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RETRACTED ARTICLE: Image encryption techniques: A comprehensive review

Multimedia Tools and Applications 2024年第29期83卷 73789-73789页

作者： Kolivand, Hoshang Hamood, Sabah Fadhel Asadianfam, Shiva Rahim, Mohd Shafry School of Computer Science and Mathematics Faculty of Engineering and Technology Liverpool John Moores University (LJMU) Liverpool UK School of Computing and Digital Technologies Staffordshire University Stoke-on-Trent UK MAGICX (Media and Games Innovation Centre of Excellence) Institute of Human Centred Engineering Universiti Teknologi Malaysia Skudai Malaysia Faculty of Electrical & Computer Engineering Qom University of Technology Qom Iran

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Illness duration and symptom profile in symptomatic UK school-aged children tested for SARS-CoV-2 (vol 5, pg 708, 2021)

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LANCET CHILD & ADOLESCENT HEALTH 2021年第10期5卷 708-718页

作者： Molteni, E. Sudre, C. H. Canas, L. S. School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine School of Life Course Sciences King's College London London UK MRC Unit for Lifelong Health and Ageing Department of Population Health Sciences and Centre for Medical Image Computing Department of Computer Science University College London London UK Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle upon Tyne UK Department of Population Health Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Zoe Limited London UK King's College London and Guy's and St Thomas' PET Centre London UK Department of Twin Research and Genetic Epidemiology Faculty of Life Sciences and Medicine School of Life Course Sciences King's College London London UK Department of Endocrinology Guys and St Thomas' NHS Foundation Trust London UK

Background In children, SARS-CoV-2 infection is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, one of the largest UK citizen participatory epidemiological studies to date. Methods In this prospective cohort study, data from UK school-aged children (age 5–17 years) were reported by an adult proxy. Participants were voluntary, and used a mobile application (app) launched jointly by Zoe Limited and King's College London. Illness duration and symptom prevalence, duration, and burden were analysed for children testing positive for SARS-CoV-2 for whom illness duration could be determined, and were assessed overall and for younger (age 5–11 years) and older (age 12–17 years) groups. Children with longer than 1 week between symptomatic reports on the app were excluded from analysis. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. Findings 258 790 children aged 5–17 years were reported by an adult proxy between March 24, 2020, and Feb 22, 2021, of whom 75 529 had valid test results for SARS-CoV-2. 1734 children (588 younger and 1146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study timeframe (illness onset between Sept 1, 2020, and Jan 24, 2021). The most common symptoms were headache (1079 [62·2%] of 1734 children), and fatigue (954 [55·0%] of 1734 children). Median illness duration was 6 days (IQR 3–11) versus 3 days (2–7) in children testing negative, and was positively associated with age (Spearman's rank-order r s 0·19, p<0·0001). Median illness duration was longer for older children (7 days, IQR 3–12) than younger children (5 days, 2–9). 77 (4·4%) of 1734 children

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ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics for Fostering STEAM and Develop Computational Thinking*

ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics...

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International Symposium on computers in Education (SIIE)

作者： Miguel Á. Conde Francisco J. Rodríguez-Sedano Francisco J. García-Peñalvo Jose Gonçalves Ilkka Jormanainen Andrea Anzanello Jonny Filipe Ribeiro Alves Raquel Fernández Hernáandez Alina- Andreia Ailincai Dept. of Mechanics Computer Science and Aerospace Engineering Universidad de Leon Leon Spain Dept. Electrical Systems and Automation Engineering Universidad de Leon Leon Spain Dpto. de Informatica y Autormatica - IUCE Universidad de Salamanca Salamanca Espana Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politecnico de Braganca Braganca Portugal School of Computing University of Eastern Finland Joensuu Finland Dept of Project Management Pixel Florence Italy Colegio Internato Dos Carvalhos Carvalhos Portugal Colegio de Educacion Especial Ntra. Sra. del Sagrado Corazon de Leon Leon Spain EuroEd Primary School Lasi Romania

ISBN: (数字)9798350376616

ISBN: (纸本)9798350376623

Our contemporary society necessitates professionals equipped with 21st-century skills. Disciplines within science, Technology, engineering, Arts, and Mathematics (known as STEAM) have been particularly effective in fostering these skills. However, when considering students with disabilities, especially those with intellectual or developmental disabilities (IDD), this assertion often falls short. In this context, the RoboSTEAMSEN project emerges as an initiative designed to enhance educational processes by providing teachers of IDD students with the necessary resources to promote STEAM engagement. The project proposes the use of active learning methodologies and robotics to achieve this goal. The primary objective of the project is realized through several strategies: understanding the needs of students with disabilities and adapting the use of robotics and active learning methodologies accordingly; training teachers in the use of these resources; and creating a platform to exchange experiences, resources, lessons learned, tools, case scenarios, etc., while reaching other potential stakeholders such as caregivers and policymakers. The main outcomes of the project are teacher training programs and the development of associated competencies, tools to identify and classify resources for the students, and technological platforms to ensure the sustain ability of the project once it concludes.

关键词： Training computers Ecosystems Collaboration Fasteners People with disabilities Mathematics

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Prototype-Guided Spatial-Spectral Interaction Network for Hyperspectral Anomaly Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Cheng, Xi Wang, Chenhao Huo, Yu Zhang, Min Wang, Hai Ren, Jinchang Xidian University School of Aerospace Science and Technology Xi’an710071 China Guangdong Polytechnic Normal University School of Computer Sciences Guangzhou510640 China Robert Gordon University National Subsea Centre School of Computing Engineering and Technology AberdeenAB21 0BH United Kingdom

In recent years, deep learning has emerged as one of the most widely utilized techniques in hyperspectral anomaly detection (HAD) with an impressive detection accuracy. However, the investigation into the diverse background representation and the spatial-spectral interaction remains underexplored. To tackle with this, we propose a novel framework namely the prototype-guided and spatial-spectral interaction network (PSSIN) for HAD in this paper. Specifically, an adaptive anomaly mask module is utilized to mitigate the interference of the background reconstruction caused by the blending of potential anomalies. Subsequently, we design a background-guided prototype autoencoder (BP-AE) to represent the backgrounds with various land cover types, incorporating two critical components: the background prototype module (BPM) and the spatial spectral interaction block (SSIB). To characterize different typical background features by a global perspective, BPM utilizes a prototype learning strategy with a self-attention mechanism, and a multivariate ensemble loss is employed for BPM to optimize the transformation of background features and the updating of a prototype codebook. To enhance the spatial-spectral utilization of window-based approach, SSIB first introduce a spatial-spectral interaction paradigm for HAD. The window-based self-attention branch is to mine spatial features characteristics, while the depth-wise convolution branch is to extract spectral features. These two branches in a parallel configuration interact with each other's features and then perform feature fusion. SSIB architecture not only broadens the receptive fields by concurrently modeling the intra-window and cross-window relationships but also facilitates bi-directional interactions between the spatial and spectral branches. Furthermore, the comprehensive experiments conducted on six authentic datasets have fully validated its superior performance. © 1980-2012 IEEE.

关键词： Hyperspectral anomaly detection (HAD) multivariate background representation prototype learning spatial-spectral interaction

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Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

arXiv

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arXiv 2021年

作者： Wang, Shen Qureshi, M. Atif Miralles-Pechuán, Luis Huynh-The, Thien Gadekallu, Thippa Reddy Liyanage, Madhusanka The School of Computer Science University College Dublin Ireland ADAPT Centre eXplainable Analytics Group Faculty of Business Technological University Dublin Ireland The School of Computing Technological University Dublin Central Quad Grangegorman Dublin Ireland The Department of Computer and Communication Engineering Ho Chi Minh City University of Technology and Education Viet Nam The School of Information Technology and Engineering Vellore Institute of Technology India The Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Leveraging Deep Neural Networks for Massive MIMO Data Detection

arXiv

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arXiv 2022年

作者： Nguyen, Ly V. Nguyen, Nhan T. Tran, Nghi H. Juntti, Markku Swindlehurst, A. Lee Nguyen, Duy H.N. The Computational Science Research Center San Diego State University San DiegoCA92182 United States Centre for Wireless Communications University of Oulu P.O.Box 4500 FI-90014 Finland The Department of Electrical and Computer Engineering University of Akron OH44325 United States The Center for Pervasive Communications and Computing Henry Samueli School of Engineering University of California IrvineCA92697 United States The Department of Electrical and Computer Engineering San Diego State University San DiegoCA92182 United States

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

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Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

TechRxiv

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TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

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From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

arXiv

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arXiv 2025年

作者： Li, Wenran Cadet, Xavier F. Medina-Ortiz, David Davari, Mehdi D. Sowdhamini, Ramanathan Damour, Cedric Li, Yu Miranville, Alain Cadet, Frederic University of Paris City University of Reunion INSERM BIGR DSIMB ParisF-75015 France Department of Computing Imperial College London United Kingdom Department of Computer Engineering University of Magallanes Punta Arenas Chile Centre for Biotechnology and Bioengineering Universidad de Chile Santiago Chile Department of Bioorganic Chemistry Leibniz Institute of Plant Biochemistry Halle06120 Germany National Centre for Biological Science TIFR Bangalore India Molecular Biophysics Unit Indian Institute of Science Bangalore India Institute of Bioinformatics and Applied Biotechnology Bangalore India EnergyLab EA 4079 Faculty of Sciences and Technology University of Reunion France School of Information Science and Technology Beijing Institute of Artificial Intelligence China Laboratoire de Mathématiques Appliquées University Le Havre Normandie le Havre France PEACCEL Artificial Intelligence Department AI for Bi-ologics Paris75013 France

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models (DDPM) and Score-based Generative Models (SGM), where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance in the E(3) group can maintain the physical stability of the N − Cα − C frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation. © 2025, CC BY.

关键词： Biomolecules

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