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arXiv 2021年

作者： Yu, Nengkun Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

The max-flow min-cut theorem is a cornerstone result in combinatorial optimization. Calegari et al. (J Am Math Soc 23(1):107–188, 2010) initialized the study of quantum max-flow min-cut conjecture, which connects the rank of a tensor network and the min-cut. Cui et al. (J Math Phys 57:062206, 2016) showed that this conjecture is false generally. In this paper, we establish a quantum max-flow min-cut theorem for a new definition of quantum maximum flow. In particular, we show that for any quantum tensor network, there are infinitely many n, such that quantum max-flow equals quantum min-cut, after attaching dimension n maximally entangled state to each edge as ancilla. Our result implies that the ratio of the quantum max-flow to the quantum min-cut converges to 1 as the dimension n tends to infinity. As a direct application, we prove the validity of the asymptotical version of the open problem about the quantum max-flow and the min-cut, proposed in Cui et al. (J Math Phys 57:062206, 2016). Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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M2UNet: MetaFormer Multi-Scale Upsampling Network for Polyp Segmentation

M2UNet: MetaFormer Multi-Scale Upsampling Network for Polyp ...

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European Signal Processing Conference (EUSIPCO)

作者： Quoc-Huy Trinh Nhat-Tan Bui Trong-Hieu Nguyen-Mau Minh-Van Nguyen Hai-Minh Phan Minh-Triet Tran Hai-Dang Nguyen Faculty of Information Technology Software Engineering Laboratory International Training and Education Center and John von Neumann Institute University of Science VNU-HCM Vietnam National University Ho Chi Minh City Vietnam

Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network $\overline{(}\mathrm{M}^{2}\text{UNet})$ for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods.

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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Automatic Video Segmentation Based on information Centroid and Optimized SaliencyCut

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Journal of Computer Science & technology 2020年第3期35卷 564-575页

作者： Hui-Si Wu Meng-Shu Liu Lu-Lu Yin Ping Li Zhen-Kun Wen Hon-Cheng Wong College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical *** the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation ***,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a *** effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the *** saliency maps for all frames in the video are calculated based on the detected *** applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex ***,we obtain the segmentation results based on IC-based saliency maps and optimized *** method is evaluated on the DAVIS dataset,consisting of different kinds of challenging *** with the state-of-the-art methods are also conducted to evaluate our *** visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.

关键词： automatic video segmentation information centroid saliency detection optimized SaliencyCut

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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An Efficient Distributed Framework for Secured Multimedia Content Using Cloud Storage

An Efficient Distributed Framework for Secured Multimedia Co...

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Computer Technologies (ICCTech), International Conference on

作者： Yunus Parvej Faniband Iskandar Ishak Fatimah Sidi Marzanah A. Jabar Deloria Shaniska Razin C. H. Yap Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.

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Parallel Global Sequence Alignment with Arbitrary Scoring Scheme: Algorithm Formulation and Evaluation

SSRN

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SSRN 2022年

作者： Sadiq, Muhammad Umair Yousaf, Muhammad Murtaza Department of Computer Science Government College University Lahore54000 Pakistan Department of Software Engineering Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan

Alignment of the sequences is useful in many biological and non-biological domains. Needleman-Wunsch algorithm is deemed as the basic algorithm for finding the optimal alignment between two sequences. It is a dynamic programming based algorithm which has quadratic time and space requirement. Nonetheless, as a result of increase in the capacity of sequence databases in recent years, sizes of sequences may contain billions of characters for computationally intensive problems. Comparison of such large sequences using sequential methods is avoided as it takes unacceptable amount of time for producing the results. In this paper, we proposed a parallel variation of the Needleman-Wunsch algorithm by reformulating the dependencies in the prevalent dynamic programming solution. Proof of correctness of the proposed algorithm is established. It evinces certain characteristics such as it allows arbitrary linear scoring scheme when comparing the sequences which is important to obtain peerless alignment. Moreover, it ensures load balancing among the computational units and maximizes the utilization of resources. It is compared with various state-of-the-art approaches. Experimental results using the real DNA and user trajectories sequences over graphic processing units (GPUs) suggest that the proposed algorithm is notably superior to the existing approaches that allow arbitrary linear scoring schemes. It achieved the GCUPs up to 14.06 and maximum speedup of 48.18x compared to the sequential implementation. © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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DeST 3.0:A new-generation building performance simulation platform

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Building Simulation 2022年第11期15卷 1849-1868页

作者： Da Yan Xin Zhou Jingjing An Xuyuan Kang Fan Bu Youming Chen Yiqun Pan Yan Gao Qunli Zhang Hui Zhou Kuining Qiu Jing Liu Yan Liu Honglian Li Lei Zhang Hong Dong Lixin Sun Song Pan Xiang Zhou Zhe Tian Wenjie Zhang Ruhong Wu Hongsan Sun Yu Huang Xiaohong Su Yongwei Zhang Rui Shen Diankun Chen Guangyuan Wei Yixing Chen Jinqing Peng Building Energy Research Center School of ArchitectureTsinghua UniversityBeijing 100084China School of Architecture Southeast UniversityNanjingJiangsu 210096China School of Environment and Energy Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China College of Civil Engineering Hunan UniversityChangshaHunan 410082China School of Mechanical Engineering Tongji UniversityShanghai200092China Beijing Key Lab of Heating Gas SupplyVentilating and Air Conditioning EngineeringBeijing University of Civil Engineering and ArchitectureBeijing 100044China China State Construction Technology Center Beijing 101320China School of Architecture Harbin Institute of TechnologyHarbinHeilongjiang 150090China School of Architecture Xi'an University of Architecture and TechnologyXi'anShaanxi 710055China School of Architecture South China University of TechnologyGuangzhouGuangdong 510641China Institute of Building Environment and Energy China Academy of Building ResearchBeijing 100013China Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology Beijing University of TechnologyBeijing 100124China Key Lab of Built Environment and Energy of Tianjin School of Environmental Science and EngineeringTianjin UniversityTianjin 300350China School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Civil Engineering Guangzhou UniversityGuangzhou 510006China Faculty of Computing Harbin Institute of TechnologyHarbinHeilongjiang 150001China Beijing Glory PKPM Technology Co. Ltd.Beijing 100013China Beijing Tangent Software Co. LtdBeijing 100081China Shanghai Huadianyuan Information Technology Co. LtdShanghai200092China Luoyang Hongye Information Technology Co. Ltd.LuoyangHenan 471822China

Buildings contribute to almost 30%of total energy consumption *** building energy modeling programs is of great significance for lifecycle building performance assessment and *** in novel building technologies,the requirements of high-performance computation,and the demands for multi-objective models have brought new challenges for building energy modeling software and *** meet the increasing simulation demands,DeST 3.0,a new-generation building performance simulation platform,was developed and *** structure of DeST 3.0 incorporates four simulation engines,including building analysis and simulation(BAS)engine,HVAC system engine,combined plant simulation(CPS)engine,and energy system(ES)engine,connected by air loop and water loop balancing *** 3.0 offers numerous new simulation features,such as advanced simulation modules for building envelopes,occupant behavior and energy systems,cross-platform and compatible simulation kernel,FMI/FMU-based co-simulation functionalities,and high-performance parallel simulation *** 3.0 has been thoroughly evaluated and validated using code verification,inter-program comparison,and case-study *** 3.0 has been applied in various aspects throughout the building lifecycle,supporting building design,operation,retrofit analysis,code appliance,technology adaptability evaluation as well as research and *** new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization.

关键词： DeST building performance simulation building energy modeling

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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