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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Zhong, Wenhao Du, Yugen Shan, Chuang Wang, Hanting Chen, Fan Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)1891706543

Facing a large number of candidate Web service with the same function, user wishes to get the most appropriate one. Quality-of-Service (QoS) which represents non-functional attributes of Web services, has become a major concern for choosing service. But it is time-consuming and resource-consuming to assess all the QoS values by invoking candidate services one by one. Thus, QoS prediction is considered an effective method to obtain QoS information. Although most of QoS prediction methods claim be able to capture the interaction between users and services, few of them take account non-interaction factors, especially the factors arising from the network environment. In this paper, the non-interaction factors from the network environment are referred as network bias, and a network biased matrix factorization (NBMF) method is proposed for QoS prediction. The method packages network bias into a linear regression model and puts the user-service interaction into a matrix factorization model, which is more sophisticated in adapting diversified circumstance, particularly in complex network environment. In addition, extensive experiments are conduct on real-world QoS dataset, and the result prove that the NBMF method achieves better performance than other state-of-the-art methods. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Forecasting

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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Fine-Grained Features-based Code Search for Precise Query-Code Matching 31

Fine-Grained Features-based Code Search for Precise Query-Co...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Xinting Cheng, Mengqiu Wang, Mengzhen Gong, Songwen Cai, Yi Xie, Jiayuan Li, Qing Department of Computing The Hong Kong Polytechnic University Hong Kong School of Software Engineering South China University of Technology China Department of Mathematics The University of Hong Kong Hong Kong Guangdong Neusoft University China

ISBN: (纸本)9798891761964

Code search aims to quickly locate target code snippets from databases using natural language queries, which promotes code reusability. Existing methods can effectively obtain aligned token-level and query word-level features. However, these studies usually represent the semantics of code and query by averaging the features of each token and word respectively, which makes it difficult to accurately capture the code details that are closely related to the query. To address this issue, we propose a fine-grained code search model that consists of a cross-modal encoder, a mapping layer, and a classification layer. Specifically, we utilize a pre-trained model, GraphCodeBERT, in the cross-modal encoder to align features. In the mapping layer, we introduce a co-attention network to capture the fine-grained interactions between code and query, ensuring a model can precisely identify key code segments relevant to the query. Finally, in the classification layer, we incorporate instruction learning techniques that leverage contextual reasoning to improve the accuracy of query-code matching. Experimental results show that our proposed model significantly outperforms existing methods across multiple programming language datasets. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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ONNXPruner: ONNX-Based General Model Pruning Adapter

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 5806-5817页

作者： Ren, Dongdong Li, Wenbin Ding, Tianyu Wang, Lei Fan, Qi Huo, Jing Pan, Hongbing Gao, Yang Nanjing University School of Electronic Science and Engineering China Nanjing University State Key Laboratory for Novel Software Technology School of Intelligence Science and Technology China Microsoft Corporation Applied Sciences Group RedmondWA98052 United States University of Wollongong School of Computing and Information Technology NSW2522 Australia

Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various models and platforms remains a significant challenge. To address this challenge, we propose ONNXPruner, a versatile pruning adapter designed for the ONNX format models. ONNXPruner streamlines the adaptation process across diverse deep learning frameworks and hardware platforms. A novel aspect of ONNXPruner is its use of node association trees, which automatically adapt to various model architectures. These trees clarify the structural relationships between nodes, guiding the pruning process, particularly highlighting the impact on interconnected nodes. Furthermore, we introduce a tree-level evaluation method. By leveraging node association trees, this method allows for a comprehensive analysis beyond traditional single-node evaluations, enhancing pruning performance without the need for extra operations. Experiments across multiple models and datasets confirm ONNXPruner's strong adaptability and increased efficacy. Our work aims to advance the practical application of model pruning. © 1979-2012 IEEE.

关键词： Adaptation Models Training Filters Deep Learning Computational Modeling software Interoperability Load Modeling software Algorithms Electronic Mail General Model Pruning ONNX Tree Level Evaluation Deep Neural Network Model Pruning Deep Learning Model Architecture Deep Learning Framework Hardware Platform Superior Performance Deep Neural Network Feature Maps Py Torch Image Net Training Images Sparse Matrix Root Node Type Of Operation Single Node Deep Neural Network Model Validation Images Extra Components Importance Of Filtering Pruning Method Squeeze Net

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

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Towards Attention-Aware Interactive 360-Degree Video Streaming on Smartphones

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IEEE Network 2025年

作者： Zhang, Lei Zhou, Haobin Shen, Linfeng Liu, Jiangchuan Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering China Simon Fraser University School of Computing Science Canada

Browsing 360-degree videos on smartphones is becoming increasingly popular, which is challenging due to heavy user interactions, limited hardware capabilities, and constrained batteries. To improve resource efficiency and user experience, we propose a framework for attention-aware interactive 360-degree video streaming for smartphone users. We first explore the built-in capabilities of off-the-shelf smartphones to capture user attention during video browsing. Using the information of user attention to predict viewport and analyze user-perceptive quality, we then investigate the approaches for improving video encoding, transmission, and rendering in 360-degree video streaming. Further, we discuss the opportunities for enhancing user interactive experience with error tolerance, protocol design, and on-device computation. Finally, our prototype implementation is presented as a case study to validate our design principles. Our framework integrates and coordinates advanced techniques for different components of 360-degree video streaming systems to achieve better user experience, which can also serve as a foundation for leveraging modern attention-based multi-modal learning architectures. © 2025 IEEE.

关键词： Video streaming

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A Speckle Noise Suppression Method Based on Quality Map 28

A Speckle Noise Suppression Method Based on Quality Map

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28th International Conference on Automation and computing, ICAC 2023

作者： Zhong, Chengxin Kong, Chao Zhang, Chaolong Xu, Yuanping Xu, Zhijie Li, Tukun Jin, Jin Wen, Qin School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld United Kingdom College of Artificial Intelligence Neijiang Normal University China

ISBN: (纸本)9798350335859

Digital holography is a technique for measuring the shape of an object in 3D. It uses interference patterns generated by a laser or other light source, which record both the phase and intensity information of the light reflected from the surface of the object. This information is then encoded into digital holograms and reconstructed using computational methods to produce a 3D shape of the object. In the process of digital holography measurement, due to the high coherence of the laser, a large amount of speckle noise is generated when measuring rough surfaces due to diffuse reflection, leading to errors in the reconstruction results. Although traditional phase filtering has a good effect on noise filtering, it may cause distortion of normal phase. In order to suppress speckle noise, this study compared the common quality maps, proposed a local threshold adaptive speckle noise extraction method based on quality maps, and finally obtained the correct phase map by restoring the speckle noise points. © 2023 IEEE.

关键词： Light sources

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