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Smart Innovation, Systems and Technologies 2023年 301卷 v-vi页

作者： Das, Biplab Patgiri, Ripon Balas, Valentina Emilia Department of Mechanical Engineering National Institute of Technology Silchar Assam Silchar India Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

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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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Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs

arXiv

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arXiv 2021年

作者： Bokor, Peter Hudec, Lukas Fabian, Ondrej Benesova, Wanda Slovak University of Technology Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Ilkovicova 2 Bratislava84216 Slovakia Institute for Clinical and Experimental Medicine Clinical and Transplant Pathology Centre Videnska 1958/9 Prague 4140 21 Czech Republic Charles University Thomayer University Hospital 3rd Faculty of Medicine Department of Pathology and Molecular medicine Videnska 800 Prague 4140 59 Czech Republic

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSIs), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSIs based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%. © 2021, CC BY.

关键词： Medical imaging

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Anytime diagnosis for reconfiguration

arXiv

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arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Accelerating SDN control plane with GPGPU-based packet classification

Accelerating SDN control plane with GPGPU-based packet class...

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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Martin Baláž Pavol Helebrandt Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9781728149677

ISBN: (纸本)9781728149684

The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms.

关键词： Graphics processing units Acceleration Classification algorithms Firewalls (computing) Telecommunication traffic Process control

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Convex Lifting Based Inverse Parametric Optimization for Implicit Model Predictive Control: A Case Study

Convex Lifting Based Inverse Parametric Optimization for Imp...

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IEEE Conference on Decision and Control

作者： Martin Gulan Ngoc Anh Nguyen Gergely Takács Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia AVL List GmbH Graz Austria

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper we revisit the problem of finding an equivalent inverse optimality formulation to a given linear model predictive control (MPC) problem using the technique of inverse parametric convex programming based on convex lifting. In particular, we show that the parametric solution to a typical QP-based MPC problem can be used to formulate an equivalent LP problem with a significantly reduced space of decision variables and a new set of constraints, the solution of which in both explicit and implicit fashion implies a lower online implementation effort. This is also demonstrated in a practical case study focused on active vibration control, using various optimization methods to solve both the nominal and the reformulated MPC problem. The obtained results suggest a potential of the convex lifting based inverse optimality technique for example in embedded MPC applications where, based on hardware specifications, either explicit or implicit solution is more suitable.

关键词： Programming Cost function Vibration control Predictive control Partitioning algorithms Optimal control Faces

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Retraction Note: Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover applied Sciences 2025年第1期7卷 1-1页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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