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Controllable image generation based on causal representation learning

Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 135-148页

作者： Shanshan HUANG Yuanhao WANG Zhili GONG Jun LIAO Shu WANG Li LIU School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China School of Materials and Energy Southwest UniversityChongqing 400715China

Artificial intelligence generated content(AIGC)has emerged as an indispensable tool for producing large-scale content in various forms,such as images,thanks to the significant role that AI plays in imitation and ***,interpretability and controllability remain *** AI methods often face challenges in producing images that are both flexible and controllable while considering causal relationships within the *** address this issue,we have developed a novel method for causal controllable image generation(CCIG)that combines causal representation learning with bi-directional generative adversarial networks(GANs).This approach enables humans to control image attributes while considering the rationality and interpretability of the generated images and also allows for the generation of counterfactual *** key of our approach,CCIG,lies in the use of a causal structure learning module to learn the causal relationships between image attributes and joint optimization with the encoder,generator,and joint discriminator in the image generation *** doing so,we can learn causal representations in image’s latent space and use causal intervention operations to control image *** conduct extensive experiments on a real-world dataset,*** experimental results illustrate the effectiveness of CCIG.

关键词： Image generation Controllable image editing Causal structure learning Causal representation learning

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Tuning-up Learning Parameters for Deep Convolutional Neural Network:A Case Study for Hand-Drawn Sketch Images

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Journal of Electronic Science and Technology 2022年第3期20卷 305-318页

作者： Shaukat Hayat Kun She Muhammad Mateen Parinya Suwansrikham Muhammad Abdullah Ahmed Alghaili the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 the College of Information Science and Engineering Hunan UniversityChangsha 410082

Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved ***(HPs)tuning is a key factor affecting the performance of machine learning(ML)*** state-of-the-art DL models use different HPs in different ways for classification tasks on different *** manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification *** analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and *** analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.

关键词： Deep learning(DL) hand-drawn sketches learning parameters

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NCD: Normal-Guided Chamfer Distance Loss for Watertight Mesh Reconstruction from Unoriented Point Clouds

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Computer Graphics Forum 2025年第0期

作者： Li, Jiaxin Tan, Jiawei Ou, Zhilong Wang, Hongxing School of Big Data and Software Engineering Chongqing University Chongqing China

As a widely used loss function in learnable watertight mesh reconstruction from unoriented point clouds, Chamfer Distance (CD) efficiently quantifies the alignment between the sampled point cloud from the reconstructed mesh and its corresponding input point cloud. Occasionally, to enhance reconstruction fidelity, CD incorporates a normal consistency term, albeit at the cost of efficiency. In this context, normal estimation for unoriented point clouds requires computationally intensive matrix decomposition or specialized pre-trained models, whereas deriving normals for mesh-sampled points can be readily achieved using the cross product of mesh vertices. However, the reconstruction models employing CD and its variants typically rely solely on the spatial coordinates of the points, which omits normal information in favor of efficiency and deployability. To tackle this challenge, we propose a novel loss function for watertight mesh reconstruction from unoriented point clouds, termed Normal-guided Chamfer Distance (NCD). Building upon CD, NCD introduces a normal-steered weighting mechanism based on the angle between the normal at each mesh-sampled point and the vector to its corresponding input point, offering several advantages: (i) it leverages readily available mesh-sampled point normals to weight coordinate-based Euclidean distances, thus extending the capability of CD;(ii) it eliminates the need for normal estimation from input unoriented point clouds;(iii) it incurs a negligible increase in computational complexity compared to CD. We employ NCD as the training loss for point-to-mesh reconstruction with multiple models and initial watertight meshes on benchmark datasets, demonstrating its superiority over state-of-the-art CD variants. © 2025 Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

关键词： Mesh generation

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Designing Group-Level Multi-User Precoding in Multi-Subcarrier Networks: Is EESM the Solution? 24

Designing Group-Level Multi-User Precoding in Multi-Subcarri...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Guan, Xin Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665496261

Existing researches on multi-subcarrie multi-antenna networks usually apply the subcarrier-level precoding, by which each tone is allocated with a dedicated precoder. Such kind of approaches are far from practical implementation due to unbearably high design complexity. By recognizing the fact that multiple subcarriers in a practical cellular network are assigned into one group and share one common precoder, we investigate a more realistic group-level precoding approach. Inspired by the commonly used link adaptation technique namely EESM (Exponential Effective SNR Mapping), a novel spectral efficiency (SE) evaluation scheme is proposed, from which a sumrate maximization of multiple users with group-level precoding is formulated. Via leveraging successive convex approximation (SCA) framework, we tackle the non-convex problem by solving a sequence of convex problems. Furthermore, we also develop a low-complexity solution using gradient projection which are free of any numerical solvers. Simulation results verify the superiority of our proposed novel SE model as well as the efficiency of the proposed algorithms. © 2023 IEEE.

关键词： Spectrum efficiency

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Towards Pervasive Sensing: A multimodal approach via CSI and RGB image modalities fusion 3

Towards Pervasive Sensing: A multimodal approach via CSI and...

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3rd ACM MobiCom Workshop on Integrated Sensing and Communication Systems, ISACom 2023

作者： Zhou, Fujia Zhu, Guangxu Li, Xiaoyang Li, Hang Shi, Qingjiang School of Software Engineering Tongji University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798400703645

Channel state information (CSI) and RGB (Red, Green, Blue) image are two fundamental modalities for accurate human activity recognition (HAR). CSI data is privacy preserving, independent of viewing angle and lighting, while RGB image data can provide useful information after processed by deep learning (DL) methods. However, both modalities have inherent disadvantages. CSI modality is with high environmental dependence, causing unstable fluctuations in wireless signals. RGB image modality is heavily restricted by camera facing angle and lighting condition. Hence, we propose a general multimodal fusion framework for HAR tasks based on both CSI and RGB image modalities. Distinct from other literatures on multimodal fusion mainly based on vision modalities, we utilized CSI modality from wireless domain along with RGB images to realize more comprehensive human activity sensing. Both early fusion and late fusion strategies are formulated and developed, with multiple models constructed from two modern unimodal frameworks to constitute corresponding fusion models. Experiments are conducted on a synchronous CSI and RGB image human activity dataset and validate the advantage of the proposed multimodal fusion design comparing with unimodal approaches. © 2023 ACM.

关键词： Channel state information

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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big data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Time Series data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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A Large-Capacity Information Hiding Algorithm Based on the Fractal Carrier 4

A Large-Capacity Information Hiding Algorithm Based on the F...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Tao, Xuejiao Leng, Yahong Yang, Qian Bai, Sen Chongqing Institute of Engineering School of Software Chongqing China Chongqing Institute of Engineering Office of Teaching Affairs Chongqing China Chongqing Institute of Engineering School of Big Data and Artificial Intelligence Chongqing China

ISBN: (纸本)9798350361643

Information security is a pressing concern in the era of the Internet. Within the realm of information security, significant research focus has been placed on information hiding technology. Researchers strive to enhance both the information hiding capacity and the anti-attack capability. This paper explores the concept of concealing information within fractal images. Fractal images possess a notable attribute of being affine transformed through simple IFS code parameters, thereby generating unique fractal representations. The simplicity of the transmission parameters facilitates an effortless restoration of the carrier. By employing straightforward affine or iterative parameters, the recipient can successfully extract the originally embedded carrier, subsequently unveiling the concealed information. To further augment the security of the information hiding process, an encryption strategy may be implemented to transmit sensitive data. In this scenario, the secret carrier serves as the public key, while the fractal parameter information functions as the private key, thereby intensifying the complexity of steganography analysis. Consequently, this paper introduces a high-capacity fractal carrier information hiding algorithm, which effectively embeds information within fractal images, amplifies and adapts fractal parameters based on requirements, and enhances the overall capacity of the fractal carrier. Exploiting fractal images for information hiding purposes conveys advantages such as minimized reliance on key information and exceptional concealment abilities, thus bolstering the secrecy of hidden data, expanding the storage capacity, and fortifying the resistance against steganalysis. © 2024 IEEE.

关键词： Fractals

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