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arXiv 2020年

作者： Wang, Xiyue Yang, Sen Tang, Mingxuan Wei, Yunpeng He, Ling Zhang, Jing Han, Xiao College of Electrical Engineering Sichuan University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Tencent AI Lab Shenzhen China

In the clinical environment, myocardial infarction (MI) as one common cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). The automatic segmentations of left ventricle (LV), right ventricle (RV), and left ventricular myocardium (LVM) in the LGE CMRIs are desired for the aided diagnosis in clinic. To accomplish this segmentation task, this paper proposes a modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to assist the segmentation in the LGE CMRIs. In this segmentation network, the squeeze-and-excitation residual (SE-Res) and selective kernel (SK) modules are inserted in the down-sampling and up-sampling stages, respectively. The SK module makes the obtained feature maps more informative in both spatial and channel-wise space, and attains more precise segmentation result. The utilized dataset is from the MICCAI challenge (MS-CMRSeg 2019), which is acquired from 45 patients including three CMR sequences. The cine and T2-weighted CMRIs acquired from 35 patients and the LGE CMRIs acquired from 5 patients are labeled. Our method achieves the mean dice score of 0.922 (LV), 0.827 (LVM), and 0.874 (RV) in the LGE CMRIs. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

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Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

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SNIFF: Reverse engineering of Neural Networks with Fault Attacks

arXiv

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arXiv 2020年

作者： Breier, Jakub Jap, Dirmanto Hou, Xiaolu Bhasin, Shivam Liu, Yang Silicon Austria Labs TU-Graz SAL DES Lab Graz University of Technology Graz Austria Temasek Laboratories Nanyang Technological University Singapore Faculty of Informatics and Information Technologies Slovak University of Technology Slovakia School of Computer Science and Engineering Nanyang Technological University Singapore

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. Previous works have mostly explored fault attacks for output misclassification, thus affecting the reliability of neural networks. In this paper we investigate the possibility to reverse engineer neural networks with fault attacks. Sign bit flip fault (SNIFF) attack enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of proprietary model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than 10−13 with the usage of 64-bit floats, which improves the current state of the art by 6 orders of magnitude. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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SpiderWalk: Circumstance-aware transportation activity detection using a novel contact vibration sensor

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第1期2卷 1-30页

作者： Wang, Liang Cheng, Wen Pan, Lijia Gu, Tao Wu, Tianheng Tao, Xianping Lu, Jian State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China School of Computer Science and Information Technology RMIT University Australia

This paper presents the design and implementation of the SpiderWalk system for circumstance-aware transportation activity detection using a novel contact vibration sensor. Different from existing systems that only report the type of activity, our system detects not only the activity but also its circumstances (e.g., road surface, vehicle, and shoe types) to provide better support for applications such as activity logging, location tracking, and smart persuasive applications. Inspired by but different from existing audio-based context detection approaches using microphones, the SpiderWalk system is designed and implemented using an ultra-sensitive, flexible contact vibration sensor which mimics the spiders sensory slit organs. By sensing vibration patterns from the soles of shoes, the system can accurately detect transportation activities with rich circumstance information while resisting undesirable external signals from other sources or speech that May cause the data assignment and privacy preserving issues. Moreover, our system is implemented by reusing existing audio devices and can be used by an unmodified smartphone, making it ready for large-scale deployments. Finally, a novel temporal and spatial correlated classification approach is proposed to accurately detect the complex combinations of transportation activities and circumstances based on the output of each individual classifiers. Experiments conducted on a real-world data set suggest our system can accurately detect different transportation activities and their circumstances with an average detection accuracy of 93.8% with resource overheads comparable to existing audio- and GPS-based systems. 2018 Association for Computing Machinery.

关键词： Machinery

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Evaluating the effect of land use land cover changes on soil moisture index and vegetation indices using multi-spectral satellite data

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Journal of Earth System Science 2025年第2期134卷 1-14页

作者： Arshad, Muhammad Zaib Iqbal, Zafar Saeed, Muhammad Saleem, Muhammad Mansha, Rizwan Abdullah-Al-Wadud, M. Tariq, Aqil Department of Environmental Sciences COMSATS University Islamabad Vehari Campus Punjab Vehari 61100 Pakistan Department of Geography University of Gujrat Punjab Gujrat 50700 Pakistan Institute of Geography University of the Punjab Punjab Lahore 05422 Pakistan Department of Geography Govt. Graduate College Chowk Azam Layyeh Punjab Chowk Azam 31450 Pakistan Institute of Geographical Information Systems School of Civil and Environmental Engineering National University of Sciences and Technology Islamabad Pakistan Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Wildlife Fisheries and Aquaculture Mississippi State University Mississippi State 39762-9690 MS United States

Human activity causes changes in land use and land cover (LULC), which restricts the availability of goods and services for both people and animals. This study aimed to determine the spatiotemporal fluctuations in LULC, vegetation cover, and moisture index through multi-temporal satellite images from the district of Rahimyar Khan, Pakistan. Satellite data analysis was carried out using ArcGIS 10.7. The supervised classification was used to accomplish the LULC classes. This method changed, based on ground-truthing. Consistent with the LULC change study between 2000 and 2020, 7.59% of bare soil was transformed into vegetation and other areas, and vegetative area was increased by 6.97% in the study area region. For 2000, 2010, and 2020, the average NDVI values were determined to be 0.17, 0.21, and –0.038, respectively. The common NDMI values for 2000, 2010, and 2020 for the region looked at were 0.48, 0.38, and 0.25, respectively. Our studies suggest that, as build-up areas rise, Rahimyar Khan tends to an increase in vegetation, moisture index, and water bodies. For planners and policy makers, estimations of the environmental effect and tests of LULC and NDMI modifications are of great significance. © Indian Academy of Sciences 2025.

关键词： change detection GIS land use land cover Moisture index NDVI remote sensing

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Minimum Age-Energy Aware Cost in Wireless Powered Fog Computing Networks

Minimum Age-Energy Aware Cost in Wireless Powered Fog Comput...

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IEEE International Conference on Communications (ICC)

作者： Haina Zheng Ke Xiong Pingyi Fan Zhangdui Zhong Khaled Ben Letaief School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

关键词： Edge computing Wireless communication Real-time systems Cost function Internet of Things System performance Time-frequency analysis

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A unified object motion and affinity model for online Multi-Object Tracking

arXiv

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arXiv 2020年

作者： Yin, Junbo Wang, Wenguan Meng, Qinghao Yang, Ruigang Shen, Jianbing Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China ETH Zurich Switzerland Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China Inception Institute of Artificial Intelligence United Arab Emirates University of Kentucky Kentucky United States

Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant – requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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A new parallel simulation method for massive crowd

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Procedia computer Science 2019年 147卷 283-287页

作者： Xiukai Zhao Lei Lyu Chen Lyu Cun Ji School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

With the advent of multi-core and even many-core processors, massively parallel computing processing has presented a trend of low cost and popularity. It provides new technical means and feasible solutions for the rapid simulation calculation of large-scale group movements and the simulable problem of super-large group movements. In the process of parallel computing, the design of parallel architecture and parallel algorithm is closely related to the group motion simulation algorithm itself due to the original task needs to be decomposed. A parallel simulation algorithm for the continuum crowds has been proposed in this paper. We use the transition block-based scene segmentation algorithm on the management node to divide the simulated scene after initializing the virtual scene, and assign each block to a different computing node for processing. Then, the compute node gets information about the individual it is responsible for. Based on the method, a parallel simulation prototype system was developed on the Sugon high-performance computing platform. Experimental results show that our parallel simulation algorithm can increase the efficiency of scene-rendering and solve the bottleneck in group scale.

关键词： parallel computing massive crowd compute node scene segmentation

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A Kind of new parallax algorithm based on symmetric continuous optimization

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Journal of Physics: Conference Series 2021年第3期1748卷

作者： Kaiwen Jiang Degan Zhang Haixia Xu Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin 300410 China

With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D reconstruction and reduce the possibility of other algorithms falling into local optimal solutions, attempting to get better and faster performance, a new parallax calculation based on symmetric continuous optimization is proposed in this paper. The algorithm is proposed here will be tested respectively in the same data, in order to certificate the algorithm applied is better than traditional minimize El algorithm.

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