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Scrutinizing the level of awareness and adoption of distributed ledger technology in the Nigerian construction industry

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Technological Sustainability 2024年第4期3卷 373-391页

作者： Aliu, John Oke, Ayodeji Emmanuel Akinwumi, Isaac I. Abdulazeez Kanya, Rislan Uyi Ehiosun, Lydia Institute for Resilient Infrastructure Systems College of Engineering University of Georgia Athens GA United States Department of Quantity Surveying Research Group on Sustainable Infrastructure Management Plus (RG-SIM+) Federal University of Technology Akure Akure Nigeria Department of Civil Engineering Covenant University Ota Nigeria Department of Computer Science and Information Technology Baze University Abuja Nigeria Department of Quantity Surveying Federal University of Technology Akure Akure Nigeria

Purpose: This study aimed to investigate and analyze the level of awareness and adoption of distributed ledger technologies (DLTs) within the Nigerian construction industry. The focus was on addressing the current state of DLT utilization, identifying challenges and opportunities and proposing strategies to enhance the integration of DLTs into the construction processes and practices of Nigerian professionals and organizations. Design/methodology/approach: The research was underpinned by a robust theoretical and conceptual framework, drawing from established theories of technology adoption. A comprehensive literature review guided the identification of various DLT types. This informed the development of a well-structured questionnaire, which was then distributed to Nigerian construction professionals. The collected data underwent analysis using percentages, frequencies, mean scores, the Kruskal–Wallis H-test and the Shapiro–Wilk test. Findings: A significant finding of this study reveals a generally low awareness and implementation of DLT among construction professionals in Nigeria. These findings emphasize the urgent need for comprehensive strategies to bridge the gap between awareness and adoption of DLT within the Nigerian construction industry. Practical implications: Industry associations, regulatory bodies and educational institutions can collaborate to develop specialized programs aimed at familiarizing professionals with the benefits and applications of DLTs. Additionally, technology providers and policymakers can leverage these findings to design user-friendly interfaces and guidelines for seamless DLT integration into construction processes. Originality/value: This study contributes to the existing body of knowledge by providing a comprehensive assessment of the awareness and adoption of DLTs specifically within the Nigerian construction industry. While the global recognition of DLT’s potential in construction is acknowledged, this research delves into a r

关键词： Asset lifecycle Blockchain technology Construction industry Distributed ledger technology Smart contracts Supply chain management

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Safeguarding Audio Integrity: Ensemble Architectures for Real-World Fake Audio Identification

Safeguarding Audio Integrity: Ensemble Architectures for Rea...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Soundarya Lahari Kasturi S Nivetha B Selva Rani S Vairamuthu Sourabh Tiwari Devendra Yadav Rashmi T Shankarappa School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Voice Intelligence Group Samsung R&D Institute Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Through this research, we introduce a unique Audio Spoof Detection System for distinguishing between actual, fraudulent, and generated audio recordings. Strong detection algorithms are required since the spread of audio modification techniques poses a major threat to the integrity of audio-based systems. Combination of 7 key voice features from cepstral coefficients and spectrograms were used to cover all possible spoofing. We are proposing ensemble methods to enhance the accuracy of conventional machine learning models such as Random Forest, State Vector Classifier (SVC), K-Nearest Neighbors (KNN) etc. and deep learning models such as Recurrent Neural Networks (RNN), Time-Delay Neural Networks (TDNN), SqueezeNet etc. The proposed model is evaluated using the ASVspoof 2021 logical access (LA) and Deep Voice dataset. Experimental results show that our proposed model significantly elevates performance compared to the baseline and state-of-the-art models.

关键词： Deep learning Accuracy Recurrent neural networks Machine learning algorithms Static VAr compensators computer architecture Nearest neighbor methods Feature extraction Vectors Standards

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New paper-by-paper classification for Scopus based on references reclassified by the origin of the papers citing them

arXiv

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arXiv 2024年

作者： Álvarez-Llorente, Jesús M. Guerrero-Bote, Vicente P. de Moya-Anegón, Félix Department of Computer and Telematic Systems Engineering University of Extremadura Badajoz Spain Department of Information and Communication University of Extremadura Badajoz Spain SCImago Research Group Granada Spain

A reference-based classification system for individual Scopus publications is presented which takes into account the categories of the papers citing those references instead of the journals in which those cited papers are published. It supports multiple assignments of up to 5 categories within the Scopus ASJC structure, but eliminates the Multidisciplinary Area and the miscellaneous categories, and it allows for the reclassification of a greater number of publications (potentially 100%) than traditional reference-based systems. Twelve variants of the system were obtained by adjusting different parameters, which were applied to the more than 3.2 million citable papers from the active Scientific Journals in 2020 indexed in Scopus. The results were analyzed and compared with other classification systems such as the original journal-based Scopus ASJC, the 2-generation-reference based M3-AWC-0.8 (Álvarez-Llorente et al., 2024), and the corresponding authors' assignment based AAC (Álvarez-Llorente et al., 2023). The different variants obtained of the classification give results that improve those used as references in multiple scientometric fields. The variation called U1-F-0.8 seems especially promising due to its restraint in assigning multiple categories, consistency with reference classifications and the fact of applying normalization processes to avoid the overinfluence of articles that have a greater number of references. © 2024, CC BY-NC-ND.

关键词： Taxonomies

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IMPLICATIONS OF NOISE IN RESISTIVE MEMORY ON DEEP NEURAL NETWORKS FOR IMAGE CLASSIFICATION

arXiv

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arXiv 2024年

作者： Emonds, Yannick Xi, Kai Fröning, Holger Computing Systems Group Institute of Computer Engineering Heidelberg University Germany Key Laboratory of Microelectronics Device & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences China

Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoid the associated costs in terms of area, time and energy, the present work is concerned with exploring how much noise in memory operations can be tolerated by image classification tasks based on neural networks. We introduce a special noisy operator that mimics the noise in an exemplary resistive memory unit, explore the resilience of convolutional neural networks on the CIFAR-10 classification task, and discuss a couple of countermeasures to improve this resilience. © 2024, CC BY-SA.

关键词： Image classification

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Towards Threat Modelling of IoT Context-Sharing Platforms

Towards Threat Modelling of IoT Context-Sharing Platforms

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IEEE International Symposium on Network Computing and Applications

作者： Mohammad Goudarzi Arash Shaghaghi Simon Finn Burkhard Stillerd Sanjay Jha School of Computer Science and Engineering UNSW Sydney Australia Cyber Security Cooperative Research Centre (CSCRC) Department of Informatics Communication Systems Group University of Zurich Switzerland

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on contextsharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these “systems of systems.” In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms.

关键词： Threat modeling Industries Interconnected systems Security Internet of Things Reliability Interoperability Information exchange System of systems Resilience

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Borsdorf, Marvin Pan, Zexu Himmelmann, Pascal Li, Haizhou Schultz, Tanja Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (纸本)9798350368741

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time. © 2025 IEEE.

关键词： cocktail party problem GlobalPhone low-resource language multilingual speech separation

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Abstracting Linear Stochastic systems via Knowledge Filtering

arXiv

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arXiv 2023年

作者： Engelaar, Maico Hendrikus Wilhelmus Romao, Licio Gao, Yulong Lazar, Mircea Abate, Alessandro Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Department of Computer Science University of Oxford United Kingdom

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stochastic system, while preserving any specification on the output. Although initially the reduced model will be time-varying, a method will be provided with which the reduced model can become time-invariant if it satisfies some minor technical conditions. We present our theoretical findings with an example that supports the proposed framework and illustrates how model reduction and controller refinement of stochastic systems can be achieved. We finish the paper by considering specific examples to analyze both completeness with respect to controller synthesis and model order reduction with respect to the state. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

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Direct Data-Driven State-Feedback Control of General Nonlinear systems

Direct Data-Driven State-Feedback Control of General Nonline...

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IEEE Conference on Decision and Control

作者： Chris Verhoek Patrick J. W. Koelewijn Sofie Haesaert Roland Tóth Dept. of Electrical Engineering Control Systems group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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