Remote File Research Articles

Remote File Sharing System for Education Institution

Remote File Sharing System for Education Institution

The effects of document's format, size, and storage media on memory forensics

Main memory or RAM contains volatile but critical data about the system's state and its recent activities. Often, RAM based artifacts are hard to be found elsewhere. Digital investigators can find in this volatile data an essential information about the recent usage of a system including the used documents. Nowadays, documents are often fetched from a variety of storage media, most of which are internet based. This can complicate the digital investigation process due to the remote nature of these storage media; most of these remote files cannot be traced on the local hard disk drive (HDD) of the captured machine. However, whenever the document's contents are successfully recovered from RAM images, it can ensure the actual usage of the document. This paper studies the effects of various storage media (local and remote) on the amount of volatile artifacts of different types of documents. Experiments are designed to evaluate the effects of local hard drives, removable media, and a set of cloud based platforms such as Dropbox, Google Drive, and OneDrive on the RAM based artifacts of a used document. Results show that the recovered contents are significantly affected by the used storage media. Moreover, the document's type has an effect too. Frequently, a good ratio of the document's contents are recovered from RAM even when the document is living on the cloud, the document is closed, and the connection is terminated.

Secure Remote Cloud File Sharing With Attribute-Based Access Control and Performance Optimization

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. The framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on SPM, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experiments indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

ITWIN Technology

The USB (Universal Serial Bus) flash drive is a data storage device. The data on the intermediary server is also stored in cloud storage. However, the USB flash drive has the disadvantage of being too compact in size, making it easily misplaced or lost. If someone else hacks the username and password for cloud storage, data can be misused. iTwin is the ideal solution for all of these issues. It's a secure USB gadget that lets you access, share, and edit any of your files and media between any two online computers in the world. It enables remote file access without compromising security or privacy.

Skyline Batch: An Intuitive User Interface for Batch Processing with Skyline.

Skyline Batch is a newly developed Windows forms application that enables the easy and consistent reprocessing of data with Skyline. Skyline has made previous advances in this direction; however, none enable seamless automated reprocessing of local and remote files. Skyline keeps a log of all of the steps that were taken in the document; however, reproducing these steps takes time and allows room for human error. Skyline also has a command-line interface, enabling it to be run from a batch script, but using the program in this way requires expertise in editing these scripts. By formalizing the workflow of a highly used set of batch scripts into an intuitive and powerful user interface, Skyline Batch can reprocess data stored in remote repositories just by opening and running a Skyline Batch configuration file. When run, a Skyline Batch configuration downloads all necessary remote files and then runs a four-step Skyline workflow. By condensing the steps needed to reprocess the data into one file, Skyline Batch gives researchers the opportunity to publish their processing along with their data and other analysis files. These easily run configuration files will greatly increase the transparency and reproducibility of published work. Skyline Batch is freely available at https://skyline.ms/batch.url.

A fully homomorphism encryption scheme based on LWR

We believe that isomorphic encryption technology can provide strong technical support for users’ privacy protection in a distributed computing environment. There are three types of quasi-homomorphism encryption methods: partial homomorphism encryption, shallow homomorphism encryption, and full homomorphism encryption. homomorphism encryption methods have important applications for ciphertext data computing in distributed computing environments, such as secure cloud computing, fee computing, and remote file storage ciphertext retrieval. It is pointed out that the construction of the homomorphism encryption method is still in the theoretical stage and cannot be used for real high-density data calculation problems. How to design (natural) isomorphic encryption schemes according to algebraic systems is still a challenging research. This question discusses the problem of Learning With Rounding (LWR). Based on the difficulty of LWR, multiple IDs, and attribute categories, a fully homomorphism encryption method corresponding to an ID is proposed. In this paper, in order to reflect the effectiveness of the proposed method, we propose a homomorphism encryption technology based on the password search attribute.

Assessing the Performance Analysis of OSPFV3 and EIGRP in Applications in IPV6 Analysis for Articles Published in Scopus between 2016 and 2021

The Analysis of common conceptual frameworks associated with Performance analysis of OSPFV3 and EIGRP in applications in IPV6 for analysis of articles published in Scopus between 2016 and 2021 by applying the Corresponding method analysis. The number of times an article is downloaded is also being considered as a measurement instrument or method of analysis. The Corresponding analysis method has analysis 117 articles from 2016 to 2021. All the articles are based on performance analysis of OSPFV3 AND IPV6.IPv6 has gained legitimacy and inevitability as a result of the internet's expansion, which has resulted in IPv4 address space exhaustion. An internet next-generation protocol that will replace eventually IPv4 is IPv6. Using Riverbed Modeler Academic Edition, 2state link protocols’ performance for IPv6, IS–IS and OSPFv3 was compared and tested for the greatest commonly utilized applications enterprise for example remote login, database query, file transfer, web surfing, and email. The major characteristics used to assess performance include IPv6 packets dropped, network convergence time, link utilization, throughput, remote login response time, file upload/download response times, http page response times, email, and database query response time,. The primary goal of this dissertation is to compare, simulate, and assess both routing protocols’ performance in order to decide which one is best for routing IPv6 network traffic. Based on the parameters utilized, the protocol that performed better than the others would be suggested for routing network traffic in IPv6. The study was separated into two scenarios to achieve this goal: the IS–IS and OSPFv3 scenario. After the simulation for the IS–IS scenario was completed, the data from both scenarios were compared and examined using the provided parameters to see which protocol worked better. Based on the majority of the simulation parameters employed, the simulation results showed that OSPFv3 was performed as compared toIS–IS.

Performance Evaluation of NVMe-over-TCP Using Journaling File Systems in International WAN

Distributing Big Data for science is pushing the capabilities of networks and computing systems. However, the fundamental concept of copying data from one machine to another has not been challenged in collaborative science. As recent storage system development uses modern fabrics to provide faster remote data access with lower overhead, traditional data movement using Data Transfer Nodes must cope with the paradigm shift from a store-and-forward model to streaming data with direct storage access over the networks. This study evaluates NVMe-over-TCP (NVMe-TCP) in a long-distance network using different file systems and configurations to characterize remote NVMe file system access performance in MAN and WAN data moving scenarios. We found that NVMe-TCP is more suitable for remote data read than remote data write over the networks, and using RAID0 can significantly improve performance in a long-distance network. Additionally, a fine-tuning file system can improve remote write performance in DTNs with a long-distance network.

Exploring Efficient Architectures on Remote In-Memory NVM over RDMA

Efficiently accessing remote file data remains a challenging problem for data processing systems. Development of technologies in non-volatile dual in-line memory modules (NVDIMMs), in-memory file systems, and RDMA networks provide new opportunities towards solving the problem of remote data access. A general understanding about NVDIMMs, such as Intel Optane DC Persistent Memory (DCPM), is that they expand main memory capacity with a cost of multiple times lower performance than DRAM. With an in-depth exploration presented in this paper, however, we show an interesting finding that the potential of NVDIMMs for high-performance, remote in-memory accesses can be revealed through careful design. We explore multiple architectural structures for accessing remote NVDIMMs in a real system using Optane DCPM, and compare the performance of various structures. Experiments are conducted to show significant performance gaps among different ways of using NVDIMMs as memory address space accessible through RDMA interface. Furthermore, we design and implement a prototype of user-level, in-memory file system, RIMFS, in the device DAX mode on Optane DCPM. By comparing against the DAX-supported Linux file system, Ext4-DAX, we show that the performance of remote reads on RIMFS over RDMA is 11.44 higher than that on a remote Ext4-DAX on average. The experimental results also show that the performance of remote accesses on RIMFS is maintained on a heavily loaded data server with CPU utilization as high as 90%, while the performance of remote reads on Ext4-DAX is significantly reduced by 49.3%, and the performance of local reads on Ext4-DAX is even more significantly reduced by 90.1%. The performance comparisons of writes exhibit the same trends.

AN EXTENSION OF CITYJSON TO SUPPORT POINT CLOUDS

Abstract. The combination between dense point clouds and 3D vector objects permits new cartographic representation of urban information. This paper proposes an extension for the CityJSON encoding to support point clouds. Following the 3.0 CityGML specifications, attributes and features are added to the core module of v1.0.1 CityJSON. Two solutions are proposed: inline complex geometries and external link to a remote file. The extended schema can be illustrated in four scenarios: detailed features visualization, fall-back solution in features reconstruction processes, simulating urban climate represented as vector fields, and true-to-life representation solution for complex elements such as solitary vegetation objects. It permits 3D city modelers to handle points clouds in a native way reducing files size and avoiding redundancy. All developments and documentation are available open-source.

Incident Command Futurism

ABSTRACT The availability of integrated remote sensing platforms and digital data collection and sharing tools is changing spill response. These tools facilitate a more effective and rapid decision-making process that can increase resource protection, improve responder safety, and reduce response costs. Early detection and response are a key to preventing smaller incidents from becoming larger. A variety of innovative tools now exist or are in development that could assist facilities and responders in the early stages and throughout an incident event to reduce human, environmental, and economic impacts. Real time field data collection for key parameters such as oil thickness and trajectory, SCAT data, oiled wildlife details, and resources at risk identification, allows for more robust data to be shared rapidly throughout the response operation. This information facilitates more effectively targeted deployment and re-deployment of human and mechanical response assets, and more immediate assessment of both environmental impacts and cleanup progress. Auto-population of incident command system forms as well as better document sharing and document retention through remote and cloud-based file saving platforms can improve the administrative and functional aspects of the response, contributing to enhanced efficiency. The ability to identify and effectively respond to rapidly changing circumstances provides the Unified Command with new tools to make better decisions and keep the public informed of progress. This paper considers new and emerging technologies as they may be applied to the work in a unified command setting, and how they may allow us to compress the operational period for decision-making and action, more accurately and more rapidly understand and share the spill response status, and how we may be able to enhance responder safety and recognize cost savings. We also consider some inherent risks associated with reliance on integrated technologies and digital information and will offer suggestions for drills and exercises to test and optimize these tools.

Fine-grained data caching approaches to speedup a distributed RDataFrame analysis

Thanks to its RDataFrame interface, ROOT now supports the execution of the same physics analysis code both on a single machine and on a cluster of distributed resources. In the latter scenario, it is common to read the input ROOT datasets over the network from remote storage systems, which often increases the time it takes for physicists to obtain their results. Storing the remote files much closer to where the computations will run can bring latency and execution time down. Such a solution can be improved further by caching only the actual portion of the dataset that will be processed on each machine in the cluster, reusing it in subsequent executions on the same input data. This paper shows the benefits of applying different means of caching input data in a distributed ROOT RDataFrame analysis. Two such mechanisms will be applied to this kind of workflow with different configurations, namely caching on the same nodes that process data or caching on a separate server.

Bases epistemológicas da educação matemática crítica: impressões de pedagogos em formação

A docência nos anos iniciais para o ensino da Matemática ainda é um desafio e ainda se observa lacunas de investigação a respeito. Esse cenário gerou a motivação desse estudo, porém sendo o tema amplo, delimitou-se para este artigo, contribuições de grupo de estudos sobre o ensino e a aprendizagem da Matemática sob o ponto de vista da Educação Matemática Crítica. Para este artigo, o objetivo é divulgar o que já foi realizado, por meio de troca de ideias e estudos colaborativos, sobre a proposta da Educação Matemática Crítica nos anos iniciais. A pesquisa de iniciação científica, em andamento, se dá com discentes de Pedagogia, Administração e egresso de Licenciatura em Matemática e Sistemas de Informação da Faculdade Estácio de Vitória, Espírito Santo. É formativa, exploratória descritiva e de caráter qualitativa. Os dados são coletados a partir de gravação de vídeos dos encontros remotos, fichamento e resenha de artigos, assim como levantamento e apresentação de práticas pedagógicas. Os resultados iniciais apontam para propostas de práticas docentes que já refletem os estudos iniciais da Educação Matemática Crítica.

Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows

In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the open-source pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source.

Mass storage interface LTSM for FAIR Phase 0 data acquisition

Since 2018 several FAIR Phase 0 beamtimes have been operated at GSI, Darmstadt. Here the new challenging technologies for the upcoming FAIR facility shall be tested while various physics experiments are performed with the existing GSI accelerators. One of these challenges concerns the performance, reliability, and scalability of the experiment data storage. Raw data as collected by event building software of large scale detector data acquisition has to be safely written to a mass storage system like a magnetic tape library. Besides this long term archive, it is often required to process this data as soon as possible on a high performance compute farm. The C library LTSM (“Lightweight Tivoli Storage Management”) has been developed at the GSI IT department based on the IBM TSM software. It provides a file API that allows for writing raw listmode data files via TCP/IP sockets directly to an IBM TSM storage server. Moreover, the LTSM library offers Lustre HSM (“Hierarchical Storage Management”) capabilities for seamlessly archiving and retrieving data stored on Lustre file system and TSM server. In spring 2019 LTSM has been employed at the FAIR Phase 0 beamtimes at GSI. For the HADES experiment LTSM was implemented into the DABC (“Data Acquisition Backbone Core”) event building software. During the 4 weeks of Ag+Ag@1.58 AGeV beam, the HADES event builders have transferred about 400 TB of data via 8 parallel 10 GbE sockets, both to the TSM archive and to the “GSI green cube” HPC farm. For other FAIR Phase 0 experiments using the vintage MBS (“Multi Branch System”) event builders, an LTSM gateway application has been developed to connect the legacy RFIO (“Remote File I/O”) protocol of these DAQ systems with the new storage interface.

Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows

In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the opensource pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source. It is successfully used in tt̄H cross section measurements and searches for di-Higgs boson production with the CMS experiment.

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals.

Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.

Smart Governance With Cloud Computing

A typical comprehension of is constantly advancing, and the phrasing and was begat for what happens when services and applications are moved into the internet that is cloud. Cloud processing is not something that abruptly seemed overnight; in some structure it may follow back to a period when computer system frameworks remotely time-shared registering assets and applications. All the more presently however, cloud computing alludes to the a wide range of sorts of applications and services being conveyed in the cloud and the way that, much of the time, the devices used to get to these services don't require any unique applications. With the help of cloud computing, you can utilize programming conveyed through the Internet on the pro gram with no installation, have an application on the Internet, set up your own storage of remote file and database framework and that's just the beginning. Cloud itself is a virtualisation of resources like servers, networks, applications, services and data storage) and allows on-demand access for the users. Due to numerous advantages of cloud computing - government sector is also affected. In this paper I give an overview, how cloud computing grows in government sector across the globe.

Data prefetching and file synchronizing for performance optimization in Hadoop-based hybrid cloud

Driven by the technical factors such as system reliability, bandwidth constraints, data confidentiality and security, as well as the economic factors such as initial capital expenditure and re-occurring operating expenditure, today's cloud computing tends to adopt hybrid cloud model. However, because hybrid clouds scale both numerically and geographically, the network delay becomes the main constraint in remote file system access. To hide network latency and reduce job completion time in Hadoop-based hybrid cloud data access, a scheduling-aware data prefetching scheme to enhance non-local map task's data locality in Hadoop-based centralized hybrid cloud (CHCDLOS-Prefetch) and a file synchronizing method to decrease job execution delay in Hadoop-based distributed hybrid cloud (DHCDLO-Sync) are proposed. In the former, input data for non-local map tasks are fetched ahead of time to target compute nodes by making use of idle network bandwidth. In the latter, considered from job level scheduling, data files with high popularity are proactively synchronized beforehand among sub-clouds to strength intra sub-cloud data locality in distributed hybrid cloud. Extensive experimental results illustrate that compared to the Capacity, the Fair and the DARE algorithms, our proposed algorithms improve hybrid cloud performance more significantly in data locality and job completion time.

An Explainable Password Strength Meter Addon via Textual Pattern Recognition

Textual passwords are still dominating the authentication of remote file sharing and website logins, although researchers recently showed several vulnerabilities about this authentication mechanism. When a user creates or changes a password, a website usually leverages a password strength meter (PSM for short) to show the strength of the password. When the password is evaluated as a weak one, the user may replace the password with a stronger or securer one. However, the user is usually confused when the password, especially a frequently used password, is shown as a weak one. We argue that an explainable password strength meter addon, which could show the reasons of weak, may help users to more effectively create a secure password. Unfortunately, we find few sites in Alexa global top 100 showing these details. Motivated to help users with an explainable PSM, this paper proposes an addon to PSMs providing feedbacks in the form of pattern passwords explaining why a password is weak. This PSM addon can detect twelve types of patterns, which cover a very large proportion among 70 million of leaked real passwords from high-profile websites. According to our evaluation and user study, our PSM addon, which leverages textual pattern passwords, can effectively detect these popular patterns and effectively help users create securer passwords.