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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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FogBus: A blockchain-based lightweight framework for edge and fog computing

arXiv

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arXiv 2018年

作者： Tuli, Shreshth Mahmud, Redowan Tuli, Shikhar Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia Delhi India Delhi India

The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog and cloud infrastructure. Although there are a number of real-world frameworks attempt to support such integration, they have many limitations from various perspectives including platform independence, security, resource management and multi-application assistance. To address these limitations, we propose a simplified but effective framework, named FogBus for facilitating end-to-end IoT-Fog(Edge)-cloud integration. FogBus offers a platform independent interface to IoT applications and computing instances for execution and interaction. It not only assists developers in building applications but also helps users in running multiple applications at a time and service providers to manage their resources. In addition, FogBus applies Blockchain, authentication and encryption techniques to secure operations on sensitive data. Because of its lightweight and cross platform software systems, it is easy to deploy, scalable and cost efficient. We demonstrate the effectiveness of our framework by creating a computing environment with it that integrates finger pulse oximeter as IoT devices with Smartphone-based gateway and Raspberry Pi-based Fog nodes for Sleep Apnea analysis. We also run several experiments on this computing environment varying FogBus settings. The experimental results show that different FogBus settings can improve latency, energy, network and CPU usage of the computing infrastructure. Copyright © 2018, The Authors. All rights reserved.

关键词： Blockchain

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Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

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information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

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Collective online learning via decentralized Gaussian processes in massive multi-agent systems

arXiv

引用

arXiv 2018年

作者： Hoang, Trong Nghia How, Jonathan Hoang, Quang Minh Low, Kian Hsiang Laboratory for Information and Decision Systems Massachusetts Institute of Technology School of Computing National University of Singapore

distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i.e., agents) for better scalability. However, a typical distributed system is usually implemented with a central server that collects data statistics from multiple independent machines operating on different subsets of data to build a global analytic model. This centralized communication architecture however exposes a single choke point for operational failure and places severe bottlenecks on the server's communication and computation capacities as it has to process a growing volume of communication from a crowd of learning agents. To mitigate these bottlenecks, this paper introduces a novel Collective Online Learning Gaussian Process (COOL-GP) framework for massive distributed systems that allows each agent to build its local model, which can be exchanged and combined efficiently with others via peer-to-peer communication to converge on a global model of higher quality. Finally, our empirical results consistently demonstrate the efficiency of our framework on both synthetic and real-world datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Multi agent systems

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Roadmap on Data-Centric Materials Science

arXiv

引用

arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard Dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, Lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, Luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, Lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt Leitherer, Andreas Li, Yue Liebscher, Christian H. Logsdail, Andrew J. Lu, Zhongwei Luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, Dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, Luigi Scheidgen, Markus Schloz, Marcel Schmidt, Daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： Data Analytics

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Robust unsupervised feature selection by nonnegative sparse subspace learning

引用

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Unfolding the Mutual Relation between Timeliness and Scalability in cloud Monitoring

Unfolding the Mutual Relation between Timeliness and Scalabi...

引用

2018 IEEE Symposium on Computers and Communications, ISCC 2018

作者： Da Cunha Rodrigues, Guilherme Calheiros, Rodrigo N. Dos Santos, Glederson Lessa Guimaraes, Vinicius Tavares Granville, Lisandro Zambenedetti Tarouco, Liane Buyya, Rajkumar Science and Technology sul Rio-Grandense Federal Institute of Education Charqueadas Brazil School of Computing Engineering and Mathematics Western Sydney University Sydney Australia Computer Networks Group Institute of Informatics Federal University of Rio Grande Do sul Porto Alegre Brazil Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9781538669501

cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among themselves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation. © 2018 IEEE.

关键词： Scalability

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Hand Gesture Recognition System Based on Local Binary Pattern Approach for Mobile Devices 1

引用

17th International Conference on Intelligent systems Design and Applications, ISDA 2017

作者： Lahiani, Houssem Kherallah, Monji Neji, Mahmoud National School of Electronics and Telecommunications University of Sfax Sfax Tunisia Faculty of Sciences University of Sfax Sfax Tunisia Faculty of Economics and Management University of Sfax Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (数字)9783319763484

ISBN: (纸本)9783319763477

Since the appearance of mobile devices, gesture recognition is being a challenging task in the field of computer vision. In this paper, a simple and fast algorithm for static hand gesture recognition for mobile device is described. The hand pose is recognized by using gentle AdaBoost learning algorithm and Local Binary Pattern features. The system is developed on an Android OS platform. The method used consists of two steps: a real-time gesture captured by a smartphone’s camera and the recognition of the hand gestures. It presents a system based on a real-time hand posture recognition algorithm for mobile devices. The aim of this work is to allow the mobile device interpreting the sign made by the user without the need to touch the screen. In this system, the device is able to perform all necessary steps to recognize hand posture without the need to connect to any distant device. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Local binary pattern

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Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

Inequality-Type Zhang Equivalency Originating from Neural Dy...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yunong Zhang Liangjie Ming Min Yang Binbin Qiu Zhonghua Li School of Data and Computer Science Sun Yat-sen University Guangzhou P. R. China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen P. R. China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.

关键词： Mechatronics Automation Conferences Linear matrix inequalities Problem-solving Robots

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Hand Pose Estimation System Based on a Cascade Approach for Mobile Devices 17th

Hand Pose Estimation System Based on a Cascade Approach for ...

引用

17th International Conference on Intelligent systems Design and Applications, ISDA 2017

ISBN: (纸本)9783319763477

The rise in the use of mobile devices requires finding new ways to interact with this type of devices. Gestures are an effective way to interact with the mobile device and to place order to it. However, gesture recognition in this context constitute a challenging task due the limited computational capacities of this type of devices. In this work, we present a hand pose estimation system for mobile device. The gesture is recognized by using a boosting algorithm and Haar-like features. The system is designed for Android devices. The method used consists of capturing gestures by a smartphone’s camera to recognize the hand sign. It presents a system based on a real-time hand posture recognition algorithm for mobile devices. The aim of this system is to allow the mobile device interpreting hand signs made by users without the need to touch the screen. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Adaptive boosting

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