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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Assessing quality of decision reducts 24

Assessing quality of decision reducts

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

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Story ARtist

Story ARtist

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Marie Kegeleers Rafael Bidarra Computer Graphics and Visualization Group Delft University of Technology Delft The Netherlands

ISBN: (数字)9781728174631

ISBN: (纸本)9781728174648

Most content creation applications currently in use are conventional PC applications with visualisation on a 2D screen and indirect interaction, e.g. through mouse and keyboard. Augmented Reality (AR) is a medium that can provide actual 3D visualisation and more hands-on interaction for these applications, due to its technology adding virtual elements to a real-world environment. We explored how AR can be used for story authoring, a specific type of content creation, and investigated how both types of existing AR interfaces, tangible and touch-less, can be combined in a useful way in that context [1]. The Story ARtist application was developed to evaluate the designed interactions and AR visualisation for story authoring. It features a tabletop environment to dynamically visualise the story authoring elements, augmented by the 3D space that AR provides. Story authoring is kept simple, with a linear plot point structure focused on core story elements like actions, characters and objects.

关键词： Visualization Robots Augmented reality Three-dimensional displays Wearable computers Ubiquitous computing Two dimensional displays

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IB-MVS: An iterative algorithm for deep multi-view stereo based on binary decisions

arXiv

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arXiv 2021年

作者： Sormann, Christian Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Austria Sony Europe B.V. R&D Center - Stuttgart Laboratory 1 Germany

We present a novel deep-learning-based method for Multi-View Stereo. Our method estimates high resolution and highly precise depth maps iteratively, by traversing the continuous space of feasible depth values at each pixel in a binary decision fashion. The decision process leverages a deep-network architecture: this computes a pixelwise binary mask that establishes whether each pixel actual depth is in front or behind its current iteration individual depth hypothesis. Moreover, in order to handle occluded regions, at each iteration the results from different source images are fused using pixelwise weights estimated by a second network. Thanks to the adopted binary decision strategy, which permits an efficient exploration of the depth space, our method can handle high resolution images without trading resolution and precision. This sets it apart from most alternative learning-based Multi-View Stereo methods, where the explicit discretization of the depth space requires the processing of large cost volumes. We compare our method with state-of-the-art Multi-View Stereo methods on the DTU, Tanks and Temples and the challenging ETH3D benchmarks and show competitive results. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

引用

8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： Image segmentation

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Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements

arXiv

引用

arXiv 2021年

作者： Thaler, Franz Payer, Christian Bischof, Horst Štern, Darko Medical University of Graz Austria Institute of Computer Graphics Vision Graz University of Technology Austria Gottfried Schatz Research Center Biophysics Medical University of Graz Austria

Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice. The two main concerns are generalization to unseen data with a different visual appearance, e.g., images acquired using a different scanner, and efficiency in terms of computation time and required graphics Processing Unit (GPU) memory. In this work, we employ a multi-organ segmentation model based on the SpatialConfiguration-Net (SCN), which integrates prior knowledge of the spatial configuration among the labelled organs to resolve spurious responses in the network outputs. Furthermore, we modified the architecture of the segmentation model to reduce its memory footprint as much as possible without drastically impacting the quality of the predictions. Lastly, we implemented a minimal inference script for which we optimized both, execution time and required GPU memory. © 2021, CC BY-SA.

关键词： graphics processing unit

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Reduct-based ranking of attributes 24

Reduct-based ranking of attributes

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Zielosko, Beata Stanczyk, Urszula Institute of Computer Science University of Silesia in Katowice Bedzi nska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. © 2020 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

引用

IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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An Attack on Facial Soft-biometric Privacy Enhancement

arXiv

引用

arXiv 2021年

作者： Osorio-Roig, Dailé Rathgeb, Christian Drozdowski, Pawel Terhörst, Philipp Štruc, Vitomir Busch, Christoph Department of Computer Science Hochschule Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research Darmstadt Germany Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes;(2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker’s database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Evolution of a construction graphics course for engineering education 17

Evolution of a construction graphics course for engineering ...

引用

17th International Workshop on Intelligent Computing in Engineering, EG-ICE 2010

作者： Cory, Clark Dibb, Hazar Howard, Elizabeth Hall, Christian Purdue University Department of Computer Graphics Technology West LafayetteIN United States

ISBN: (纸本)9781907284601

The construction industry is being inundated with new technology utilized to help expedite all processes of construction. Visualization of the entire process and individual assemblies of materials has become a standard requirement for individuals in the construction industry. The traditional means of education needed for construction was gathered by hands on experience and could take several years for the individual to obtain. Many AEC firms do not have the time to train carpenters, subcontractors, or superintendents how the construction assembly processes come together. Today's engineer or construction manager that goes through a higher educational system has the ability to utilize new technologies enabling her/him to obtain the visualization education quicker than traditional means. technology is providing marketing alternatives such as three-dimensional models for visualization, simulation, and spatial analysis. This paper discusses the evolution of the construction graphics course taught to Engineering & Construction Management students at Purdue over the last 10 years. The course originally was introduced to get Engineering and Construction Management students visually cognizant of all traditional 2D graphics used in industry. Today's course was designed and delivered to assist students in learning the creation, integration of every graphic type used in construction as well as examining 3D geometry, spatial relationships, geographic information, quantities of materials, and properties of building components. The paper will discuss the evolution of graphics education in the civil engineering and construction management course at Purdue University and how the curriculum came about which was influenced by new technology utilized by the AEC industry. © Nottingham University Press

关键词： Engineering education

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