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arXiv 2021年

作者： Tell, Paolo Klünder, Jil Küpper, Steffen Raffo, David MacDonell, Stephen Münch, Jürgen Pfahl, Dietmar Linssen, Oliver Kuhrmann, Marco Department of Computer Science IT University Copenhagen Denmark Software Engineering Group Leibniz University Hannover Hannover Germany Center for Digital Technologies Clausthal University of Technology Clausthal-Zellerfeld Germany Department of Computer Science School of Business Portland State University PortlandOR United States Department of Information Technology and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Reutlingen University Reutlingen Germany Institute of Computer Science University of Tartu Tartu Estonia FOM Hochschule für Oekonomie & Management Essen Germany Software Engineering I University of Passau Passau Germany

Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large-scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

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Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Latency variation aware read performance optimization on 3D high density NAND flash memory 20

Latency variation aware read performance optimization on 3D ...

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30th Great Lakes Symposium on VLSI, GLSVLSI 2020

作者： Lv, Yina Shi, Liang Xue, Chun Jason Zhuge, Qingfeng Sha, Edwin H.-M. Software/Hardware Co-design Engineering Research Center Ministry of Education United States School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9781450379441

State-of-the-art high density NAND flash memory has been recommended as read intensive storage device due to their excellent read performance. However, recent studies and reports show that the read latency of high density NAND flash memory is increasing. The reason comes from at least two aspects: First, high density flash generally adopts multiple bits per cell technique, where the access latency of the most significant bits is largely increased. Second, due to the reliability variation among these bits, the access latency of the most significant bits is further increased. We introduce RLV, a read performance optimization scheme is proposed to exploit the read latency variation among the multiple bits. The basic idea is that firstly identify the hotness of read data and then move them to the places with corresponding read latency. Our evaluation shows that RLV incurs negligible overhead, while improving read performance by 14% on average compared with state-of-the-arts. © 2020 Association for Computing Machinery.

关键词： NAND circuits

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One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

arXiv

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arXiv 2023年

作者： Guo, Hang Dai, Tao Zhu, Mingyan Meng, Guanghao Chen, Bin Wang, Zhi Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Shenzhen China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Research Center of Artificial Intelligence Peng Cheng Laboratory Shenzhen China

Recognizing characters from low-resolution (LR) text images poses a significant challenge due to the information deficiency as well as the noise and blur in low-quality images. Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition. Although this pipeline is straightforward and intuitive, it has to use an additional super-resolution network, which causes inefficiencies during training and testing. Moreover, the recognition accuracy of the second stage heavily depends on the reconstruction quality of the first stage, causing ineffectiveness. In this work, we attempt to address these challenges from a novel perspective: adapting the recognizer to low-resolution inputs by transferring the knowledge from the high-resolution. Guided by this idea, we propose an efficient and effective knowledge distillation framework to achieve multi-level knowledge transfer. Specifically, the visual focus loss is proposed to extract the character position knowledge with resolution gap reduction and character region focus, the semantic contrastive loss is employed to exploit the contextual semantic knowledge with contrastive learning, and the soft logits loss facilitates both local word-level and global sequence-level learning from the soft teacher label. Extensive experiments show that the proposed one-stage pipeline significantly outperforms super-resolution based two-stage frameworks in terms of effectiveness and efficiency, accompanied by favorable robustness. Code is available at https://***/csguoh/KD-LTR. © 2023, CC BY-NC-ND.

关键词： Distillation

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

Towards Cluster-Based Split Federated Learning Approach for ...

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Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

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HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

arXiv

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arXiv 2022年

作者： Suo, Jiashun Wang, Tianyi Zhang, Xingzhou Chen, Haiyang Zhou, Wei Shi, Weisong Engineering Research Center of Cyberspace Yunnan University Kunming650091 China School of Software Yunnan University Kunming650091 China Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer and Information Sciences University of Delaware NewarkDE19716 United States

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2, 898 infrared thermal images extracted from 43, 470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://***/suojiashun/HIT-UAV-Infrared-Thermal-Dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Optimal Control for Connected and Autonomous Vehicles at Signal-Free Intersections

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IFAC-PapersOnLine 2020年第2期53卷 15306-15311页

作者： Boli Chen Xiao Pan Simos A. Evangelou Stelios Timotheou Department of Electronic and Electrical Engineering at University College London UK Department of Electrical and Electronic Engineering Imperial College London UK Department of Electrical and Computer Engineering and the KIOS Research and Innovation Center of Excellence University of Cyprus

The development of connected and autonomous vehicles (CAVs) is one of the central aspects in the pathway towards future intelligent mobility systems. This paper addresses the problem of coordinating CAVs crossing an uncontrolled intersection so as to maintain safe and efficient traffic flow. The proposed control strategy is based on an optimal control framework that is formulated to minimize a weighted sum of total energy consumption and travel time of all CAVs by finding the optimal velocity trajectory of each vehicle. The design procedure starts with a proper formulation of the autonomous intersection crossing problem for CAVs, with various cases of energy recovery capability by the CAVs considered, to also investigate the influence of powertrain electrification on the intersection crossing problem. This yields an optimal control problem (OCP) with nonlinear and nonconvex dynamics and constraints. In order to ensure a rapid solution search and a unique global optimum, the OCP is reformulated via convex modeling techniques. Numerical results validate the effectiveness of the proposed approaches, while the trade-off between energy consumption and travel time is illustrated by Pareto optimal solutions.

关键词： Connected autonomous vehicles Intersections crossing Energy consumption Optimization Velocity control

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Advances in adversarial attacks and defenses in computer vision: A survey

arXiv

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arXiv 2021年

作者： Akhtar, Naveed Mian, Ajmal Kardan, Navid Shah, Mubarak Department of Computer Science and Software Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States

—Deep Learning (DL) is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical applications. However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. Since the discovery of this phenomenon in 2013 [1], it has attracted significant attention of researchers from multiple sub-fields of machine intelligence. In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018. Many of those contributions have inspired new directions in this area, which has matured significantly since witnessing the first generation methods. Hence, as a legacy sequel of [2], this literature review focuses on the advances in this area since 2018. To ensure authenticity, we mainly consider peer-reviewed contributions published in the prestigious sources of computer vision and machine learning research. Besides a comprehensive literature review, the article also provides concise definitions of technical terminologies for non-experts in this domain. Finally, this article discusses challenges and future outlook of this direction based on the literature reviewed herein and [2]. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Physical Adversarial Attack Meets computer Vision: A Decade Survey

arXiv

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arXiv 2022年

作者： Wei, Hui Tang, Hao Jia, Xuemei Wang, Zhixiang Yu, Hanxun Li, Zhubo Satoh, Shin'ichi Van Gool, Luc Wang, Zheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China Colleage of Software and Technology Zhejiang University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Digital Content and Media Sciences Research Division National Institute of Informatics Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan Computer Vision Lab of ETH Zurich Zürich8092 Switzerland KU Leuven Leuven3000 Belgium INSAIT Sofia Bulgaria

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Genetic evidence for facial variation being a composite phenotype of cranial variation and facial soft tissue thickness

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Journal of Genetics and Genomics 2022年第10期49卷 934-942页

作者： Wei Qian Manfei Zhang Kaiwen Wan Yunxia Xie Siyuan Du Jiarui Li Xiongzheng Mu Jiange Qiu Xiangyang Xue Xiahai Zhuang Yingzhi Wu Fan Liu Sijia Wang State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterFudan UniversityShanghai 201203China School of Computer Science Fudan UniversityShanghai 200438China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Data Science Fudan UniversityShanghai 200433China Academy of Medical Science Zhengzhou UniversityZhengzhouHenan 450052China Department of Plastic Surgery Huashan HospitalFudan UniversityShanghai 200040China CAS Key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsUniversity of Chinese Academy of SciencesBeijing 100101China School of Future Technology University of Chinese Academy of SciencesBeijing 101408China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunmingYunnan 650223China

Facial and cranial variation represent a multidimensional set of highly correlated and heritable *** is known about the genetic basis explaining this *** develop a software package ALo SFL for simultaneous localization of facial and cranial landmarks from head computed tomography(CT)images,apply it in the analysis of head CT images of 777 Han Chinese women,and obtain a set of phenotypes representing variation in face,skull and facial soft tissue thickness(FSTT).Association analysis of 301 single nucleotide polymorphisms(SNPs)from 191 distinct genomic loci previously associated with facial variation reveals an unexpected larger number of loci showing significant associations(P<1e-3)with cranial phenotypes than expected under the null(O/E=3.39),suggesting facial and cranial phenotypes share a substantial proportion of genetic *** FSTT to a SNP-only model shows a large impact in explaining facial variance.A gene ontology analysis reveals that bone morphogenesis and osteoblast differentiation likely underlie our cranial-significant ***,this study simultaneously investigates the genetic effects on both facial and cranial variation of the same sample,supporting that facial variation is a composite phenotype of cranial variation and FSTT.

关键词： Facial variation Cranial variation Association analysis Single nucleotide polymorphism Facial soft tissue thickness Computed tomography

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