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Learning from group supervision: the impact of supervision deficiency on multi-label learning

Science China(Information Sciences) 2021年第3期64卷 5-17页

作者： Miao XU Lan-Zhe GUO School of Information Technology and Electrical Engineering The University of Queensland RIKEN Center for Advanced Intelligence Project National Key Laboratory for Novel Software Technology Nanjing University

Multi-label learning studies the problem where one instance is associated with multiple *** supervised multi-label learning has attracted considerable research attention because of the annotation difficulty. Majority of the studies on weakly supervised multi-label learning assume that one group of weak annotations is available for each instance; however, none of these studies considers multiple groups of weak annotations that can be easily acquired through crowdsourcing. Recent studies on crowdsourced multilabel learning observed that the current query strategies do not agree well with human habits and that data cannot be collected as expected. Therefore, this study aims to design a new query strategy in accordance with human behavior patterns to obtain multiple groups of weak annotations. Further, a learning algorithm is proposed based on neural networks for such type of data. In addition, this study qualitatively and empirically analyzes factors in the proposed query strategy that may impact further learning and provides insights to obtain better query strategy with respect to future crowdsourcing in case of multi-label data.

关键词： multi-label learning weakly supervised leaning weakly supervised multi-label learning query strategy group supervision

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Development of a JIRA Plugin for Tracking and Managing Time Allocations for Tasks and projects

Development of a JIRA Plugin for Tracking and Managing Time ...

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World Conference on Complex Systems (WCCS)

作者： Fatiha El Aouni Karima Moumane Siham Bendaouya Aymane Ait Laasri Yacine Wahi Software Project Management Research Team ENSIAS Mohammed V University in Rabat Rabat Morocco Adria Business & Technology Casablanca Morocco Study and development engineer Adria Business & Technology Casablanca Morocco Engineering Student ENSIAS Mohammed V University Rabat Morocco

ISBN: (数字)9798331519131

ISBN: (纸本)9798331519148

The success of any project depends heavily on effective project management. By implementing best practices in project management, organizations can improve project outcomes, streamline operations, minimize risks, and achieve better results and KPIs. This involves practices such as tracking costs, managing risks and resources, and optimizing time management. This paper introduces a new plugin specifically designed to address the challenges of time tracking and cost management for organizations using Jira for their software projects. The developed plugin enables the organization to save costs by replacing the paid solution they previously used, and it offers more functionality adapted to their needs. To ensure its effectiveness and usability, the plugin was evaluated through a questionnaire designed for managers and team members within the organization. This research provides valuable insights for organizations seeking to enhance their project management practices and who want to develop their own plugin.

关键词： Costs project management Organizations software Resource management Usability Complex systems Best practices

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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FF-KGAT: Feature Fusion Based Knowledge Graph Attention Network for Recommendation 5

FF-KGAT: Feature Fusion Based Knowledge Graph Attention Netw...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tao, Li Xiaoge, Li Chaodong, Wang Xianliang, Li Shuai, Gao Dan, Han Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China Inspur Software Technology Co Ltd Software Development Project Management China Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Communication and Information Engineering Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9781450396899

It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention Network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods. © 2022 ACM.

关键词： Recommender systems

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Reflectance outperforms force and position in model-free needle puncture detection

Reflectance outperforms force and position in model-free nee...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rachael L’Orsa Anupam Bisht Linhui Yu Kartikeya Murari David T. Westwick Garnette R. Sutherland Katherine J. Kuchenbecker Engineering’s Department of Electrical and Software Engineering Schulich School University of Calgary Calgary Canada Project NeuroArm Cumming School of Medicine University of Calgary Calgary Canada Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Canada Department of Biomedical Engineering Schulich School of Engineering’s University of Calgary Calgary Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The surgical procedure of needle thoracostomy temporarily corrects accidental over-pressurization of the space between the chest wall and the lungs. However, failure rates of up to 94.1% have been reported, likely because this procedure is done blind: operators estimate by feel when the needle has reached its target. We believe instrumented needles could help operators discern entry into the target space, but limited success has been achieved using force and/or position to try to discriminate needle puncture events during simulated surgical procedures. We thus augmented our needle insertion system with a novel in-bore double-fiber optical setup. Tissue reflectance measurements as well as 3D force, torque, position, and orientation were recorded while two experimenters repeatedly inserted a bevel-tipped percutaneous needle into ex vivo porcine ribs. We applied model-free puncture detection to various filtered time derivatives of each sensor data stream offline. In the held-out test set of insertions, puncture-detection precision improved substantially using reflectance measurements compared to needle insertion force alone (3.3-fold increase) or position alone (11.6-fold increase).

关键词： Reflectivity Force measurement Torque Three-dimensional displays Force Surgery Position measurement Aerospace electronics Needles Torque measurement

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Towards semi-automated pleural cavity access for pneumothorax in austere environments

Towards semi-automated pleural cavity access for pneumothora...

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73rd International Astronautical Congress, IAC 2022

作者： L'Orsa, Rachael Lama, Sanju Westwick, David Sutherland, Garnette Kuchenbecker, Katherine J. Department of Electrical and Software Engineering University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Project neuroArm Health Research Innovation Centre University of Calgary Canada Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany

Astronauts are at risk for pneumothorax, a condition where injury or disease introduces air between the chest wall and the lungs (i.e., the pleural cavity). In a worst-case scenario, it can rapidly lead to a fatality if left unmanaged and will require prompt treatment in situ if developed during spaceflight. Chest tube insertion is the definitive treatment for pneumothorax, but it requires a high level of skill and frequent practice for safe use. Physician astronauts may struggle to maintain this skill on medium- and long-duration exploration-class missions, and it is inappropriate for pure just-in-time learning or skill refreshment paradigms. This paper proposes semi-automating tool insertion to reduce the risk of complications in austere environments and describes preliminary experiments providing initial validation of an intelligent prototype system. Specifically, we showcase and analyse motion and force recordings from a sensorized percutaneous access needle inserted repeatedly into an ex vivo tissue phantom, along with relevant physiological data simultaneously recorded from the operator. When coupled with minimal just-in-time training and/or augmented reality guidance, the proposed system may enable non-expert operators to safely perform emergency chest tube insertion without the use of ground resources. Copyright © 2022 by Ms. Rachael L'Orsa. Published by the IAF, with permission and released to the IAF to publish in all forms.

关键词： Augmented reality

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Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction 13th

Evaluating a Comparing Deep Learning Architectures for Blood...

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13th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2020

作者： El Idrissi, Touria Idri, Ali Department of Computer Sciences EMI University Mohamed V Rabat Morocco Software Project Management Research Team RITC ENSIAS University Mohamed V Rabat Morocco Complex Systems Engineering University Mohammed VI Polytechnic Ben Guerir Morocco

ISBN: (纸本)9783030723781

To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30 min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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