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

作者： Guohui Li Li Zou Zhiying Deng Qi Chen School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

The lack of interaction data of new items in recommendation systems leads to the problem of cold-start item recommendations. Current methods usually approximate content features of the items to interaction embeddings and then use content features for prediction. However, these methods typically learn content features with the same item’s interaction embeddings, resulting in sparse supervised signals for content features and suboptimal recommendation performance. To address this issue, we propose a novel approach called Neighborhood-Enhanced Multimodal Collaborative Filtering (NEMCF) that captures the neighborhood of content features and interaction embeddings in order to enrich the content features. Specifically, we first design a dual graph neural network to aggregate interaction embeddings and content features of co-occurring neighbors of items. Then, we use the aggregated interaction embeddings and content features of items to supplement the content features via contrastive learning. Finally, to enhance the robustness of NEMCF, we randomly employ item content features and interaction embeddings for recommendations. Extensive experimental studies on four benchmark datasets demonstrate that the NEMCF method outperforms several typical cold-start methods and achieves state-of-the-art results.

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Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Su, Huaqiang Lei, Haijun Chen, Guoliang Chen, Xin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Guangzhou First People's Hospital Department of Radiology Guangzhou China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China

ISBN: (纸本)9798350337488

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network's ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16. © 2023 IEEE.

关键词： Diffusion probability model feature fusion module medical segmentation mutual graph learning network

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Exploring Representation-Level Augmentation for Code Search

Exploring Representation-Level Augmentation for Code Search

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Haochen Miao, Chunyan Leung, Cyril Huang, Yanxian Huang, Yuan Zhang, Hongyu Wang, Yanlin School of Computer Science and Engineering Nanyang Technological University Singapore China School of Software Engineering Sun Yat-Sen University China The University of Newcastle Australia

Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice. Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving program transformation) are proposed to learn better representations. However, these augmentations are at the raw-data level, which requires additional code analysis in the preprocessing stage and additional training costs in the training stage. In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representation-level augmentation that unifies existing methods. Then, we propose three new augmentation methods (linear extrapolation, binary interpolation, and Gaussian scaling) based on the general format. Furthermore, we theoretically analyze the advantages of the proposed augmentation methods over traditional contrastive learning methods on code search. We experimentally evaluate the proposed representation-level augmentation methods with state-of-the-art code search models on a large-scale public dataset consisting of six programming languages. The experimental results show that our approach can consistently boost the performance of the studied code search models. Our source code is available at https://***/Alex-HaochenLi/RACS. © 2022 Association for Computational Linguistics.

关键词： Semantics

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ER-Net: Efficient Recalibration Network for Multi-view Multi-person 3D Pose Estimation 8

ER-Net: Efficient Recalibration Network for Multi-view Multi...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Zhou, Mi Liu, Rui Yi, Pengfei Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng School of Software Engineering Dalian University National and Local Joint Engineering Laboratory of Computer Aided Design Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-To-end direct regression methods, the field has reached a new stage of development. However, the regression results are still not accurate enough even for the optimal method for the joints that are more heavily influenced by external factors. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and an optimal calibration strategy, which is applied to a multi-view multi-person 3D human pose estimation task backbone network to achieve improved detection accuracy for joints that are more severely affected by external factors. Specifically, we achieve optimal weight adjustment of joint feature information through a recalibration module based on a channel attention mechanism and an optimal calibration strategy, which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints. We call this method the Efficient Recalibration Network (ER-Net). Finally, experiments were conducted on two benchmark datasets for this task, Campus, and Shelf, PCP reached 97.3 percent and 98.3 percent, respectively. © 2022 IEEE.

关键词： computer vision

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Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation 7

Magnetic Type Classification in Sunspot Group Based on Semi-...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Liu, Junhong Li, Baoping Luo, Zihui School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665478571

Sunspot group, known as active solar regions, is the main sources of solar storms. The morphological and magnetic characteristics of solar active regions play a very important role in solar storm forecasting and is well described by Mount Wilson Sunspot Classification Scheme. The development of convolutional neural network methods in the field of image processing makes efficient magnetic type classification possible. In this paper, we propose a method based on semi-supervised learning and knowledge distillation for magnetic type classification in sunspot group. On the sunspot magnetic type classification dataset, our method achieves 95.14% total classification accuracy, 97.4%, 94.43% and 85.71% F1-scores of Alpha, Beta, and Beta-x types respectively. © 2022 IEEE.

关键词： Solar energy

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao 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 Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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Design and Implementation of Script Module with Recording and Playback Function in CAE software

Design and Implementation of Script Module with Recording an...

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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Wang, Jingyu Fu, Chengjie Wang, Tiejun Wang, Yuntao Chen, Liangzhong School of Aeronautics and Astronautics Sichuan University Chengdu China Tianfu Engineering-Oriented Numerical Simulation & Software Innovation Center Sichuan University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9781510665040

In large-scale engineering experiments, such as hydrodynamics and aerodynamics experiments, computer aided software (CAE) is always used to manage a large number of parameters and experimental data to simulate the physical field through finite element analysis. To improve work efficiency, one of the most practical methods at present is to record user operations in the CAE software to script files with domain specific language (DSL), such as Python, and replay those script files to reproduce user operations. In this paper, a common script module ScriptEngine supporting the recording and playback function is introduced, and it can be embedded into any CAE software to automatically complete grid division, flow field calculation, pre-processing and post-processing by running the scripts. Furthermore, a demo program embedding ScriptEngine into an opensource FastCAE was developed and the results show that running the scripts recorded by ScriptEngine could obtain the same results as the user operations and ScriptEngine can be used as a common script module in any CAE software and other desktop software which needs the recording and playback function. © 2023 SPIE.

关键词： Python

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A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment 30th

A Multi-Armed Bandits Learning-Based Approach to Service Ca...

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30th International Conference on Web Services, ICWS 2023

作者： Li, Jinpeng Zhao, Jiale Chen, Peng Xia, Yunni Li, Fan Li, Yin Zeng, Feng Liu, Hui School of Computer Chongqing University Chongqing400030 China School of Computer and Software Engineering Xihua University Chengdu610039 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Guangzhou Institute of Software Application Technology Guangzhou510990 China Limited Shenzhen518129 China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9783031448355

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key *** the polarity of an opinion is much more useful if we also know the targeted Aspect or *** based approaches,like dependency-based rules,are quite popular and effective for this ***,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency *** popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s ***,in general,the sequential rule-based approaches have poor generalization ***,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb *** this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun ***,after rigorous analysis,we have also constructed rules for the extraction of verb *** verb rules primarily based on the association between verb and opinion *** proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review *** F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing *** improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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