Data Fetching Research Articles

OSM Sidewalkreator: A QGIS plugin for an automated drawing of sidewalk networks for OpenStreetMap

Sidewalks are a relevant part of the living space in urban environments but are still rarely mapped. In recent years, the mapping of sidewalks has grown in importance among the OSM and academic communities as a matter of concern for many UN SDGs. To cover this gap, we propose a GitHub-hosted, fully open-source QGIS Plugin entitled "OSM SidewalKreator" to automatically draw the geometries of sidewalks for OSM crossings and curb crossing interfaces. The plugin workflow encompasses the steps of input area selection; data fetching, data cleaning, sidewalk geometries generation; crossings and kerbs generation; optional sidewalk splitting; and data exporting. Furthermore, the tool gives the user the capacity to have control over the process. Our tests revealed that the proposed method embodied by the plugin surpasses the manual process in many contexts, highlighting completeness, topological and thematic accuracies. We conclude that deepening, improving, and increasing the amount of open sidewalk mapping, mainly in the widely available OSM, can be a valuable asset to improve the development of accessibility and mobility worldwide.

Analysis of Sindh School Monitoring System Smartphone App of Government Schools of Sindh

The attendance of employees is crucial in both public and private organizations as it indicates their dedication. In the schools of Government of Sindh the monitoring of attendance of teachers has become increasingly challenging, for that, the Government of Sindh has introduced an android based smart phone app called the Sindh School Monitoring Management System (SSMS app) in April 2016 to collect, analyze and disseminate the real-time data regarding school, teachers and students. The app was implemented in 15 districts in the first phase and has since been expanded to cover the entire province, resulting in improved teachers attendance, education quality and school monitoring at government schools. Previously, attendance was tracked manually, which was not secured and had flaws such as teachers not adhering to policy for signature, timing and signing in advance. While the SSMS app has improved the performance, it still faces implementation and design challenges. This research examines the SSMS app in detail, highlighting its weaknesses and aiming to improve its efficiency through a questionnaire designed with input from experts. An analysis is presented based on the feedback of monitoring assistants (MAs) on the indicators such as teacher’s attendance, punctuality, multi modal representation of SSMS, monitoring, data collection, data loading, data fetching.

Performance of an openEHR based hospital information system

BackgroundA desirable feature of hospital information systems is interoperability, which is generally quite limited due to the lack of standardization of the data model. This results in high development and maintenance costs for such systems. The openEHR standard addresses this problem. Due to its two-level modelling, it allows the separation of demographic and medical data and the storage of this data so that it can be easily processed and exchanged. However, it introduces an additional software layer that may affect system performance. This article examines the performance of a system based on the openEHR standard and compares it with the performance of a proprietary system developed in a classic way. MethodsTwo hospital information systems with the same functionality were designed and developed. One was based on an openEHR server, and another was using proprietary data model having both demographic and medical data. Systems were deployed on Azure platform and load tests using JMeter were conducted to calculate statistics of elapsed time of requests as well as throughput of both systems. ResultsEndpoints which fetch only demographic data had the same performance, but when medical data had to be queried, a decrease in performance of the openEHR based system was noticed. The system based on a proprietary data had about 6 times bigger throughput in terms of medical data fetching. ConclusionsOpenEHR adds another layer to the architecture of a hospital information system which might result in performance issues. Such a system must be designed to operate on a sufficiently strong architecture if it is intended to serve many users.

MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data.

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. A lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power, and large delays in delivery of results. We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies, and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets.

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented, which predicts the pedestal heights and widths of electron density ( $n_{\mathrm {e}}$ ) and electron temperature ( $T_{\mathrm {e}}$ ) profiles as well as the separatrix $n_{\mathrm {e}}$ from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The one modeling framework for integrated tasks (OMFIT) framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train ML models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal component analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current ( $I_{\mathrm {p}}$ ), toroidal magnetic field ( $B_{\phi }$ ), neutral beam heating power ( $P_{{\mathrm {NBI}}}$ ), and shaping quantities. The deep jointly informed neural networks (DJINNs) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple linear regression (LR) analysis, indicating nonlinear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

SLID: Exploiting Spatial Locality in Input Data as a Computational Reuse Method for Efficient CNN

Convolutional Neural Networks (CNNs) revolutionized computer vision and reached the state-of-the-art performance for image processing, object recognition, and video classification. Even though CNN inference is notoriously compute-intensive, as convolutions account for >90% of the total operation tasks, the ability to tradeoff between accuracy, performance, power, and latency to meet target application makes it an open research topic. This paper proposes the Spatial Locality Input Data (SLID) method for computational reuse during the inference stage for a pre-trained network. The method exploits input data spatial locality via skipping partial processing of the multiply-and-accumulate (MAC) operations for adjacent data and equating its value to previously computed ones. SLID improves the throughput of resource-constrained devices (Internet-of-Things, edge devices) and accelerates computations during the inference phase by reducing the number of MAC operations. Such approximate computing schema does not require a similarity quantification step nor any modification for the training stage. The computational data reuse was evaluated on three well-known distinctive CNN structures and data sets with alternating layer selections: LeNet, CIFAR-10, and AlexNet. The computational data reuse method saves up to 34.9%, 49.84%, and 31.5% of MAC operations while reducing the accuracy by 8%, 3.7%, and 5.0% for the three models mentioned earlier, respectively. Besides, the proposed method saves on memory access by eliminating data fetching of skipped inputs. Furthermore, filter size, strides, and padding on the accuracy and savings of operations are analyzed. SLID is the first work to exploit the input spatial locality for savings on CNN convolution operations with minimal accuracy loss and without memory or computational overhead. This makes it a great option to support intelligence at the edge.

Gigabit Security Switches Deployment - A Review

With the increasing users and services over the Internet, people demand faster data fetching and more reliable network services. However, the increasing network services and equipment causes the network management much more difficult. This is largely due to the increased size of networks and the requirement for increasingly faster and more efficient networks. In this paper, we discuss high available (HA) architecture for cost effective security switches. Security switch contains of a security switch engine (SSE) and a traditional layer-2 switch. They examine the packet content. These two components connected through a Gigabit Ethernet link. In this paper, we also discuss the types of switches. However, the majority of network administrators currently deploying gigabit networks will find that they are seeing far less than a full 100 Mbps of traffic moving over the wire. Even a standard PC with an off-the-shelf gigabit network card will be able to cope quite easily.

GEMM Optimization for a Decoupled Access/Execute Architecture Processor

General dense matrix multiplication operation (GEMM) has a great impact on the performance of supercomputers. The typical blocking algorithm of GEMM divides the operation in to several levels. Because of the limitation of data transmission, previous works only focuses on optimizing the lowest level of the GEMM based on generalpurpose processors. However, the Decoupled Access/Execute architecture (DAE) processor has enhanced the ability of data fetching. This paper will introduce several methods for optimizing GEMM based on a DAE processor. The Execute Processors (EP) of the platform is made up by Direct Register Access (DRA) and Direct Cache Access (DCA), which can be used to manage the data transport between registers, cache and memory. This paper pays more attention on optimizing the high levels of the blocking GEMM. The GEMM kernel based on the DAE processor was divided into 4 levels, and several levels of the new algorithm are capable to self-adjust. And the performance of our algorithm was effectively improved.

A Hardware Implementation to Compute Displacement of Moving Object in a Real Time Video

This paper presents the implementation of FPGA based real time object tracking system that is targeted to Spartan-6 IVK board. The presented work combines both hardware and software flow where the complex parts of the algorithm such as computing displacement is designed to HW modules. Other part of the algorithm such as video data fetching, window based weight calculation is targeted to software. In our work, we are using standard video format having resolution of 1280x720. The main idea of this work is to accelerate the performance of the object detection system by partitioning the algorithm between dedicated hardware IP core and software.

Distributed Shared Memory for Roaming Large Volumes

We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.

High Efficiency Counter Mode Security Architecture via Prediction and Precomputation

Encrypting data in unprotected memory has gained much interest lately for digital rights protection and security reasons. Counter Mode is a well-known encryption scheme. It is a symmetric-key encryption scheme based on any block cipher, e.g. AES. The schemeýs encryption algorithm uses a block cipher, a secret key and a counter (or a sequence number) to generate an encryption pad which is XORed with the data stored in memory. Like other memory encryption schemes, this method suffers from the inherent latency of decrypting encrypted data when loading them into the on-chip cache. One solution that parallelizes data fetching and encryption pad generation requires the sequence numbers of evicted cache lines to be cached on-chip. On-chip sequence number caching can be successful in reducing the latency at the cost of a large area overhead. In this paper, we present a novel technique to hide the latency overhead of decrypting counter mode encrypted memory by predicting the sequence number and pre-computing the encryption pad that we call one-time-pad or OTP. In contrast to the prior techniques of sequence number caching, our mechanism solves the latency issue by using idle decryption engine cycles to speculatively predict and pre-compute OTPs before the corresponding sequence number is loaded. This technique incurs very little area overhead. In addition, a novel adaptive OTP prediction technique is also presented to further improve our regular OTP prediction and precomputation mechanism. This adaptive scheme is not only able to predict encryption pads associated with static and infrequently updated cache lines but also those frequently updated ones as well. Experimental results using SPEC2000 benchmark show an 82% prediction rate. Moreover, we also explore several optimization techniques for improving the prediction accuracy. Two specific techniques, Two-level prediction and Context-based prediction are presented and evaluated. For the two-level prediction, the prediction rate was improved from 82% to 96%. With the context-based prediction, the prediction rate approaches 99%. Context-based OTP prediction outperforms a very large 512KB sequence number cache for many memory-bound SPEC programs. IPC results show an overall 15% to 40% performance improvement using our prediction and precomputation, and another 7% improvement when context-based prediction techniques is used.