failing to effectively mitigate the sparsity issue in check-in data. Meanwhile, due to the lack of labeled data that clearly distinguishes general tastes from short-term interests, existing joint modeling approaches for long- and short-term preferences struggle to provide clear supervision and differentiation for target preferences, leading to potential entanglement between these two preferences and consequently affecting the accuracy of preference modeling. Based on these, we propose a novel Disentangling Long- and Short-term Preferences Based on Latent Commonalities Enhancement for Next POI Recommendation (DLSPRec) method to accurately distinguish the differences and commonalities between users’ general tastes and short-term preferences, thereby improving the accuracy of the next location recommendation. This is the first attempt to disentangle long- and short-term preferences in the field of next POI recommendation. Through tests on three real-world datasets, we validated the superiority and rationality of the DLSPRec method. Compared to the suboptimal baseline model, DLSPRec achieved an average performance improvement of 10.39%, 9.69% and 16.39% on the PHO, NYC, and SIN datasets, respectively. The code is available at https://***/IsZSS/DLSPRec. © 2025

关键词： Contrastive learning Next POI recommendation Preferences disentanglement

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Dizygotic Conditional Variational AutoEncoder for multi-modal and partial modality absent few-shot learning

arXiv

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arXiv 2021年

作者： Zhang, Yi Huang, Sheng Peng, Xi Yang, Dan Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing400044 China The School of Big Data and Software Engineering Chongqing University Chongqing400044 China Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner. Subsequently, the generated features of two CVAEs are adaptively combined to yield the final feature, which can be converted back into its paired conditions while ensuring these conditions are consistent with the original conditions not only in representation but also in function. DCVAE essentially provides a new idea of data augmentation in various multi-modal scenarios by exploiting the complement of different modality prior information. Extensive experimental results demonstrate our work achieves state-of-the-art performances on miniImageNet, CIFAR-FS and CUB datasets, and is able to work well in the partial modality absence case. © 2021, CC BY-NC-SA.

关键词： Learning systems

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Tourism service recommendation based on user influence in social networks and time series 21

Tourism service recommendation based on user influence in so...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ye, Jianxin Xiong, Qingyu Li, Qiude Gao, Min Xu, Rui Key Laboratory of Dependable Service Computing Cyber Physical Society Chongqing University Ministry of Education School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9781728120584

In the mobile network, user can share their trip stories at any time and place, the massive user data can provides an opportunity to mining users' travel preferences. Tourism recommendation has been studied by lots of researchers, although they took into account the social relation of users and the need of travelers, but if multi-dimensional social information and the seasonality of tourism service are considered, it may lead to better personality efficiency for tourism recommendation system. In this paper, we propose a novel personalized tourism service recommendation model termed GA-LSTM-CSInf, which fused multi-modality travel information include the multi-dimensional social information, time series and tourism service category. The main work is listed as follows:(1)we combined genetic algorithm (GA) and LSTM network for predicting the popularity and score of tourism service.(2) Considering the multi-dimensional social information of users in social networks, we proposed the Explicit Social Influence (ExSoInf) and the Implicit Social Influence (ImSoInf) of users, and then integrated them into SVD algorithm.(3) Calculating user preferences for tourism service categories using matrix factorization technology. And then, the calculation results of the three methods are weighted and summed to obtain a fusion tourism service recommendation algorithm (GA-LSTM-CSInf). We conduct a comprehensive performance evaluation for our fusion recommendation algorithm using real-world dataset collected from Yelp. Experimental results show that our method achieves significantly superior recommendation quality compared to other state of art recommendation methods. © 2019 IEEE.

关键词： Genetic algorithms

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GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences

GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Chen Shijie Liao Jiaxin Hou Kesu Wang Junhao Wen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Traffic flow prediction is an important foundation for intelligent transportation systems. The traffic data are generated from a traffic network and evolved dynamically. So spatio-temporal relation exploration plays a support role on traffic data analysis. Most researches focus on spatio-temporal information fusion through a convolution operation. To the best of our knowledge, this is the first work to suggest that it is necessary to distinguish the two aspects of spatial correlations and propose the two types of spatial graphs, named as geographic graph and semantic graph. Then two novel stereo convolutions with irregular acceptive fields are proposed. The geographic-semantic-temporal contexts are dynamically jointly captured through performing the proposed convolutions on graph sequences. We propose a geographic-semantic-temporal graph convolutional network (GST-GCN) model that combines our graph convolutions and GRU units hierarchically in a unified end-to-end network. The experiment results on the Caltrans Performance Measurement System (PeMS) dataset show that our proposed model significantly outperforms other popular spatio-temporal deep learning models and suggest the effectiveness to explore geographic-semantic-temporal dependencies on deep learning models for traffic flow prediction.

关键词： Deep learning Measurement Three-dimensional displays Convolution Semantics Predictive models Meteorology

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

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arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： Data visualization

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Hybrid attention network based on progressive embedding scale-context for crowd counting

arXiv

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arXiv 2021年

作者： Wang, Fusen Sang, Jun Wu, Zhongyuan Liu, Qi Sang, Nong 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 School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China

The existing crowd counting methods usually adopted attention mechanism to tackle background noise, or applied multi-level features or multi-scales context fusion to tackle scale variation. However, these ap- proaches deal with these two problems separately. In this paper, we propose an Hybrid Attention Network (HAN) by employing Progressive Embedding Scale-context (PES) information, which enables the network to simultaneously suppress noise and adapt head scale variation. We build the hybrid attention mechanism through paralleling spatial attention and channel attention module, which makes the network to focus more on the human head area and reduce the interference of background objects. Besides, we embed certain scale-context to the hybrid attention along the spatial and channel dimensions for alleviating these count- ing errors caused by the variation of perspective and head scale. Finally, we propose a progressive learning strategy through cascading multiple hybrid attention modules with embedding diαerent scale-context, which can gradually integrate diαerent scale-context information into the current feature map from global to local. Ablation experiments provides that the network architecture can gradually learn multi-scale features and suppress background noise. Extensive experiments demonstrate that HANet obtain state-of-the-art counting performance on four mainstream datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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