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A new parallel simulation method for massive crowd

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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Developing an Effective Graph Based Block Chain Framework

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： B Ravi Prasad Swapnil Saurav A Sangeerani Devi Venkateshwaran Loganathan Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Department of Problem Management Service Now Software India Pvt. Ltd Hyderabad Telangana India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India

Blockchain's decentralized characteristic is recognized as a potential technology to deliver secure and safe resources. However, the existing blockchain networks cannot fulfil the transaction given the limited bandwidth for practical application demand. In this article, the cubic design of the conventional public blockchain is strengthened with the Directed Graph Probabilistic model. In the current structure, blocks of the lightweight structure are ordered in grades and width. We develop protocols to position newly created blocks in order to make them more effective and secure. It increases confidentiality and time for authentication of transactions in contrast with standard blockchain protocols and enjoys the accuracy and liveliness of blockchain. This paper introduces two potential methods for targeting opposition parties and we also show that the procedure against them is safe and stable. Experiments will generate three hundred inputs every second that is 64 times the output of Bitcoin's transaction. This time Bitcoin's production will hit 27 times its Ethereum's.

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A Fusion Steganographic Algorithm Based on Faster R-CNN

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computers, Materials & Continua 2018年第4期55卷 1-16页

作者： Ruohan Meng Steven G.Rice Jin Wang Xingming Sun School of Computer and Software Nanjing University of Information Science and TechnologyNing Liu RoadNo.219Nanjing210044China Jiangsu Engineering Centre of Network Monitoring Ning Liu RoadNo.219Nanjing210044China Department of Mathematics and Computer Science Northeastern State University TahlequahOK 74464USA School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China

The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover *** secret message should not be damaged on the process of the cover *** order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding *** this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret ***,complex texture regions are selected based on a kind of objects detection ***,three different steganographic methods were used to hide secret message into the selected block *** results show that the approach enhances the security and robustness.

关键词： Faster R-CNN fusion steganography object detection CNNs information hiding.

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Analysis and modeling of sequential circuits in QCA nano computing: RAM and SISO register study

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Solid State Electronics Letters 2019年第2期1卷 73-83页

作者： Md. Abdullah-Al-Shafi Rahman Ziaur Institute of Information Technology (IIT) University of Dhaka Dhaka 1000 Bangladesh School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street Melbourne VIC 3000 Australia

Quantum-dot cellular automata (QCA) is a foremost archetype of field-coupled nanoscale devices. It is a non-von-Neumann, minimal energy dissipated model for conventional nano computing by transistor free logic. The field-coupled nanoscale models rely on limited field connections among nanoscale building modules which are organized in forms to complete convenient assessments. A fundamental device in QCA is termed as a cell is created from a structure of coupled dots by a few flexible charges and the charge arrangement initiates a bit, and quantum charge channeling inside a squared cell permits device shifting. QCA operation approves extreme device thicknesses, room temperature implementation, and higher switching speeds. We propose an inventive design of two commonly used sequential circuits, namely random access memory (RAM) and serial-in/serial-out (SISO) register in this study. Noteworthy enhancements in terms of extent, cell intricacy, latency, and cost have been attained in both designs. Thorough performance assessment and analysis are achieved in several aspects to substantiate the designed circuits having an outstanding operation in contrast to existing studies. QCADesigner and QCAPro tools have been utilized to confirm the precise functionality of the outlined architectures.

关键词： Quantum-dot cellular automata (QCA) Random Access Memory (RAM) Serial-in/Serial-out (SISO) Nanoelectronics

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STR: Secure computation on additive shares using the share-transform-reveal strategy

arXiv

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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Supervised Nonnegative Tucker Decomposition for Computational Phenotyping

Supervised Nonnegative Tucker Decomposition for Computationa...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Kai Yang Wenqi Sun Junfeng Zhao Yasha Wang Bing Xie Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Beida(Binhai) Information Research Tianjing National Engineering Research Center of Software Engineering Beijing China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

With the availability of Electronic Health Records (EHR) data, lots of predictive tasks in medical practice seem solvable by building predictive models. However, EHR data always contains various medical concepts (e.g., diagnosis, medicines, lab tests) with high dimensions and mass correlations among them. To avoid the curse of dimensionality, the representation learning or dimensional reduction process is needed before prediction. Traditional methods either could not mine the correlations of different dimensions in datasets or lack of interpretability. Therefore we proposed a supervised tucker tensor decomposition (SNTD) which is guided by the prediction tasks for representation learning. Detailly speaking, SNTD constrains factor matrices by adding the label information of task and generating regularization term towards the non-negative tucker decomposition (NTD). Compared to the CP decomposition based methods, SNTD has better performance for that more flexible core tensor can contribute to the better learning of correlations among dimensions largely. We also demonstrate the accuracy and interpretability of our approach on an real-world EHR data for hospitalization task. Our result shows SNTD can not only obtain better performance of prediction than baseline methods, but also achieve the interpretability of representations.

关键词： Tensile stress Medical diagnostic imaging Matrix decomposition Task analysis Correlation Predictive models Mathematical model

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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-depth 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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Consent Verification Under Evolving Privacy Policies

Consent Verification Under Evolving Privacy Policies

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IEEE International Conference on Requirements engineering

作者： Marco Robol Travis D. Breaux Elda Paja Paolo Giorgini Information Engineering and Computer Science Department University of Trento Trento Italy Institute of Software Research Carnegie Mellon University Pittsburgh USA Computer Science Department IT University of Copenhagen Copenhagen Denmark

Personal data provides important business value, for example, in the personalization of services. In addition, companies are moving toward new business models, in which products and services are offered without charge to users, but in exchange for targeted advertising revenue. New privacy regulations require organizations to explicitly state their data practices in privacy policies, including which data types will be collected. By consenting to data collections described in a policy, the user acknowledges that he or she is granting the company the authorizations needed to access their data. When data practices change, a new version of the policy is released. This release can occur a few times a year, when requirements are rapidly changing for the collection and processing of personal data. Furthermore, the user may change his or her privacy consent by opting in or out of the policy. We propose a formal framework to support companies and users in their understanding of policies evolution under consent regime that supports both retroactive and non-retroactive consent and consent revocation. Preliminary results include an ontology for policy evolution, expressed in Description Logic, that can be used to formalize consent and data collection logs and then query for which data types can be legally accessed.

关键词： Companies Authorization Data privacy Privacy Data collection Ontologies

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A embedding model for text classification

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Expert Systems 2019年第6期36卷 e12460-e12460页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Yi, Jing Zhu, Zhenfang School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Heze University Heze China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look-up tables, we construct a two-dimensional table that is in the format to represent words in text documents. Finally, the two-dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models. © 2019 John Wiley & Sons, Ltd

关键词： Embeddings

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Multi-Dimension Projection for Non-Linear Data Via Spearman Correlation Analysis (MD-SCA)

Multi-Dimension Projection for Non-Linear Data Via Spearman ...

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International Conference on information and Communication Technologies (ICICT)

作者： Muhammad Saddam Khokhar Keyang Cheng Misbah Ayoub Zakria Lubamba Kasangu Eric School of Computer Science and Communications Engineering Jiangsu University Zhenjiang China Xi'an Jiaotong-Liverpool University Suzhou China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728123349

ISBN: (纸本)9781728123356

This paper introduces an algorithm of multidimensional informative projection or view of multiple variable and more than two random variables via Spearman correlation analysis (SCA). The proposed algorithm is an extension of Spearman correlation analysis to extract linear or nonlinear information of projections through pairwise correlation analysis. These multi-dimensional informative projections used as common patterns in pattern recognition application. The proposed algorithm extends SCA through linear algebraic solution for the optimization problem, the problem of dual representation of high multi-dimensional data, and structural dilemma issues along with deep learning model. Additionally, the proposed method decreases the quadratic algorithm complexity among linear and non-linear data through Spearman rank ability. The demonstration of proposed approached performs on two-bench mark data set: Face96 and Yale Face Database.

关键词： Correlation Mathematical model Feature extraction Face Optimization Random variables Pairwise error probability

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