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SSRN 2022年

作者： Zhang, Jia Gao, Min Zhao, Liang Hu, Jiaqi Gao, Jinyong Deng, Meiling Wan, Chao Yang, Linda School of Big Data and Software Engineering Chongqing University Chongqing401331 China China National Electric Apparatus Research Institute Co. Ltd. Guangzhou510300 China Aibo Green Reverse Supply Chain Co. Ltd. Shenzhen518000 China School of Computing University of Portsmouth Hampshire PortsmouthPO1 3HE United Kingdom

In recent years, the reverse logistics (RL) management of waste electrical and electronic equipment (WEEE) has aroused widespread interest due to its vital role in improving ecological and economic benefits. As accurate quantification and estimate of future WEEE are fundamental to adequate planning and efficient treatment, we propose the Multi-time Scale Attention Network (MULAN) model in this paper for WEEE RL return prediction. In MULAN, we take multiple windows of historical data to capture distinct characteristics presented at different time scales and use attention-based alignment to fuse information. We then propose smooth time-series embedding method based on neighborhood data aggregation and temporal feature embedding method from four kinds of time positions to tackle the challenges of dealing with high noise data and high sensitivity to temporal dependencies. We conduct extensive experiments on real-world datasets to demonstrate that MULAN has significantly improved prediction accuracy over other baseline prediction methods in most cases. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering

arXiv

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arXiv 2025年

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view’s complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025, CC BY.

关键词： Contrastive Learning

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Dual consistent constraint via disentangled consistency and complementarity for multi-view clustering

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Neurocomputing 2025年 641卷

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025

关键词： Contrastive Learning

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An Empirical Study of Solidity Language Features

An Empirical Study of Solidity Language Features

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Ziyan Wang Xiangping Chen Xiaocong Zhou Yuan Huang Zibin Zheng Jiajing Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665478373

Solidity, like other programming languages, is evolving with many new features being introduced and deprecated features being removed. However, not every feature benefits developers and sometimes it may do more harm than good. In the hope of knowing the actual use of language features and helping improving them, we summarize 41 common features, discuss their pros and cons, and conduct an empirical study. We design an extensible feature analysis tool to analyze their distribution in 172,645 real-world smart contracts. We further analyze the reason why some features are popular. We invite readers for a case study to understand the usage patterns and how seemingly benign features produce subtle bugs. It turns out that some features like high-level function invocations, visibility, events, and assertions are popular while developers cares little about some other features like function modifier overloading. After summarizing our findings, we give suggestions to different people in Solidity community.

关键词： Computer languages Conferences Smart contracts Computer bugs software quality Reliability engineering software reliability

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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Maximum profit of viral marketing: An optimal control approach 2019

Maximum profit of viral marketing: An optimal control approa...

引用

4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： Zhao, Juan Yang, Lu-Xing Yang, Xiaofan School of Big Data and Software Engineering Chongqing University Chongqing400044 China School of Information Technology Deakin University VIC3125 Australia

ISBN: (纸本)9781450362580

Viral marketing, known also as word-of-mouth marketing, is to use consumer-to-consumer communications to disseminate information about a product or service. Firms must persuade consumers, through their personal network and with the support of the Internet to become the vehicles which can disseminate the positive information. This paper addresses the modeling and analysis of the viral marketing for a consistent set of items. We develop a node-level positive comments mixed spreading model with positive comments and discount pricing mechanism, and thereby we measure the profit of the marketing. Next, we show that the model admits an optimal control, and we present the optimality system for solving the model. Finally, we give some optimal strategies by solving the corresponding optimality systems. © 2019 Association for Computing Machinery.

关键词： Profitability

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Multi-modal cyberbullying detection on social networks

Multi-modal cyberbullying detection on social networks

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International Joint Conference on Neural Networks (IJCNN)

作者： Kaige Wang Qingyu Xiong Chao Wu Min Gao Yang Yu State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Big Data and Software Engineering Chongqing University China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Because social networks have become a vital part of people's lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.

关键词： Social network services Feature extraction Visualization Logic gates Media Encoding Text analysis

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K-Core Decomposition on Super Large Graphs with Limited Resources

arXiv

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arXiv 2021年

作者： Gao, Shicheng Xu, Jie Li, Xiaosen Fu, Fangcheng Zhang, Wentao Ouyang, Wen Tao, Yangyu Cui, Bin School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Tencent Inc. China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Institute of Computational Social Science Peking University Qingdao China

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Design of a Hybrid Brain-Computer Interface and Virtual Reality System for Post-Stroke Rehabilitation

引用

IFAC-PapersOnLine 2020年第2期53卷 16010-16015页

作者： Mengjie Huang Yuting Zheng Jingjing Zhang Bin’ao Guo Chenyu Song Rui Yang Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University Suzhou 215123 China Research Institute of Big Data Analytics Xi’an Jiaotong-Liverpool University Suzhou 215123 China

As one of common diseases among elderly, stroke often leads to motor impairment and even serious disability. Post-stroke rehabilitation is of great importance to restore the motor function and improve the life quality of stroke survivors. This study therefore sets out to propose a hybrid system based on brain-computer interface and virtual reality, which can provide various training programs including action observation, motor imagery and physical therapy for post-stroke patients with different motor control levels and training demands. The present work offers new insights into the way in which the conventional rehabilitation programs can be turned into innovative and interactive training experiences with advanced technologies to make optimal rehabilitation outcomes for stroke survivors.

关键词： Rehabilitation engineering Stroke Virtual reality Brain-computer interface Motor imagery

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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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