Personal Cloud Research Articles

Unlocking Generation Y’s continuance intentions in personal cloud storage services: an extended expectation confirmation model analysis

PurposeThis study aims to investigate the factors that influence the continuance intention to use personal cloud storage services among Generation Y.Design/methodology/approachA quantitative online survey was carried out to collect data from 271 respondents. Structural equation modelling with SmartPLS 4.0 software was used to run the analysis and examine the hypothesized relationships in the research model.FindingsThe study revealed that both satisfaction and habit exert a significant influence on continuance intention, whereas self-efficacy does not demonstrate a significant effect. In addition, satisfaction was found to be influenced by confirmation, perceived usefulness and perceived security. Furthermore, confirmation and cloud storage service quality were found to impact perceived usefulness, while confirmation also had an effect on perceived security. However, the hypothesized moderating role of perceived privacy risk in the relationship between perceived usefulness, perceived security and satisfaction was not supported.Originality/valueThis study advances the field by adapting an expanded expectation-confirmation model that delineates the nuanced impacts of habit, user satisfaction and self-efficacy on Generation Y’s continuance intention to use personal cloud storage services. It challenges the conventional wisdom regarding self-efficacy’s influence on technology adoption, offering a more intricate portrayal of its role. This research contributes a distinctive theoretical perspective, emphasizing the complex interplay of factors that inform sustained user engagement with cloud technologies.

Implementation of Personal Cloud using Cryptography

Abstract: This Python-based personal cloud application leverages Flask and cryptography to establish a secure file storage system. Employing the Fernet encryption algorithm, the server encrypts uploaded files, ensuring data confidentiality. Users can securely upload, store, and download files, with each file automatically encrypted upon upload and decrypted for download. While this example prioritizes simplicity, a production-ready system should include robust user authentication, access controls, and further security enhancements. The abstract encapsulates the project's essence, highlighting its focus on file security within a personal cloud environment while acknowledging the need for additional features in a real-world deployment.

The Comparison of Experiments in Embedded System Course

The embedded system is designed to perform specific computation tasks and usually embedded in other devices. Unlike normal personal computer and cloud server, it is not for daily use but production environment with higher stability and reliability demands in various fields with fixed functionalities, smaller size and lower power consumption for the use of portable and power limit situations. The course of embedded system generally contains fundamental concepts, hardware architecture, software development, bootloader and kernel development, etc. It is a course closely integrating theory and experiments, with the purpose of making resource optimization and management, power efficiency, security and integrity. As a course in university, there are many factors that teacher needs to take into consideration before conducting the experiments in embedded system. In this paper, various factors relevant to the experiments, such as the system architecture, platform, and principles, are discussed in details for the comparison of embedded system experiments.

Exploring the Cloud Storage Service Adoption Through the Dual Perspectives: Protection Motivation Theory (PMT) and the Extended Unified Theory of Technology Acceptance Model (UTAUT2)

Cloud computing is spreading rapidly, but little research has been conducted to understand the effects of the protective perspective on acceptance by individuals. The goal of this study was to explore the impact of protective behavior on individuals’ adoption of cloud computing. Specifically, the study will concentrate on personal cloud storage service (CSS), widely recognized as a protective technology. Our theoretical framework endeavored to combine protection motivation theory with the extended unified theory of acceptance and use of technology and focused on the effect of threat in the context of CSS. This study tested two proposed models (Model A, Model B) by structural equation modeling using data from 392 individuals. Data were collected for people in voluntary CSS-usage settings through an online survey. Model A and model B explained 61.0% and 60.4% of the variance in the behavioral intention to accept CSS, respectively. Results showed that CSS adoption is a protective measure against data loss and identified that threat influence CSS adoption. Threat elements directly do not influence behavioral intention to adopt CSS. Threat elements mostly acted as antecedents of independent variables; perceived severity and perceived vulnerability directly influenced technology attributes (performance expectancy, effort expectancy), and showed slightly different results in contextual attributes (facilitating conditions, social influence) and consumer attributes (hedonic motivation, habits). Our study offers a unique perspective for researchers and professionals in understanding the impact of threat factors on information technology domains where protective technologies are operated. Thus, we recommend that academics and business practitioners in the realm of cloud computing should consider user’s protective motivation view of adopting CSS when designing, developing, delivering and spreading it.

SpatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images.

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

Key generation method from fingerprint image based on deep convolutional neural network model

Biometrics effect our live. Security applications employ biometrics. Biometric encryption is growing. Encryption requires biometric key creation. Long, random, and unexpected is the key. Information and communication security research emphasizes long, strong encryption keys. The proposed system uses fingerprint biometrics to generate a long, random biometric encryption key for symmetric encryption. Pre-processing removed noise from donor fingerprint images in the dataset. The program then trains an updateable Tuned VGG-16 convolutional neural network model and tests it on fingerprint images to learn fundamental fingerprint properties. The convolutional neural netwoprk CNN model retains the final weights for the second model to extract encryption key features. Transfer learning built a second convolutional neural network model to retrieve features without relearning. Keeping vector mean for processing. The last step generates an encryption key based on each person's vector of unique biometric features can be used for symmetric encryption algorithms to encrypt personal documents on the personal PC or personal cloud. Our CNN based method uses biometrics to recognize people and create safe and trustworthy encryption keys with over 99% accuracy in testing. Our 98%-accurate deep ANN classifier exceeds the support vector machine and random forest classifiers.

Data ownership in training artificial intelligence in healthcare

"In the framework of this article, we research whether, according to the national regulation of Latvia, the data used in the training of artificial intelligence (AI) in medicine can be considered an object of property rights. An analytical and descriptive method will be used for the research. The analytical method will help analyse the data as a property term and determine its principles. Meanwhile, the descriptive method will describe the AI technology definition and processes. The work will clarify whether Latvia should provide a more transparent and fairer approach to patient data and their right to fully manage it, based on the examples of Finland and Belgium. We cannot consider all personal data objects of property rights in civil law because it is difficult to determine the beginning and end of personal data. However, personal data obtained from an organisation and stored in the personal cloud are considered an object of property rights. At the same time, by analogy, it can be concluded that ownership rights to data may also exist in other circumstances, depending on the type of data and its storage location. Keywords: personal data, property rights, civil law, medicine law."

Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Investigation of Mobile Cloud Storage Adoption Factors in Higher Education

Mobile cloud storage provides benefits for educational institutions. Several researchers have researched cloud computing adoption, but only a few studies related to how users experience using Personal Cloud Storage Services. This research aims to investigate the adoption of the mobile cloud storage factors following the theory, as well as research that has been previously proven related to user interest in using mobile cloud storage among higher education students. This quantitative research uses data analysis techniques using GSCA to prove the theory and achieve the research goals. The research methodology consists of five main stages, namely the stage of model development and research design, the stage of preparing the instrument and its measurement, the stage of testing the instrument, the stage of survey and results, as well as the stages of analysis and discussion as well as conclusions. Five variables are investigated in this research: knowledge sharing, perceived usefulness, attitude toward using a system, trust, and intention to use. The results of hypothesis testing were conducted using GSCA; three proposed hypotheses were accepted, and one was rejected. The variables the research model can explain are 68%, and the remaining 32% are other variables not used in this study. The characteristics of respondents can provide several ways to increase the adoption of mobile cloud computing by linking research results from inferential analysis and descriptive analysis. Future research can focus on extracting these variables through user interviews regarding students' intentions to use mobile cloud computing.

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.

Implementasi Secure Storage Menggunakan Metode Full Disk Encryption dan Tamper Proof pada Cloud Storage

Currently, storage development has shifted to internet-based storage, or cloud storage, as online storage media. Cloud storage is becoming more popular among the public due to several advantages, including the fact that when the storage on personal computers runs out, users do not need to purchase new storage devices or incur additional costs for device maintenance. However, using such online storage can increase risks such as unauthorized access, data leakage, sensitive data, and privacy rights. This study proposes a prototype secure personal cloud storage system with physical database protection. The device has data encryption features that use full disk encryption to secure data on the server, VPN to secure communication between user and server, and tamper proof to physically secure the database. The test results show that the implementation of secure storage affects the performance of cloud storage, as evidenced by the decrease in data write speed from 19.87 Mb/s to 15.75 Mb/s after the implementation of LUKS full disk encryption. On the security side of data transmission, OpenVPN can secure the transmission between the user and the server. This is evidenced by sniffing testing using wireshark tools which shows that data transmission has been encapsulated by the OpenVPN application. In unit testing carried out on LUKS, it proved that the number of LUKS version 2 key slots corresponds to the reference, which is a maximum of 32 key slots. The last test proves that the tamper proof mechanism can work as expected. The tamper proof mechanism works when the device detects an indication of an attack on the physical device. The tests carried out were destroying the device casing using a hammer, opening the casing bolt, and a saw. The test results show that the device will perform damage to the SSD when the attack indication exceeds the tolerance value.

Adequacy of stationary measurements as proxies for residential personal exposure to gaseous and particle air pollutants

People are exposed to myriad of airborne pollutants in their homes. Owing to diverse potential sources of air pollution and human activity patterns, accurate assessment of residential exposures is complex. In this study, we explored the relationship between personal and stationary air pollutant measurements in residences of 37 participants working from home during the heating season. Stationary environmental monitors (SEMs) were located in the bedroom, living room or home office and personal exposure monitors (PEMs) were worn by the participants. SEMs and PEMs included both real-time sensors and passive samplers. During three consecutive weekdays, continuous data were obtained for particle number concentration (size range 0.3–10 μm), carbon dioxide (CO2), and total volatile organic compounds (TVOC), while passive samplers collected integrated measures of 36 volatile organic compounds (VOCs) and semi volatile organic compounds (SVOCs). The personal cloud effect was detected in >80% of the participants for CO2 and >50% participants for PM10. Multiple linear regression analysis showed that a single CO2 monitor placed in the bedroom efficiently represented personal exposure to CO2 (R2 = 0.90) and moderately so for PM10 (R2 = 0.55). Adding a second or third sensor in a residence did not lead to improved exposure estimates for CO2, with only 6–9% improvement for particles. Selecting data from SEMs when participants were in the same room improved personal exposure estimates by 33% for CO2 and 5% for particles. Out of 36 detected VOCs and SVOCs, 13 had at least 50% higher concentrations in personal versus stationary samples. Findings from this study aid improved understanding of the complex dynamics of gaseous and particle pollutants and their sources in residences, and could support the development of refined procedures for residential air quality monitoring and inhalation exposure assessment.

Own Cloud Storage Using Raspberry Pi

The research proposes a solution to the problem of relying on third-party cloud storage services like Dropbox, Google Drive, and iCloud. The proposed solution involves creating a personal cloud storage using a Raspberry Pi and an external hard drive. This way, the user can have full control over their data and determine the amount of memory they need for storage. One of the main concerns with using third-party cloud storage services is security. By creating a personal cloud storage using Raspberry Pi, the user can ensure that their data is secure and not accessible to other users. Additionally, the user does not have to pay a hefty amount of money to use these services, as they can use their own external hard drive as a cloud storage device. To access the data stored in the external hard drive, the research suggests using ownCloud. This way, the user can access their data from any device with an internet connection, making it easy and convenient to use. Overall, the proposed solution offers a practical and cost-effective alternative to relying on third-party cloud storage services. Key Word: Cloud storage, Low-cost, Raspberry Pi.

Personal Cloud Storage using Raspberry PI

Developing a Raspberry Pi personal cloud server that can serve as your very own personal cloud storage is the goal of the project "Personal Cloud Using Raspberry Pi." Moving your data to private cloud storage is a preferable solution because privacy protection is a major concern today. The Raspberry Pi 3 model B module that houses the cloud server is configured. The server is linked to the internet by the router. For greater data storage, the server offers an optional USB hard disc. The Raspberry Pi server's wireless network card or adapter is used to link it to Personal Cloud, an open-source cloud computing platform. The Raspberry Pi can be configured remotely from any device, such as a smartphone, using a local tunnel.Numerous services, including SSH, HTTP, and VNC, are configured on and managed by the local tunnel. Access to the Personal Cloud account is restricted to the admin. Through the local tunnel, an administrator can provide users a variety of services. You can store your data for free on our project as needed. With tight system control, this project provides you the freedom to use space efficiently.

Human personal air pollution clouds in a naturally ventilated office during the COVID-19 pandemic

Personal cloud, termed as the difference in air pollutant concentrations between breathing zone and room sites, represents the bias in approximating personal inhalation exposure that is linked to accuracy of health risk assessment. This study performed a two-week field experiment in a naturally ventilated office during the COVID-19 pandemic to assess occupants’ exposure to common air pollutants and to determine factors contributing to the personal cloud effect. During occupied periods, indoor average concentrations of endotoxin (0.09 EU/m3), TVOC (231 μg/m3), CO2 (630 ppm), and PM10 (14 μg/m3) were below the recommended limits, except for formaldehyde (58 μg/m3). Personal exposure concentrations, however, were significantly different from, and mostly higher than, concentrations measured at room stationary sampling sites. Although three participants shared the same office, their personal air pollution clouds were mutually distinct. The mean personal cloud magnitude ranged within 0–0.05 EU/m3, 35–192 μg/m3, 32–120 ppm, and 4–9 μg/m3 for endotoxin, TVOC, CO2, and PM10, respectively, and was independent from room concentrations. The use of hand sanitizer was strongly associated with an elevated personal cloud of endotoxin and alcohol-based VOCs. Reduced occupancy density in the office resulted in more pronounced personal CO2 clouds. The representativeness of room stationary sampling for capturing dynamic personal exposures was as low as 28% and 5% for CO2 and PM10, respectively. The findings of our study highlight the necessity of considering the personal cloud effect when assessing personal exposure in offices.

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.

Raspberry Pi as a Personal Cloud Server with Next-cloud

This research will show steps on how to build personal cloud storage by using Raspberry Pi (minicomputer), which will help the user to enable cloud storage mode to their external hard drive. Software solutions like Next-cloud, own-cloud are open-source cloud storage applications that allow users to store and access files, photos, and other digital assets from any device with internet access. The main objective of our project is to give a user personal Cloud Storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud service.

Cloud computing for information management and use in the 21st century library

Librarians are applying different platforms for user services and attaining exceeding satisfaction for information management and use in this Web based era. Cloud computing presents an advantage for libraries. It is providing several opportunities for libraries to link their services, use and manage information on the cloud. Cloud computing is the new type of service provided on the Internet, which has exclusively changed the way computers are used irrespective of geographical barriers to manage and use information. The paper examines cloud computing as a tool for information management and use in the 21st century library. The discourse is about the concept of cloud computing, models of cloud computing and the service, which include IaaS, PaaS and SaaS. The paper also discusses cloud. The paper also discusses cloud deployment storage models in private, community, public, hybrid and personal cloud. Information management concept are highlighted, alongside applications of cloud computing for information management and usage in the 21st century libraries. Benefits of using cloud computing for information management in the 21st century libraries including the risks associated with cloud computing for information management and use in the 21st century libraries. Concluding, the discourse opines that today libraries are shifting to cloud computing technology to process data correctly and quickly. Cloud based services are helping libraries by creating digital libraries, social networking and enhance information sharing. Though, problems regarding data security, privacy, reliability and legal rights have not been fully taken care of. All information personnel must be aware of these issues before choosing cloud based services. Despite these issues, cloud based technology is an incredible development that can improve data management and usage in libraries and offer dependable, rapid services to its users. The study recommends that base on the importance and relevance of cloud computing technology, that it should be applied in library services to manage and use information must especially university libraries in Nigeria.

Factors influencing quality and performance of cloud computing platforms defined by personal users in South Africa

Cloud computing changed our way of doing things because we now maintain our systems and data on global online platforms. The research aimed to investigate the predictive factors determining the quality of personal users' cloud computing and their effect on platform performance. The study adopted a cross-sectional, descriptive, quantitative design conducted within South African communities and received 254 responses, a response rate of 66.1%. The measurement model (SRMR = 0.071) results demonstrated that cost-effectiveness, reliability, flexibility, availability, security effectiveness, and scalability collectively define the quality of cloud computing platforms utilized by personal users. AVE (0.575 – 0.694), CR (0.844 – 0.901), Fornell-Larcker criterion, and cross-loadings confirmed convergent validity, reliability, and discriminant validity. Security effectiveness, scalability, and reliability have a statistically significant effect on platform performance with small to medium effect sizes. The theoretical implication is an enhanced domestication theory of technology based on cloud computing, thus providing insights into the needs required to fit within the prevailing environment and context. In addition, it helped understand the personal users' decisions on cloud computing. The study enhances cloud computing at a personal level through understanding that the decision and value may differ from well-researched organizational levels.

An Empirical Study to Examine Drivers of Personal Cloud Computing Usage

Cloud computing is recognized as an important area for information technology (IT) innovation and investment. Many companies are rapidly reorienting their overall IT strategies to include cloud computing. Cloud computing reduces the cost of IT since there is no installing, maintaining, or updating software as well as the elimination of IT emergencies or working after hours. Despite the potential for positive change, there are still other factors to consider before cloud computing can be accepted entirely by the individual, large business, or academic group. Therefore, the main objective of this study is to examine how individuals perceive cloud computing. The study findings reveal several factors which play an important role in their decision whether to adopt cloud computing technology. This study will contribute to the literature by proposing and empirically testing factors that impact the intention to adopt cloud computing.