Configuration Method of AWS Security Architecture That Is Applicable to the Cloud Lifecycle for Sustainable Social Network

Recently, due to the many features and advantages of cloud computing, “cloud service” is being introduced to countless industries around the world at an unbelievably rapid pace. However, with the rapid increase in the introduction of cloud computing services, security vulnerabilities are increasing and the risk of technology leakage from cloud computing services is also expected to increase in social network service. Therefore, this study will propose an AWS-based (Amazon Web Services) security architecture configuration method that can be applied for the entire life cycle (planning, establishment, and operation) of cloud services for better security in AWS Cloud Services, which is the most used cloud service in the world. The proposed AWS security guide consists of five different areas, Security Solution Selection Guide, Personal Information Safeguard Guide, Security Architecture Design Guide, Security Configuration Guide, and Operational Security Checklist, for a safe social network. The AWS Security Architecture has been designed with three reference models: Standard Security Architecture, Basic Security Architecture, and Essential Security Architecture. The AWS Security Guide and AWS Security Architecture proposed in this paper are expected to help many businesses and institutions that are hoping to establish and operate a safe and reliable AWS cloud system in the social network environment.

You Don't Need an App—Conducting Mobile Smoking Research Using a Qualtrics-Based Approach

With the increasing availability of smartphones, many tobacco researchers are exploring smartphone-delivered mobile smoking interventions as a disseminable means of treatment. Most effort has been focused on the development of smartphone applications (apps) to conduct mobile smoking research to implement and validate these interventions. However, developing project-specific smartphone apps that work across multiple mobile platforms (e.g., iOS and Android) can be costly and time-consuming. Here, using a hypothetical study, we present an alternate approach to demonstrate how mobile smoking cessation and outcome evaluation can be conducted without the need of a dedicated app. Our approach uses the Qualtrics platform, a popular online survey host that is used under license by many academic institutions. This platform allows researchers to conduct device-agnostic screening, consenting, and administration of questionnaires through Qualtrics's native survey engine. Researchers can also collect ecological momentary assessment data using text messaging prompts with the incorporation of Amazon Web Services' Pinpoint. Besides these assessment capabilities, Qualtrics has the potential for delivering personalized behavioral interventions through the use of JavaScript code. By customizing the question's web elements in Qualtrics (e.g., using texts, images, videos, and buttons), researchers can integrate interactive web-based interventions and complicated behavioral and cognitive tasks into the survey. In conclusion, this Qualtrics-based methodology represents a novel and cost-effective approach for conducting mobile smoking cessation and assessment research.

You Don’t Need Labeled Data for Open-Book Question Answering

Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset—SQuAD and Natural Questions datasets. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.

Serverless Architecture For Bulk Email Management

Sending emails in large quantities can be tediousconsidering free services do not cover bulk email and paidservices can be costly and are not easy to customize. Traditionalemail client used for basic emailing services fail to be useful inlarger volumes of emails to target people or spread informationto consented individuals. This paper proposes a serverless archi-tecture to tackle such problems by using one such offering fromthe Amazon Web Services(AWS) which can be easily replacedby a software architects choice of service. The constraints helpto make an architecture using components that can fit most ofthe needs of a serverless backend and extend it to scenariossuch mobile notifications, One Time Password (OTP) systems orother means of communication to minimize single point of failureand also decrease the dependency on physical servers for suchoperations offering a comparable solution within the cloud. Thearchitecture proposed is tested to find the time taken to sendthe emails of various quantities and see how it affects the cost.The architecture was successful able to send multiple emails in aquick and single invocation and has demonstrated a higher levelof scalability compared to conventional methods.

Serverless Architecture For Bulk Email Management

Email sending is an important tool to broadcast messages to many people to convey information. bulkemails in magnitude of thousands of emails is needed for large organization or company reaching outto its customers.Creating and maintaining a server to accomplish this is not only time-taking but notcost-effective. Products such as Gmail and Outlook are useful in basic emailing services but fail to beuseful in larger volumes of emails to target people or spread information to consented individuals.Thispaper tries to achieve bulk email management throught the use of serverless architecture to sendemails through a service from the Amazon Web Services(AWS) ecosystem.

Detailed Study of Cloud Storage Services on Aws Cloud (Amazon Web Services)

This research revolves around understanding the Cloud Storage Services offered by world's most famous Cloud Provider Amazon Web Services (AWS). We will be covering major Cloud Storage Services like EBS, S3 and EFS. But first let’s understand more about AWS. We should use these end-of-life services as a per-project and keep in mind the key benefits of these end-to-end services. Amazon EBS brings the highest end-to-end prices available with block for level of Amazon Elastic Compute Cloud (EC2) instances. Saves data to file system stored after EC2 status closure. Amazon EFS provides portable file storage, also designed for EC2. It can be used as a standard data source for any application or load that works in most cases. Using the EFS file system, you can configure file system installation settings. The main difference between EBS and EFS is that EBS is only accessible from a single EC2 state in your specific AWS region, while EFS allows you to mount a file system in multiple regions and scenarios.

Toward a Marine Road Network for Ship Passage Planning and Monitoring

Safety of navigation is essential for the global economy as maritime trade accounts for more than 80% of international trade. Carrying goods by ship is economically and environmentally efficient, however, a maritime accident can cause harm to the environment and local economies. To ensure safe passage, mariners tend to use already familiar routes as a best practice; most groundings occur when a vessel travels in unfamiliar territories or suddenly changes its route, e.g., due to extreme weather. In highly trafficked areas, the highest risk for ships is that of collision with other vessels in the area. In these situations, a network of previously traversed routes could help mariners make informed decisions for finding safe alternative routes to the destination, whereas a system that can predict the routes of nearby vessels would ease the burden for the mariner and alleviate the risk of collision. The goal of this project is to utilize Automatic Identification System data to create a network of “roads” to promote a route planning and prediction system for ships that makes finding optimal routes easier and allows mariners on the bridge and Autonomous Surface Vehicles to predict movement of ships to avoid collisions. This paper presents the first steps taken toward this goal, including data processing through the usage of Python libraries, database design and development utilizing PostgreSQL, density map generation and visualizations through our own developed libraries, an A* pathfinding algorithm implementation, and an early implementation of an Amazon Web Services deployment.

Discovery of clinically relevant fusions in pediatric cancer

Background Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. Results Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information. Conclusions The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.