File Upload Research Articles

TEACHING BUSINESS ENGLISH ONLINE. ASSIGNMENT ACTIVITIES AND TESTS IN MOODLE

In many universities around the world, the current pandemic situation confined the academic process to online interactions alone, with no physical presence in a class, the ‘face-to-face’ classes being held by means of applications or programs that provide video conferences facilities. Besides these tools for live communication, most universities – among which ours, as well – have started to make extensive use of the e-learning Moodle platform. Especially designed for the distance learning form of education, the Moodle platform has previously been used in our university mostly by the teachers involved in the distance learning form, particularly for the purpose of providing the distance learning students with study materials and other resources of interest for the subject – through the resources uploaded on the pages of the discipline -, for communication – by means of forums and chat rooms -, and for enabling a few tests and assignments along the semester. Now, with everything going online, the vast range of facilities this application offers has come to be discovered by all academics involved in the instruction process. In both synchronous and asynchronous activities organised by means of the platform, the user will find useful a series of facilities offered by the platform. Among these, the current study will refer to several diverse possibilities of using the activities of Assignment and Test for the classes of English for Business, with a series of examples of exercises that were created by means of these Moodle tools. The regular file upload assignment in which the student were instructed what to prepare, compose or solve, and upload at the specific button has now, with experience, ‘grown’ obsolete, more interesting online text types of assignment being preferred, such as video or audio records, texts written directly online in the applications or others types following templates and using materials posted. As for the tests, besides the basic types – more straightforward and easy to create – such as: multiple choice drills, matching, true / false, short answers, or drag and drop, there will be exemplifications of the embedded answers (cloze) type, which requires audacity and willingness from the teacher to put in more effort to prepare and self-instruct on how to use and enter special codes in order to create complex exercises. Needs teach us and eventually the results prove indeed rewarding and worth the effort.

Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in the number of user equipment has strengthened public concerns on human exposure to radio-frequency electromagnetic fields (RF EMFs). This requires an assessment and monitoring of RF EMF exposure, in an almost continuous way. Particular interest goes to the uplink (UL) exposure, assessed through the transmission power of the mobile phone, due to its close proximity to the human body. However, the UL transmit (TX) power is not provided by the off-the-shelf modem and RF devices. In this context, we first conduct measurement campaigns in a multi-floor indoor environment using a drive test solution to record both downlink (DL) and UL connection parameters for Long Term Evolution (LTE) networks. Several usage services (including WhatsApp voice calls, WhatsApp video calls, and file uploading) are investigated in the measurement campaigns. Then, we propose an artificial neural network (ANN) model to estimate the UL TX power, by exploiting easily available parameters such as the DL connection indicators and the information related to an indoor environment. With those easy-accessed input features, the proposed ANN model is able to obtain an accurate estimation of UL TX power with a mean absolute error (MAE) of 1.487 dB.

Engineering in Crisis – Critical Reflection Writing Prompt

Writing Prompt sent to the International Engineering, Social Justice, and Peace community and other engineering education sub-communitiess (primarily in North America:
 
 
 
 Our objective is to capture your thoughts, experiences, and responses to intersecting crises of COVID-19, white supremacy, anti-blackness, police violence, late capitalism, technologies and engineerings, power formations, state violence, academia, and engineering education over the past year.
 We wish to break the mould and create a space for the entire engineering community - students, educators, and professionals to share varied perspectives. Being oral history, this project is free from the usual academic barriers or gatekeeping. No citations needed if you do not wish to do so.
 While we aim to keep editorial interference at a minimum, we do not intend to include entries that (in our aesthetic and axiological judgement) can cause significant structural, cultural, or emotional harm to marginalised communities. We recognise that such filtering is hard to fully specify. The "objectives" statement above could be a guide for providing you a sense for what we are looking for. Entries should align with IJESJP's focus on engendering dialog on engineering practices that enhance gender, racial, class, and cultural equity and are democratic, non-oppressive, and non-violent. We acknowledge that even this filter limits the expression of particular forms of knowing and being.
 Our commitments are available here: http://esjp.org/about-esjp/our-commitments
 We are inspired by the way stories are told and archived through oral history, and feel the need to capture these stories before they become lost in the flux of our ongoing crises. Such history can be a story, anger and frustrations through rant, back of the envelope ideas and theories, poems, prose, fiction, critiques. This history is anything and everything you wish to document in time.
 Instructions: Please provide the following information by August 15th, 2021.
 
 Entry.
 Title, optional
 File upload, optional.
 Name, gender pronouns, and affiliations of authors
 Do you want your submission anonymous?
 
 
 

Performance of gender detection tools: a comparative study of name-to-gender inference services.

Objective:To evaluate the performance of gender detection tools that allow the uploading of files (e.g., Excel or CSV files) containing first names, are usable by researchers without advanced computer skills, and are at least partially free of charge.Methods:The study was conducted using four physician datasets (total number of physicians: 6,131; 50.3% female) from Switzerland, a multilingual country. Four gender detection tools met the inclusion criteria: three partially free (Gender API, NamSor, and genderize.io) and one completely free (Wiki-Gendersort). For each tool, we recorded the number of correct classifications (i.e., correct gender assigned to a name), misclassifications (i.e., wrong gender assigned to a name), and nonclassifications (i.e., no gender assigned). We computed three metrics: the proportion of misclassifications excluding nonclassifications (errorCodedWithoutNA), the proportion of nonclassifications (naCoded), and the proportion of misclassifications and nonclassifications (errorCoded).Results:The proportion of misclassifications was low for all four gender detection tools (errorCodedWithoutNA between 1.5 and 2.2%). By contrast, the proportion of unrecognized names (naCoded) varied: 0% for NamSor, 0.3% for Gender API, 4.5% for Wiki-Gendersort, and 16.4% for genderize.io. Using errorCoded, which penalizes both types of error equally, we obtained the following results: Gender API 1.8%, NamSor 2.0%, Wiki-Gendersort 6.6%, and genderize.io 17.7%.Conclusions:Gender API and NamSor were the most accurate tools. Genderize.io led to a high number of nonclassifications. Wiki-Gendersort may be a good compromise for researchers wishing to use a completely free tool. Other studies would be useful to evaluate the performance of these tools in other populations (e.g., Asian).

Erratum: Chromosome Screening of Human Preimplantation Embryos by Using Spent Culture Medium: Sample Collection and Chromosomal Ploidy Analysis.

An erratum was issued for:Chromosome Screening of Human Preimplantation Embryos by Using Spent Culture Medium: Sample Collection and Chromosomal Ploidy Analysis. The Protocol and Representaive Results sections wereupdated. In the Protocol, step 3.8.2 was updated from: After logging into the system, click Create Submission under the NICS tab. Then, select the sequencing platform, choose ChromInst for the reagent, enter the project information in the box under Project ID, set the analysis preferences and upload the files. Once all sequencing files are successfully uploaded, click Submit to start the analysis (Figure 3A). to: After logging into the system, click Create Submission under the NICS-A tab. Then, choose NGS for the platform, select corporation, choose ChromInst for the reagent, enter the project information in the box under Project ID, set the analysis preferences and upload the files. Once all sequencing files are successfully uploaded, click Submit to start the analysis (Figure 3A). In the Representative Results, Figure 3 was updated from: Figure 3. Data Analysis. (A) The page of Create Submission. There are different options for the user application. For sequencing platform, users can choose Illumina or Ion Torrent. For analysis criterion, there are two length detection resolution for selection, the whole chromosome and whole arm level. The users also can choose whether the mosaicism or gender information is reported. Finished the above parameter setting,click on the box under File upload and choose the appropriate sequencing files to upload. For Illumina, choose the files with an extension of fastq.gz. For Ion Torrent platform, choose files with an extension of bam. Click Submit to start the analysis after successfully upload. (B) The view of summary table. The summary table consists of following information: Sample Name: The name of each NICS sample is listed; Data QC: Indicates whether the sequencing file passes the QC for NICS analysis; Conclusion: Indicates whether the NICS analysis is normal or abnormal, "N/A" indicates no conclusive result is available; Gender: If the user chooses to report the sex information, this column will appear in the summary table; Karyotype: Shows the analysis results; CNV plot (Whole Genome): View the CNV profiles of all chromosomes; CNV plot (By Chromosome): View the CNV profiles of each chromosome. (C) The Save Report Page. Click Export report button next to the Summary of Results. Select the information you want to show on the final report and click Export. Select Save File in the appearing dialog window and then click OK. The reports will be saved to the Download folder of the computer. Please click here to view a larger version of this figure. to: Figure 3. Data Analysis. (A) There are different options for the user application. For sequencing platform corporation, users can choose Illumina, Ion Torrent or MGI. The users can choose whether the gender information is reported. Finished the above parameter setting, click on the box under File upload and choose the appropriate sequencing files to upload. For Illumina, choose the files with an extension of fastq.gz. Click Submitto start the analysis after successfully upload. (B) The view of summary table. The summary table consists of following information: Sample Name: The name of each NICS sample is listed; Data QC: Indicates whether the sequencing file passes the QC for NICS analysis; AI Rating: The rating (A, B or C) for each NICS sample; AI_Rating Interpretation: Evaluation of embryo implantation potential; AI Grading: The score for each NICS sample; CNV plot (Whole Genome): View the CNV profiles of all chromosomes; (C) The Save Report Page. Click Export reportbutton next to the Summary of Results. Select the information you want to show on the final report and click Export. The reports will be saved to the Downloadfolder of your computer. Please click here to view a larger version of this figure.

A Case Study of Publishing Internal APIs to External Users

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

A Set of Simple Tools For Assembling, Annotating, Versioning and Publishing Taxonomies

A Set of Simple Tools For Assembling, Annotating, Versioning and Publishing Taxonomies

Your mileage may vary: impact of data input method for a deep learning bone age app’s predictions

The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods. The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic. Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively. Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

Application of Hadoop Distributed Cluster in Big Data

Hadoop is the core technology of large data storage. Distributed data storage based on Hadoop technology plays a central role in large data storage. This paper introduces the basic concepts of Hadoop platform, focusing on the analysis of HDFS and MapReduce. Then, the process of building Hadoop cluster environment is described in detail, and Hadoop is applied to a file publishing system. Finally, time-consuming comparisons of file upload operations are made for different orders of files in different clusters [1].

Sistem Ujian Online Essay Kolektif Berbasis Gambar Studi Kasus Kampus PTKI Medan

The Covid-19 pandemic is a disaster for all of us, Vocational Education is also affected by the Covid-19 pandemic which forces online lectures, one of which is at the Industrial Chemical Technology Polytechnic (PTKI), in the implementation of the exam at PTKI Medan carried out collectively with questions exam in the form of an essay. There have been many studies on online essay exams but the scope of the discussion is for automatic correction or similarity based on text input. In vocational education, especially PTKI Medan, which has a test that is not limited to text input but there are calculations. Another weakness of text input is that the answers can be more easily copied and pasted by students. This research was carried out for 1 year starting from Odd semester 2020 to Even 2020. The method in developing information systems uses the waterfall model with the stages of needs analysis, system design, coding, implementation and the last stage of system maintenance. From the results of the research in the odd semester of 2020, the researchers used a text-based online essay exam model, but many weaknesses were found, one of which was the ease of students in using answers from the internet which were directly entered into the answer column. At the maintenance stage, the researcher updates the system by changing the image-based exam model, so students must write their answers on paper and then upload the answers to the website. The file upload system can overload the server if it is not controlled, one of the techniques used is to compress image files uploaded by students to the system and the maximum size can be compressed up to 78.48% from 1329 Kb to 286 Kb for this type. png data. In addition, to make it easier for lecturers to check answers, use the ezoom.js feature as a means to view answers in the form of images that are useful for zooming in, zooming out, rotating the answer images. Ezoom.js is a javascript that can be installed on php-based websites

GenoVault: a cloud based genomics repository

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user’s permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

REDCap on FHIR: Clinical Data Interoperability Services

BackgroundDespite widespread use of electronic data capture (EDC) systems for research and electronic health records (EHR), most transfer of data between EHR and EDC systems is manual and error prone. Increased adoption of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) application programming interfaces (APIs) in recent years by EHR systems has increased the availability of patient data for external applications such as REDCap. ObjectiveDescribe the development of the REDCap Clinical Data Interoperability Services (CDIS) module that provides seamless data exchange between the REDCap research EDC and any EHR system with a FHIR API. CDIS enables end users to independently set up their data collection projects, map EHR data to fields, and adjudicate data transfer without project-by-project involvement from Health Information Technology staff. MethodsWe identified two use cases for EHR data transfer into REDCap. Clinical Data Pull (CDP) automatically pulls EHR data into user-defined REDCap fields and replaces the workflow of having to transcribe or copy and paste data from the EHR. Clinical Data Mart (CDM) collects all specified data for a patient over a given time period and replaces the process of importing EHR data for registries from research databases. With an iterative process, we designed our access control, authentication, variable selection, and mapping interfaces in such a way that end users could easily set up and use CDIS. ResultsSince its release, the REDCap CDIS has been used to pull over 19.5 million data points for 82 projects at Vanderbilt University Medical Center. Software and documentation are available through the REDCap Consortium. ConclusionsThe new REDCap Clinical Data and Interoperability Services (CDIS) module leverages the FHIR standard to enable real-time and direct data extraction from the EHR. Researchers can self-service the mapping and adjudication of EHR data into REDCap. The uptake of CDIS at VUMC and other REDCap consortium sites is improving the accuracy and efficiency of EHR data collection by reducing the need for manual transcription and flat file uploads.

Research on Load Balancing of Cloud Storage Server Based on Fully Connected Group

The scheduling problem of online cloud storage traffic is a hot topic in current network research. Aiming at the scheduling problem of file upload requests in online cloud storage systems, the existing schemes rarely meet the requirement of providing users with different bandwidth guarantees. One of the purposes of using multiple volume servers is to let multiple volume servers share the system load evenly and avoid the system performance bottleneck. Once the load is aggregated and one of the multiple volume servers is overloaded, the volume server will become the system bottleneck again, which will greatly affect the overall performance of the system. On the basis of analyzing the existing scheduling algorithms of server clusters, this paper proposes a dormant scheduling algorithm of fully connected groups, which can automatically equip servers with I D, redirect tasks according to the actual processing capacity, and ensure load balance by unlimited increase. The experimental results show that the load balancing strategy among multi-volume servers ensures the overall reading and writing performance of the system, and effectively improves the I/O throughput and I/O times per second.

Deep Learning Frameworks for Cervical Mobilization Based on Website Images

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of ‘Download All Images’ and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

Analysis of e-learning user satisfaction itb stikom bali using end user computing satisfaction (eucs) method

The development of internet technology has spurred the emergence of various new applications that support activities in various fields, including in the field of education such as e-learning. ITB STIKOM Bali has its own developed e-learning system. In e-learning, there are several navigation menus such as course information, meeting descriptions, file uploads, discussion activities, and others that are prepared for students as users. This study aims to determine the level of satisfaction of e-learning users. The level of satisfaction was measured using the End User Computing Satisfaction (EUCS) method involving 177 students as respondents who were randomly selected. The data collection method in this study used a questionnaire. From the results of the analysis of the data that has been collected, the following results are obtained: Based on the results of the calculation of the average level of satisfaction, the value is 4.54 on the content variable, the accuracy variable is 4.48, the shape variable is 4.53. on the timeliness variable, the score was 4.5, the user convenience variable scored 4.51, and when combined with the satisfaction level according to Kaplan and Norton, it could be concluded that the level of user satisfaction with e-learning was included in the very satisfied category.

EFURMS An Efficient Scheme for File Upload and Ranked Multi-Keyword Search over Encrypted Data in Cloud

Cloud based services provide scalable storage capacities and enormous computing capability to enterprises and individuals to support big data operations in different sectors like banking, scientific research and health care. Therefore many data owners are interested to outsource their data to cloud storage servers due to their huge advantage in data processing. However, as the banking and health records usually contain sensitive data, there are privacy concerns if the data gets leaked to un-trusted third parties in cloud storage. To protect data from leakage, the widely used technique is to encrypt the data before uploading into cloud storage servers. The traditional methods implemented by many authors consumes more time to outsource the data and searching for a document is also time consuming. Sometimes there may be chances of data leakage due to insufficient security. To resolve these issues, in the current VP Search(VPS) scheme is implemented, which provides features like verifiability of search results and privacy preservation. With its features the current system consumes more time for file uploading and index generation, which slows down the searching process. In the existing VPS scheme time minimization to efficiently search for a particular document is a challenging task on the cloud. To resolve all the above drawbacks, we have designed an index generation scheme using a tree structure along with a search algorithm using Greedy Depth-first technique, that reduces the time for uploading files and file searching time. The newly implemented scheme minimizes the time required to form the index tree file for set of files in the document which are to be uploaded and helps in storing the files in a index tree format. These techniques result in reducing the document upload time and speeding up the process of accessing data efficiently using multi-keyword search with top-‘K’ value.

Proxy-Oriented Multiple File Uploading and Integrity Checking of files in Multi-Cloud Environment

Cloud servers have become an option for the organizations to store their files and data. Security in the hybrid and multi-cloud environment is always a challenge to be considered. Client can access the cloud servers to upload the files and documents. In a particular time, when cloud servers restrict access to client for storing the files, then the client can assign its proxy to process the files and upload them. Contrarily, integrity checking is also a significant security issue. It allows customers to verify if their outsourced data is kept intact without the entire data being downloaded. In Hybrid and Multi Cloud environments, a new proxy-oriented multiple file uploading and integrity checking model is proposed to resolve security problems. This concrete safety protocol is built with the aid in the Multi-Cloud Environment.

Improving Data Services of Mobile Cloud Storage with Support for Large Data Objects using OpenStack Swift

Providing data services support for large file upload and download is increasingly vital for mobile cloud storage. There is an increase in mobile users whose data access trends show more access and large file sharing. It is a challenging task for Mobile Application Developers to handle upload and retrieve large files to/from a mobile app because of difficulties with latency, bandwidth, speed, errors, and disruptions to service in a wireless mobile environment. Some scenarios require these large files to be used offline, sometimes to be updated by a single user, and sometimes be shared among all other users. The Wireless mobile environment must consider mobile user’s constraints, such as frequent disconnections and low bandwidth, which affect the ability to handle data and transactions management. The primary objective of this study is to propose a cloud-based Mobile Sync service (sometimes referred as Mobile backend as a Service) with OpenStack Swift object storage to manage large objects efficiently using two main techniques of segmentation and object chunking with compression in a mobile cloud environment. This work further contributed to a prototype implementation of the proposed framework and provides Application Programming Interface (API) consisting of Create, Read, and Delete queries and chunking operations and a lightweight sync protocol that can manage large file synchronization and access. The experimental findings with object-chunking tested size settings show that the proposed Mobile Sync framework can accommodate large files ranging from 100MB to 1GB and provides a decrease in upload/download synchronization times of 63.203% / 92.987%percent as compared to other frameworks.

The jPOST Repository as a Public Data Repository for Shotgun Proteomics.

In recent years, mass spectrometry-based proteomics approach has made significant progress and the number of datasets related to various proteomics projects has increased worldwide. To promote the sharing and reuse of promising datasets, it is important to build an appropriate, high-quality public data repository. For this purpose, several repositories have already been created. The jPOST repository that we developed in 2016 has successfully implemented several unique features, such as fast file upload, flexible file management, and an easy-to-use interface. In addition, this repository is an official member of the ProteomeXchange Consortium established to facilitate standard data submission and global dissemination of mass spectrometry proteomics data. Our repository contributes to the global partnership for sharing and storing all the datasets related to various proteomics experiments.

A Traffic Analysis on Serverless Computing Based on the Example of a File Upload Stream on AWS Lambda

The shift towards microservisation which can be observed in recent developments of the cloud landscape for applications has led towards the emergence of the Function as a Service (FaaS) concept, also called Serverless. This term describes the event-driven, reactive programming paradigm of functional components in container instances, which are scaled, deployed, executed and billed by the cloud provider on demand. However, increasing reports of issues of Serverless services have shown significant obscurity regarding its reliability. In particular, developers and especially system administrators struggle with latency compliance. In this paper, following a systematic literature review, the performance indicators influencing traffic and the effective delivery of the provider’s underlying infrastructure are determined by carrying out empirical measurements based on the example of a File Upload Stream on Amazon’s Web Service Cloud. This popular example was used as an experimental baseline in this study, based on different incoming request rates. Different parameters were used to monitor and evaluate changes through the function’s logs. It has been found that the so-called Cold-Start, meaning the time to provide a new instance, can increase the Round-Trip-Time by 15%, on average. Cold-Start happens after an instance has not been called for around 15 min, or after around 2 h have passed, which marks the end of the instance’s lifetime. The research shows how the numbers have changed in comparison to earlier related work, as Serverless is a fast-growing field of development. Furthermore, emphasis is given towards future research to improve the technology, algorithms, and support for developers.