Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks

Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistakeholder cloud environment and are therefore prone to privacy leaks. This paper aims to address such privacy concerns by examining the representative case study of shared database scenarios in 5G Open Radio Access Network (O-RAN) networks where we have a shared database within the near-real-time (near-RT) RAN intelligent controller. We focus on securing the data that can be used by machine learning (ML) models for spectrum sharing and interference mitigation applications without compromising the model and network performances. The underlying idea is to leverage a (i) Shuffling-based learnable encryption technique to encrypt the data, following which, (ii) employ a custom Vision transformer (ViT) as the trained ML model that is capable of performing accurate inferences on such encrypted data. The paper offers a thorough analysis and comparisons with analogous convolutional neural networks (CNN) as well as deeper architectures (such as ResNet-50) as baselines. Our experiments showcase that the proposed approach significantly outperforms the baseline CNN with an improvement of 24.5% and 23.9% for the percent accuracy and F1-Score respectively when operated on encrypted data. Though deeper ResNet-50 architecture is obtained as a slightly more accurate model, with an increase of 4.4%, the proposed approach boasts a reduction of parameters by 99.32%, and thus, offers a much-improved prediction time by nearly 60%

End User Satisfaction With Cloud Computing: The Case of Hamad Medical Corporation in Qatar

Cloud computing assures a faster, cheaper and more efficient rendering of resources, which leads to huge popularity among businesses and specifically the health sector. The major objective of this research is to identify the benefits of cloud computing (CC) and the factors influencing users satisfaction. Utilizing a survey collected from 219 employees, the research model was tested. Results indicated that employee compliance issues, security and privacy issues, economic benefits, operational benefits, functional benefits, and trust are all significant predictors of satisfaction. Management issues and private cloud risks were not significant predictors of satisfaction. The coefficient of determination R2 = 0.81. This study conducted comparisons between different categories of the sample based on their satisfaction level and concluded that age and education were significant discriminators, while gender, experience, and department were not. Conclusions and future research are stated in the last section

How do different devices impact users' web browsing experience?

The digital world presents many interfaces, among which the desktop and mobile device platforms are dominant. Grasping the differential user experience (UX) on these devices is a critical requirement for developing user focused interfaces that can deliver enhanced satisfaction. This study specifically focuses on the user's web browsing experience while using desktop and mobile. The thesis adopts quantitative methodology. This amalgamation presents a comprehensive understanding of the influence of device specific variables, such as loading speed, security concerns and interaction techniques, which are critically analyzed. Moreover, various UX facets including usability, user interface (UI) design, accessibility, content organization, and user satisfaction on both devices were also discussed. Substantial differences are observed in the UX delivered by desktop and mobile devices, dictated by inherent device attributes and user behaviors. Mobile UX is often associated with personal, context sensitive use, while desktop caters more effectively to intensive, extended sessions. A surprising revelation is the existing discrepancy between the increasing popularity of mobile devices and the persistent inability of many websites and applications to provide a satisfactory mobile UX. This issue primarily arises from the ineffective adaptation of desktop-focused designs to the mobile, underscoring the necessity for distinct, device specific strategies in UI development. By furnishing pragmatic strategies for designing efficient, user-friendly and inclusive digital interfaces for both devices; the thesis contributes significantly to the existing body of literature. An emphasis is placed on a device-neutral approach in UX design, taking into consideration the unique capabilities and constraints of each device, thereby enriching the expanding discourse on multiservice user experience. As well as this study contributes to digital marketing and targe­ted advertising perspe­ctives

How do different devices impact users' web browsing experience?

The digital world presents many interfaces, among which the desktop and mobile device platforms are dominant. Grasping the differential user experience (UX) on these devices is a critical requirement for developing user focused interfaces that can deliver enhanced satisfaction. This study specifically focuses on the user's web browsing experience while using desktop and mobile. The thesis adopts quantitative methodology. This amalgamation presents a comprehensive understanding of the influence of device specific variables, such as loading speed, security concerns and interaction techniques, which are critically analyzed. Moreover, various UX facets including usability, user interface (UI) design, accessibility, content organization, and user satisfaction on both devices were also discussed. Substantial differences are observed in the UX delivered by desktop and mobile devices, dictated by inherent device attributes and user behaviors. Mobile UX is often associated with personal, context sensitive use, while desktop caters more effectively to intensive, extended sessions. A surprising revelation is the existing discrepancy between the increasing popularity of mobile devices and the persistent inability of many websites and applications to provide a satisfactory mobile UX. This issue primarily arises from the ineffective adaptation of desktop-focused designs to the mobile, underscoring the necessity for distinct, device specific strategies in UI development. By furnishing pragmatic strategies for designing efficient, user-friendly and inclusive digital interfaces for both devices; the thesis contributes significantly to the existing body of literature. An emphasis is placed on a device-neutral approach in UX design, taking into consideration the unique capabilities and constraints of each device, thereby enriching the expanding discourse on multiservice user experience. As well as this study contributes to digital marketing and targe­ted advertising perspe­ctives

Using Privacy Calculus Theory To Assess Users´ Acceptance Of Video Conferencing Apps During The Covid-19 Pandemic

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementVideoconferencing (VC) applications (apps) are getting notable attention worldwide, from common citizens to professionals as an alternative to vis-à-vis communication specifically during COVID-19. The growth of VC apps is expected to rise even more in the future with the prediction that widespread adoption of remote work will continue to hold even after the pandemic. This research investigates the key drivers for individuals’ intentions into continuing to use this technology in professional settings. Considering the importance of professionals’ perceptions of privacy in professionals’ settings, this study proposes a conceptual model rooted in the theoretical foundations of privacy calculus theory, extended with the conceptualization of privacy concerns for mobile users (MUIPC), ubiquity, and theoretical underpinnings from social presence theory. The conceptual research model was empirically tested by using data collected from a survey of 487 actual users of videoconferencing apps across Europe. Structural equation modeling (SEM) is performed to test the model. The study revealed several findings (1) perceived value in using VC apps motivates the professionals to continue using VC apps and shapes their perception as they evaluate the risk-benefit trade-off they are making when using VC apps. (2) professionals’ indeed form and articulate their own assessment of value based on the perceived risks and benefits associated with using VC apps. However, professionals' perceptions of value are strongly influenced by potential benefits received from using VC apps than by potential risks associated with using VC apps. (3) professionals’ perceived risk is determined by MUIPC and trust. (4) professionals’ perceived benefits are shaped by ubiquity and social presence. For researchers, this study highlights the usefulness of integrating privacy calculus theory, social presence theory and trust in studying the individuals’ behavioral intentions towards new technologies. For practitioners, understanding the key determinants is pivotal to design and build mobile video-conferencing apps that achieve higher consumer acceptance and higher rates of continued usage of VC apps in professional settings

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Factors influencing the adoption postponement of mobile payment services in the hospitality sector during a pandemic

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Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey

Modern language models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based code intelligence, such as automated bug repair, and test case generation. Despite their great potential, language models for code intelligence (LM4Code) are susceptible to potential pitfalls, which hinder realistic performance and further impact their reliability and applicability in real-world deployment. Such challenges drive the need for a comprehensive understanding - not just identifying these issues but delving into their possible implications and existing solutions to build more reliable language models tailored to code intelligence. Based on a well-defined systematic research approach, we conducted an extensive literature review to uncover the pitfalls inherent in LM4Code. Finally, 67 primary studies from top-tier venues have been identified. After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, implications, current solutions, and challenges of different pitfalls for LM4Code systems. We developed a comprehensive classification scheme that dissects pitfalls across four crucial aspects: data collection and labeling, system design and learning, performance evaluation, and deployment and maintenance. Through this study, we aim to provide a roadmap for researchers and practitioners, facilitating their understanding and utilization of LM4Code in reliable and trustworthy ways