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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Shogo Morita Yan Zhang Takuto Yamauchi Sinan Chen Jialong Li Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan International Research Center for Neurointelligence University of Tokyo Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context; and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application.

关键词： Green products Color Faces Consumer electronics Augmented reality Context modeling

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Inferring Prerequisite Relationships Among Learning Resources for HPC Education 11th

Inferring Prerequisite Relationships Among Learning Resource...

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Wu, Run Luo, Chenyu Hu, Miao Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9789811600098

For online HPC (High-Performance Computing) education, identifying the prerequisite relationships among different HPC learning resources is important for generating learning paths and personalized course recommendations for students. Previously, such prerequisite relationships were often created by domain experts manually. In this paper, we propose a novel framework to infer both concept and learning resource prerequisite relationships and integrate such a framework into an online HPC education platform. We first construct a bi-directional long short-term (BiLSTM) neural network with attention mechanism to automatically mine the latent semantic features from the formal description of concepts. Then, we input two kinds of features into a fully connected neural network for classification and obtain the relationships among concepts. Considering the asymmetry and directivity of the prerequisite relation, we adopt a network embedding model to learn the vector representations of each concept as the prerequisite and subsequent roles. The concept vectors are weighted and summed to generate the feature vectors of learning resources. For a given pair of learning resources, their feature vectors are input into a classifier that outputs the result of relation classification. Finally, we conduct a series of experiments on two large benchmark datasets. The experimental results show that our method outperforms existing methods with significant improvements. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Vectors

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FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

arXiv

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arXiv 2023年

作者： Yi, Liping Yu, Han Shi, Zhuan Wang, Gang Liu, Xiaoguang Cui, Lizhen Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore Switzerland School of Software Shandong University Jinan China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client’s model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Towards harmless rawlsian fairness regardless of demographic prior 24

Towards harmless rawlsian fairness regardless of demographic...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xuanqian Wang Jing Li Ivor W. Tsang Yew-Soon Ong School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore and Centre for Frontier AI Research Agency for Science Technology and Research Singapore and College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair.

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RGB-D Based Visual Navigation Using Direction Estimation Module 18th

RGB-D Based Visual Navigation Using Direction Estimation Mod...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Luo, Chao Bi, Sheng Dong, Min Nie, Hongxu School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangdong Guangzhou510006 China

ISBN: (纸本)9783030893699

Target-driven visual navigation without mapping works to solve navigation problems that given a target object, mobile robots can navigate to the target object. Recently, visual navigation has been researched and improved largely by learning-based methods. However, their methods lack depth information and spatial perception, using only single RGB images. To overcome these problems, two methods are presented in this paper. Firstly, we encode visual features of objects by dynamic graph convolutional network and extract 3D spatial features for objects by 3D geometry, a high level visual feature for agent to easily understand object relationship. Secondly, as human beings, they solve this problem in two steps, first exploring a new environment to find the target object and second planning a path to arrive. Inspired by the way of humans navigation, we propose direction estimation module (DEM) based on RGB-D images. DEM provides direction estimation of the target object to our learning model by a wheel odometry. Given a target object, first stage, our agent explores an unseen scene to detect the target object. Second stage, when detected the target object, we can estimate current location of the target object by 3D geometry, after that, each step of the agent, DEM will estimate new location of target object, and give direction information of the target object from a first-view image. It can guide our agent to navigate to the target object. Our experiment results outperforms the result of state of the art method in the artificial environment AI2-Thor. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Lin, Zheng Zhu, Guangyu Deng, Yiqin Chen, Xianhao Gao, Yue Huang, Kaibin Fang, Yuguang The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Computer Science Fudan University Shanghai200438 China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States The School of Control Science and Engineering Shandong University Shandong Jinan250061 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of activations’ gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Transparency to the Extreme: An In-Depth Study of the Bitcoin Exchange Ecosystem 3rd

Transparency to the Extreme: An In-Depth Study of the Bitcoi...

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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Xue, Gengsheng Li, Yang Zheng, Zibin School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou510275 China

ISBN: (纸本)9789811679926

Bitcoin exchanges, which are digital platforms for investors to trade Bitcoins freely, operate between digital and fiat currency networks. Thus they provide an opportunity to connect real-world identities to pseudonymous Bitcoin addresses, which is critical to anti-money laundering efforts. To this end, in this paper, we conduct a systematic investigation on the whole Bitcoin exchange ecosystem and seek to characterize, understand, and identify patterns around exchanges (including exchanges’ internal wallet structure and transfer activity). Specifically, we first construct the Bitcoin Transfer Graph (BTG) using the on-chain transaction records and propose an approach to identify the Bitcoin addresses belonging to Bitcoin exchanges. Then, based on the found addresses, we conduct a preliminary analysis of mainstream Bitcoin exchanges and provide an overview of their internal wallet architecture. Lastly, we obtain many observations and findings by analyzing these graphs, which help us obtain a deep understanding of the Bitcoin exchange ecosystem. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Smart Retail Store Surveillance and Security with Cloud-Powered Video Analytics and Transfer Learning Algorithms

Smart Retail Store Surveillance and Security with Cloud-Powe...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： P. Sivalakshmi U. Kavitha R. Usha Ojasvi Pattanaik S.P. Maniraj C. Srinivasan Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India School of Commerce Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vignan Institute of Management and Technology for Women Ghatkesar JNTU Hyderabad India Department of Data Science and Business Systems Faculty of Engineering & Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveillance by combining transfer learning (TL) algorithms with cloud-powered video analytics. It is now possible to remotely interpret, analyze, and store massive amounts of video data produced by security cameras using cloud computing capabilities. In addition, TL methods are used to customize pre-trained deep learning models for retail settings, allowing for precise event and anomaly identification. This method drastically reduces the computing load on local devices and makes surveillance systems more accurate and responsive. One of the main advantages of the proposed system is that it can monitor the store premises in real time. It can also automatically identify suspicious actions like stealing or damage and inform staff or security about possible risks. By using cloud infrastructure, the system can also adapt to changing security needs and expand dynamically to handle increasing data volumes. Retailers may find useful information for refining shop layouts, product placements, and marketing tactics by integrating cloud-powered video analytics, which allows advanced functions like crowd analysis, heat mapping, and customer behavior analysis. A thorough plan for smart retail store security and surveillance, using cloud computing, video analytics, and TL algorithms to build a strong and effective system for protecting digital retail spaces.

关键词： Cloud computing Machine learning algorithms Accuracy Surveillance Visual analytics Scalability Transfer learning Real-time systems Safety Security

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations Research Forum 2023年第1期4卷 1-20页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations 39

Cross-Modal Few-Shot Learning with Second-Order Neural Ordin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

ISBN: (纸本)157735897X

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP's robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Differential equations

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