15,802 research outputs found
A Privacy Calculus Perspective
Sandhu, R. K., Vasconcelos-Gomes, J., Thomas, M. A., & Oliveira, T. (2023). Unfolding the Popularity of Video Conferencing Apps: A Privacy Calculus Perspective. International Journal Of Information Management, 68(February), 1-17. [102569]. https://doi.org/10.1016/j.ijinfomgt.2022.102569. Funding: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).Videoconferencing (VC) applications (apps) have surged in popularity as an alternative to face-to-face communications especially during the COVID-19 pandemic. Although VC apps offer myriad benefits, it has caught much media attention owing to concerns of privacy infringements. This study examines the key determinants of working professional’s intentions to use VC apps in the backdrop of this conflicting duality. A conceptual research model is proposed that is based on theoretical foundations of privacy calculus and extended with conceptualizations of mobile users’ information privacy concerns (MUIPC), trust, technicality, ubiquity, as well as theoretical underpinnings of social presence theory. Structural equation modelling (SEM) is used to empirically test the model using data collected from 487 working professionals. For researchers, the study offers insights on the extent to which social richness and technological capabilities afforded by the virtual environment serve as predictors of the continuance intentions of using VC apps. Researchers may also find the model applicable to other studies of surveillance-based technologies. For practitioners, key recommendations pivotal to the design and development mobile video-conferencing apps are presented to ensure higher acceptance and continued usage of VC apps in professional settings.preprintauthorsversionepub_ahead_of_prin
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures
Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation.
Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents.
In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industry’s hidden aspects, address statistical issues in measurement, and explore the industry’s transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE project’s case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
Key technologies for safe and autonomous drones
Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
Recently, self-supervised neural networks have shown excellent image
denoising performance. However, current dataset free methods are either
computationally expensive, require a noise model, or have inadequate image
quality. In this work we show that a simple 2-layer network, without any
training data or knowledge of the noise distribution, can enable high-quality
image denoising at low computational cost. Our approach is motivated by
Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise
independent noise. Our experiments on artificial, real-world camera, and
microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise)
often outperforms existing dataset-free methods at a reduced cost, making it
suitable for use cases with scarce data availability and limited compute
resources. A demo of our implementation including our code and hyperparameters
can be found in the following colab notebook:
https://colab.research.google.com/drive/1i82nyizTdszyHkaHBuKPbWnTzao8HF9
A Qualitative Study on the Effect of Misattributed Parentage Experiences
Identity formation is a lifelong process, significantly influenced by factors involving social groups such as family, culture, and life events. Identity confusion can result from Misattributed Parentage Experiences (MPE), when people learn they are not biologically related to a parent(s) who raised them as such, possibly triggering genealogical bewilderment, the state when uncertain knowledge of biological parents, or lack thereof, leads to maladjustment, confusion, and uncertainty (Leighton, 2012) in identity. The present study is a qualitative analysis of the effect genealogical bewilderment has on identity formation and crises for MPE adults in the United States between 2012 and 2022. Using Marcia (1966) as the primary model for identity development, this study frames the appreciable difference genealogical bewilderment causes when one no longer knows the ancestry or culture from which they came. However, identity is also a fluid concept influenced by life events; therefore, this study expands the conceptual framework to include Erikson’s (1968) psychosocial stages and Tajfel and Turner’s (1979) social identity theory. A phenomenological methodology using a critical hermeneutic (interpretive) approach and thematic analysis was used to analyze an open-ended survey of 123 participants. The findings identified two main themes obtained through open-ended survey questions: the presence of identity crisis and negative family dynamics. The research implications point to concrete ways MPEs successfully heal from the identity crisis and resulting negative family dynamic changes and can bridge the research practice gap for professional mental health clinicians
Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity
IoT networks are increasingly becoming target of sophisticated new
cyber-attacks. Anomaly-based detection methods are promising in finding new
attacks, but there are certain practical challenges like false-positive alarms,
hard to explain, and difficult to scale cost-effectively. The IETF recent
standard called Manufacturer Usage Description (MUD) seems promising to limit
the attack surface on IoT devices by formally specifying their intended network
behavior. In this paper, we use SDN to enforce and monitor the expected
behaviors of each IoT device, and train one-class classifier models to detect
volumetric attacks.
Our specific contributions are fourfold. (1) We develop a multi-level
inferencing model to dynamically detect anomalous patterns in network activity
of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection
of anomalous flows. This provides enhanced fine-grained visibility into
distributed and direct attacks, allowing us to precisely isolate volumetric
attacks with microflow (5-tuple) resolution. (2) We collect traffic traces
(benign and a variety of volumetric attacks) from network behavior of IoT
devices in our lab, generate labeled datasets, and make them available to the
public. (3) We prototype a full working system (modules are released as
open-source), demonstrates its efficacy in detecting volumetric attacks on
several consumer IoT devices with high accuracy while maintaining low false
positives, and provides insights into cost and performance of our system. (4)
We demonstrate how our models scale in environments with a large number of
connected IoTs (with datasets collected from a network of IP cameras in our
university campus) by considering various training strategies (per device unit
versus per device type), and balancing the accuracy of prediction against the
cost of models in terms of size and training time.Comment: 18 pages, 13 figure
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