Through the looking glass: perceiving risk and emotions toward cosmetic procedure engagement

Purpose This research aims to develop a user risk segmentation typology and implement a method that traces how user emotions adapt before, after and toward a next cosmetic procedure. It introduces the user risk segments to an empirical framework to explain re-engagement with the procedure. Design/methodology/approach A survey was self-administered to online consumer panels in the USA. The survey targeted users who had previously undertaken one of three elective procedures, namely, Botox (N = 550), hair transplant (N = 350) or liposuction (N = 350). Findings The typology identified timid image seekers, daring image crafters, approval-seeking socialites and mainstream image adopters. The method tracking user emotions found significant differences before, after and toward a next cosmetic procedure in the user risk segments. The framework predicted user re-engagement with the procedure for each segment. Research limitations/implications The typology presents more sophisticated user risk profiles. The method maps adapting user emotions toward engagement pre- and post-procedure. However, findings are limited to the USA and three cosmetic procedures. Practical implications The typology offers a profile of users and their risk perceptions of a behavior. The method presents an instrument that follows how user emotions adapt. The framework advances understanding of user re-engagement with the behavior. Originality/value Arguably, to the best of the authors’ knowledge, this is the first research to explore how perceived risk operates on emotional states and adaptation, which manifest user well-being and impact user behavior.

Depositing annotated sequences in GenBank: there needs to be a better way

Submitting sequences to the National Center for Biotechnology Information (NCBI) is an integral part of research and the publication process for many disciplines within the life sciences, and it will only become more important as sequencing technologies continue to improve. Here, I argue that the available infrastructure and resources for uploading data to NCBI—especially the associated annotations of eukaryotic genomes—are inefficient, hard to use and sometimes just plain bad. This, in turn, is causing some researchers to forgo annotations entirely in their submissions. The time is overdue for the development of sophisticated, user-friendly software for depositing annotated sequences in GenBank.

Detection of Phishing Attacks using Content Analysis in the Cloud

In the world of internet and technology, technical advancement is widely accepted by both types of users - with and without technical knowledge. Advancement in technologies also brings in different risks involved along with it. These risks involve risks of being compromised at any point of time, leading to identity theft or financial loss or loss of very confidential information. Phishing attacks are one such kind of attack which can trap anyone into it, let it be a novice user or a sophisticated user. This paper involves what phishing attacks are, how the phishers target cloud services, how they deceive users, how the phishers send phish sites to its target. It also includes the background process that happens in normal scenarios and during phishing, a proposed mechanism which can be used for detection, safety measures which if taken can reduce the chances of falling in the trap and mechanisms used by researchers in order to detect and prevent phishing sites.

Electrophysiology prediction of single neurons based on their morphology

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model.Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

A Novel Method For Android Version Updates To Avoid Fragmentation

Android is the most widely used operating system worldwide, running on over seventy percent of the entire smartphone market. However, Android OS suffers from a big problem, known as fragmentation, that causes many complications both on the user side and the development side. Various solutions are currently live to eliminate these complications in the near future, but there is currently no direct solution for the same. A project was developed with the name SENSE IT ALL - DEVICE TEST to help counter and minimise these complications caused due to fragmentation on the platform. Sense It All is an Android application, built using the native Android SDK, that provides diagnostic tests to the deviceincorporated sensors, features, trending technologies, internal system, and other tools, to help a naive user, or a sophisticated user, to understand, reduce, and even eliminated the consequential complications due to fragmentation.

Network Traffic Monitoring Analysis System with Built-in Monitoring Data Gathering

Networking, that is one among the foremost vital aspects of knowledge technology revolution, is developing progressively day when day. this is often as a result of it offers an enormous quantity of information, resources and human experiences. On the one hand, it contains a substantial quantity of harmful content, due to misusing. On the opposite hand, sitting for an extended time ahead of PC’s or alternative network-based devices will have an effect on body badly. As enterprise computing environments become a lot of network-oriented, the importance of network traffic observation and analysis intensifies. Most existing traffic observation and analysis tools specialise in measure the traffic a lot of individual network segments. Further, they generally have sophisticated user interfaces. This paper introduces associated presents the planning associate application and implementation of an MS Windows-compatible software system tool that's accustomed manage networks usage and keep track of each network user activity. associate application consists of 2 components consumer and server. The consumer aspect could be a backgroundapplication runs whenever the computer is run, it turns off only if the computer is turned off and launched with its startup. The server aspect is a lot of complex-GUI application that's accountable in the main for receiving information sent by purchasers cluster, managing and change information to produce network owner up to this point read. The effectiveness of associate application has been verified by applying it to associate enterprise network atmosphere.

Spot Weather Forecasts: Improving Utilization, Communication, and Perceptions of Accuracy in Sophisticated User Groups

Spot weather forecasts (SWFs) are issued by Weather Service offices throughout the United States and are primarily for use by wildfire and prescribed fire practitioners for monitoring local-scale weather conditions. This paper focuses on use of SWFs by prescribed fire practitioners. Based on qualitative, in-depth interviews with fire practitioners and National Weather Service forecasters, this paper examines factors that influence perceptions of accuracy and utilization of SWFs. Results indicate that, while several well-understood climatological, topographical, and data-driven factors influence forecast accuracy, social factors likely have the greater impact on perceptions of accuracy, quantitative accuracy, and utilization. These include challenges with building and maintaining relationships between forecasters and fire managers, communication issues around updating SWFs, and communicating forecast confidence and uncertainty. Operationally, improved quantitative skill in a forecast is always desirable, but key opportunities for improving accuracy and utilization of these forecasts lie in 1) enhancing the processes and mechanisms for communication between a Weather Forecast Office and fire practitioners—before, during, and after an SWFs is issued—and 2) working with the wildland fire community to experiment with forecast uncertainty and confidence information in SWFs and evaluate impacts of these approaches.

Populating the wilderness: crowdsourcing database of the smokies

Purpose – Researchers at the University of Tennessee Libraries experimented with crowdsourcing to determine if contributions by members of the public could be utilized to add citations and subject tags to a new online bibliography, Database of the Smokies (DOTS: dots.lib.utk.edu). The paper aims to discuss this issue. Design/methodology/approach – The database is built in Drupal, an open source platform that provides a crowdsourcing mechanism. The public was offered the opportunity to create accounts and add content to DOTS. After three months, the project team performed a transaction log analysis of user submissions in order to determine whether an editorial process was necessary. Findings – This analysis revealed that 18 percent of database content was the result of crowdsourcing and that much of the content submitted by participants was either obscure or difficult to locate. The analysis also showed that while contributors added valuable citations, an editorial review process was necessary to ensure this crowdsourced content could be published in the database. In addition, contributor supplied subject tags were not of significant uniqueness or quantity to substantially influence the existing taxonomy. Finally, the publicity of the crowdsourcing feature allowed other institutions to contribute to the project and add rare material. Originality/value – This paper offers a model for utilizing crowdsourcing to entice a sophisticated user group to help build a bibliographic database.

User-Centric Design for Mathematical Web Services

A web service is a programmatically available application logic exposed over the internet and it has attracted much attention in recent years with the rapid development of e-commerce. Very few web services exist in the field of mathematics. The aim of this paper is to seamlessly provide user-centric mathematical web services to the service requester. In particular, this paper focuses on mathematical web services for prepositional logic and set theory which comes under discrete mathematics. A sophisticated user interface with virtual keyboard is created for accessing web services. Experimental results show that the web services and the created user interface are efficient and practical.