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arXiv 2023年

作者： Mosaiyebzadeh, Fatemeh Pouriyeh, Seyedamin Parizi, Reza M. Sheng, Quan Z. Han, Meng Zhao, Liang Sannino, Giovanna Batista, Daniel Macêdo Department of Computer Science University of São Paulo Brazil Department of Information and Technology Kennesaw State University MariettaGA United States Decentralized Science Lab. Kennesaw State University MariettaGA United States School of Computing Macquarie University Sydney Australia Binjiang Institute of Zhejiang University Zhejiang Hangzhou China Institute of High Performance Computing and Networking National Research Council Naples Italy

Advancements in wearable medical devices in IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), we are witnessing how efficient healthcare services are provided to patients and how healthcare professionals are effectively used AI-based models to analyze the data collected from IoHT devices for the treatment of various diseases. To avoid privacy breaches, these data must be processed and analyzed in compliance with the legal rules and regulations such as HIPAA and GDPR. Federated learning is a machine leaning based approach that allows multiple entities to collaboratively train a ML model without sharing their data. This is particularly useful in the healthcare domain where data privacy and security are big concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy Enhancing Technologies (PETs) are a set of tools and techniques that are designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper reviews PETs in detail and comprehensively in relation to FL in the IoHT setting and identifies several key challenges for future research. © 2023, CC BY.

关键词： Laws and legislation

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Specificity-preserving RGB-D Saliency Detection

arXiv

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arXiv 2021年

作者： Zhou, Tao Fan, Deng-Ping Chen, Geng Zhou, Yi Fu, Huazhu School of Computer Science and Engineering Nanjing University of Science and Technology China CVL ETH Zurich Switzerland School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China School of Computer Science and Engineering Southeast University China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing RGB-D SOD models usually adopt different fusion strategies to learn a shared representation from the two modalities (i.e., RGB and depth), while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, termed SPNet (Specificity-Preserving Network), which benefits SOD performance by exploring both the shared information and modality-specific properties (e.g., specificity). Specifically, we propose to adopt two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps, respectively. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information. Moreover, to capture rich complementary multi-modal information for boosting the SOD performance, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using a skip connection, the hierarchical features between the encoder and decoder layers can be fully combined. Extensive experiments demonstrate that our SP-Net outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at: https://***/taozh2017/SPNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks

Sparsification and Optimization for Energy-Efficient Federat...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Lei Lei Yaxiong Yuan Yang Yang Yu Luo Lina Pu Symeon Chatzinotas School of Information and Communications Engineering Xi’ an Jiaotong University China Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Competence Center for High Performance Computing Fraunhofer ITWM Germany Department of Electrical and Computer Engineering Mississippi State University USA Department of Computer Science University of Alabama USA

ISBN: (纸本)9781665435413

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

关键词： Wireless communication Training Energy consumption Federated learning Neural networks Bandwidth Energy efficiency

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A modular spectrum sensing node for Resources-Oriented Radio Monitoring

A modular spectrum sensing node for Resources-Oriented Radio...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Meixia Yang JingJing Yang Zhe Xiao Ming Huang Wireless Innovation Lab. of Yunnan University School of Information Science and Engineering of Yunnan University Kunming China Institute of High Performance Computing Singapore

ISBN: (纸本)9781728158754;9781728192994

The existing radio monitoring practices suffer from two critical limitations: 1) current practices have been limited to isolated measuring and are unable to integrate data from different spectrum sensors; 2) these practices still require human operators to interpret signals, which prevent automated and intelligent processing. To address these limitations, we present a novel design of a spectrum sensing node based on the Representational State Transfer (REST) architecture. The spectrum sensing node exploits the REST architecture to integrate resources (data) into the web and to make it effortless to collect and share resources by exposing resources as service-oriented Web APIs. In addition, the spectrum sensing node introduces standardization and automation to radio monitoring. We outline the procedures of spectrum analysis and signal analysis for radio monitoring. Furthermore, to automate spectrum sensing node functionalities, we implement spectrum analysis and signal analysis using machine learning methods, which reliably extract and learn intrinsic features of complex data. The spectrum sensing node has been packaged and deployed in Honghe Hani and Yi Autonomous Prefecture of China for radio spectrum management. We describe and discuss our implemented prototype of the spectrum sensing node to demonstrate its practical advantages.

关键词： Support vector machines Wireless sensor networks Frequency modulation Automation computer architecture Broadcasting Feature extraction

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Regularly Truncated M-estimators for Learning with Noisy labels

arXiv

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arXiv 2023年

作者： Xia, Xiaobo Lu, Pengqian Gong, Chen Han, Bo Yu, Jun Yu, Jun Liu, Tongliang The Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Australian AI Institute Faculty of Engineering and IT The University of Technology Sydney BroadwayNSW2007 Australia The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University Hong Kong The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China The Department of Automation University of Science and Technology of China Hefei230026 China

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples;(b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels. The implementation is available at https://***/xiaoboxia/RTM_LNL. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Harnessing Resource and Demand Flexibility for Energy Management in Urban Micro-grids

Harnessing Resource and Demand Flexibility for Energy Manage...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Lalitha Subramanian Wu Jiyan Rudy Tjandra Sebastian Troitzsch Tobias Massier Erine Siew Pheng Teh Romain Migne Xu Yan EDF Lab Singapore TUMCREATE Ltd. Singapore Engineering Cluster Singapore Institute of Technology Institute for High Performance Computing Agency for Science Technology and Research Chemical Engineering and Food Technology Cluster Singapore Institute of Technology School of Electrical and Electronic Engineering Nanyang Technological University

ISBN: (纸本)9798350399677

Successful adoption of distributed clean energy resources requires the enhancement of system flexibility from both the generation and the demand side. This paper presents a case study on the Punggol Digital District of Singapore exploring both distributed generation, storage, and demand side flexibility. The distributed generation resources investigated include rooftop photovoltaic systems and waste-to-electricity generation via hydrogen and fuel cell technology. In the urban use case, the local demand to be served far exceeds the local distributed clean energy generation potential. Therefore in this context, flexibility is the key added value of collective self-consumption, which could be leveraged as a valuable grid service in the city-state to support the integration of variable renewable energy resources. In this context, this work presents the assessment of the flexibility that can be provided by the cooling demand and electric vehicles by the control of respective demand-side resources.

关键词： Photovoltaic systems Renewable energy sources Privacy Energy resources Asia Hydrogen Fuel cells

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Thermal-aware Energy Efficient Task Scheduling Framework

Thermal-aware Energy Efficient Task Scheduling Framework

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Jian Nong Jia Chen Yinqing Wang Wei Qin Xi He Faculty of Innovation Engineering-School of Computer Science and Engineering Macau University of Science and Technology Macau China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China High Performance Computing Lab Wuzhou University Wuzhou China

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.

关键词： Data centers Temperature distribution Schedules Processor scheduling Thermal management Ubiquitous computing Energy efficiency

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Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

arXiv

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arXiv 2024年

作者： Yuan, Han Hong, Chuan Jiang, Pengtao Zhao, Gangming Tran, Nguyen Tuan Anh Xu, Xinxing Yan, Yet Yen Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States College of Computer Science Nankai University China Faculty of Engineering The University of Hong Kong Hong Kong Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Radiology Changi General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template’s boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. Conclusions: In the context of pneumothorax diagnos

关键词： Diagnosis

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MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-...

引用

Supercomputing Conference

作者： Kiran Ranganath Joshua D. Suetterlein Joseph B. Manzano Shuaiwen Leon Song Daniel Wong Department of Electrical and Computer Engineering University of California Riverside CA USA High-Performance Computing Group Pacific Northwest National Lab WA USA Future System Architecture Lab School of Computer Science University of Sydney Sydney Australia

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

关键词： Network topology Processor scheduling high performance computing Graphics processing units Throughput Hardware Topology

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