检索结果-内蒙古大学图书馆

Intuitive User-Guided Portrait Image Editing with Asymmetric Conditional GAN

Computational Visual Media 2025年第2期11卷 361-379页

作者： Linlin Liu Qian Fu Fei Hou Ying He Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group Singapore Singapore Data61 Commonwealth Scientific and Industrial Research Organisation Sydney Australia Key Laboratory of System Software (CAS) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

We propose PortraitACG, a novel framework for user-guided portrait image editing that leverages an asymmetric conditional generative adversarial network (GAN), which supports the fine-grained editing of geometries, colors, lights, and shadows using a single neural network model. Existing conditional GAN-based approaches usually feed the same conditional information into generators and discriminators, which is sub-optimal because these two modules are designed for different purposes. To facilitate flexible user-guided editing, we propose a novel asymmetric conditional GAN, where the generators take the transformed conditional inputs, such as edge maps, color palettes, sliders, and masks, that can be directly edited by the user, and the discriminators take the conditional inputs in a way that can guide controllable image generation more effectively. This allows image editing operations to be performed in a simpler and more intuitive manner. For example, the user can directly use a color palette to specify the desired colors of hair, skin, eyes, lips, and background and use a slider to blend colors. Moreover, users can edit the lights and shadows by modifying their corresponding masks.

关键词： Image color analysis Image edge detection Generative adversarial networks Generators Training Geometry Feature extraction Hair Feeds Image synthesis

来源：评论

学校读者我要写书评

暂无评论

On EDA-Driven Learning for SAT Solving 23

On EDA-Driven Learning for SAT Solving

引用

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Min Li Zhengyuan Shi Qiuxia Lai Sadaf Khan Shaowei Cai Qiang Xu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong S.A.R Communication University of China China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

ISBN: (纸本)9798350323481

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic '1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges

引用

Artificial Intelligence Review 2025年第8期58卷 1-57页

作者： Naz, Anam Khan, Hikmat Ullah Bukhari, Amal Alshemaimri, Bader Daud, Ali Ramzan, Muhammad Department of Computer Science University of Sargodha Punjab Pakistan Department of Information Technology University of Sargodha Punjab Pakistan Department of Information Systems and Technology Collage of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Department of Software Engineering University of Sargodha Punjab Pakistan

Natural language processing (NLP), a prominent research domain of Artificial Intelligence (AI), analyzes users’ generated content on social media for various purposes like sentiment analysis, text summarization, chatbots, fake news detection, etc. Recent advancements in NLP have helped for analysis of human behavior analysis and predicting various human personality traits. Understanding personality traits has long been a fundamental pursuit in psychology and cognitive sciences due to its vast applications for understanding from individuals to social dynamics. Due to online social platforms where people express their views, experiences and comments, NLP is applied for users’ behavior and personality analysis, which is helpful in defining marketing strategies, consumers’ behavior analysis, team building, etc. This research study provides a comprehensive overview of existing methodologies, applications, and challenges in the field of personality traits detection using shallow machine learning, ensemble learning and deep learning. To conduct this study, recent research publications relevant to NLP for this new but emerging research domain are reviewed. The background knowledge of personality models of various nature is discussed for better domain understanding. The study encompasses machine learning and deep learning models with thorough analysis of traditional and innovative techniques including ensemble learning and transformer-based models in chronological order highlighting the trend analysis showing evolution of application of advanced methods. The review also presents and compares the widely used datasets which may guide the researchers for selection of datasets in future studies. Performance evaluation metrics have been discussed which are used in the relevant literature. Furthermore, it explores the application of research of personality traits detection in various domains highlighting its significance. We have also carried out extensive empirical analysis using

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Encryption Traffic Classification Based on Mining Traffic Context and Transport Relationship

Encryption Traffic Classification Based on Mining Traffic Co...

引用

IEEE Conference on Wireless Communications and Networking

作者： Weilin Gai Runqing Zhang Huiyuan Zhang Yu Guo Jun Yin Peng Zhang TCA Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response TechnicalTeam/Coordination Center of China Beijing China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules of Datagram Token conversion, pretraining and fine-tuning, the method uses large-scale unlabeled encrypted traffic for pretraining, mining and learning the traffic context and transmission relationship of encrypted traffic classification tasks, so that a small number of labeled data samples can be effectively used in the fine-tuning stage. Significantly improve the performance of the model on specific downstream classification tasks, enhance the accuracy, adaptability and robustness of the model in diverse environments, limited resources and new encryption security protocols, and realize efficient encryption traffic classification in multi-scenario and low-resource background. The results show that ETC-MTCTR achieves the best performance on three tasks: encryption malware classification, VPN encrypted traffic classification, and TLS 1.3 encryption application classification. Its F1 score is improved by 0.22% in the classification task of encrypted malware, 1.4% in the classification task of VPN encrypted traffic App, 4.56% in the classification task of VPN encrypted traffic Service, and 9.89% in the classification task of TLS 1.3 encrypted application, which is significantly better than other comparison methods.

关键词： Adaptation models Accuracy Protocols Malware Robustness Encryption Virtual private networks Data mining

来源：评论

学校读者我要写书评

暂无评论

PROTOTYPE CONTRASTIVE CONSISTENCY LEARNING FOR SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION

arXiv

引用

arXiv 2025年

作者： He, Shihuan Lai, Zhihui Wang, Ruxin Kong, Heng Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Breast and Thyroid Surgery Baoan Central Hospital of Shenzhen China

Medical image segmentation is a crucial task in medical image analysis, but it can be very challenging especially when there are less labeled data but with large unlabeled data. Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning by constructing contrastive samples from partial pixels. However, although previous contrastive learning methods can mine semantic information from partial pixels within images, they ignore the whole context information of unlabeled images, which is very important to precise segmentation. In order to solve this problem, we propose a novel prototype contrastive learning method called Prototype Contrastive Consistency Segmentation (PCCS) for semi-supervised medical image segmentation. The core idea is to enforce the prototypes of the same semantic class to be closer and push the prototypes in different semantic classes far away from each other. Specifically, we construct a signed distance map and an uncertainty map from unlabeled images. The signed distance map is used to construct prototypes for contrastive learning, and then we estimate the prototype uncertainty from the uncertainty map as tradeoff among prototypes. In order to obtain better prototypes, based on the student-teacher architecture, a new mechanism named prototype updating prototype is designed to assist in updating the prototypes for contrastive learning. In addition, we propose an uncertainty-consistency loss to mine more reliable information from unlabeled data. Extensive experiments on medical image segmentation demonstrate that PCCS achieves better segmentation performance than the state-of-the-art methods. The code is available at https://***/comphsh/PCCS. © 2025, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Tactile-Driven Rule Configuration and Anomaly Detection in Industrial IoT Systems

引用

IEEE Internet of Things Journal 2025年

作者： Tan, Lizhuang Singh, Amritpal Zhang, Wei Pei, Hongjuan Zhang, Peiying Chahal, Prabhjot Kaur Singh, Maninderpal Ji'nan250014 China Pohang University of Science and Technology Department of Computer Science and Engineering Pohang37673 Korea Republic of Northumbria University Department of Computer and Information Sciences United Kingdom University of Chinese Academy of Sciences School of Engineering Science Beijing100049 China China University of Petroleum Qingdao Institute of Software College of Computer Science and Technology East China Qingdao266580 China Chandigarh Group of Colleges CEC Punjab Mohali India Southern Methodist University Electrical and Computer Engineering Department Dallas United States

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data is subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the Self-Organized Maps approach to distinguish between industrial traffic requiring tactile latencies and non-tactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic. © 2014 IEEE.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

DDA: Distillation-Driven Acceleration of the Reverse Diffusion Process for Stochastic Multi-Ship Trajectory Prediction

DDA: Distillation-Driven Acceleration of the Reverse Diffusi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kun Ma Qilong Han Jingzheng Yao Changmao Wu Yuntao Zhang College of Computer Science And Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Institute of Software Chinese Academy of Sciences Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Modeling stochastic multi-ship trajectories is vital for maritime safety and interaction efficiency. Recent researches show that diffusion models excel in trajectory prediction, surpassing GANs and VAEs in generation quality, diversity and stability. However, their slow sampling speed remains a major limitation, as producing high-quality trajectories typically requires hundreds of denoising steps. We introduce DDA, a novel method that accelerates multi-ship trajectory generation by distilling the reverse diffusion process, progressively reducing sampling steps by half while minimizing quality loss. We use CVAE-based encoder to map multimodal inputs into state embeddings in the latent space, and use distillation diffusion in the latent space to more quickly and better represent multi-ship trajectories. The diffusion model uses Transformer-based core, and we incorporate SO(2) invariance and equivariance to enhance model representation. Validation on real-world AIS datasets shows that the student model retains high-quality trajectory generation while sampling speed is approximately 30 times faster.

关键词： Diffusion processes Stochastic processes Transformer cores Signal processing Diffusion models Transformers Stability analysis Trajectory Artificial intelligence Speech processing

来源：评论

学校读者我要写书评

暂无评论

Instantaneous Trajectory Prediction via Latent Bidirectional Cooperative Diffusion

Instantaneous Trajectory Prediction via Latent Bidirectional...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kun Ma Qilong Han Jingzheng Yao Changmao Wu Chunrui Na College of Computer Science And Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Chinese Academy of Sciences Institute of Software Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world scenarios, extreme cases where pedestrians suddenly emerge from blind spots or occlusions, leaving only a minimal amount of observable trajectory points, occur frequently. This presents a significant challenge for autonomous driving and robotic navigation, where pedestrian safety and timely response are critical considerations. To address this challenge, we propose a framework for instantaneous trajectory prediction using Latent bidirectional Cooperative Diffusion (LCD). It designs a complementary mechanism that constructs a coupled bidirectional cooperative diffusion model. LCD simultaneously and progressively generates unobserved past trajectories and future trajectories, feeding each other as conditions into the cross-attention module for mutual guidance. This framework employs CVAE as its encoder to map the observed multi-model trajectories into a high-dimensional latent space to enhance complex representations. Experiments conducted on the ETH/UCY and SDD datasets demonstrate the superiority of our framework.

关键词： Pedestrians Navigation Signal processing Diffusion models Liquid crystal displays Trajectory Safety Speech processing Robots Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

DatawiseAgent: A Notebook-Centric LLM Agent Framework for Automated Data science

arXiv

引用

arXiv 2025年

作者： You, Ziming Zhang, Yumiao Xu, Dexuan Lou, Yiwei Yan, Yandong Wang, Wei Zhang, Huaming Huang, Yu National Engineering Research Center for Software Engineering Peking University China School of Software & Microelectronics Peking University China School of Computer Science Peking University China Xi’an Jiaotong University China Institute of Basic Theory of Chinese Medicine China Academy of Chinese Medical Sciences China

Data science tasks are multifaceted, dynamic, and often domain-specific. Existing LLM-based approaches largely concentrate on isolated phases, neglecting the interdependent nature of many data science tasks and limiting their capacity for comprehensive end-to-end support. We propose DatawiseAgent, a notebook-centric LLM agent framework that unifies interactions among user, agent and the computational environment through markdown and executable code cells, supporting flexible and adaptive automated data science. Built on a Finite State Transducer(FST), DatawiseAgent orchestrates four stages, including DSF-like planning, incremental execution, self-debugging, and post-filtering. Specifically, the DFS-like planning stage systematically explores the solution space, while incremental execution harnesses real-time feedback and accommodates LLMs’ limited capabilities to progressively complete tasks. The self-debugging and post-filtering modules further enhance reliability by diagnosing and correcting errors and pruning extraneous information. Extensive experiments on diverse tasks, including data analysis, visualization, and data modeling, show that DatawiseAgent consistently outperforms or matches state-of-the-art methods across multiple model settings. These results highlight its potential to generalize across data science scenarios and lay the groundwork for more efficient, fully automated workflows. Copyright © 2025, The Authors. All rights reserved.

关键词： Data science

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：