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IEEE International Conference on systems, Man and Cybernetics

作者： Cheng-Ju Kuo Ding-Chau Wang Tzu-Ting Chen Yung-Chien Chou Mao-Yuan Pai Gwo-Jiun Horng Min-Hsiung Hung Yu-Chuan Lin Tz-Heng Hsu Chao-Chun Chen Department of Computer Science and Information Engineering Institute of Manufacturing Information and Systems National Cheng Kung University (NCKU) Taiwan Dept. of Comp. Sci. & Info. Engr. Southern Taiwan University of Science and Technology (STUST) Taiwan General Research Service Center National Pingtung University of Science and Technology Taiwan Department of Computer Science and Information Engineering Chinese Culture University Taiwan

In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

关键词： Feature extraction Transforms Inspection Industries Object detection Shape Data structures

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A study on authentication mechanism in SEaaS for SDN 11

A study on authentication mechanism in SEaaS for SDN

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11th International Conference on Ubiquitous information Management and Communication, IMCOM 2017

作者： Kim, Green An, Junghyun Kim, Keecheon Dept. of IT Convergence Information Security Konkuk University Seoul05029 Korea Republic of Dept. of Computer Engineering Konkuk University Seoul05029 Korea Republic of

ISBN: (纸本)9781450348881

SDN(Software Defined network) separates control plane from data plane(forwarding plane) which leads programmability of network and scalability, flexibility of existed network. nevertheless, the reason that security requirements had never considered during the initial design of SDN brought various security issues. And this security issues includes vulnerabilities(such as unauthorized access, data modification and leakage) and attacks(for example, DoS(Denial of Service) and MIITM(Main-in-the-Middle attack)). In this paper, we proposed SEaaS(security as a Service) which can provide security within SDN environment. Also, we suggest authentication mechanism between application and controller among the various services of SEaaS) © 2017 ACM.

关键词： Authentication

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Composite risk modeling for automated threat mitigation in medical devices 4

Composite risk modeling for automated threat mitigation in m...

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4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017

作者： Rao, Aakarsh Rozenblit, Jerzy Lysecky, Roman Sametinger, Johannes Dept. of Electrical and Computer Engineering University of Arizona TucsonAZ United States Dept. of Information Systems - Software Engineering Johannes Kepler University Linz Austria

ISBN: (纸本)9781510838253

Medical device security is a growing concern with increasing incorporation of complex software and hardware. security threats exploiting vulnerabilities in medical devices may directly impact patient safety. Standardization and federal organizations are hence, actively involved in setting up new paradigms for guidance and regulation of security throughout the lifecycle. To protect medical devices against threats a risk-based framework that continually manages and assesses security risks along with their proactive addressing is highly recommended. In this paper, we model a multi-modal design approach for risk assessment in medical devices and propose an adaptive remediation scheme to mitigate security threats. Our multi-modal approach is integrated into the hardware-software development with a middleware for interaction between the modes. This provides an effective premarket risk management while the adaptive remediation scheme pro-actively mitigate risk during postmarket deployment. We model our approaches in detail and demonstrate them in a pacemaker design model and deployment scenario. ©2017 Society for Modeling & Simulation International (SCS).

关键词： Risk management

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Towards Self-Explaining Digital systems: A Design Methodology for the Next Generation

Towards Self-Explaining Digital Systems: A Design Methodolog...

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IEEE International Verification and security Workshop (IVSW)

作者： Rolf Drechsler Christoph Lüth Goerschwin Fey Tim Güneysu Dept. Mathematics and Computer Science University of Bremen Bremen Germany Hamburg University of Technology (TUHH) Institute of Embedded Systems Hamburg Germany Dept. Electrical Engineering & Information Technology Ruhr-Universiat Bochum Bochum Germany

As digital systems get ever more complex, their behaviour may at times appear unfathomable. Users will only be prepared to accept this if they are convinced that the system does indeed work correctly. Thus, we argue the need for self-explaining systems: systems that are able to explain their behaviour, and the reasons for it. In this paper, we propose first steps towards a design methodology for such systems, and argue that beyond user acceptance, self-explanation also has other applications such as self-verification and reconfiguration. We propose a conceptual framework for self-explaining systems, discuss how to achieve completeness, and consider implementation aspects.

关键词： Runtime Safety security Manipulators Temperature sensors

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Genetic algorithm based patching scheme for worm containment on social network 17

Genetic algorithm based patching scheme for worm containment...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Kang, Donghwa Kim, Seoyeon Jung, Jinman Kim, Bongjae Min, Hong Heo, Junyoung Korea Internet and Security Agency Korea Republic of Dept. of Unmanned System Engineering Hannam University Daejeon Korea Republic of Dept. of Information and Communication Engineering Hannam University Daejeon Korea Republic of Dept. of Computer Science and Engineering Sun Moon University Asan Korea Republic of School of Computer Information Engineering Hoseo University Asan Korea Republic of Dept. of Computer Engineering Hansung University Seoul Korea Republic of

ISBN: (纸本)9781450344869

With the increasing number of online social networking site users, there is increasing cyber attacks on online social networks. Strong connectivity among users of the social networking site makes the information of interest rapidly spread. If the worm disguised as information to attract users' interest spreads on online social networking sites, it can cause great damage. Therefore, a fast patch propagation schemes need to be able to inhibit the activity of the worm. In this paper, we propose a fast patch propagation scheme based on genetic algorithms for online social networks. Copyright 2017 ACM.

关键词： Genetic algorithms

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A compact tree representation of an antidictionary

A compact tree representation of an antidictionary

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作者： Ota, Takahiro Morita, Hiroyoshi Dept. of Computer and Systems Engineering Nagano Prefectural Institute of Technology Ueda-shi386-1211 Japan Dept. of Computer and Network Engineering Graduate School of Informatics and Engineering University of Electro-Communications Chofu-shi182-8585 Japan

In both theoretical analysis and practical use for an anti-dictionary coding algorithm, an important problem is how to encode an antidictionary of an input source. This paper presents a proposal for a com-pact tree representation of an antidictionary built from a circular string for an input source. We use a technique for encoding a tree in the compression via substring enumeration to encode a tree representation of the antidic-tionary. Moreover, we propose a new two-pass universal antidictionary coding algorithm by means of the proposal tree representation. We prove that the proposed algorithm is asymptotic optimal for a stationary ergodic source. Copyright © 2017 The Institute of Electronics, information and Communication Engineers.

关键词： Forestry

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

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Discovering communities of malapps on android-based mobile cyber-physical systems

arXiv

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arXiv 2018年

作者： Su, Dan Liu, Jiqiang Wang, Wei Wang, Xiaoyang Du, Xiaojiang Guizani, Mohsen Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Dept. of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States Dept. of Electrical and Computer Engineering University of Idaho MoscowID United States

Android-based devices like smartphones have become ideal mobile cyber-physical systems (MCPS) due to their powerful processors and variety of sensors. In recent years, an explosively and continuously growing number of malicious applications (malapps) have posed a great threat to Android-based MCPS as well as users' privacy. The effective detection of malapps is an emerging yet crucial task. How to establish relationships among malapps, discover their potential communities, and explore their evolution process has become a challenging issue in effective detection of malapps. To deal with this issue, in this work, we are motivated to propose an automated community detection method for Android malapps by building a relation graph based on their static features. First, we construct a large feature set to profile the behaviors of malapps. Second, we propose an E-N algorithm by combining epsilon graph and k-nearest neighbor (k-NN) graph for graph construction. It solves the problem of an incomplete graph led by epsilon method and the problem of noise generated by k-NN graph. Finally, a community detection method, Infomap, is employed to explore the underlying structures of the relation graph, and obtain the communities of malapps. We evaluate our community detection method with 3996 malapp samples. Extensive experimental results show that our method outperforms the traditional clustering methods and achieves the best performance with rand statistic of 94.93% and accuracy of 79.53%. Copyright © 2018, The Authors. All rights reserved.

关键词： Embedded systems

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Towards a cyber defense framework for SCADA systems based on power consumption monitoring 50

Towards a cyber defense framework for SCADA systems based on...

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50th Annual Hawaii International Conference on System Sciences, HICSS 2017

作者： Hernández, Jarilyn M. Chen, Qian Calhoun, Chelsea Sykes, Summer Nichols, Jerey A. Lane Dept. of Computer Science and Electrical Engineering West Virginia University United States Department of Engineering Computer Science Technology Savannah State University Cyber and Information Security Research Group Oak Ridge National Laboratory

ISBN: (纸本)9780998133102

Supervisory control and data acquisition (SCADA) systems are industrial automation systems that remotely monitor and control critical infrastructures. SCADA systems are major targets for espionage and sabotage attackers. Current commercial off-the-shelf security solutions are insufficient in protecting SCADA systems against sophisticated cyber-attacks. Furthermore, these breaches are not detected in real-time or fast enough to prevent further damages. To address this challenge we present a feasibility study that proves monitoring power consumption of SCADA devices is an effective approach to detect cyber-attacks. We built a testbed containing a Programmable Logic Controller (PLC) that was instrumented to record its power usage. Three SCADA-specific cyber-attacks were simulated and we report the power consumption of the PLC under these normal and anomalous scenarios. We show that it is possible to distinguish the PLC power utilization between these scenarios. In route to this result we found and describe vulnerabilities in the DF-1 protocol. © 2017 Proceedings of the Annual Hawaii International Conference on System Sciences. All rights reserved.

关键词： Electric power utilization

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