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Developing capabilities for responsible research and innovation (RRI)

Journal of Responsible Technology 2023年 15卷

作者： Ogoh, George Akintoye, Simisola Eke, Damian Farisco, Michele Fernow, Josepine Grasenick, Karin Guerrero, Manuel Rosemann, Achim Salles, Arleen Ulnicane, Inga School of Computer Science University of Nottingham Nottingham United Kingdom Centre for Research Ethics & Bioethics (CRB) Uppsala University Uppsala Sweden Biogem Biology and Molecular Genetics Research Institute AV Ariano Irpino Italy Department of Bioethics and Medical Humanities University of Chile Santiago Chile Centre for Law Faculty of Business and Law Justice and Society De Montfort Law School De Montfort University Leicester United Kingdom Centre for Computing and Social Responsibility Faculty of Computing School of Computer Science and Informatics Engineering and Media De Montfort University Leicester United Kingdom

This paper examines the notion of capability development through the lens of Responsible Research and Innovation (RRI). It describes how RRI capabilities can be promoted and developed through capacity-building activities by drawing on a reflection of the experiences of some members of the Ethics and Society team of the EU-funded Human Brain Project (HBP) who have contributed to the development of an extensive RRI capacity building programme for HBP researchers, EBRAINS research infrastructure developers and users, as well as interested external audiences. It also highlights some of the outcomes of the RRI capacity-building exercise and the challenge of assessing the impact of such activities. © 2023

关键词： Capability development Capacity building Ethics Research and innovation RRI

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues

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Intelligence-Based Medicine 2025年 11卷 100223-100223页

作者： Murugavel, Kiruthiga Devi R, Parthasarathy Mathivanan, Sandeep Kumar Srinivasan, Saravanan Shivahare, Basu Dev Shah, Mohd Asif Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai 600026 India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computing Science and Engineering Galgotias University Greater Noida 203201 India Department of Economics Kardan University Parwane Du Kabul 1001 Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Punjab Phagwara 144001 India

Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. Deep Denoising Autoencoders (DDAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces Deep Denoising Autoencoders (DDAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-ROC of 0.85 & less interface time of 6.5 ms/sample scores the effectiveness of integrating mult

关键词： Audio-visual modality Bipolar disorder Deep denoising autoencoders (DDAEs) Fisher vectors (FVs) Multimodal

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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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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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An Empirical Study on Secure Usage of Mobile Health Apps: The Attack Simulation Approach

arXiv

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arXiv 2022年

作者： Aljedaani, Bakheet Ahmad, Aakash Zahedi, Mansooreh Babar, M. Ali Computer Science Department Aljumum University College Umm Alqura University Makkah Saudi Arabia School of Computing and Communications Lancaster University Leipzig Germany School of Computing and Information Systems University of Melbourne Australia CREST – the Centre for Research on Engineering Software Technologies University of Adelaide Australia Australia

Context: Mobile applications (mobile apps for short) have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context-sensitive computing. In smart healthcare context, mobile health (mHealth) apps - representing a specific genre of mobile apps that manage health-critical information - face some critical challenges relating to security and privacy of device and user data. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure development and usage of mHealth apps. However, such studies rely on self-reported behaviors documented via interviews or survey questions that lack a practical, i.e. action-based approach to monitor and synthesise users’ actions and behaviors in security critical scenarios. Objective and Method: Our objective was to conduct an empirical study - engaging participants with attack simulation scenarios and analyse their actions - for investigating the security awareness of mHealth app users via action-based research. To conduct the study, we simulated some common security attack scenarios in mHealth context and engaged a total of 105 app users (14 countries across 5 continents) to monitor their actions and analyse their behavior. We analysed users' data with statistical analysis including reliability and correlations tests, descriptive analysis, and qualitative data analysis (i.e., thematic analysis method) for the open-ended questions. Results: Our results indicate that whilst the minority of our participants perceived access permissions positively, the majority had negative views by indicating that such an app could violate or cost them to lose privacy. Users provide their consent, granting permissions, without a careful review of privacy policies that leads to undesired or malicious access to health critical data. The results also indicated that 73.3% of our participants had denied at l

关键词： mHealth

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud

Watermarking and Encryption for Electronic Medical Records (...

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Cyber Resilience (ICCR), International Conference on

作者： Haider Rasheed Abdulshaheed Yaseen Saadi Abbas Jamal F. Tawfeq Karamath Ateeq Saif. E. A. Alnawayseh Taher M. Ghazal Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing “big EMR data” from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article.

关键词： Cloud computing Hospitals Watermarking Big Data Mobile handsets Robustness Safety

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Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation

Minimizing the Accumulated Trajectory Error to Improve Datas...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiawei Du Yidi Jiang Vincent Y.F. Tan Joey Tianyi Zhou Haizhou Li Centre for Frontier AI Research (CFAR) Agency for Science Technology and Research (A*STAR) Singapore Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Department of Electrical and Computer Engineering National University of Singapore Department of Mathematics National University of Singapore SRIBD School of Data Science The Chinese University of Hong Kong Shenzhen China

Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thus recently come to the fore. This paradigm involves distilling information from large real-world datasets into tiny and compact synthetic datasets such that processing the latter ideally yields similar performances as the former. State-of-the-art methods primarily rely on learning the synthetic dataset by matching the gradients obtained during training between the real and synthetic data. However, these gradient-matching methods suffer from the so-called accumulated trajectory error caused by the discrepancy between the distillation and subsequent evaluation. To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7% on a subset of images of the ImageNet dataset with higher resolution images. We also validate the effectiveness and generalizability of our method with datasets of different resolutions and demonstrate its applicability to neural architecture search. Code is available at. https://***/AngusDujw/FTD-distillation.

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Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

arXiv

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arXiv 2022年

作者： Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Hammoudeh, Mohammad Karimipour, Hadis Srivastava, Gautam The Cyber Science Lab University of Guelph Canada The College of Computing and Software Engineering Kennesaw State University GA United States Information & Computer Science Department King Fahd University of Petroleum & Minerals Saudi Arabia The School of Engineering Department of Electrical and Software Engineering The University of Calgary Alberta Canada The Department of Math and Computer Science Brandon University Manitoba Canada The Research Centre for Interneural Computing China Medical University Taichung Taiwan

Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block Hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, Block Hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth. © 2022, CC BY.

关键词： Blockchain

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Revocable and certificateless public auditing for cloud storage

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science China(Information sciences) 2020年第10期63卷 282-284页

作者： Yinghui ZHANG Tiantian ZHANG Shengmin XU Guowen XU Dong ZHENG School of Cyberspace Security Xi'an University of Posts and Telecommunications Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications Secure Mobile Centre School of Information SystemsSingapore Management University School of Computer Science and Engineering University of Electronic Science and Technology of China

Dear editor,Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources,which has attracted the attention of many researchers[1,2].A generic blockchain-based cloud data auditing scheme[3] is proposed,which is compatible with any blockchains including the bitcoin *** the data integrity checking scheme,certificate-

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