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Annals of Emerging Technologies in computing 2024年第1期8卷 13-21页

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024 by the author(s).

关键词： Phase locked loops

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Babangida-Sabo, Sumayya Avemaria Utulu, Samuel C. Ajibesin, Abel American University of Nigeria Department of Informaton Systems School of Informtaon Technology and Computing Yola Nigeria Amerucan University of Nigeria Department of Software Engineering School of Information Technology and Computing Yola Nigeria

ISBN: (纸本)9798350358155

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria's attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR. © 2024 IEEE.

关键词： Sustainable development goals

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An Entity Extraction and Categorization Technique on Twitter Streams

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International Journal of Information Technology and Decision Making 2024年第3期23卷 1203-1228页

作者： Narayanasamy, Senthil Kumar Chang, Maiga School of Information Technology & Engineering VIT Tamil Nadu Vellore India School of Computing and Information Systems Athabasca University AthabascaAB Canada Multidisciplinary Academic Research Center National Dong Hwa University Hualien Taiwan

As social media platforms have gained huge momentum in recent years, the amount of information generated from the social media sites is growing exponentially and gives the information retrieval systems a great challenge to extract the potential named entities. Researchers have utilized the semantic annotation mechanism to retrieve the entities from the unstructured documents, but the mechanism returns with too many ambiguous entities. In this work, the DBpedia knowledge base is adopted for entity extraction and categorization. To achieve the entity extraction task precisely, a two-step process is proposed: (a) train the unstructured datasets with Word2Vec and classify the entities into their respective categories. (b) crawl the web pages, forums, and other web sources to identifying the entities that are not present in the DBpedia. The evaluation shows the results with more precision and promising F1 score. © 2024 World Scientific Publishing Company.

关键词： Information retrieval systems

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Privacy-Preserving Spatial Crowdsourcing in Smart Cities Using Federated and Incremental Learning Approach 100

Privacy-Preserving Spatial Crowdsourcing in Smart Cities Usi...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Rahman, Md Mujibur Mamun, Quazi Wu, Jun Charles Sturt University School of Computing Mathematics and Engineering NSW Australia Waseda University Graduate School of Information Production and Systems Tokyo Japan

ISBN: (纸本)9798331517786

Spatial crowdsourcing (SC) systems have emerged as an advanced crowdsourcing paradigm to revolutionise the efficient development of smart city services. SC engages participants and their sensitive data to accomplish spatiotemporal tasks on platforms. However, revealing sensitive data in SC for smart cities exacerbates cybersecurity and privacy concerns, especially Membership Inference Attacks (MIA). To address the problems, this research proposes a Federated Learning (FL) and Incremental Learning (IL) based framework in SC that integrates advanced privacy-preserving techniques. By leveraging FL and adaptive differential privacy, sensitive data remains in decentralised devices while local models are trained without exchanging raw data to a server. We integrate additive secret sharing, a secure multi-party computation technique to protect data during transmission and aggregation. IL enhances the framework using a generative replay approach to ensure continuous adaptation to new data without forgetting existing knowledge to overcome catastrophic forgetting. We broadly evaluate our work against MIA and catastrophic forgetting using Yelp datasets. Compared with other baseline approaches, our experimental results demonstrate that the proposed framework significantly mitigates the risk of MIAs by around 50% and improves forgetting accuracy by up to 13%, thereby providing robust privacy-preserving mechanisms. © 2024 IEEE.

关键词： Federated learning

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年 73卷 2070-2086页

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder 3

Impact of Latent Space Dimension on IoT Botnet Detection Per...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Wasswa, Hassan Nanyonga, Aziida Lynar, Timothy University of New South Wales School of Systems and Computing Canberra Australia University of New South Wales School of Engineering and Technology Canberra Australia

ISBN: (纸本)9798350381931

The rapid evolution of Internet of Things (IoT) technology has led to a significant increase in the number of IoT devices, applications, and services. This surge in IoT devices, along with their widespread presence, has made them a prime target for various cyber-attacks, particularly through IoT botnets. As a result, security has become a major concern within the IoT ecosystem. This study focuses on investigating how the latent dimension impacts the performance of different deep learning classifiers when trained on latent vector representations of the train dataset. The primary objective is to compare the outcomes of these models when encoder components from two cutting-edge architectures: the Vision Transformer (ViT) and the Variational Auto-Encoder (VAE) are utilized to project the high dimensional train dataset to the learned low dimensional latent space. The encoder components are employed to project high-dimensional *** IoT botnet traffic datasets to various latent sizes. Evaluated on N-BaIoT and CICIoT2022 datasets, findings reveal that VAE-encoder based dimension reduction outperforms ViT-encoder based dimension reduction for both datasets in terms of four performance metrics including accuracy, precision, recall, and F1-score for all models which can be attributed to absence of spatial patterns in the datasets the ViT model attempts to learn and extract from image instances. © 2024 IEEE.

关键词： Internet of things

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Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-Stationary Learning

Preserving Seasonal and Trend Information: A Variational Aut...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Wasswa, Hassan Nanyonga, Aziida Lynar, Timothy School of Systems and Computing University of New South Wales Canberra Australia School of Engineering and Technology University of New South Wales Canberra Australia

ISBN: (纸本)9798350364866

AI models have garnered significant research attention towards predictive task automation. However, a stationary training environment is an underlying assumption for most models and such models simply do not work on non-stationary data since a stationary relationship is learned. The existing solutions propose making data stationary prior to model training and evaluation. This leads to loss of trend and seasonal patterns which are vital components for learning temporal dependencies of the system under study. This research aims to address this limitation by proposing a method for enforcing stationary behavior within the latent space while preserving trend and seasonal information. The method deploys techniques including Differencing, Time-series decomposition, and Latent Space Arithmetic (LSA), to learn information vital for efficient approximation of trend and seasonal information which is then stored as embeddings within the latent space of a Variational Autoencoder (VAE). The approach's ability to preserve trend and seasonal information was evaluated on two time-series non-stationary datasets. For predictive performance evaluation, four deep learning models were trained on the latent vector representations of the datasets after application of the proposed method and all models produced competitive results in comparison with state-of-the-art techniques using RMSE as the performance metric. © 2024 IEEE.

关键词： Spatio-temporal data

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Enhanced Interpretability in Project Risk Management: Interpreting the Antecedents by Capturing the Interdependencies in Risk Factors 51

Enhanced Interpretability in Project Risk Management: Interp...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Badhon, Bodrunnessa Anavatti, Sreenatha G. Chakrabortty, Ripon K. School of Systems & Computing UNSW ADFA CanberraACT2610 Australia School of Engineering & Technology UNSW ADFA CanberraACT2610 Australia

This paper proposes two frameworks-Interpretive Structural Modelling with Local Interpretable Model-agnostic Explanations (ISM-LIME) and Knowledge Graph with LIME (KG-LIME)-to enhance interpretability in project risk management. While machine learning has proven to be a powerful tool for predicting and analyzing risks in increasingly complex projects, its"black box" nature often obscures decision-making processes. Current explainable AI (XAI) techniques, such as LIME, SHAP (SHapley Additive exPlanations), and G-LIME (Global-LIME), provide valuable insights into feature importance but struggle with capturing the intricate relationship and cascading effects among multiple risk factors. Our proposed frameworks address these limitations by not only elucidating the impact of individual risk factors and interpreting the antecedents but also by revealing their interconnections and cascading effects. Through a comparative analysis, this study demonstrates how ISM-LIME and KG-LIME overcome the shortcomings of traditional XAI methods. ISM-LIME offers a clear hierarchical representation of risk interactions, while KG-LIME provides a detailed network-based view that captures complex interdependencies. This comparison highlights the strengths of each approach in improving risk assessment and decision-making. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Knowledge graph

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of Computer Science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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