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Assessing responsible innovation training

Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

arXiv

引用

arXiv 2021年

作者： Deng, Xiumei Li, Jun Ma, Chuan Wei, Kang Shi, Long Ding, Ming Chen, Wen Poor, H. Vincent The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Hangzhou China Nanjing University of Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Data61 CSIRO KensingtonWA6152 Australia The Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [O(1/V), O(√V)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter V. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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Triplet Deep Subspace Clustering via Self-Supervised data Augmentation

Triplet Deep Subspace Clustering via Self-Supervised Data Au...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Xianzhen Li Haijun Zhang Yi Yang Shuicheng Yan Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology (Shenzhen) Shenzhen China Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia Sea AI Lab (SAIL) & National University of Singapore Singapore

ISBN: (纸本)9781665423991

Deep subspace clustering (DSC) with the auto-encoder and self-expression layer is of great concern due to encouraging performance. However, existing methods usually adopt a “single-task” strategy based on a single dataset, without considering other related tasks or data. As such, they cannot discover other useful information to improve the clustering task. Besides, the local structure preservation of the latent codes in mapping is usually ignored. In this paper, we therefore present an effective “multi-task” strategy via the self-supervised data augmentation, and propose a new end-to-end trainable Triplet Deep Subspace Clustering Network (TDSC-net). Specifically, TDSC-net firstly generates triplet data (i.e., anchor, positive and negative data) from input data by a spectral clustering module and a self-supervised data augmentation module. This can enable it to inherit the merits of self-supervised learning and multitask learning implicitly. After that, TDSC-net builds a triplet deep autoencoder network with a self-expression layer, which takes the triplet data as input, where they share the common network layers (i.e., autoencoder and self-expression layers) over the triple tasks for complementary learning and mutual supervision. A triplet loss is also included to retain the local information of deep latent codes, which will also benefit the self-expression. Furthermore, TDSC-net separates the self-expression layer from decoding process to improve the efficiency of reconstruction. Extensive results on several public datasets demonstrate the effectiveness of our triplet-task DSC strategy.

关键词： Codes Conferences Multitasking Decoding data mining Task analysis

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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ZSTAD: Zero-Shot Temporal Activity Detection

ZSTAD: Zero-Shot Temporal Activity Detection

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lingling Zhang Xiaojun Chang Jun Liu Minnan Luo Sen Wang Zongyuan Ge Alexander Hauptmann School of Computer Science and Technology Xi'an Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xian China School of Information Technology and Electrical Engineering The University of Queensland Australia School of Computer Science Carnegie Mellon University USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.

关键词： Semantics Proposals Training Task analysis Feature extraction Microsoft Windows Three-dimensional displays

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FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics

arXiv

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arXiv 2025年

作者： Li, Yixuan Tian, Yu Huang, Yipo Lu, Wei Wang, Shiqi Lin, Weisi Rocha, Anderson College of Computing City University of Hong Kong Hong Kong School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou510006 China College of Computing and Data Science Nanyang Technological University Singapore Artificial Intelligence Lab Recod.ai University of Campinas Campinas13084-851 Brazil

The rapid and unrestrained advancement of generative artificial intelligence (AI) presents a double-edged sword: while enabling unprecedented creativity, it also facilitates the generation of highly convincing deceptive content, undermining societal trust. As image generation techniques become increasingly sophisticated, detecting synthetic images is no longer just a binary task—it necessitates interpretable, context-aware methodologies that enhance trustworthiness and transparency. However, existing detection models primarily focus on classification, offering limited explanatory insights into image authenticity. In this work, we propose FakeScope, an expert multimodal model (LMM) tailored for AI-generated image forensics, which not only identifies AI-synthetic images with high accuracy but also provides rich, interpretable, and query-driven forensic insights. To this end, we first construct FakeChain dataset that contains linguistic authenticity reasoning based on visual trace evidence, developed through a novel human-machine collaborative framework. Building upon it, we further present FakeInstruct, the largest multimodal instruction tuning dataset containing 2 million visual instructions tailored to enhance forensic awareness in LMMs. Leveraging the knowledge of FakeInstruct, FakeScope achieves state-of-the-art performance in both closed-ended and open-ended forensic scenarios. It can distinguish synthetic images with high accuracy while offering coherent and insightful explanations, free-form discussions on fine-grained forgery attributes, and actionable enhancement strategies. Notably, despite being trained exclusively on qualitative hard labels, FakeScope demonstrates remarkable zero-shot quantitative capability on detection, enabled by our proposed token-based probability estimation strategy. Furthermore, FakeScope exhibits strong generalization and in-the-wild ability, ensuring its applicability in real-world scenarios. The data, model, and demo will be publ

关键词： computer forensics

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Learning to augment expressions for few-shot fine-grained facial expression recognition

arXiv

引用

arXiv 2020年

作者： Wang, Wenxuan Fu, Yanwei Sun, Qiang Chen, Tao Cao, Chenjie Zheng, Ziqi Xu, Guoqiang Qiu, Han Jiang, Yu-Gang Xue, Xiangyang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China School of Data Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Ping An OneConnect Shanghai China

—Affective computing and cognitive theory are widely used in modern human-computer interaction scenarios. Human faces, as the most prominent and easily accessible features, have attracted great attention from researchers. Since humans have rich emotions and developed musculature, there exist a lot of subtle and fine-grained expressions in real-world applications. However, it is extremely time-consuming to collect and annotate a large number of facial images, of which some subtle expressions may even require psychologists to correctly categorize them. To the best of our knowledge, the existing expression datasets are only limited to several basic facial expressions, which are not sufficient to support our ambitions in developing successful human-computer interaction systems. To this end, a novel Fine-grained Facial Expression database – F2ED is contributed in this paper;and such a dataset costs one year to be collected by us and annotated with the help of psychological annotators. Totally, it includes more than 200k images with 54 facial expressions from 119 persons. So far as we know, this is the first large dataset to label faces with subtle emotion changes for the recognition of facial expressions. Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances. These tasks mimic human performance to learn robust and general representation from few examples. To address such few-shot tasks, we propose a unified task-driven framework – Compositional Generative Adversarial Network (Comp-GAN) learning to synthesize facial images and thus augmenting the instances of few-shot expression classes. Essentially, Comp-GAN consists of two generators: one for editing faces with desired expression and the other for changing the face posture;so it can generate many re

关键词： Generative adversarial networks

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

引用

arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Lightweight Deep Network for 3D Medical Image Segmentation

A Lightweight Deep Network for 3D Medical Image Segmentation

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Toshiki Kawahara Yinhao Li Yutaro Iwamoto Lanfen Lin Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China

ISBN: (数字)9781728198026

ISBN: (纸本)9781728198033

Medical image segmentation is an important preprocessing task for computer-aided diagnosis and computer-aided surgery. As medical images such as computed tomography scans provide 3D volumetric data, 3D deep learning (DL) models (e.g., 3DU-Net) have been proposed for medical image segmentation. Compared with conventional 2D DL, 3D DL models have more network parameters, which may lead to overfitting when small training sets are available. In addition, 3D image processing has a high computational cost and consequently a long computation time. To reduce both the number of network parameters and computation time, we propose a lightweight formulation of 3DU-Net based on three-component decomposition. The proposed method can substantially reduce the number of parameters and computation time for 3D image segmentation while maintaining a comparable accuracy with the conventional 3DU-Net.

关键词： Image segmentation Solid modeling Three-dimensional displays Computed tomography Computational modeling Two dimensional displays Medical diagnostic imaging

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A Continuous information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

A Continuous Information Gain Measure to Find the Most Discr...

引用

Congress on Evolutionary Computation

作者： Matthew Stephenson Damien Anderson Ahmed Khalifa John Levine Jochen Renz Julian Togelius Christoph Salge Department of Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands Computer and Information Sciences Department University of Strathclyde Glasgow UK Game Innovation Lab Tandon School of Engineering New York University New York USA Research School of Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.

关键词： Games Correlation Artificial intelligence Testing Task analysis Noise measurement Technological innovation

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