scholarly journals TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

2021 ◽  
Vol 11 (22) ◽  
pp. 10803
Author(s):  
Jiagang Song ◽  
Yunwu Lin ◽  
Jiayu Song ◽  
Weiren Yu ◽  
Leyuan Zhang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Location Based Services ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Geographical Information ◽  
Superior Performance ◽  
Hybrid Index ◽  
High Level ◽  
Hash Codes

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

scholarly journals Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval

2021 ◽  
Vol 11 (18) ◽  
pp. 8769
Author(s):  
Jun Long ◽  
Longzhi Sun ◽  
Liujie Hua ◽  
Zhan Yang
Keyword(s):  
Code Generation ◽  
Hash Function ◽  
Large Scale ◽  
Semantic Information ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Coordinate Descent Algorithm ◽  
Hash Code ◽  
Benchmark Datasets ◽  
Hash Codes

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods.

Performance on an Adaptive Automated Team Tracking Task with Human and Computer Teammates

1997 ◽  
Vol 41 (1) ◽  
pp. 551-555 ◽  
Author(s):  
Kristin Krahl ◽  
Mark W. Scerbo
Keyword(s):  
Adaptive Systems ◽  
High Performance ◽  
Vertical Axis ◽  
Tracking Task ◽  
Superior Performance ◽  
Performance Skills ◽  
Group Assignment ◽  
Pursuit Tracking ◽  
Human Participants ◽  
High Level

The present study examined team performance on an adaptive pursuit tracking task with human-human and human-computer teams. The participants were randomly assigned to one of three team conditions where their partner was either a computer novice, computer expert, or human. Participants began the experiment with control over either the horizontal or vertical axis, but had the option of taking control of their teammate's axis if they achieved superior performance on the previous trial. A control condition was also run where a single participant controlled both axes. Performance was assessed by RMSE scores over 100 trials. The results showed that performance along the horizontal axis improved over the session regardless of the experimental condition, but the degree of improvement was dependent upon group assignment. Individuals working alone or paired with an expert computer maintained a high level of performance throughout the experiment. Those paired with a computer-novice or another human performed poorly initially, but eventually reached the level of those in the other conditions. The results showed that team training can be as effective as individual training, but that the quality of training is moderated by the skill level of one's teammate. Moreover, these findings suggest that task partitioning of high performance skills between a human and a computer is not only possible but may be considered a viable option in the design of adaptive systems.

scholarly journals CENTIMETRE-ACCURACY IN FORESTS AND URBAN CANYONS – COMBINING A HIGH-PERFORMANCE IMAGE-BASED MOBILE MAPPING BACKPACK WITH NEW GEOREFERENCING METHODS

2020 ◽  
Vol V-1-2020 ◽  
pp. 333-341
Author(s):  
S. Blaser ◽  
J. Meyer ◽  
S. Nebiker ◽  
L. Fricker ◽  
D. Weber
Keyword(s):  
Web Services ◽  
High Performance ◽  
Large Scale ◽  
Superior Performance ◽  
City Centre ◽  
Visual Localization ◽  
Mobile Mapping ◽  
Coordinate Measurement ◽  
Collaborative Processes ◽  
Mobile Mapping System

Abstract. Advances in digitalization technologies lead to rapid and massive changes in infrastructure management. New collaborative processes and workflows require detailed, accurate and up-to-date 3D geodata. Image-based web services with 3D measurement functionality, for example, transfer dangerous and costly inspection and measurement tasks from the field to the office workplace. In this contribution, we introduced an image-based backpack mobile mapping system and new georeferencing methods for capture previously inaccessible outdoor locations. We carried out large-scale performance investigations at two different test sites located in a city centre and in a forest area. We compared the performance of direct, SLAM-based and image-based georeferencing under demanding real-world conditions. Both test sites include areas with restricted GNSS reception, poor illumination, and uniform or ambiguous geometry, which create major challenges for reliable and accurate georeferencing. In our comparison of georeferencing methods, image-based georeferencing improved the median precision of coordinate measurement over direct georeferencing by a factor of 10–15 to 3 mm. Image-based georeferencing also showed a superior performance in terms of absolute accuracies with results in the range from 4.3 cm to 13.2 cm. Our investigations showed a great potential for complementing 3D image-based geospatial web-services of cities as well as for creating such web services for forest applications. In addition, such accurately georeferenced 3D imagery has an enormous potential for future visual localization and augmented reality applications.

scholarly journals Performance Analysis of Specification Computer and Mobile with Implementation Tawaf Virtual Reality using A* Algorithm and RVO System

2019 ◽  
Vol 7 (1) ◽  
pp. 55-70
Author(s):  
Moh. Zikky ◽  
M. Jainal Arifin ◽  
Kholid Fathoni ◽  
Agus Zainal Arifin
Keyword(s):  
Virtual Reality ◽  
High Performance ◽  
Large Scale ◽  
3D Models ◽  
A Algorithm ◽  
Virtual Reality Technology ◽  
Performance Technology ◽  
Outer Line ◽  
High Performance Computer ◽  
High Level

High-Performance Computer (HPC) is computer systems that are built to be able to solve computational loads. HPC can provide a high-performance technology and short the computing processes timing. This technology was often used in large-scale industries and several activities that require high-level computing, such as rendering virtual reality technology. In this research, we provide Tawaf’s Virtual Reality with 1000 of Pilgrims and realistic surroundings of Masjidil-Haram as the interactive and immersive simulation technology by imitating them with 3D models. Thus, the main purpose of this study is to calculate and to understand the processing time of its Virtual Reality with the implementation of tawaf activities using various platforms; such as computer and Android smartphone. The results showed that the outer-line or outer rotation of Kaa’bah mostly consumes minimum times although he must pass the longer distance than the closer one.  It happened because the agent with the closer area to Kaabah is facing the crowded peoples. It means an obstacle has the more impact than the distances in this case.

scholarly journals Cloud Computing Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Yassine Sabri ◽  
Aouad Siham
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Processing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Remote Sensing Data ◽  
Cloud Service ◽  
Intermediate Data ◽  
High Level

Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform.

scholarly journals Discriminative Deep Hashing for Scalable Face Image Retrieval

2017 ◽  
Author(s):  
Jie Lin ◽  
Zechao Li ◽  
Jinhui Tang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Prediction Errors ◽  
Unified Framework ◽  
Multi Scale ◽  
Deep Hashing ◽  
Hash Codes

With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.

scholarly journals Granular layEr Simulator: Design and Multi-GPU Simulation of the Cerebellar Granular Layer

2021 ◽  
Vol 15 ◽  
Author(s):  
Giordana Florimbi ◽  
Emanuele Torti ◽  
Stefano Masoli ◽  
Egidio D'Angelo ◽  
Francesco Leporati
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Granular Layer ◽  
Graphics Processing Unit ◽  
Mossy Fibers ◽  
Processing Unit ◽  
Large Network ◽  
Processing Times ◽  
3D Space ◽  
High Level

In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-scale networks, where neurons are described by highly complex mathematical models. These aspects strongly increase the computational load of the simulations, which can be efficiently performed by exploiting parallel systems to reduce the processing times. Graphics Processing Unit (GPU) devices meet this need providing on desktop High Performance Computing. In this work, authors describe a novel Granular layEr Simulator development implemented on a multi-GPU system capable of reconstructing the cerebellar granular layer in a 3D space and reproducing its neuronal activity. The reconstruction is characterized by a high level of novelty and realism considering axonal/dendritic field geometries, oriented in the 3D space, and following convergence/divergence rates provided in literature. Neurons are modeled using Hodgkin and Huxley representations. The network is validated by reproducing typical behaviors which are well-documented in the literature, such as the center-surround organization. The reconstruction of a network, whose volume is 600 × 150 × 1,200 μm3 with 432,000 granules, 972 Golgi cells, 32,399 glomeruli, and 4,051 mossy fibers, takes 235 s on an Intel i9 processor. The 10 s activity reproduction takes only 4.34 and 3.37 h exploiting a single and multi-GPU desktop system (with one or two NVIDIA RTX 2080 GPU, respectively). Moreover, the code takes only 3.52 and 2.44 h if run on one or two NVIDIA V100 GPU, respectively. The relevant speedups reached (up to ~38× in the single-GPU version, and ~55× in the multi-GPU) clearly demonstrate that the GPU technology is highly suitable for realistic large network simulations.

Efficient Multimedia Similarity Measurement Using Similar Elements

2019 ◽  
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Xinpan Yuan ◽  
Longzhi Sun
Keyword(s):  
Large Scale ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Similarity Measurement ◽  
Visual Words ◽  
Indexing Structure ◽  
Measurement Framework ◽  
The Arts ◽  
Definition Of ◽  
Similar Elements

Online social networking techniques and large-scale multimedia retrieval are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data as well. This trend has put forward higher requirements and greater challenges on massive multimedia retrieval. In this paper, we investigate the problem of image similarity measurement, which is one of the key problems of multimedia retrieval. Firstly, the definition of similarity measurement of images and the related notions are proposed. Then, an efficient similarity measurement framework is proposed. Besides, we present a novel basic method of similarity measurement named SMIN. To improve the performance of similarity measurement, we carefully design a novel indexing structure called SMI Temp Index (SMII for short). Moreover, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that the proposed approach outperforms state-of-the-arts.

Identifying Distinctive Features of Productive and Socially Efficient Schools

2019 ◽  
Vol 7 (4) ◽  
pp. 15-23
Author(s):  
Марина Матюшкина ◽  
Marina Matyushkina ◽  
Константин Белоусов ◽  
Konstantin Belousov
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Empirical Studies ◽  
Basic Research ◽  
Successful Schools ◽  
Distinctive Features ◽  
Effi Ciency ◽  
Level Of Use ◽  
High Level ◽  
State Examination

The article presents the results of a series of empirical studies devoted to the analysis of the relationship between school performance (according to the Unified State Examination criterion), its social efficiency (according to the criterion of the frequency of student circulation to tutors) and various social and pedagogical characteristics of the school. A correlation analysis was carried out on an array of data obtained over 5 years of regular comprehensive surveys in schools of St. Petersburg. The sets of signs that are most characteristic for schools with high performance and for schools with high social effi ciency are identified and described. Distinctive features of successful schools are associated with a high level of use of tutoring services by students, with good material and technical conditions, teachers’ competence in the use of design and research methods, etc. In socially eff ective schools, the achievement of students’ academic results is based on the use of their own school strengths — teachers’ potential, innovative technologies with large-scale attraction of the Internet and electronic resources. The study was carried out with the fi nancial support of the Russian Foundation for Basic Research in the framework of the scientifi c project “Signs of an eff ective school in conditions of the mass distribution of tutoring practices” No. 19-013-00455.

scholarly journals From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

2013 ◽  
Vol 21 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
Marek Blazewicz ◽  
Ian Hinder ◽  
David M. Koppelman ◽  
Steven R. Brandt ◽  
Milosz Ciznicki ◽  
...  
Keyword(s):  
Code Generation ◽  
High Performance ◽  
Large Scale ◽  
Einstein Equations ◽  
Efficient Manner ◽  
Code Transformations ◽  
Wide Range ◽  
Problem Description ◽  
Physics Model ◽  
High Level

Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, theChemoraframework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretization is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.

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