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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xiangyu Su, Weiheng Dong, Yuxing Li, Yang Zhang, Xianli Gong, Tieliang MacHado, Ines Prata Crispin-Ortuzar, Mireia Li, Chen Gao, Zeyu Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Interactive Entertainment Group Global Tencent Singapore University of Cambridge Department of Oncology United Kingdom University of Cambridge Cruk Cambridge Centre United Kingdom

ISBN: (纸本)9798350386226

Accurate histopathological image segmentation is crucial for precise disease diagnosis and prognosis. Yet, challenges like staining variations, imaging conditions, and tissue diversity impede model generalization across domains, such as different institutes or organs. Traditional domain generalization (DG) techniques, such as data augmentation and feature alignment, excel in classification tasks but face challenges in segmentation tasks due to their dense prediction requirements. These tasks are particularly computationally demanding, and are complicated due to the fine-grained feature variability that arises from the domain differences in histopathological images. To tackle this, we propose the Shallow-Deep Synergy (SDS) approach for the U-Net-based segmentation framework, which capitalizes on the distinctive characteristics of both shallow and deep layers of the U-Net. Specifically, we introduce the fine-grained domain variations in image intensities and textures for shallow layers, while focusing on aligning the pixel-level classification decision boundaries in deep layers by adjusting the optimization trajectory through class-wise gradient and feature alignment. Moreover, the SDS is equipped with a big-batch strategy further boosting alignment efficiency, achieving high accuracy without substantial GPU memory. Extensive experiments conducted on two histopathological segmentation datasets, each representing different domain types, demonstrate that the proposed SDS achieves superior generalization performance compared to existing domain generalization methods, even being competitive with intra-domain models in some cases. © 2024 IEEE.

关键词： Stochastic systems

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations 40

Stable Heterogeneous Treatment Effect Estimation across Out-...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Yuling Wu, Anpeng Kuang, Kun Du, Liang Sun, Zixun Wang, Zhi Tsinghua University Tsinghua Shenzhen International Graduate School China Zhejiang University College of Computer Science and Technology China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department United Arab Emirates Interactive Entertainment Group Tencent Tsinghua University Tsinghua-Berkeley Shenzhen Institute China

ISBN: (纸本)9798350317152

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation Learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. © 2024 IEEE.

关键词： Population distribution

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An Industrial Internet Cloud-Edge Collaborative Scheme and Evaluation System 3

An Industrial Internet Cloud-Edge Collaborative Scheme and E...

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Sun, Tianwen Wang, Yushen Yang, Guang Xiong, Runqun Yang, Kai Zhang, Wenhua System integration center China Mobile Group Jiangsu Co. Ltd. Nanjing China China Mobile Group Jiangsu Co. Ltd. Group Customer Department Nanjing China School of Computer Science Southeast University Nanjing China Migu Interactive Entertainment Co. Ltd. Cloud network technology Department Nanjing China

ISBN: (纸本)9798331506582

The scale of the global cloud computing market is growing rapidly. In the context of the global digital economy, cloud computing has become an inevitable choice for digital transformation of enterprises. With the rapid development of 5G, Internet of things and other technologies, edge computing has attracted more and more attention, and cloud-edge collaboration has become an important trend. Only cloud computing and edge computing can meet the needs of various scenarios through close collaboration, so as to maximize the application value of cloud computing and edge computing. This paper systematically puts forward a cloud edge collaboration scheme in the field of industrial Internet, and provides a standard industrial Internet cloud edge collaboration depth integration rating standard for manufacturing enterprises, which is convenient for scientific evaluation of cloud edge synergy effect. © 2024 IEEE.

关键词： Cloud platforms

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A Preliminary Study on the Collaborative Playing Piano Automatic with Deep Learning

A Preliminary Study on the Collaborative Playing Piano Autom...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Karuna Yampray Department of Electronic Engineering International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan College of Creative Design and Entertainment Technology Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A great number of deep learning-based models have been recently proposed for automatic piano classification. In this paper, we describe our contribution to the challenge of automatic piano classification when the performer performs at the concert or stage. Among these models in deep learning, we use init-1D-WaveNet and init-2D-MLNet for comparison the accuracy in the piano beginning level of the Christmas song (Jingle bells). Our experimental results show that the assessment using the init-2D-MLNet still achieve high accuracy of 87.5%.

关键词： Deep learning Accuracy Instruments Collaboration Consumer electronics

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Shallow-Deep Synergy: Boosting Cross-Domain Generalization in Histopathological Image Segmentation

Shallow-Deep Synergy: Boosting Cross-Domain Generalization i...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiangyu Wang Weiheng Su Yuxing Dong Yang Li Xianli Zhang Tieliang Gong Ines Prata Machado Mireia Crispin-Ortuzar Chen Li Zeyu Gao School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Interactive Entertainment Group Global Tencent Singapore Department of Oncology University of Cambridge UK CRUK Cambridge Centre University of Cambridge UK

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Image segmentation Accuracy Perturbation methods Stochastic processes Boosting Robustness Trajectory Prognostics and health management Optimization Biomedical imaging

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Design and implementation of a virtual reality insects learning system with its user experience evaluation 8

Design and implementation of a virtual reality insects learn...

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8th International Conference on Orange technology, ICOT 2020

作者： Tsai, An-Chao Tajen University Department of Computer Science and Entertainment Technology Pingtung County Taiwan

ISBN: (纸本)9781665418522

This work proposed a Virtual Reality Insects Learning System for elementary school students. Since the application of Virtual Reality has been gradually involved into education recently, the investigation of the effect and its usefulness could be an interesting topic for both content provider and teachers. The proposed work includes fifteen different kinds of insects with individual explanation;students can interact with the system to learn to know each insect. The proposed system is further used to evaluate students' learning experience and perceptions. The experimental results show that students perceived positive motivation and attitudes accompany with less cognitive load in Virtual Reality learning. The results of this work also demonstrate that the Virtual Reality related content provide a brand new learning experience which is helpful for students' study. © 2020 IEEE.

关键词： Virtual reality

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Methods for pitch analysis in contemporary popular music: highlighting pitch uncertainty in Primaal’s commercial works

arXiv

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arXiv 2025年

作者： Deruty, Emmanuel Leroy, Luc Macé, Yann Meredith, David Sony Computer Science Laboratories 6 rue Amyot Paris75005 France Neodrome Entertainment 5 rue Vernet Paris75008 France Department of Architecture Design and Media Technology Aalborg University Rendsburggade 14 Aalborg9000 Denmark

We identify characteristic features of how pitch is manipulated for expressive purposes by Hyper Music, a mainstream commercial music company specialising in advertisement music for global corporations. The study shows that the use and organisation of pitch in the company’s ‘Primaal’ brand differs from Western classical music. Through interviews with producers and in-depth analysis of their work, we reveal that their methods centre on a conscious aim to construct a musical discourse based on pitch uncertainty, contrasting with the clear transmission of well-defined pitches in Western classical traditions. According to the Primaal producers, who acknowledge the influence of artists such as Kanye West and Daft Punk and use widely available technology, pitch uncertainty captures the listener’s attention. We provide analyses of musical excerpts demonstrating their approach, alongside descriptions of the tools and methods employed to achieve their expressive goals. These goals and methods are placed in a broader historical context, contrasting with fundamental principles of pitch organisation in Western music. Techniques used by Hyper Music to introduce and control pitch uncertainty include boosting upper partials, expressive use of inharmonicity, continuous pitch distributions around ‘poles’ tied to specific ‘modes’, and continuously evolving pitch. We examine these techniques from a psychoacoustic perspective, and conduct listening tests corroborating some of the observations. The ultimate goal of the study is to introduce a set of methods suited to the analysis of pitch in contemporary popular *** Codes 00A65 © 2025, CC BY.

关键词： Music

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations

Stable Heterogeneous Treatment Effect Estimation across Out-...

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International Conference on Data Engineering

作者： Yuling Zhang Anpeng Wu Kun Kuang Liang Du Zixun Sun Zhi Wang Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Technology Zhejiang University Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Tencent Interactive Entertainment Group Tsinghua-Berkeley Shenzhen Institute Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation Learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL- HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods.

关键词： Training Representation learning Measurement Sociology Estimation Data engineering Data models

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Research on Efficient Software Defect Prediction Using Deep Learning Approaches 2

Research on Efficient Software Defect Prediction Using Deep ...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Razauddin Madhuri G, Sindhu Oberoi, Ashish Vats, Aman Sivasangari, A. Siwach, Kuldeep Gulf Colleges Department of Computer and Information Sciences Saudi Arabia Faculty of Engineering & Technology Department of CSE Karnataka Bengaluru India Rimt University Department of CSE Gobindgarh Mandi India Manav Rachna International Institute of Research and Studies Faculty of Media Studies and Humanities Department of Journalism and Mass Communication Haryana Faridabad India Sathyabama Institute of Science and Technology Department of Ut Tamilnadu Chennai India School of Media and Entertainment SoME Gd Goenka University Gurgaon India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data;it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner. © 2022 IEEE.

关键词： Defects

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Occlusion resistant face detection and recognition system 8

Occlusion resistant face detection and recognition system

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8th International Conference on Orange technology, ICOT 2020

作者： Tsai, An-Chao Ou, Yang-Yen Wu, Wei-Ching Wang, Jhing-Fa Tajen University Department of Computer Science and Entertainment Technology Taiwan National Cheng Kung University Department of Electrical Engineering Taiwan

ISBN: (纸本)9781665418522

The application of face recognition has become more and more popular with the development of deep learning algorithms and the calculation of hardware chips for accelerating neural networks. In face recognition, the recognition accuracy is easily affected by light, distance and occlusion. However, the occlusion is the most difficult issue to deal with. This work presents a convolutional neural network which was trained to improve the accuracy of face detection with the ability to capture facial features. The proposed method overcomes the situation when the face accompany with occlusion. All of the face regions and facial landmark are calculated via the face detection network for the inputted image. The face is then aligned by facial landmark and input into the face recognition network for identification. The experimental results of accuracy could reach 96.15% and 88.46% with the occlusion ration 25% and 50%, respectively. The proposed system successfully improves the face recognition accuracy while the face is occluded. © 2020 IEEE.

关键词： Face recognition

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