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International Conference on data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG.

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An Overview of Controllable Image Synthesis: Current Challenges and Future Trends

SSRN

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SSRN 2022年

作者： Huang, Shanshan Li, Qingsong Liao, Jun Liu, Li Li, Lian School of Big Data & software Engineering Chongqing University Chongqing400000 China School of Computers Science and Information Engineering Hefei University of Technology Hefei230601 China

Controllable image synthesis is a method by which users can manipulate a particular attribute in an image in a semantically meaningful way without affecting other attributes. This research has been a hot topic in image processing, with significant advances in visual authenticity, diversity, and controllability over the past few years. However, there are still some challenges in this field, such as the controllability and interpretability of synthesis images. This paper is the first comprehensive and systematic discussion of controllable image synthesis, from problem definition, taxonomy and evaluation systems to existing challenges and future research directions. Firstly, the definition of controllable image synthesis is given, and several representative deep generative models are introduced in detail. Secondly, the existing controllable image synthesis methods are divided into three categories according to the different control manners used and discuss the typical work in each category critically. Next, this paper introduces the public datasets and evaluation metrics commonly used in image synthesis and analyzes the representative controllable image synthesis methods. Finally, we summarize the whole literature, and some open topics and future development direction of controllable image synthesis are discussed. © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

A Complex Belief Jensen-Shannon Divergence in Complex Eviden...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Linlu Gao Mingrui Yin Fuyuan Xiao Zehong Cao The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China STEM Mawson Lakes Campus University of South Australia Adelaide Australia

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority.

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

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arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

arXiv

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

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SPDM: Spatiotemporal-Periodic Diffusion Model for Multivariate Time Series Imputation

SPDM: Spatiotemporal-Periodic Diffusion Model for Multivaria...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qia Zhang Renfang Wang Hong Qiu Xiufeng Liu Xu Cheng College of Information Technology Shanghai Ocean University Shanghai China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China Department of Technology Management and Economics Technical University of Denmark Kgs. Lyngby Denmark School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

This paper presents SPDM, an innovative spatiotemporal-periodic diffusion model for multivariate time series interpolation, which addresses the challenges of spatiotemporal dependency and periodicity inherent in real-world datasets. Unlike prior methods, SPDM integrates conditional features capturing spatiotemporal correlations and geographic relationships, enhancing the model's ability to account for complex interdependencies within time series data. A noise prediction module, leveraging Fast Fourier Transform, decomposes time series into periodic components, thereby enabling the model to capture both intra- and inter-period dynamics and inter-channel correlations effectively. Experimental results on multiple industrial datasets show that SPDM outperforms state-of-the-art methods across various missing data scenarios, highlighting its robustness and effectiveness. This work establishes a new approach to time series interpolation by combining conditional information construction with periodicity-aware diffusion modeling, offering promising insights for further applications in time series analysis.

关键词： Interpolation Correlation Fast Fourier transforms Time series analysis Noise Predictive models Diffusion models Imputation Robustness Spatiotemporal phenomena

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Rgmef: A Lightweight Multi-Exposure Image Fusion Network Based on Retinex and Gradient Guidance

SSRN

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SSRN 2024年

作者： Feng, Yunfei Zhang, Mei Zhu, Jinhui School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Software Engineering South China University of Technology Guangzhou510641 China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou510641 China

The goal of the multi-exposure image fusion (MEF) is to create a high dynamic range (HDR) image with balanced exposures and more details by extracting and enhancing valid information from multiple low dynamic range (LDR) images with varied exposures. In recent years, deep learning has provided many solutions for MEF, and they have achieved remarkable effects. However, because of the high network complexity, these approaches often have low efficiency and limit the potential improvement of the effect. In order to fuse valuable information from different images, we introduce a lightweight but robust MEF network, which focus on the image internal characteristics rather than long-distance information. In this study, we first design a decomposed network inspired by Retinex theory to extract the illumination map from an image and obtain the gradient map through Sobel operator. Subsequently, we introduce two lightweight feature extraction modules to extract image features with the guidance of illumination and gradient maps respectively, and then fuse these features to produce an HDR result through three fusion blocks. Furthermore, we design a loss function to obtain a more suitable brightness range for the current scene. Numerous comparative experiments demonstrate the superiority of our method in terms of image texture, pixel intensity, and color retention. Moreover, some ablation studies indicate that our approach has a significant adaptability to source images. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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A Federated Framework for Air Quality Prediction with Pre-defined Graph and Adaptive Graph

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IEEE Internet of Things Journal 2025年

作者： Huang, Wei Wang, Feng Chen, Junling Liu, Jia Liu, Zhiquan Li, Tianrui Fuzhou350108 China Fuzhou University College of Computer and Big Data Fuzhou350108 China Xihua University School of Computer and Software Engineering Chengdu610039 China Jinan University College of Cyber Security Guangzhou510632 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China

Air quality prediction utilizes IoT technologies to collect data centrally for model training, which may cause regulatory risks, privacy concerns, and high costs of integrating data. Meanwhile, distributed training through federated learning relies on the pre-defined graph structure generated by the geographic locations of air monitoring stations, which fails to capture the potential spatial relationships between air monitoring stations. In addition, the inherent multi-period attribute of air quality data makes time series changes extremely complex. To this end, this paper proposes a Federated framework for air quality prediction with Pre-defined graph and Adaptive Graph (FedPAG). Specifically, the client encodes the air quality data and meteorological data provided by the Internet of Things (IoT) system using the multi-period interaction and encoder modules to capture the proximity and periodicity features of the time series data. Next, the server combines the hidden states uploaded by the clients with pre-defined graph and adaptive graph respectively and forms node embeddings to capture the spatial features among the clients. Then, the client concatenates the hidden states with node embeddings to fuse the spatial information and feeds them into the decoder to obtain the final predicted values. Finally, we conduct experiments on the Beijing and Shijiazhuang datasets to demonstrate the effectiveness of the proposed method. © 2014 IEEE.

关键词： Adaptive graph Air quality prediction Federated learning Multi-period interaction

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Design and Implementation of Student Comprehensive Assessment System

Design and Implementation of Student Comprehensive Assessmen...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Zhongjie Liu Jiachen Cai Meifang Xu Dawei Tao Shih-Pang Tseng School of Software and Big Data Changzhou College of Information Technology Changzhou China School of Cyberspace Security Changzhou College of Information Technology Changzhou School of Electronic Engineering Changzhou College of Information Technology Changzhou School of Information Science and Technology Sanda University

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

With the continuous deepening of educational informatization, student comprehensive assessment has become increasingly important as a core link in evaluating academic achievements and promoting the improvement of teaching quality. However, some schools still lack sufficient educational informatization and rely on Excel spreadsheets to analyze students comprehensive assessment data, resulting in low work efficiency. This paper proposes a student comprehensive assessment system based on the Django framework, which deeply investigates the functional requirements of existing student comprehensive assessments and designs corresponding functional modules such as honor declaration, moral education evaluation, academic performance inquiry, dormitory performance accounting, and comprehensive performance inquiry. Based on the different weights of the entered data, the system can quickly calculate the comprehensive assessment scores for each student. The system also integrates a large language model from Qianfan, developing an intelligent question assistant to deeply learn the school's policy knowledge base to answer questions encountered by teachers and students in their learning and daily lives. The deployment and implementation of the system have verified the accuracy of the student comprehensive assessment results, improved the level of educational informatization management in student work, and promoted the development of the student comprehensive assessment system.

关键词： Ethics Accuracy Large language models Education Knowledge based systems

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