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arXiv 2018年

作者： Valadares, Dalton Cézane Gomes da Silva, Matteus Sthefano Leite Brito, Andrey Elísio Monteiro Salvador, Ewerton Monteiro Federal University of Campina Grande Informatics and Electrical Engineering Center Computer Science Campina Grande Paraba Brazil Federal Institute of Pernambuco Mechanical Engineering Department Caruaru Pernambuco Brazil Federal University of Paraíba João Pessoa Paraíba Brazil

The Internet of Things (IoT) field has gained much attention from industry and academia, being the main subject for numerous research and development projects. Frequently, the dense amount of generated data from IoT applications is sent to a cloud service, that is responsible for processing and storage. Many of these applications demand security and privacy for their data because of their sensitive nature. This is specially true when such data must be processed in entities hosted in public clouds, where the environment in which applications run may not be trusted. Some concerns are then raised since it is not trivial to provide the needed protection for these sensitive data. We present a solution that considers the security components of FIWARE and the Intel SGX capabilities. FIWARE is a platform created to support the development of Smart Applications, including IoT systems, and SGX is the Intel solution for Trusted Execution Environment (TEE). We propose a new component for key management that, together with other FIWARE components, can be used to provide privacy, confidentiality, and integrity guarantees for IoT data. A case study illustrates how this proposed solution can be employed in a realistic scenario, which allows the dissemination of sensitive data through public clouds without risking privacy issues. The results of the experiments provide evidence that our approach does not harm scalability or availability of the system. In addition, it presents acceptable memory costs when considering the benefit of the privacy guarantees achieved. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

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Designing ecosystems of intelligence from first principles

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Collective Intelligence 2024年第1期3卷

作者： Karl J Friston Maxwell JD Ramstead Alex B Kiefer Alexander Tschantz Christopher L Buckley Mahault Albarracin Riddhi J Pitliya Conor Heins Brennan Klein Beren Millidge Dalton AR Sakthivadivel Toby St Clere Smithe Magnus Koudahl Safae Essafi Tremblay Capm Petersen Kaiser Fung Jason G Fox Steven Swanson Dan Mapes Gabriel René VERSES Research Lab Los Angeles CA USA (KJF MJDR ABK AT CLB MA RJP CH BK BM DARS TSCS MK SET CP KF JGF SS DM GR) Wellcome Centre for Human Neuroimaging University College London London UK (KJF MJDR) Department of Philosophy Monash University Melbourne VIC Australia (ABK) Department of Informatics University of Sussex Brighton UK (CLB) Department of Computer Science Université du Québec à Montréal Montréal QC Canada (MA) Department of Experimental Psychology University of Oxford Oxford UK (RJP TSCS) Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany (CH) Department of Biology University of Konstanz Konstanz Germany (CH) Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany (CH) Network Science Institute Northeastern University Boston MA USA (BK) Brain Network Dynamics Unit University of Oxford Oxford UK (BM) Department of Mathematics Stony Brook University New York NY USA (DARS) Department of Physics and Astronomy Stony Brook University New York NY USA (DARS) Department of Biomedical Engineering Stony Brook University New York NY USA (DARS) Topos Institute Berkeley CA USA (TSCS) Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands (MK) Department of Philosophy Université du Québec à Montréal Montréal QC Canada (SET)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

关键词： Active inference free energy principle artificial intelligence belief updating belief propagation

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The application of particle swarm optimization using neural network to optimize classification of employee performance assessment

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Mohammad Humam Oman Somantri Maylane Boni Abdillah Syaefani Arif Romadhon Mohammad Khambali Robbi Rahim Department of Computer Engineering Politeknik Harapan Bersama Tegal Indonesia Department of Informatics Politeknik Harapan Bersama Tegal Indonesia Department of Electrical Engineering Politeknik Harapan Bersama Tegal Indonesia Department of Mechanical Engineering Politeknik Harapan Bersama Tegal Indonesia Department of Electrical Engineering Politeknik Negeri Semarang Indonesia Sekolah Tinggi Ilmu Manajemen Sukma Medan Indonesia

The measurement of employee value performance in an institution is very important for the future evaluation and planning. The employee performance assessment absolutely must be done to determine the accomplishments to be achieved by every employee. Policy makers are difficult to determine the optimal classification toward assessment of the performance appraisal. Selection of the best attributes weights in optimizing Neural Network for the performance appraisal that has been not optimal yet and the effect on level of accuracy that is generated. Practitioners of Particle Swarm Optimization (PSO) on Neural Network (NN) are expected to optimize the selection of the best attribute weights to the performance appraisal classification resulting in increased accuracy.

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Retracted: Implementation Method CUSUM to Determine the Accident Prone Areas in Web Based

Retracted: Implementation Method CUSUM to Determine the Acci...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer Science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Dinata, Rozzi Kesuma Abdullah, Dahlan Hartono, H. Erliana, Cut Ita Riasti, Berliana Kusuma Iskandar, Akbar Sari, Ifit Novita Nurmawati, N. Nanuru, Ricardo Freedom Putri, Lusi Dwi Manurung, Rosida Tiurma Sriadhi, S. Saleh, A.A. Astari, T. Khaddafi, Muammar Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics Universitas Sebelas Maret Surakarta Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Departement Of: Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Department of Civil Engineering Universitas Lancang Kuning Pekanbaru Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Elementary School Teacher Education STKIP Citra Bangsa Aceh Utara Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

At the Unit Laka Lantas Polres Lhokseumawe determining accident-prone areas on roads in Lhokseumawe still using manual systems. Determination of accident-prone areas are less effective because the police Unit Laka Lantas Polres Lhokseumawe just using the data of the last year and had to repeatedly calculate manually accident-prone areas based on the number of human casualties. In this study the system implementation CUSUM method (Cummulative Summary) to determine the accident-prone areas designed using the web-based programming language PHP. In this system there are three processes to determine areas prone to accidents, the accident rate calculations based on the weighted severity, blacksite calculations using the Z-Score method for determining areas prone to accident blackspots and calculations using the CUSUM method to determine the critical points of an accident. The data used are secondary data obtained from the Police Unit Laka Lantas Polres Lhokseumawe years 2009-2013. © Published under licence by IOP Publishing Ltd.

关键词： Accidents

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Total Variation L1 Fidelity Salt-and-Pepper Denoising with Adaptive Regularization Parameter 5

Total Variation L1 Fidelity Salt-and-Pepper Denoising with A...

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5th NAFOSTED Conference on Information and computer Science, NICS 2018

作者： Thanh, Dang N. H. Surya Prasath, V.B. Thanh, Le Thi Department of Information Technology Hue College of Industry Hue530000 Viet Nam Division of Biomedical Informatics Cincinnati Childrens Hospital Medical Center CincinnatiOH45229 United States Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Biomedical Informatics College of Medicine University of CincinnatiOH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States Department of Basic Sciences Ho Chi Minh City University of Transport Ho Chi Minh700000 Viet Nam

ISBN: (纸本)9781538679838

Total variation (TV) is an effective tool to solve the image denoising and many other image processing problems. For the denoising problem, it is necessary to create automatic image processing methods based on parameters estimation of the corresponding models. For TV image denoising problem, almost methods focus on TV-L2 norm. The works for parameter estimation of denoising model based on TV-L1 norm is very little. The denoising model with TV-L1 norm is impressive to treat the salt-and-pepper noise. In this paper, we propose a parameter estimation method based on characteristics of the salt-and-pepper noise. This method is especially effective for the images without very high contrast and with high noise level. We will handle the comparison to other salt-and-pepper denoising methods, such as TV-L1 method and the BPDF method to prove the effectiveness of the proposed parameter estimation method for the adaptive TVL1 denoising model. © 2018 IEEE.

关键词： Salt and pepper noise

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The effects of visual and control latency on piloting a quadcopter using a head-mounted display

arXiv

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arXiv 2018年

作者： Zhao, Jingbo Allison, Robert S. Vinnikov, Margarita Jennings, Sion Department of Electrical Engineering and Computer Science York University Toronto Canada Department of Informatics New Jersey Institute of Technology Newark United States Flight Research Laboratory National Research Council Ottawa Canada

Recent research has proposed teleoperation of robotic and aerial vehicles using head motion tracked by a head-mounted display (HMD). First-person views of the vehicles are usually captured by onboard cameras and presented to users through the display panels of HMDs. This provides users with a direct, immersive and intuitive interface for viewing and control. However, a typically overlooked factor in such designs is the latency introduced by the vehicle dynamics. As head motion is coupled with visual updates in such applications, visual and control latency always exists between the issue of control commands by head movements and the visual feedback received at the completion of the attitude adjustment. This causes a discrepancy between the intended motion, the vestibular cue and the visual cue and may potentially result in simulator sickness. No research has been conducted on how various levels of visual and control latency introduced by dynamics in robots or aerial vehicles affect users' performance and the degree of simulator sickness elicited. Thus, it is uncertain how much performance is degraded by latency and whether such designs are comfortable from the perspective of users. To address these issues, we studied a prototyped scenario of a head motion controlled quadcopter using an HMD. We present a virtual reality (VR) paradigm to systematically assess the effects of visual and control latency in simulated drone control scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Helmet mounted displays

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Defect Classification of Printed Circuit Boards based on Transfer Learning

Defect Classification of Printed Circuit Boards based on Tra...

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IEEE Applied Signal Processing Conference (ASPCON)

作者： Barnajit Ghosh M. K. Bhuyan Pradipta Sasmal Yuji Iwahori Prathik Gadde Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati Assam India Department of Computer Science Chubu University Kasugai Japan School of Informatics and Computing Indiana University Purdue University Indianapolis IN USA

ISBN: (纸本)9781538666876

In this paper, we propose a transfer learning based method to classify PCB images into two classes - i) True Defect and ii) Pseudo Defect A pre-trained Inception-V3 model is used for transfer learning and the mid-level representations of the PCB images are extracted from the output of an intermediate layer of the pre-trained model. These mid-level representations of the PCB images are used to train a small adaptation network, which makes the overall network suitable for classification of PCB images. In order to tackle overfitting, regularization strategies are implemented. The results of our experiment, conducted on real world PCB images, show a significant improvement in the classification accuracy as compared to previous work.

关键词： Feature extraction Training Image segmentation Convolution Printed circuits Conferences

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Forecasting intracranial hypertension using multi-scale waveform metrics

arXiv

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arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

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Multi-Objective Design of Compact Microwave Components with Data-Driven Surrogates and Pareto Front Decomposition

Multi-Objective Design of Compact Microwave Components with ...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Adrian Bekasiewicz Slawomir Koziel John W. Bandler Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Engineering Optimization & Modeling Center Reykjavik University Reykjavik Iceland Department of Electrical and Computer Eng. McMaster University Hamilton ON Canada

The paper discusses low-cost multi-objective optimization of compact microwave components using variablefidelity EM simulation models and data-driven surrogates. Our approach builds upon a recently reported method where the initial approximation of the Pareto set is obtained by optimizing the kriging surrogate constructed from sampled data of the coarsediscretization EM model of the structure at hand, with selected designs further refined to obtain the high-fidelity Pareto set. The drawback of the method is a large number of training data samples required to set up the surrogate. Here, considerable savings concerning the training data set size are achieved by Pareto front decomposition based on auxiliary points identified along the front and setting up the kriging models in the corresponding subdomains. The key factor is that the total volume of the sub-domains is considerably smaller than the volume of the original domain. Our considerations are illustrated using a compact rat-race coupler with design optimization cost savings of 29- and 30-percent for two and three sub-domains, respectively.

关键词： Optimization Couplers Training data Microwave theory and techniques Radio frequency Numerical models Microwave FET integrated circuits

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Color Based Person Tracking with Illumination Compensation by using Multiple Statistics of Scene

Color Based Person Tracking with Illumination Compensation b...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Motonori Doi Akihiro Tsujimoto Akira Kimachi Shogo Nishi Norikazu Ikoma Dept. of Telecommunications and Computer Networks Osaka Electro-Communication University Neyagawa Japan Graduate School of Engineering Osaka Electro-Communication University Neyagawa Japan Dept. of Engineering Informatics Osaka Electro-Communication University Neyagawa Japan Dept. of Electrical Electronics and Communication Engineering Nippon Institute of Technology Saitama Japan

ISBN: (纸本)9781538626344;9781538626337

Person tracking is an important technology for several applications of video surveillance, robot vision and human interface. Key features of the person tracking are the shape and color. Especially, color information provides important information to distinguish individuals in scenes including multiple persons. However, color information is influenced by illumination color. In this paper, we propose a novel person tracking method based on color tracking with illumination color compensation. The illumination color is estimated from three statistical features obtained from the whole scene. The scene statistics are an average value, a maximum value and "gray-edge" value that means an average of edge component of the scene. Person is tracked as the concentration of registered colors by using the particle filter. In the proposed method, the positions and velocity of points are defined as a system model of the particle filter. Three likelihoods of each particle are calculated by colors compensated by three scene statistics. Then, the maximum likelihood of three likelihoods is selected as the likelihood of the particle. We conducted experiments for evaluation of the proposed method. The results showed the improvement of the accuracy of tracking in comparing with the previous method that compensated color by only average value of the scene.

关键词： Image color analysis Lighting Target tracking Particle filters Color Image sequences Clothing

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