Large Scale Real Time Research Articles

SHA 512 Enhanced Secure Hash Decentralized Response System

Today a very huge lack of confidence about the current EVM/ballot response system like voting has made voting response collection in any democratic country very condemnatory. People have watched their fundamental rights which are being violated. On the other hand the other digital voting systems are also in danger due to a minimal of transparency. Most of the traditional response systems are not clear enough so as to meet today's requirements; this crucial reason makes it very sweaty for the government to come up with voters' trust. The motive behind the downfall of the traditional and current cyber voting system is that it can be easily compromised due to centralized network. The primary objective here is to counter the issues of the traditional digital voting and such response systems, which has any type of mistrust or inequality during the process of accepting responses through response systems like voting. Blockchain technology can be used in such response systems like voting to have a fair response system and reduce wrongdoing. The current physical response systems like voting have many downs in it as well as the digital voting systems are not up to the mark to be implemented on a huge scale. Such all problems raises the need for a solution to secure the democratic rights of the people. This article put-ups a framework based on up-to-date blockchain algorithm technology that provides high clarity and reliability of the system to construct a healthy relationship between response systems and election authorities. The insisted software provides a framework that can be implemented in real world to conduct voting like procedures digitally through blockchain rather than involving any physical polling stations and centralized ledger. Our proposed framework provides an extendible hashing, by using the latest consensus algorithms. The Secure Hash Algorithms applied in the voting system along with Chain Security makes the response systems more secure. Distributed ledger contracts also provide a protected connection between the user and the consensus network while running a transaction in the chain. The security of the blockchain based response system has also been discussed. In Addition to this, encryption of responses using cryptographic hash functions and prevention of exploitation attacks about 51% on the blockchain has also been elaborated. Also, the procedure for carrying out blockchain transactions during the process of response collection has been elaborated using Blockchain. Finally, the throughput performance evaluation of the proposed system shows that such a system can be deployed in a large-scale real time population.

Diabetes mellitus prediction using machine learning within the scope of a generic framework

<span>Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.</span>

MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data

BackgroundThe rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings.ResultsMeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. Availability: https://skynet.unimi.it/index.php/tools/meltingplot/.ConclusionsThe analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.

Retraction: Andersen S. S. L. Real time large scale in vivo observations reveal intrinsic synchrony, plasticity and growth cone dynamics of midline crossing axons at the ventral floor plate of the zebrafish spinal cord. J. Integr. Neurosci. 18(4), 351-368.

The above-mentioned article (Andersen,2019), published online on December 30, 2019 has been withdrawn by Dr. Andersen. The retraction has been issued following additional information received by IMR Press and reviewed by the Chief Editor.

E-Metering and Fault Detection in Smart Water Distribution Systems using Wireless Network

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB

E-Metering and Fault Detection in Smart Water Distribution Systems using Wireless Network

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB

A Wireless Sensor Network Model for E-Metering and Fault Identification in Smart Water Distribution Systems

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB.

The regulatory actions of retinoic acid on M2 polarization of porcine macrophages

We previously demonstrated that the most bioactive vitamin A metabolite, all-trans retinoic acid (ATRA), increased T helper 2-associated responses induced in pigs by infection with the parasitic nematode Ascaris suum We also showed that ATRA potentiated the mRNA expression of several IL-4 induced chemokines (chemokine (CC motif) ligand 11 [(CCL11), CCL17, CCL22 and CCL26] associated with alternative activation (M2a) in porcine macrophages in vitro. Herein, several mechanisms whereby ATRA affects IL-4 signaling are profiled using large-scale real time PCR and RNA-Seq analysis. Twenty-three genes associated with M2a markers in other species were independently upregulated by both IL-4 and ATRA, including the adenosine receptor A2B (ADORA2B), cysteinyl leukotriene receptor 2 (CYSLTR2) and the vitamin D receptor (VDR). ATRA synergistically enhanced IL-4 up-regulation of Hepatitis A virus cellular receptor 2 (HAVCR2) and transglutaminase 2 (TGM2) and further repressed IL-4 down-regulated CD163 and Cytochrome b-245, beta polypeptide (CYBB) mRNA. Macrophages treated with ATRA exhibited a dose-dependent reduction in phagocytosis of opsonized Staphylococcus aureus. In addition, the combination of IL-4 and ATRA up-regulated the anti-inflammatory protein, IL-1R antagonist (IL1RN) and TGM2. These data indicate that ATRA induces a state of partial alternative activation in porcine macrophages, and amplifies certain aspects of M2a activation induced by IL-4. Given the prevalence of allergic and parasitic diseases worldwide and the close similarities in the porcine and human immune responses, these findings have important implications for the nutritional regulation of allergic inflammation at mucosal surfaces.

Real time large scale in vivo observations reveal intrinsic synchrony, plasticity and growth cone dynamics of midline crossing axons at the ventral floor plate of the zebrafish spinal cord.

How axons are wiring the vertebrate spinal cord has in particular been studied at the ventral floor plate using fixed samples or looking at single growing axons with various microscopy techniques. Thereby may remain hidden important live organismal scale information concerning dynamics and concurrent timing of the many axons simultaneously crossing the floor plate. Here then, applying light-sheet microscopy, axonal growth and guidance at the floor plate are followed in vivo in real time at high resolution along several hundred micrometers of the zebrafish spinal cord by using an interneuron expressing GFP as a model axon. The commissural axons are observed crossing the ventral floor plate midline perpendicularly at about 20 microns/h and in a manner dependent on the Robo3 receptor. Commissural growth rate reaches a minimum at the midline, confirming previous observations. Ipsilateral axons extend concurrently, at three to six times higher growth rates. At guidance points, commissural axons are seen to decrease their growth rate and growth cones increase in size. Commissural filopodia appear to interact with the nascent neural network, and thereby trigger immediate plastic and reversible sinusoidal-shaped bending movements of neighboring commissural shafts. A simple protocol isolating single neuronal cells from the spinal cord is developed to facilitate further molecular characterization. The recordings show the strikingly stereotyped spatio-temporal control that governs midline crossing. The live observations give renewed perspective on the mechanisms of axonal guidance in the spinal cord that provide for a discussion of the current distinction between diffusible long-range versus substrate-bound short-range guidance cues.

Retraction.

Retraction: Søren S. L. Andersen. (2017). Real time large scale in vivo observations reveal intrinsic synchrony, plasticity and growth cone dynamics of midline crossing axons during neuronal wiring of the zebrafish spinal cord. The Journal of Comparative Neurology, doi.org/10.1002/cne.24314 The above-mentioned article, published online on August 30, 2017 in Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the journal Editor-in-Chief, Patrick R. Hof and Wiley Periodicals, Inc. The retraction has been issued following an investigation conducted by Uppsala University and reviewed by the Editor-in-Chief.

MIS-SLAM: Real-Time Large-Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing

Real-time simultaneously localization and dense mapping is very helpful for providing Virtual Reality and Augmented Reality for surgeons or even surgical robots. In this paper, we propose MIS-SLAM: a complete real-time large scale dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB- SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large scale scope localizing and dense mapping in real-time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.

Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access. The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.

A multi-version document storage strategy based on chord in large-scale real time collaborative environments

In the research field of CSCW, the issue of data management is given little attention compared with other issues. Previous studies mainly focus on how to produce multiple versions. As for the large-scale real time collaborative environment, new storage strategy must be devised because: 1) There are many versions due to the multiple conflict operations, which should be saved to reflect the different intentions of the workers; 2) The new-comer may want to retrieve some old versions to catch up with the work process of the team. The paper devises a multi-version document storage model according to the characteristics of the large-scale real time collaborative environment. This strategy makes full use of the participating sites as the expandable storage resources, based on chord ring as the underlying storage structure and integrates the two storage modes: full storage and incremental storage. Besides, this strategy supports the dynamic change of the chord structure. The strategy realises the document access flexibility and reliability in large-scale collaborative environment, and ensures the storage load balancing between storage nodes. The storage schema is realised in the Co-AutoCAD system and experiments analysis is given to show the effectiveness of the schema.

A multi-version document storage strategy based on chord in large-scale real time collaborative environments

In the research field of CSCW, the issue of data management is given little attention compared with other issues. Previous studies mainly focus on how to produce multiple versions. As for the large-scale real time collaborative environment, new storage strategy must be devised because: 1) There are many versions due to the multiple conflict operations, which should be saved to reflect the different intentions of the workers; 2) The new-comer may want to retrieve some old versions to catch up with the work process of the team. The paper devises a multi-version document storage model according to the characteristics of the large-scale real time collaborative environment. This strategy makes full use of the participating sites as the expandable storage resources, based on chord ring as the underlying storage structure and integrates the two storage modes: full storage and incremental storage. Besides, this strategy supports the dynamic change of the chord structure. The strategy realises the document access flexibility and reliability in large-scale collaborative environment, and ensures the storage load balancing between storage nodes. The storage schema is realised in the Co-AutoCAD system and experiments analysis is given to show the effectiveness of the schema.

ATUS-PRO: A FEM-based solver for the time-dependent and stationary Gross–Pitaevskii equation

ATUS-PRO is a solver-package written in C++ designed for the calculation of numerical solutions of the stationary- and the time dependent Gross–Pitaevskii equation for local two-particle contact interaction utilising finite element methods. These are implemented by means of the deal.II library (Bangerth et al., 0000) [1], (Bangerth et al., 2007) [2]. The code can be used in order to perform simulations of Bose–Einstein condensates in gravito-optical surface traps, isotropic and full anisotropic harmonic traps, as well as for arbitrary trap geometries. A special feature of this package is the possibility to calculate non-ground state solutions (topological modes, excited states) (Marojević et al., 2013), (Yukalov et al., 1997, 2004) [3,4] for an arbitrarily high non-linearity term. The solver-package is designed to run on parallel distributed machines and can be applied to problems in one, two, or three spatial dimensions with axial symmetry or in Cartesian coordinates. The time dependent Gross–Pitaevskii equation is solved by means of the fully implicit Crank–Nicolson method, whereas stationary states are obtained with a modified version based on our own constrained Newton method (Marojević et al., 2013). The latter method enables to find the excited state solutions. Program summaryProgram title: ATUS-PROCatalogue identifier: AEZD_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEZD_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 12757No. of bytes in distributed program, including test data, etc.: 412782Distribution format: tar.gzProgramming language: C++.Computer: PCs and distributed memory machines.Operating system: Linux, UNIX.RAM: Depending on the problem; megabytes to gigabytesSupplementary material: A file containing the expected results from the test run can be downloaded.Classification: 4.3, 4.12.External routines: MPI, GSL, LAPACK, P4EST, PETSC, deal.IINature of problem: Solving the Gross–Pitaevskii equation for Bose–Einstein condensates in external traps. Stationary solutions: computation of ground as well as excited states. Real time propagation: calculation of time dependent solutions.Solution method: The method of solving for stationary states is based on an enhanced version of the Newton algorithm developed in [1]. An implicit Crank–Nicolson scheme is used for real-time propagation. Both methods use adaptive finite element methods based on the library deal.II.Restrictions: The one-dimensional programs run only on single core.Additional comments: This package generates 8 executables, (i) breed_1, (ii) breed, (iii) breed_cs, (iv) rtprop_1, (v) rtprop, (vi) rtprop_cs, (vii) gen_params, (viii) gen_params_csRunning time: Depending on size of problem: from seconds (ground state calculations) to minutes (small no. of excited states, short timescale real-time propagation) up to several days (large no. of excited states and large scale real time propagation).

Three Dimensional Hypercube Model and Mechanism

The automated access control mechanism afforded to an automated system control in order to attain the maximum objectives of preserving the confidentiality, integrity, authentication & high availability of information system resources. The risk optimization is the process of identifying vulnerabilities, risk, uncertainties and threats to operating system resources to achieving the maximum business objectives and deciding the maximum counter measures in to optimizing the lowest level of risk. This proposed three dimensional hypercube security models and mechanism is going to be providing high level accountability for individuals who are accessing sensitive information on multiple relation functions, operation and services (RFOS) on multiple application, system software, server and network. This accountability is accomplished through access control mechanisms & services that require Confidentiality, Integrity, Authentication, Access control, Non repudiation, Availability & Privacy through operating system control and audit function. One has to develop the computer algebraic system over a relation, function, operation & services for multiple business, resources and location for risk optimization on complex real time large scale operating system. This paper contributes to the development of an optimization mechanism that objective to determine the optimal cost to be invested into security mechanisms on the measure component of the system security. Furthermore, the model optimize the cost, time & resources is supposed to optimize the system risks and maximize the business throughput and high security system. One has to develop one solution (ACM) for multiple issue which is satisfying to the pervasive and ubiquitous computing based on distributed object oriented system.

3D measures exploitation for a monocular semi-supervised fall detection system

Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. At the same time, it exploits 3D space's measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning approach. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.

Finger knuckle biometrics – A review

Biometric-based personal authentication is receiving a widespread interest in the area of research due to its high applicability in a wide range of security applications. Among these, hand-based biometric systems are considered to be more successful in terms of accuracy and computational complexity. In hand-based biometrics, finger knuckle surface is considered as one of the emerging potential biometric traits for personal authentication. This is due to its stable and unique inherent patterns present in the outer surface of the finger back knuckle region. Further, this finger knuckle has a high potentiality towards discriminating individuals with high accuracy. In this paper, we present a review of various system models that are implemented for personal authentication using finger knuckle biometrics. Furthermore, the challenges that could arise during the implementation of the large scale real time biometric system with finger knuckle print are explored.

Scalable digital spatial light modulator-micromesh heterostructures for real time wave optical applications.

For large-scale real time wave optical applications, we propose and demonstrate scalable simple digital spatial light modulator (SLM)-micromesh (μM) heterostructures, which fully harness ubiquitous well developed consumer information displays for real time large-scale SLMs and advanced patterning technologies for promoting the wave optical properties of SLMs of any size. Weakly diffractive projection mode large-scale SLMs with poor demultiplexity are transformed to highly diffractive mode heterostructures with fine patterned micromeshes as efficient demultiplexers or wave optical promoters. As a result, diffraction efficiency, diffraction angle, demultiplexity, multiplexity, reconstructed image quality and numbers of visibly reconstructed images largely increase even though the pixel pitches of the employed SLMs are many orders of magnitude larger than the wavelength of the illuminating light. The approach shown in this study can be applicable even for any sized weakly diffractive SLMs, and can simultaneously increase the effective spatial bandwidth and the physical dimension required for their wave optical applications. This can't be achieved by presently available SLMs alone.

Parallel and Distributed Code Clone Detection using Sequential Pattern Mining

This research presents a parallel and distributed data mining approach to code clone detection. It aims to prove the value and importance of deploying parallel and distributed computing for real-time large scale code clone detection. It is implemented this approach in a family of clone detectors, called PD EgyCD (Parallel and Distributed Egypt Clone Detector). In this approach, This research builds on an earlier work of the authors for code clone and plagiarism detection using sequential pattern mining by adding parallelism and distribution to our earlier tool EgyCD. Our approach uses data mining through a tailored Apriori-based algorithm for code clone detection. And it uses parallelization and distribution to achieve excellent performance to scale up to clone detection on very large systems. This approach has been implemented as a database application which leverages the capabilities of modern database tools. Two versions have been developed of this distributed technique. The first one uses client-server technique in which all clients and the server deal with only one database. The second one uses agents where each client acts as a separate agent and has its own database and after working on a sub-problem, it submits its partial solution to the server to finally get the complete solution (set of code clones). Experiments show that agents technique is faster than clientserver one. Distribution enhances performance very much. Speed improvement is a function of the number of clients/agents used. Our conclusion is that data mining, combined with parallel and distributed computing, can efficiently be deployed for code clone detection of very large systems.