38,821 research outputs found
Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions
The advent of Blockchain technology (BT) revolutionised the way remittance
transactions are recorded. Banks and remittance organisations have shown a
growing interest in exploring blockchain's potential advantages over
traditional practices. This paper presents a data-driven predictive decision
support approach as an innovative artefact designed for the blockchain-oriented
remittance industry. Employing a theory-generating Design Science Research
(DSR) approach, we have uncovered the emergence of predictive capabilities
driven by transactional big data. The artefact integrates predictive analytics
and Machine Learning (ML) to enable real-time remittance monitoring, empowering
management decision-makers to address challenges in the uncertain digitised
landscape of blockchain-oriented remittance companies. Bridging the gap between
theory and practice, this research not only enhances the security of the
remittance ecosystem but also lays the foundation for future predictive
decision support solutions, extending the potential of predictive analytics to
other domains. Additionally, the generated theory from the artifact's
implementation enriches the DSR approach and fosters grounded and stakeholder
theory development in the information systems domain.Comment: Ppaper has been accepted for presenting in the Australasian
Conference on Information Systems 2023, Dec 6 to 8, Wellington, N
Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol and the NIST-approved Quantum-Resistant Cryptographic Algorithms: Red Teaming Generative AI and Quantum Cryptography
In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing both unprecedented opportunities and potential vulnerabilities.
This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms. Utilising Python and C++ as primary computational tools, the study employs a "red teaming" approach, simulating potential cyber-attacks to assess the robustness of quantum security measures. Preliminary research over 12 months laid the groundwork, which this study seeks to expand upon, aiming to translate theoretical insights into actionable, real-world cybersecurity solutions. Located at the University of Oxford's technology precinct, the research benefits from state-of-the-art infrastructure and a rich collaborative environment. The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats. The research aims to foster a safer, quantum-ready digital future through iterative testing, feedback integration, and continuous improvement. The findings are intended for broad dissemination, ensuring that the knowledge benefits academia and the global community, emphasising the responsible and secure harnessing of quantum technology
Combining behavioural types with security analysis
Today's software systems are highly distributed and interconnected, and they
increasingly rely on communication to achieve their goals; due to their
societal importance, security and trustworthiness are crucial aspects for the
correctness of these systems. Behavioural types, which extend data types by
describing also the structured behaviour of programs, are a widely studied
approach to the enforcement of correctness properties in communicating systems.
This paper offers a unified overview of proposals based on behavioural types
which are aimed at the analysis of security properties
Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions
Blockchain technology (BT) revolutionised remittance transactions recording, banks and remittance institutes have shown growing interest in exploring blockchain\u27s potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an innovative artefact designed for blockchain-oriented remittance industry. Employing theory-generating Design Science Research (DSR) approach, the transaction Big Data (BD) driven predictive emerged. The artefact integrates Predictive Analytics (PA) and Machine Learning (ML) to enable real-time transactions monitoring, empowering management decision-makers to address challenges in the uncertain digitized landscape of blockchain-oriented remittance companies. Bridging the gap between theory and the practice, this research safeguards the remittance ecosystem while fostering future predictive decision support solution with its PA advancement in other domains. Additionally, the generation of theory from the artifact\u27s implementation enriches the DSR approach and fosters grounded and stakeholder theory development in the Information Systems (IS) domain
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Evaluating Software Architectures: Development Stability and Evolution
We survey seminal work on software architecture evaluationmethods. We then look at an emerging class of methodsthat explicates evaluating software architectures forstability and evolution. We define architectural stabilityand formulate the problem of evaluating software architecturesfor stability and evolution. We draw the attention onthe use of Architectures Description Languages (ADLs) forsupporting the evaluation of software architectures in generaland for architectural stability in specific
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