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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Andreas Hauptmann Mustafa Al-Rubaye Miika T. Nieminen Mikael A. K. Brix Research Unit of Mathematical Sciences University of Oulu Finland Department of Computer Science University College London United Kingdom Research Unit of Health Sciences and Technology University of Oulu Finland Department of Diagnostic Radiology Oulu University Hospital Finland

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Learned reconstructions for 3D cone-beam computed tomography (CBCT) require significant hardware resources for training as well as evaluation. In this challenge paper we aim to improve performance of the U-Net architecture for post-processing by creating multiple inputs to the network using varying frequency filters. The networks are able to be trained on a single GPU and achieved 3rd place in the ICASSP 2024 3D-CBCT grand challenge.

关键词： Training Three-dimensional displays Filters Computed tomography Conferences Graphics processing units computer architecture

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A Meta-Analysis of Artificial Intelligence Applications for Tracking COVID-19: The Case of the U.A.E. 18

A Meta-Analysis of Artificial Intelligence Applications for ...

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18th International Learning and technology Conference, L and T 2021

作者： Haneya, Hala Alkaf, Dhekra Bajammal, Faigah Brahimi, Tayeb Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first found in Wuhan, China, on December 31, 2019. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of November 10, 2020, more than fifty million cases have been confirmed, and more than one million deaths have been reported globally. This situation has created a serious challenge for all countries to institute a variety of control measures to track and slow down the spread of the virus and prevent the increasing number of deaths. In recent years, there has been an ongoing interest in using Artificial Intelligence (A.I.) in healthcare to create new treatments and detecting diseases. The objective of this study is to analyze the application and the impact of A.I. on the breakout of COVID-19 and discuss the contribution of A.I. to the fight against the pandemic based on the most recent applications used in the united Arab Emirates, including Dubai Police Movement Restriction Monitoring System, Taxis Preventive Measures Compliance System, Mobile App "Wai-Eye,"Smart Helmets, Virtual Doctor, and The Department of Health - Abu Dhabi (DoH) Remote Healthcare App. The method used in this study is based on a meta-analysis of recent COVID-19 studies from various databases such as scienceDirect, Sage Journals, SpringerLink, ResearchGate, Emerald Open Research, and IEEE Xplore. The COVID-19 data was based on Johns Hopkins University Center for Systems science and Engineering (JHU CCSE). Results showed that A.I. applications provided the necessary prevention of the spread of COVID-19, assisted in monitoring restrictions and preventive measures violations, and provided remote healthcare, which directly impacted the number of hospital visits amidst the lockdown. The study concluded that A.I. has proven to be effective in supporting governments in fighting the pandemic. © 2021 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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A Survey of Artificial Intelligence Solutions in Response to the COVID-19 Pandemic in Saudi Arabia 18

A Survey of Artificial Intelligence Solutions in Response to...

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18th International Learning and technology Conference, L and T 2021

作者： Aljizawi, Jailan Dalloul, Dina Ghryani, Layal Aldabbagh, Shaymaa Brahimi, Tayeb Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia

Artificial Intelligence (AI) has a significant impact on the course of the COVID-19 pandemic from various aspects. This paper aims to explore the trends of AI applications and how they help predict and prevent the progress of COVID-19 in Saudi Arabia. The method used in this study is based on a narrative review of recent literature on AI and COVID-19 and on a survey conducted on 211 participants. Results show that AI is a crucial element to overcoming COVID 19. The use of COVID-19 related Apps that helped reduce the pandemic spread was more common among the age group participants ranging from 15 - 30. The study concluded that COVID-19 made a positive impact on everyone, and by the use of technology, things got much more comfortable to help in enduring any long-term health consequences. © 2021 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran The Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India The AI Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as ‘attacks,’ underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, and CalTech256, focusing on image classification with 12 different NUI attack models. The resilience of VGG, ResNet, MobilenetV3-small and InceptionV3 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models’ classification accuracy when subjected to NUI attacks, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models’ resilience against NUI attacks. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures

Surrogate-Assisted Multi-objective Genetic Fuzzy Associative...

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Advancement in technology (ICONAT), International Conference for

作者： Ajit Kumar Behera Satchidananda Dehuri Ashish Ghosh Department of Computer Science & Engineering Silicon Institute of Technology Bhubaneswar India Department of Computer Science Fakir Mohan University Balasore India Machine Intelligence Unit Indian Statistical Institute 203 B.T. Road Kolkata India

This paper presents a new surrogate-assisted multi-objective genetic fuzzy associative classification model by learning multiple granularities. The specific method is the hybridization of multi-objective genetic algorithms (MOGAs), radial basis function neural networks (RBFNs), and rough set. We show that our approach requires only a few numbers of fitness evaluations compared to the methods proposed in [34] without compromising to maintain an average classification ability in almost all the datasets considered in this work for evaluation of the model.

关键词： Search methods Rough sets Radial basis function networks Genetics Classification algorithms Kernel Genetic algorithms

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Self-supervised transfer learning of physiological representations from free-living wearable data 21

Self-supervised transfer learning of physiological represent...

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2021 ACM Conference on Health, Inference, and Learning, CHIL 2021

作者： Spathis, Dimitris Perez-Pozuelo, Ignacio Brage, Soren Wareham, Nicholas J. Mascolo, Cecilia Department of Computer Science and Technology University of Cambridge United Kingdom MRC Epidemiology Unit School of Clinical Medicine University of Cambridge United Kingdom

ISBN: (纸本)9781450383592

Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions. To date, research has primarily focused on the purely supervised task of human activity recognition, demonstrating limited success in inferring high-level health outcomes from low-level signals. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their underlying physiological relationship. In addition, we propose a custom quantile loss function that accounts for the long-tailed HR distribution present in the general population. We evaluate our model in the largest free-living combined-sensing dataset (comprising >280k hours of wrist accelerometer & wearable ECG data). Our contributions are two-fold: I) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level. Notably, we show that the embeddings can generalize in various downstream tasks through transfer learning with linear classifiers, capturing physiologically meaningful, personalized information. For instance, they can be used to predict variables associated with individuals' health, fitness and demographic characteristics (AUC >70), outperforming unsupervised autoencoders and common bio-markers. Overall, we propose the first multimodal self-supervised method for behavioral and physiological data with implications for large-scale health and lifestyle monitoring. Code: Https://***/sdimi/Step2heart. © 2021 Owner/Author.

关键词： Embeddings

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points

An Innovative Way to Increase the Speed and Efficiency of Re...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Ryan Saptarshi Ray Debendranath Das Aritro Sengupta Pankaj Kumar Arun Kumar Sahani Information Technology Govt. College Of Engineering & Ceramic Technology Kolkata India Cryptology and Security Research Unit Indian Statistical Institute Kolkata India Department of Computer Science & Engineering Lingaya's Vidyapeeth Faridabad Haryana India

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form.

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Analytical Approximation for ATTR with Respect to Node Removals

Analytical Approximation for ATTR with Respect to Node Remov...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Fenghua Wang Jinyi Zou Robert E. Kooij Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Unit ICT Strategy and Policy Netherlands Organisation for Applied Scientific Research (TNO) Den Haag The Netherlands

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

We propose an analytical approach to approximate the average two-terminal reliability (ATT R) for graphs where a fraction of the nodes is removed. The approximation is based on the generating function of the network's degree distribution under random node removals and stochastic degree-based node removals. Through validation on synthetic graphs, including Erdos Renyi random graphs and Barabasi-Albert graphs, as well as four real-world networks from the Internet Topology Zoo, we observe that the analytical method effectively approximates the average two-terminal reliability under random node removals for synthetic graphs. In the case of real-world graphs under random and stochastic degree-based node removals or synthetic graphs under stochastic degree-based node removals, the analytical ap-proximation yields reasonably accurate results when the fraction of removed nodes is small, specifically less than 10%, provided that the initial analytical approximation closely aligns with the real ATT R values.

关键词： Reliability engineering Communication networks Internet topology

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A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots

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ASME Letters in Dynamic Systems and Control 2022年第1期2卷 011009页

作者： Vallachira, Sathish Norrlof, Mikael Orkisz, Michal Butail, Sachit Robotics Service Intelligence Unit ABB Ability Innovation Center Karnataka Bangalore560048 India Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi New Delhi110020 India ABB Robotics Division Vasteras721 68 Sweden ABB Corporate Technology Center Krakow31-038 Poland Department of Mechanical Engineering Northern Illinois University DeKalbIL60115 United States

In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the related efficacy of the information-theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear-induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy-based approach is able to detect the axis of failure more than 80% of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time series analysis used to identify directional dependence among processes. Copyright © 2021 by ASME.

关键词： Industrial robots

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Arbitrary Motion Style Transfer with Multi-Condition Motion Latent Diffusion Model

Arbitrary Motion Style Transfer with Multi-Condition Motion ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xingliang Jin Shuai Li Chenglizhao Chen Aimin Hao Xia Hou Ning Li Hong Qin Beijing Information Science and Technology University China Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University College of Computer Science and Technology China University of Petroleum (East China) Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences Department of Computer Science Stony Brook University (SUNY at Stony Brook) Stony Brook New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

computer animation's quest to bridge content and style has historically been a challenging venture, with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality, ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories, recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory, motion content, and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow, guiding the overall structure and movement, while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow, enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers, setting a new benchmark in the realm of computer animation. The source code and model are available at https://***/XingliangJin/***.

关键词： Bridges computer vision Fluids Computational modeling Source coding Animation Diffusion models

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