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7th Competitive Advantage in the Digital Economy, CADE 2021

作者： Khan, Hajra Alam, Masoom Al-Kuwari, Saif Faheem, Yasir CyberSec Lab Department of Computer Science COMSATS University Islamabad Pakistan Division of Information and Computing Technology Hamad Bin Khalifa University Qatar NUST Islamabad Pakistan

ISBN: (纸本)9781839536045

A rising digital economy implies more cybersecurity challenges. As organisations continue with their digital transformations, they need to implement pervasive cyber defense measures to comply with the corresponding severe security threats. The number of organisations and individuals falling victim to targeted attacks such as spear-phishing attacks is growing rapidly. Regardless of substantial exploration in mitigation systems, attackers today are becoming more sophisticated as they cultivate their techniques, employing advanced natural language (NL) capabilities to deceive email security systems. Game theory approaches based on cybersecurity are mostly concentrated on proposing defence algorithms against attacks. This work is comprehensively centred on the role of the attacker in spear-phishing attacks, using OpenAI text generating model Generative Pre-trained Transformer 2 (GPT-2) to generate emails with various malicious content. Attackers use those emails to attack a target and attempt to deceive the defence system. Considering the lack of theoretic analysis from the attacker's perspective, a non-cooperative zero-sum spear-phishing game model is proposed that allows an attacker to choose an optimal strategy for maximising payoff. Moreover, we calculated the Nash equilibrium (NE) in mixed strategies for the attacker-defender game and provided a reasonable scheme for an attacker to gain an advantage over the target. © 2021 Institution of Engineering and Technology. All rights reserved.

关键词： Game theory

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Pre-trained Convolutional Neural Networks and Otsu’s Multi-level Thresholding based Alzheimer’s Classification

Pre-trained Convolutional Neural Networks and Otsu’s Multi-...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Duraimurugan Samiayya Venkatesan Rajinikanth Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India School of Computer Science and Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Technology St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Division of Research and Innovation SIMATS Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Disorders affecting the brain and the nerves that supply the entire body are referred to as neurological diseases. Changes in the brain’s chemistry, structure, or electrical system can cause a variety of symptoms. Alzheimer’s disease (AD), a degenerative neurological condition that impairs cognitive function, is one example of this type of illness. The most prevalent type of dementia for which there is no effective treatment. Early detection and treatment, however, may help the patient’s lifestyle and reduce symptoms. Brain imaging techniques, including MRI, CT, PET, and others, are widely used to diagnose neurological problems. It has been discovered that the Artificial Intelligence based approaches, such as the deep learning algorithms and machine learning algorithms work well for assessing these photos and provide improved accuracy for identifying AD. In this paper, we classified AD and Control cases using four different Convolutional Neural Networks (CNN) versions. Additionally, in order to increase the accuracy of AD’s classification, we have segmented the images using Otsu’s thresholding technique. The outcomes demonstrate that, both before and after thresholding was implemented, the CNN AlexNet offers a significant improvement over the other deep learning models.

关键词： Neurological diseases Deep learning Thresholding (Imaging) Machine learning algorithms Magnetic resonance imaging Computed tomography Computational modeling

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SegMorph: Concurrent Motion Estimation and Segmentation for Cardiac MRI Sequences

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IEEE Transactions on Medical Imaging 2024年 PP卷 1-1页

作者： Bi, Ning Zakeri, Arezoo Xia, Yan Cheng, Nina Taylor, Zeike A. Frangi, Alejandro F. Gooya, Ali University of Leeds Leeds UK Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK CISTIB the School of Computing University of Leeds Leeds UK CISTIB School of Mechanical Engineering University of Leeds UK Christabel Pankhurst Institute Department of Computer Science Faculty of Science and Engineering and the Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health The University of Manchester Manchester UK School of Computing Science University of Glasgow Glasgow UK

We propose a novel recurrent variational network, SegMorph, to perform concurrent segmentation and motion estimation on cardiac cine magnetic resonance image (CMR) sequences. Our model establishes a recurrent latent space that captures spatiotemporal features from cine-MRI sequences for multitask inference and synthesis. The proposed model follows a recurrent variational auto-encoder framework and adopts a learnt prior from the temporal inputs. We utilise a multi-branch decoder to handle bi-ventricular segmentation and motion estimation simultaneously. In addition to the spatiotemporal features from the latent space, motion estimation enriches the supervision of sequential segmentation tasks by providing pseudo-ground truth. On the other hand, the segmentation branch helps with motion estimation by predicting deformation vector fields (DVFs) based on anatomical information. Experimental results demonstrate that the proposed method performs better than state-of-the-art approaches qualitatively and quantitatively for both segmentation and motion estimation tasks. We achieved an 81% average Dice Similarity Coefficient (DSC) and a less than 3.5 mm average Hausdorff distance on segmentation. Meanwhile, we achieved a motion estimation Dice Similarity Coefficient of over 79%, with approximately 0.14% of pixels displaying a negative Jacobian determinant in the estimated DVFs. IEEE

关键词： Motion estimation

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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Mobile Technology and Teens: Understanding the Changing Needs of Sociocultural and Technical Landscape

Mobile Technology and Teens: Understanding the Changing Need...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Cho, Janghee Song, Inhwa Agha, Zainab Cagiltay, Bengisu Calambur, Veena Rheu, Minjin Huh-Yoo, Jina Division of Industrial Design National University of Singapore Singapore Singapore School of Computing KAIST Daejeon Korea Republic of Department of Computer Science San Francisco State University San FranciscoCA United States Department of Computer Sciences University of Wisconsin - Madison MadisonWI United States Stevens Institute of Technology HobokenNJ United States School of Communication Loyola University Chicago ChicagoIL United States Stevens Institute of Technology HobokenNJ United States

ISBN: (纸本)9798400713958

Teens’ mobile technology use can help teens connect with one another, but it also raises concerns around overuse, addiction, and exposure to harmful content. Traditional tools and methods for parental controls and guidance for mobile technology use among children, such as screen time limits, often fail to address teens’ nuanced experiences on the benefits and harm of their mobile technology use. This workshop brings together interdisciplinary researchers, practitioners, and teen advocates to examine how the CHI community can foster healthy teen mobile-technology relationships. Our goals are to: (1) co-design research agenda, (2) foster cross-sociocultural collaboration, (3) generate guidance for stakeholders (e.g., public, policymakers, parents, healthcare providers), and (4) plan actionable steps for ongoing impact. The workshop will explore themes like engaging broader stakeholders, embracing marginalized voices, and navigating the implications of emerging technologies through panel presentations and interactive sessions. By examining these themes, we aim to re-explore the HCI community’s discourse on teen mobile technology use and well-being, fostering a comprehensive understanding and inclusive approaches to navigate the multifaceted challenges in the modern digital landscape in diverse sociocultural contexts. © 2025 Copyright held by the owner/author(s).

关键词： Wearable computers

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Real-Time Fish Detection and Tracking using Ensemble based Deep Learning Approach for Aquatic Species Monitoring

Real-Time Fish Detection and Tracking using Ensemble based D...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Nishanth R Sai sudhindra sasank v Sriya Simran Dash Vinod Kumar Yadav Senthil Kumar Thangavel K Somasundaram Department of Mathematics Coimbatore Amrita Vishwa Vidyapeetham Amrita School Of Physical Sciences Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Department of Computer Science and Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Tamil Nadu India Fisheries Economics Extension & Statistics Division (FEESD) ICAR-Central Institute of Fisheries Education Mumbai India Department of Computer science and Engineering Amrita Vishwa Vidyapeetham Amrita school of Engineering Tamil Nadu India Department of Mathematics Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This study presents an advanced deep learning framework for real-time fish detection, classification, and motion tracking in underwater environments. YOLOv8 is utilized for fast and accurate fish detection, while motion-based keyframe extraction and optical flow algorithms like Lucas-Kanade and Farneback enhance tracking performance. ByteTrack ensures consistent object tracking across frames, and classification models, including SVM, Random Forest, and CNN, improve species identification. To optimize real-time performance, the system applies preprocessing techniques such as Gaussian blurring and contrast enhancement, along with post-processing methods like Non-Maximum Suppression (NMS) to refine detections. Designed for live underwater monitoring, this approach enhances fish behavior analysis, ecological research, and fisheries management.

关键词： Deep learning Accuracy Recurrent neural networks Biological system modeling Real-time systems Convolutional neural networks Object tracking Monitoring Random forests Optical flow

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A 3D Implementation of Convolutional Neural Network for Fast Inference

A 3D Implementation of Convolutional Neural Network for Fast...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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Attention Assessment in Children with Autism Using Head Pose and Motion Parameters from Real Videos

Attention Assessment in Children with Autism Using Head Pose...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Elizabeth B Varghese Marwa Qaraqe Dena Al Thani Hazım Kemal Ekenel Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of Computer Engineering Istanbul Technical University Istanbul Turkey

In children with autism spectrum disorders (ASD), attention assessment plays a crucial role in understanding their behavioral and cognitive functioning. Difficulties with attention are a common feature of children with autism and have a significant impact on their ability to learn and socialize. In this paper, we propose a non-invasive and objective method to assess attention in children with autism from real videos by utilizing the head poses and motion parameters. The proposed approach is an ensemble of a deep learning model that extracts head pose parameters, an optical flow approach that extracts motion parameters from consecutive frames, temporal head pose parameters extraction and an autoencoder for attention assessment. The experimental study was conducted on 39 children (ASD = 19, neurotypical children = 20) by giving different attention tasks and capturing their video using an attached webcam. Results are analyzed for participant and task differences, which demonstrate that our approach is successful in measuring a child's attention control and inattention. In particular, the assessment of the head poses and motion parameters will enable the development of real-time attention recognition systems that can be used for both learning and targeted intervention.

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Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning

arXiv

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arXiv 2024年

作者： Chang, Tyler Gillette, Andrew Maulik, Romit Mathematics and Computer Science Division Argonne National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Information Sciences and Technology Department The Pennsylvania State University United States

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently;(2) comparative performance among distinct interpolants can aid in validation goals;(3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository. © 2024, CC BY-NC-SA.

关键词： Interpolation

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A Robust Protocol for Smart eHealthcare based on Elliptic Curve Cryptography and Fuzzy logic in IoT

A Robust Protocol for Smart eHealthcare based on Elliptic Cu...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Abdaoui, Abderrazak Erbad, Aiman Al-Ali, Abdulla Mohamed, Amr Guizani, Mohsen Qatar University Computer Science and Engineering Department College of Engineering Doha Qatar College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology Doha Qatar

ISBN: (纸本)9781665423908

Emerging technologies change the qualities of modern healthcare by employing smart systems for patient monitoring. To well use the data surrounding the patient, tiny sensing devices and smart gateways are involved. These sensing systems have been used to collect and analyze the real-time data remotely in Internet of Medical Thinks (IoM). Since the patient sensed information is so sensitive, the security and privacy of medical data are becoming challenging problem in IoM. It is then important to ensure the security, privacy and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for the IoM. In this paper, in order to improve the authentication and communications in health care applications, we present a novel secure and anonymous authentication scheme. We will use elliptic curve cryptography (ECC) with random numbers generated by fuzzy logic. We simulate IoM scheme using network simulator 3 (NS3) and we employ optimized link state routing protocol (OLSR) algorithm and ECC at each node of the network. We apply some attack algorithms such as Pollard's ρ and Baby-step Giant-step to evaluate the vulnerability of the proposed scheme. © 2021 IEEE.

关键词： Fuzzy logic

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