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arXiv 2022年

作者： Wei, Hui Tang, Hao Jia, Xuemei Wang, Zhixiang Yu, Hanxun Li, Zhubo Satoh, Shin'ichi Van Gool, Luc Wang, Zheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China Colleage of Software and Technology Zhejiang University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Digital Content and Media Sciences Research Division National Institute of Informatics Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan Computer Vision Lab of ETH Zurich Zürich8092 Switzerland KU Leuven Leuven3000 Belgium INSAIT Sofia Bulgaria

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Surveying Cyber Threat Intelligence and Collaboration: A Concise Analysis of Current Landscape and Trends

Surveying Cyber Threat Intelligence and Collaboration: A Con...

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IEEE International Conference on Cloud Computing technology and Science (CloudCom)

作者： Panagiotis Radoglou-Grammatikis Elisavet Kioseoglou Dimitrios Asimopoulos Miltiadis Siavvas Ioannis Nanos Thomas Lagkas Vasileios Argyriou Konstantinos E. Psannis Sotirios Goudos Panagiotis Sarigiannidis Department of Electrical and Computer Engineering Campus ZEP Kozani University of Western Macedonia Kozani Greece William Gladstone 31 K3Y Ltd Sofia Bulgaria MetaMind Innovations P.C. Kila Kozani Greece Centre for Research and Technology Hellas/Information Technologies Institute Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus Department of Computer Science Kavala Campus International Hellenic University Kavala Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

The evolution of cyberattacks has been significantly impacted by the rise of Artificial Intelligence (AI). In particular, AI-driven attacks leverage Machine Learning (ML) and Deep Learning (DL) methods to automate tasks like identifying vulnerabilities, crafting convincing phishing emails, and evading conventional security measures. These cyberattacks can adapt in real time, making them more elusive and challenging to detect. Furthermore, AI has enabled the development of AI-powered malware that can learn and evolve, making it even more dangerous. As AI continues to evolve, both attackers and defenders are engaged in a relentless arms race, with cybersecurity professionals striving to harness AI for threat detection and response while cybercriminals seek to exploit AI’s capabilities for their malicious purposes. This ongoing battle underscores the need for proactive and adaptive cybersecurity strategies to mitigate the evolving threats posed by AI-driven cyberattacks. Based on the aforementioned remarks, it is evident that efficient and adaptable countermeasures are necessary. In this paper, we focus our attention on Cyber Threat Intelligence (cti) mechanisms. cti is the process of collecting, analysing, and sharing information about potential cybersecurity threats to help organisations proactively defend against cyberattacks. In particular, after providing an overview of the cti use cases, a brief analysis of existing solutions follows, highlighting the current trends and directions for future work in this research field.

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Detection of Violence in Cartoon Videos Using Visual Features

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Procedia computer Science 2021年 192卷 4962-4971页

作者： Tahira Khalil Javed Iqbal Bangash Abdul Waheed Khan Saima Anwar Lashari Abdullah Khan Dzati Athiar Ramli Department of Computing Abasyn University Peshawar Pakistan nstitute of Computer Science and IT (ICS/IT) The Agriculture University Peshawar Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Haripur Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

technology play an important role in today era. Tremendous growth has been observed in the field of communication and technology. Same is noteworthy in case of video and multimedia. An enormous amount of visual clips containing all types of scenes can easily be accessed both online and offline. In the use of internet has increase the trend of watching cartoons as a source of entertainment among children. There is a tendency that cartoon video has scenes that make some people uncomfortable because of the contents of the video that may be against their feelings and acceptability levels. There could be a number of objectionable elements in cartoon videos. This work presents a framework based on knowledgebase, with visual features which is divided into two categories i.e. character and object database. That deal with the detection of violent contents in cartoon videos which can have negative impact on child personality. A prototype of this system is developed and implemented in Mat lab. The experimental results show that the proposed system successfully detects the violent scenes in cartoon videos.

关键词： Low-Level Features Object Extraction Violence Detection Cartoon Videos

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Speech separation using an asynchronous fully recurrent convolutional neural network 21

Speech separation using an asynchronous fully recurrent conv...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Xiaolin Hu Kai Li Weiyi Zhang Yi Luo Jean-Marie Lemercier Timo Gerkmann Department of Computer Science and Technology Tsinghua Laboratory of Brain and Intelligence (THBI) IDG/McGovern Institute of Brain Research Tsinghua University Beijing China Department of Electrical Engineering Columbia University NY Department of Informatics University of Hamburg Hamburg Germany

ISBN: (纸本)9781713845393

Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation performance. In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network (FRCNN) to solve the separation task. This model contains bottom-up, top-down and lateral connections to fuse information processed at various time-scales represented by stages. In contrast to the traditional approach updating stages in parallel, we propose to first update the stages one by one in the bottom-up direction, then fuse information from adjacent stages simultaneously and finally fuse information from all stages to the bottom stage together. Experiments showed that this asynchronous updating scheme achieved significantly better results with much fewer parameters than the traditional synchronous updating scheme. In addition, the proposed model achieved good balance between speech separation accuracy and computational efficiency as compared to other state-of-the-art models on three benchmark datasets.

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Combining Attentional CNN and GRU Networks for Ocean Current Prediction based on HF Radar Observations 19

Combining Attentional CNN and GRU Networks for Ocean Current...

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Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

作者： Nathachai Thongniran Kulsawasd Jitkajornwanich Siam Lawawirojwong Panu Srestasathiern Peerapon Vateekul Department of Computer Engineering Chulalongkorn University Department of Computer Science King Mongkut's Institute of Technology Ladkrabang GISTDA Academy Geo-Informatics & Space Technology Development Agency

ISBN: (纸本)9781450376570

Lately, CNN-GRU demonstrates the ability of deep learning techniques on ocean surface current prediction. Improvement of the current prediction model creates positive impact on variety of marine activities, such as search-and-rescue, disaster monitoring and power forecasting. Deep learning techniques was successfully deployed to improve model performance in many areas due to their ability to handle enormous amounts of information in a variety of inputs and their huge growth in recent years. Latest ocean current prediction employed a combination of two mature techniques, which are Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), to capture spatial and temporal characteristics of its nature. However, there is still room for improvement due to many modern techniques that still have not been employed, and domain knowledge in oceanic, such as lunar illumination, is not taken into account to improve prediction performance. This paper introduces the ocean surface prediction model that employs soft attention mechanism, transfer learning, and incorporation of domain knowledge inputs which are month number, lunar effect, and hour number. An experimental dataset from 2014 to 2016, provided by GISTDA, is collected by using high frequency (HF) radar stations located along the coastal Gulf of Thailand. The experiment compares an existing CNN-GRU and our proposed model. The result shows an improvement of the prediction model in terms of RMSE by 2.57%, and 3.44% on U and V components.

关键词： convolutional neural network deep learning HF radar gated recurrent unit spatio-temporal Surface current forecasting attention mechanism transfer learning

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AI recognition of patient race in medical imaging: a modelling study

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The Lancet Digital Health 2022年第6期4卷 e406-e414页

作者： Gichoya, Judy Wawira Banerjee, Imon Bhimireddy, Ananth Reddy Burns, John L Celi, Leo Anthony Chen, Li-Ching Correa, Ramon Dullerud, Natalie Ghassemi, Marzyeh Huang, Shih-Cheng Kuo, Po-Chih Lungren, Matthew P Palmer, Lyle J Price, Brandon J Purkayastha, Saptarshi Pyrros, Ayis T Oakden-Rayner, Lauren Okechukwu, Chima Seyyed-Kalantari, Laleh Trivedi, Hari Wang, Ryan Zaiman, Zachary Zhang, Haoran Department of Radiology Emory University Atlanta GA United States Department of Computer Science Emory University Atlanta GA United States School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States School of Informatics and Computing Indiana University–Purdue University Indianapolis IN United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science University of Toronto Toronto ON Canada Stanford University School of Medicine Palo Alto CA United States Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia School of Public Health University of Adelaide Adelaide SA Australia Florida State University College of Medicine Tallahassee FL United States Dupage Medical Group Hinsdale IL United States Department of Computer Science Georgia Institute of Technology Atlanta GA United States Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada

Background: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that t

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A mutualistic framework for sustainable capacity sharing in manufacturing 53

A mutualistic framework for sustainable capacity sharing in ...

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53rd CIRP Conference on Manufacturing Systems, CMS 2020

作者： Szaller, Ádám Pedone, Gianfranco Egri, Péter Szalóki, Ádám Nick, Gábor Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest1111 Hungary EPIC InnoLabs Ltd. Budapest1111 Hungary

In order to decrease the ecological impact and footprint of supply chain actors, a promising mechanism for sustainability in the industry is represented by sharing resources with each other in a mutualistic way. The paper aims at introducing a novel framework that facilitates the collaborative work of manufacturing facilities and helps participants dynamically and safely negotiate available capacities shared within the federation. The center of the framework is an open API cloud- and simulation-based platform which supports the strategic collaboration of federated companies while helping them optimize resource utilization, avoid shortages and minimize the environmental impact of their logistics. © 2020 The Authors.

关键词： Supply chains

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Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba

arXiv

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arXiv 2024年

作者： Huang, Jiahao Yang, Liutao Wang, Fanwen Nan, Yang Wu, Weiwen Wang, Chengyan Shi, Kuangyu Aviles-Rivero, Angelica I. Schönlieb, Carola-Bibiane Zhang, Daoqiang Yang, Guang Department of Bioengineering and Imperial-X Imperial College London London United Kingdom National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong China Human Phenome Institute Fudan University Shanghai China Department of Nuclear Medicine Inselspital University of Bern Bern Switzerland Department of Informatics Technical University of Munich Munich Germany Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 14784 LNCS卷 v页

作者： Bai, Guangdong Ishikawa, Fuyuki Aït-Ameur, Yamine Papadopoulos, George A. School of Electrical Engineering and Computer Science Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD Australia Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Chiyoda-ku Japan IRIT University of Toulouse Toulouse France Department of Computer Science University of Cyprus Nicosia Cyprus

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