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SSRN 2022年

作者： Roy, Bishwajit Malviya, Lokesh Mal, Sandip Kumar, Amrendra Kumar, Radhikesh Jahed Armaghani, Danial Kumar, Deepak Banerjee, Avishek School of Computer Science UPES Uttarakhand Dehradun248007 India School of SCSE VIT MP Bhopal466114 India Dept.of CE RIT UK Roorkee247667 India Dept. of CSE NIT Bihar Patna800001 India Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State Universit 76 Lenin Pros ect Chelyabinsk454080 Russia Dept. of CE NIT Bihar Patna800001 India Department of CSBS Asansol Engineering College Asansol713305 India

Due to the intricate nature of erodibility, sensitivity of soil particles and limited amount of data, the consequent modelling of soil wind erodibility is a challenging task for researchers. Therefore, the robust prediction of soil wind erodibility along with the sensitive factors is often an indispensable task. This study analyses portable wind tunnel-based measured wind erosion rate (gm −2 s −1 ) from 118 sites in Kerman Province, southeast Iran where 17 different predictors were considered for each site. This paper investigates the importance of a set of predictors which are most affected by soil erosion rate. Furthermore, this study proposes a novel an optimum feature selection-based heterogeneous ensemble meta-estimator (voting ensemble (VE)) for predicting wind erosion rate. A linear regression-based recursive feature elimination technique with 2-fold cross validation is used to identify the 7significant important predictors, called i) VFS (very fine sand), ii) CS (coarse sand), iii) VCS (very coarse sand), iv) GC (gravel coverage), v) MWD (mean weight diameter), vi) surface crust, and vii) moisture, that affects the soil erodibility rate. Then, a VE with 3 unfitted estimators is proposed, which averages the predictions of singleton estimators to enhance the prediction accuracy. The 3 singleton unfitted estimators, called support vector machine (linear kernel), random forest and gradient boosting machine, are used with grid search approach. The investigation identifies the surface crust as the most effective parameter in predicting soil erosion rate. © 2022, The Authors. All rights reserved.

关键词： Soils

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A spatio-temporal multimedia big data framework for a large crowd

A spatio-temporal multimedia big data framework for a large ...

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IEEE International Conference on Big Data

作者： Bilal Sadiq Faizan Ur Rehman Akhlaq Ahmad Md. Abdur Rahman Sohaib Ghani Abdullah Murad Saleh Basalamah Ahmad Lbath KACST GIS Technology Innovation Center Umm Al Oura University Saudi Arabia Dept. of Computer Science University of Grenoble Alpes France College of Engineering and Islamic Architecture Umm Al Qura University Saudi Arabia Kulliyah of Information and Communication Technology International Islamic University Kuala Lumpur Malaysia College of Computer and Information Systems Umm Al Qura University Saudi Arabia Innovation and Entrepreneurship Institute Umm Al Qura University Saudi Arabia

Crowdsourced multimedia data poses several challenges when it is collected, stored, indexed, retrieved, and visualized. Examples of crowd source multimedia data are social sensors, vehicle sensors, physical sensors, human sensors, etc. Analyzing such multimodal and diversified crowdsourced data provides very rich understanding about the need of individuals within a crowd. Such understanding makes it possible to tailor services to individuals' needs, also called context-aware services. In this paper, we propose a spatial multimedia big data framework that can collect multimedia data from 1) a very large crowd equipped with multi-sensory smartphones, 2) vehicles, and 3) social networks. A set of multimedia services are offered to users to support their spatio-temporal activities. These include but not limited to 1) simple user interfaces to utilize multimedia services for instant guidance, 2) navigation to points of interests (POI), and 3) efficient and cost effective intra-city rides to users. The big data framework is designed to handle a very large number of multimedia spatio-temporal queries in real-time. The system is a pilot project and will be deployed during the event of Hajj 2015 when over three million pilgrims from all over the world will visit Makkah, Saudi Arabia to perform their Hajj rituals.

关键词： Multimedia communication Big data Sensors Streaming media Multimedia databases Crowdsourcing Semantics

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A framework for crowd-sourced data collection and context-aware services in Hajj and Umrah

A framework for crowd-sourced data collection and context-aw...

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ACS/IEEE International Conference on computer systems and Applications

作者： Akhlaq Ahmad Md. Abdur Rahman Faizan Ur Rehman Ahmed Lbath Imad Afyouni Abdelmajid Khelil Syed Osama Hussain Bilal Sadiq Mohamed Ridza Wahiddin College of Engineering and Islamic Architecture Umm Al Qura University Makkah Saudi Arabia Kulliyyah of Information and Communication Technology International Islamic University Malaysia College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Science and Technology Unit Umm Al-Qura University Makkah Saudi Arabia Dept. of Computer Science University of Grenoble Alpes France Umm Al-Qura University Makkah SA TU Darmstadt Germany

We propose a context aware framework that offers a set of cloud-based services to support a very large Hajj and Umrah crowd by capturing their contexts using smartphones. The proposed framework captures the individual's context, provides a set of adapted services, and allows being in touch with a subset of one's community of interest. We leverage the spatiotemporal sensory data captured by our framework to define users' contexts for optimized services. Our proposed framework is also envisioned to assist the Hajj and Umrah authorities to (1) improve Hajj & Umrah documentation, (2) improve Hajj organization through better understanding of pilgrims' (individual and crowd) spatial and temporal behavior and needs, and (3) protect pilgrims' environment through environmental monitoring. In particular, the developed methods, techniques, and algorithms will support the pilgrimage quality of experience. We have tested our system through end-user subjects and due to apply for the upcoming Hajj events. We present our implementation details and the general impression of end users about our system.

关键词： Context Smart phones Social network services Communities Context-aware services Temperature sensors

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Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models

Weight Sparsity Complements Activity Sparsity in Neuromorphi...

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Neuromorphic systems (ICONS), International Conference on

作者： Rishav Mukherji Mark Schöne Khaleelulla Khan Nazeer Christian Mayr David Kappel Anand Subramoney Birla Institute of Technology and Science Pilani - Goa Campus Goa India Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics TU Dresden Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) TU Dresden Dresden Germany Centre for Tactile Internet (CeTI) with Human-in-the-Loop TU Dresden Dresden Germany Ruhr University Bochum Institut für Neuroinformatik Germany Dept. of Computer Science Royal Holloway University of London Egham United Kingdom

ISBN: (数字)9798350368659

ISBN: (纸本)9798350368666

Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient. Event-based architectures such as spiking neural networks (SNNs) naturally exhibit activity sparsity, and many methods exist to sparsify their connectivity by pruning weights. While the effect of weight pruning on feed-forward SNNs has been previously studied for computer vision tasks, the effects of pruning for complex sequence tasks like language modeling are less well studied since SNNs have traditionally struggled to achieve meaningful performance on these tasks. Using a recently published SNN-like architecture that works well on small-scale language modeling, we study the effects of weight pruning when combined with activity sparsity. Specifically, we study the tradeoff between the multiplicative efficiency gains the combination affords and its effect on task performance for language modeling. To dissect the effects of the two sparsities, we conduct a comparative analysis between densely activated models and sparsely activated event-based models across varying degrees of connectivity sparsity. We demonstrate that sparse activity and sparse connectivity complement each other without a proportional drop in task performance for an event-based neural network trained on the Penn Treebank and WikiText-2 language modeling datasets. Our results suggest sparsely connected event-based neural networks are promising candidates for effective and efficient sequence modeling.

关键词： Analytical models computer vision Event detection Neuromorphics Computational modeling Neural networks computer architecture Spiking neural networks Computational efficiency Standards

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Characterization of Laser Inscribed On-Chip Photonic Lanterns with Different Core Distances

Characterization of Laser Inscribed On-Chip Photonic Lantern...

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European Conference on Optical Communication

作者： Justus Bohn Joel Carpenter Simon Gross Michael Withford Jochen Schroder Centre for Ultrahigh bandwidth Devices for Optical Systems (CUDOS) Architecture and Information Technology University of Queensland Australia Dept. of Physics and Astronomy Macquarie University Australia School of Electrical and Computer Engineering RMIT University Melbourne Australia

We measured the wavelength-dependent transfer matrix of multiple photonic lanterns with different core spacings using a spatially diverse swept-wavelength interferometer. We characterised the mode-coupling and mode-de... 详细信息

关键词： Photonics Multiplexing Couplings Wavelength measurement Optical losses Optical interferometry Optical devices

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Inductor Design Optimization Using FEA Supervised Machine Learning

Inductor Design Optimization Using FEA Supervised Machine Le...

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European Conference on Power Electronics and Applications

作者： D. Cajander I. Viarouge P. Viarouge D. Aguglia ENERGY Inst. Haute Ecole d'Ingénieurie et d’Architecture Fribourg HES-SO University of Applied Sciences and Arts Western Switzerland Boulevard de Pérolles 80 Fribourg Switzerland Electrical and Computer Eng. Dept. LEEPCI Lab. Laval University GIK 7P4 Quebec (QC) Canada ELECTROTECHNOLOGIES Selem Inc 2610 Rue Gérard-Lajoie GIP 3G1 Quebec (QC) Canada Accelerator Systems Dept. Electric Power Converter Group CERN - European Organization for Nuclear Research Geneva 23 Switzerland

ISBN: (数字)9789075815399

ISBN: (纸本)9781665487009

An optimal inductor design methodology using dimensioning models derived from Finite Element Analysis (FEA) supervised Artificial Neural Networks (ANN) is presented. The efficiency of such trained ANN dimensioning models in terms of compromise between precision and computing time is demonstrated for the cylindrical inductor topology with air and magnetic material core including saturation.

关键词： Training Analytical models Systematics Network topology Computational modeling Atmospheric modeling Artificial neural networks

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CONCEPTIONAL CONSIDERATIONS FOR A TOWED VESSEL SAFETY KNOWLEDGE SYSTEM

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NAVAL ENGINEERS JOURNAL 1990年第1期102卷 68-73页

作者： LATORRE, R LESAGE, M Dr. Robert Latorre:received a BSE (Naval Architecture and Marine Engineering) in 1971 and an MSC in 1972 from the University of Michigan. After language training at Middle Language College he studied at the University of Tokyo's Department of Naval Architecture where he received a MSE in 1975 and a doctor of engineering degree in 1978. As an assistant professor at the University of Michigan (1979–1983) Dr. Latorre began his study of towed vessel safety and river tow design. This resulted in a multiyear project sponsored by the U.S. Dept. of Transportation on towed vessel safety. This work was continued after he joined the University of New Orleans where he designed a 38.3m × 4.6m × 0.03–2.1m variable depth towing tank for ship offshore and inland waterway hydrodynamic studies. His research interests include cavitation noise high speed craft design and computational fluid dynamics. During the 1986 academic year he taught as a professor at the University of Tokyo's Mechanical Engineering Department. During this time he began to interact with artificial intelligence robotics and computer vision researchers in Japan. Mauricio Le Sage:received his BSC (bachelor's degree) in 1986. He is a graduate student at the University of New Orleans. His research interests are in the areas of artificial intelligence computer graphics and expert systems. At the time of this paper a joint effort entitled “An Inductive Approach To Plagiarism Detection In Computer Programs” by Gerard K. Rambally and Mauricio Le Sage was pending publication.

A new model is introduced for avoiding accidents where a tugboat capsizes or trips when towing a larger vessel. The development of the Tripping Avoidance Knowledge Base (TAKB) is described in terms of the relevant descriptive parameters, the graphical representation of the knowledge base and its conversion to a decision matrix is also shown. As with any AI application in a new and promising area, there are many unclear points which this paper highlights. The paper closes with the authors' recommendations for future development and refinement of the knowledge base.

关键词： Tugboats

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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