Search Results for author: Qinghua Lu

Found 38 papers, 4 papers with code

Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents

no code implementations16 May 2024 Yue Liu, Sin Kit Lo, Qinghua Lu, Liming Zhu, Dehai Zhao, Xiwei Xu, Stefan Harrer, Jon Whittle

Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents, which can leverage distinguished reasoning and language processing capabilities to takes a proactive, autonomous role to pursue users' goals.

Resolving Ethics Trade-offs in Implementing Responsible AI

no code implementations16 Jan 2024 Conrad Sanderson, Emma Schleiger, David Douglas, Petra Kuhnert, Qinghua Lu

While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects.


Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI

no code implementations30 Nov 2023 Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang, Zhenchang Xing, Mark Staples, Qinghua Lu, Liming Zhu

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns.

Data Poisoning Machine Unlearning

Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents

no code implementations22 Nov 2023 Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Stefan Harrer, Jon Whittle

Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.

Language Modelling Large Language Model

Towards Self-Interpretable Graph-Level Anomaly Detection

1 code implementation NeurIPS 2023 Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i. e., the vital subgraph that leads to the predictions.

Graph Anomaly Detection

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Horst Joachim Mayer, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Isabell Tributsch, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marina Camacho, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.


Decentralised Governance-Driven Architecture for Designing Foundation Model based Systems: Exploring the Role of Blockchain in Responsible AI

no code implementations11 Aug 2023 Yue Liu, Qinghua Lu, Liming Zhu, Hye-Young Paik

Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.

Test-takers have a say: understanding the implications of the use of AI in language tests

no code implementations19 Jul 2023 Dawen Zhang, Thong Hoang, Shidong Pan, Yongquan Hu, Zhenchang Xing, Mark Staples, Xiwei Xu, Qinghua Lu, Aaron Quigley

To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective.


Enhancing Virtual Assistant Intelligence: Precise Area Targeting for Instance-level User Intents beyond Metadata

no code implementations7 Jun 2023 Mengyu Chen, Zhenchang Xing, Jieshan Chen, Chunyang Chen, Qinghua Lu

Although their capabilities of processing user intents have been developed rapidly, virtual assistants in most platforms are only capable of handling pre-defined high-level tasks supported by extra manual efforts of developers.

Distributed Trust Through the Lens of Software Architecture

no code implementations25 May 2023 Sin Kit Lo, Yue Liu, Guangsheng Yu, Qinghua Lu, Xiwei Xu, Liming Zhu

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.

Attribute Federated Learning

A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture

no code implementations9 May 2023 Qinghua Lu, Liming Zhu, Xiwei Xu, Yue Liu, Zhenchang Xing, Jon Whittle

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models.

Language Modelling Large Language Model

Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects

no code implementations17 Apr 2023 Conrad Sanderson, David Douglas, Qinghua Lu

Many sets of ethics principles for responsible AI have been proposed to allay concerns about misuse and abuse of AI/ML systems.

Ethics Fairness

A Reference Architecture for Designing Foundation Model based Systems

no code implementations13 Apr 2023 Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Jon Whittle

The release of ChatGPT, Gemini, and other large language model has drawn huge interests on foundations models.

Language Modelling Large Language Model

Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges

no code implementations12 Mar 2023 Linh T. Nguyen, Lam Duc Nguyen, Thong Hoang, Dilum Bandara, Qin Wang, Qinghua Lu, Xiwei Xu, Liming Zhu, Petar Popovski, Shiping Chen

Second, we focus on the convergence of blockchain and data sharing to give a clear picture of this landscape and propose a reference architecture for blockchain-based data sharing.

Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach

no code implementations3 Jan 2023 Qinghua Lu, Yuxiu Luo, Liming Zhu, Mingjian Tang, Xiwei Xu, Jon Whittle

In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges.

Chatbot Fairness

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

1 code implementation23 Nov 2022 Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang

Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.

Federated Learning Graph Learning

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

no code implementations12 Sep 2022 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Didar Zowghi, Aurelie Jacquet

Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.

Ethics Fairness

Decision Models for Selecting Federated Learning Architecture Patterns

no code implementations28 Apr 2022 Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu

Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning.

Federated Learning Management

Towards a Roadmap on Software Engineering for Responsible AI

no code implementations9 Mar 2022 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly.

Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

no code implementations2 Mar 2022 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle

In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis.

Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm

no code implementations3 Feb 2022 Peiying Zhang, Chao Wang, Chunxiao Jiang, Neeraj Kumar, Qinghua Lu

Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs.

Attribute Management +1

Feature-context driven Federated Meta-Learning for Rare Disease Prediction

no code implementations29 Dec 2021 Bingyang Chen, Tao Chen, Xingjie Zeng, Weishan Zhang, Qinghua Lu, Zhaoxiang Hou, Jiehan Zhou, Sumi Helal

Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.

Disease Prediction Federated Learning +1

M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search

no code implementations15 Dec 2021 Junjun Wu, Huiyu Kuang, Qinghua Lu, Zeqin Lin, Qingwu Shi, Xilin Liu, Xiaoman Zhu

In the search process, combine the self-attention network structure module to adjust the searched neural network structure, and then combine the semantic segmentation network searched by different branches to form a fast semantic segmentation network structure, and input the picture into the network structure to get the final forecast result.

Neural Architecture Search Segmentation +1

AI Ethics Principles in Practice: Perspectives of Designers and Developers

no code implementations14 Dec 2021 Conrad Sanderson, David Douglas, Qinghua Lu, Emma Schleiger, Jon Whittle, Justine Lacey, Glenn Newnham, Stefan Hajkowicz, Cathy Robinson, David Hansen

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems.

Ethics Fairness

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

1 code implementation NeurIPS 2021 Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang

Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode.

Continuous Control reinforcement-learning +1

Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns

no code implementations18 Nov 2021 Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, David Douglas, Conrad Sanderson

These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.


Blockchain-based Trustworthy Federated Learning Architecture

no code implementations16 Aug 2021 Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, Liming Zhu

To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.

Fairness Federated Learning +1

FLRA: A Reference Architecture for Federated Learning Systems

no code implementations22 Jun 2021 Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu

The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.

BIG-bench Machine Learning Federated Learning

AI and Ethics -- Operationalising Responsible AI

no code implementations19 May 2021 Liming Zhu, Xiwei Xu, Qinghua Lu, Guido Governatori, Jon Whittle

In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.


FedProto: Federated Prototype Learning across Heterogeneous Clients

4 code implementations1 May 2021 Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.

Federated Learning

A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things

no code implementations21 Apr 2021 Jiehan Zhou, Shouhua Zhang, Qinghua Lu, Wenbin Dai, Min Chen, Xin Liu, Susanna Pirttikangas, Yang Shi, Weishan Zhang, Enrique Herrera-Viedma

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.

Edge-computing Federated Learning +1

Architectural Patterns for the Design of Federated Learning Systems

no code implementations7 Jan 2021 Sin Kit Lo, Qinghua Lu, Liming Zhu, Hye-Young Paik, Xiwei Xu, Chen Wang

Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.

BIG-bench Machine Learning Federated Learning +1

Dynamic Fusion based Federated Learning for COVID-19 Detection

no code implementations22 Sep 2020 Weishan Zhang, Tao Zhou, Qinghua Lu, Xiao Wang, Chunsheng Zhu, Haoyun Sun, Zhipeng Wang, Sin Kit Lo, Fei-Yue Wang

To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.

BIG-bench Machine Learning Decision Making +3

Blockchain-based Federated Learning for Failure Detection in Industrial IoT

no code implementations6 Sep 2020 Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Sin Kit Lo, Shiping Chen, Xiwei Xu, Liming Zhu

Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.

Federated Learning Privacy Preserving

A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

no code implementations22 Jul 2020 Sin Kit Lo, Qinghua Lu, Chen Wang, Hye-Young Paik, Liming Zhu

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.

BIG-bench Machine Learning Federated Learning

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