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Hybrid architectures for Large-Scale Blended Classrooms: A Case Study 9

Hybrid Architectures for Large-Scale Blended Classrooms: A C...

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9th International conference on Computing, Communication and networking Technologies, ICCCNT 2018

作者： Kumar, R Ajith Soman, T Suchith Narayanan, Sankaran Bijlani, Kamal Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9781538644300

Blended Classrooms (BC) have become popular in the milieu of higher education in India with several Indian Massive open Online Course (MOOC) initiatives adopting this format as well. The size of BC participants is growing at a much rapid pace, of the order of several hundred, to even a few thousand students. Numerous challenges need to be reckoned with in the design of interactive virtual classrooms (VCs) for such large-scale BCs: network bandwidth, server scalability, reliable access to Information and Communications Technologies (ICT) platforms. This paper presents a hybrid terrestrial-satellite multicast internet architecture to address some of these challenges developed under the aegis of Amrita Virtual Interactive e-Learning World's (A-VIEW) BC research. A-VIEW service integrates terrestrial Internet Protocol (IP) networks and satellite networks using overlay multicast network architecture, to ensure reliable experience. The bandwidth-sensitive multicast framework fosters server scalability. The wider reachability and consistent in-class experience of the proposed architecture were validated in several real-world VC trials. © 2018 ieee.

关键词： Scalability

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Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning

Small Steps and Giant Leaps: Minimal Newton Solvers for Deep...

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International conference on Computer Vision (ICCV)

作者： Joao Henriques Sebastien Ehrhardt Samuel Albanie Andrea Vedaldi University of Oxford Visual Geometry Group University of Oxford

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in SGD. No estimate of the Hessian is maintained. We first validate our method, called CURVEBALL, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly-generated architectures.

关键词： Machine learning Optimization Jacobian matrices Computational modeling Linear programming Stochastic processes Convergence

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Lightweight and efficient octave convolutional neural network for fire recognition

Lightweight and efficient octave convolutional neural networ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Angel Ayala Estanislau Lima Bruno Fernandes Byron L. D. Bezerra Francisco Cruz Escola Politécnica de Pernambuco Universidade de Pernambuco Recife Brasil School of Information Technology Deakin University Geelong Australia

ISBN: (数字)9781728156668

ISBN: (纸本)9781728156675

Fire recognition from visual scenes is a demanding task due to the high variance of color and texture. In recent years, several fire-recognition approaches based on deep learning methods have been proposed to overcome this problem. However, building deep convolutional neural networks usually involves hundreds of layers and thousands of channels, thus requiring excessive computational cost, and a considerable amount of data. Therefore, applying deep networks in real-world scenarios remains an open challenge, especially when using devices with limitations in hardware and computing power, e.g., robots or mobile devices. To address this challenge, in this paper, we propose a lightweight and efficient octave convolutional neural network for fire recognition in visual scenes. Extensive experiments are conducted on FireSense, CairFire, FireNet, and FiSmo datasets. In overall, our architecture comprises fewer layers and fewer parameters in comparison with previously proposed architectures. Experimental results show that our model achieves higher accuracy recognition, in comparison to state-of-the-art methods, for all tested datasets.

关键词： Convolution Image color analysis Task analysis Computer architecture Accidents Training Convolutional neural networks

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RESTful Interfaces for Application Initiated D2D Communications 10

RESTful Interfaces for Application Initiated D2D Communicati...

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10th International conference on Ubiquitous and Future networks (ICUFN)

作者： Pencheva, Evelina Atanasov, Ivaylo Kireva-Mihova, Denitsa Trifonov, Ventsislav Tech Univ Sofia Fac Telecommun Sofia Bulgaria

ISBN: (纸本)9781538646465

Device-to-Device (D2D) communications feature ultra low latency and efficient resource utilization. D2D communications may be empowered by Multi-access Edge Computing (MEC) which brings cloud capabilities for storage and processing into radio access network. In this paper, we present an approach to define RESTful interfaces for open access to proximity-based service, which may be used by mobile edge applications to initiate device proximity discovery and direct communications. The approach is illustrated by typical use cases, data model, interface definition and proximity-based service state models, which are formally described and verified.

关键词： Multi-access Edge Computing Traffic Offloading Application programming Interfaces Finite State Machines

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Cascaded CNN Method for Far Object Detection in Outdoor Surveillance 14

Cascaded CNN Method for Far Object Detection in Outdoor Surv...

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14th International conference on Signal Image Technology & Internet Based Systems (SITIS)

作者： Ghahremani, Amir Bondarev, Egor de With, Peter H. N. Eindhoven Univ Technol Video Coding & Architectures Res Grp VCA Eindhoven Netherlands

ISBN: (纸本)9781538693858

In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and Fl-measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.

关键词： Far object detection Cascaded CNNs Vessel detection Maritime surveillance

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Behavioral Modeling of Power Amplifiers With Modern Machine Learning Techniques

Behavioral Modeling of Power Amplifiers With Modern Machine ...

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ieee MTT-S International Microwave Workshop Series on 5G Hardware and System Technologies (IMWS-5G)

作者： Sener Dikmese Lauri Anttila Pablo Pascual Campo Mikko Valkama Markku Renfors Ericsson AB Stockholm Sweden Electrical Engineering Faculty of Information and Communication Sciences Tampere University Tampere Finland

ISBN: (数字)9781728131436

ISBN: (纸本)9781728131443

In this study, modern machine learning (ML) methods are proposed to predict the dynamic non-linear behavior of wideband RF power amplifiers (PAs). Neural networks, k- nearest neighbor, and several tree-based ML algorithms are first adapted to handle complex-valued signals and then applied to the PA modeling problem. Their modeling performance is evaluated with measured data from two basestation PAs. Gradient boosting is seen to outperform the other ML approaches and to give comparable performance to the generalized memory polynomial (GMP) reference model in terms of both the normalized mean squared error (NMSE) and adjacent channel error power ratio (ACEPR). This is the first study int he open literature to consider modern ML approaches, besides neural networks, for PA behavioral modeling.

关键词： Behavioral modeling power amplifiers generalized memory polynomial machine learning neural networks gradient boosting tree based approaches decision tree Power amplifiers behavioral models Neural network Decision Trees Artificial Intelligence Machine Learning Computer programming Languages content-based image retrieval ecosystem-based approach Art Therapy Modeling

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Code Generation for Multiprocessor Distributed Computing Systems 21

Code Generation for Multiprocessor Distributed Computing Sys...

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21st conference of open-Innovations-Association (FRUCT)

作者： Volkov, Victor Ivanova, Vera Syschikov, Alexey St Petersburg State Univ Aerosp Instrumentat St Petersburg Russia

ISBN: (纸本)9789526865324

Distributed computing systems is a well-known instrument to rise system performance and avoid bottlenecks of shared memory architectures. In some cases, distributed architectures are used in embedded systems: to simplify every single embedded processor, decrease power consumption, increase system tolerance and predictability, etc. However, when working with such systems, software developers face all kinds of complications related to distributed systems programming: workload distribution, communications implementation, computations synchronization etc. For embedded distributed systems it's impossibility to use distributed programming approaches like MPI: they are too "heavy" for embedded systems. We propose the extended application of VIPE visual programming technology that covers a software development for distributed multi-processor systems. VPL program structure and extensible code generation backend allows to automate most aspects of distributed programming and apply a single VPL programs both to shared memory and distributed systems without additional programming effort.

关键词： programming Embedded systems Instruments System performance Memory architecture

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Control Plane architectures Enabling Transport network Adaptive and Autonomic Operation 19

Control Plane Architectures Enabling Transport Network Adapt...

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19th International conference on Transparent Optical networks (ICTON)

作者： Casellas, Ramon Vilalta, Ricard Mayoral, Arturo Martinez, Ricardo Munoz, Raul Contreras, Luis M. CERCA CTTC Castelldefels Spain Telefon Global CTO Unit Madrid Spain

ISBN: (纸本)9781538608593

The maturity and flexibility of Software Defined networking principles, favouring centralized control deployments, featured application programming interfaces and the development of a related application ecosystem, has paved the way for the design and implementation of advanced and adapted control plane architectures that specially target and enable efficient and massive monitoring and data collection. In this line, the steady increase in the use of open and standard interfaces and data modelling languages, as well as the wide adoption (in a vendor independent way) of model-driven solutions and a unified approach for data collection and processing facilitates shifting the focus to the actual processing of information, towards autonomic networks and self-* capabilities. Scoped to the control of a flexi-grid optical transport network, and in line with major industry trends, in this paper we cover our requirements for efficient data collection and processing, and we propose and detail a control and management architecture, building on the concepts of front-end and back-end entities, dynamic instantiation of control plane functions within the ETSI NFV framework and the applicability to use cases such as in-operation network planning, on-demand network optimization and parameter tuning.

关键词： SDN/NFV control of optical networks active stateful PCE front-end/back-end architectures in operation network planning on-demand control functions service function placement

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Review of programming and Performance Optimization on CPU-MIC Heterogeneous System

Review of Programming and Performance Optimization on CPU-MI...

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International conference on Image, Vision and Computing (ICIVC)

作者： Cheng Chen Fujun Yang Fang Wang Liang Deng Dan Zhao Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang Sichuan China

ISBN: (纸本)9781538649923

Heterogeneous architectures are widely adopted in high performance computing. The MIC (Many Integrated Cores) processor, unveiled by Intel, has been widely used and draws attention by simplifying heterogeneous programming and improving performance. In this paper, we first introduce the architecture and executing models of MIC. Then we discuss programming and performance optimization on MIC system. Finally, some open issues and future directions in the heterogeneous system are discussed.

关键词： Microwave integrated circuits programming Computer architecture Computational modeling Optimization Task analysis Central Processing Unit

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Towards Statistical Machine Learning for Edge Analytics in Large Scale networks: Real-Time Gaussian Function Generation with Generic DSP 1

Towards Statistical Machine Learning for Edge Analytics in L...

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1st International Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Oyekanlu, Emmanuel Onidare, Samuel Oladele, Paul Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Univ Lancaster Dept Comp & Commun Lancaster England Lulea Univ Technol Dept Comp Sci & Space Tech Lulea Sweden

ISBN: (纸本)9789531842358

The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based s

关键词： digital signal processor powerline communication smart grid machine learning open-source embedded C big data

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