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2023 International Conference on Machine Learning and Automation, CONF-MLA 2023

作者： Lee, Hsiang-Hung Chen, Yiting Department of Computer Science & Information Engineering National Taiwan Normal University Taipei City106 Taiwan Department of Computer Software and Theory Donghua University Shanghai201620 China

This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Blockchain

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TezNet: An Efficient Convolutional Neural Network for Optical Coherence Tomography Classification 6

TezNet: An Efficient Convolutional Neural Network for Optica...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Qaiser, Muhid Abbas, Muhammad Sohail Muhammad, Asif Haider, Khadija Khan, Haroon National University of Computer and Emerging Sciences Department of Artificial Intelligence and Data Science Islamabad Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Retinal diseases such as Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME) can severely impair vision and are challenging to reverse if detected late. Optical Coherence Tomography (OCT) serves as a non-invasive imaging modality to diagnose these conditions through cross-sectional retinal images. Current advancements in retinal disease detection predominantly focus on fine-tuning larger models to achieve high accuracy, often at the expense of substantial training time and data requirements. We introduce an innovative Convolutional Neural Network (CNN) named TezNet that is deeper and wider than existing custom CNNs for the dataset used in this research yet maintains a lightweight architecture with only 3.6 million parameters. This high efficiency is accomplished by leveraging depthwise and spatial separable convolutional techniques without employing preprocessing techniques or noise reduction filters. The data augmentation techniques generate diverse image qualities, ensuring robust predictions across various scenarios. By training exclusively on a single dataset, our model achieves rapid training times and optimal resource utilization, minimizing the risk of overfitting. This blend of advanced parameter optimization, minimal preprocessing, and strategic data augmentation underscores the potential of our CNN model for swift, precise, and resource-efficient retinal disease detection. TezNet model achieves a remarkable 96% accuracy, competing closely with state-of-the-art networks like ResNet50 (over 23M parameters), InceptionV3 (over 25M parameters), and ResNeXt (89M parameters). © 2024 IEEE.

关键词： Optical coherence tomography

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Voice Presentation Attacks Detection using Acoustic MLTP Features and BiLSTM 3

Voice Presentation Attacks Detection using Acoustic MLTP Fea...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Ibrar, Sundas Javed, Ali Ilyas, Hafsa Uet Taxila Department of Computer Science Punjab Pakistan Uet Taxila Department of Software Engineering Punjab Pakistan

ISBN: (纸本)9798350332391

Automatic speaker verification (ASV) systems have been used in numerous voice-controlled technologies including voice banking, automobiles, smartphones, smart homes, etc. The growth of voice-operated devices is increasing exponentially, and it is mandatory to secure the ASV systems against voice spoofing attacks such as cloned and synthetic voices. The research community has worked to develop spoofing countermeasures to improve the security of ASV systems. However, existing spoofing countermeasures have limitations to detect voice conversion, synthetic speech, and voice replay attacks. There still exists a need for the development of voice spoofing detectors to further strengthen the security of voice-controlled systems. Therefore, in this study, we designed a novel spoofing countermeasure for identifying physical access (PA) and logical access (LA) attacks. More precisely, we proposed a novel acoustic feature descriptor, namely, modified local ternary patterns (MLTP), and used them to train a BiLSTM classifier for voice spoofing detection. We assessed the performance of our approach using the standard ASVspoof-2019 dataset and attained an equal error rate (EER) of 1.87% and 2.66% for the PA and LA attacks, respectively. Experimental results obtained from a detailed assessment signify that our approach has the capability to accurately classify the bonafide and spoofed audio samples over contemporary methods. © 2023 IEEE.

关键词： Automation

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Variational Auto-Encoder Model and Federated Approach for Non-Intrusive Load Monitoring in Smart Homes 28

Variational Auto-Encoder Model and Federated Approach for No...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Kaspour, Shamisa Yassine, Abdulsalam Department of Computer Science Thunder Bay Canada Lakehead University Department of Software Engineering Thunder Bay Canada

ISBN: (纸本)9798350300482

Non-Intrusive Load Monitoring (NILM) is a technique used for identifying individual appliances' energy consumption from a household's total power usage. This study examines a novel energy disaggregation model called Variational Auto-Encoder (VAE) with Federated Learning (FL). Specifically, VAE has a complex structure that resolves the issues in Short Sequence-to-Point (Short S2P) with fewer samples as input windows for each appliance. Short S2P cannot be generalized and might confront some challenges while disaggregating multi-state appliances. To this end, we examine a series of experiments using a real-life dataset of appliance-level power from the UK: UK-DALE. We also investigate additional protection of model parameters using Differential Privacy (DP). The findings show that FL with the VAE model achieves comparable performance to its centralized counterpart and improves all the metrics significantly compared to the Short S2P model. © 2023 IEEE.

关键词： Energy utilization

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FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection 39

FakeDiffer: Distributional Disparity Learning on Differentia...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Bo Zhang, Zhao Zhao, Suiyi Ye, Xianming Zhang, Haijun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunnan China Department of Electronic and Computer Engineering University of Pretoria South Africa Shenzhen China

ISBN: (纸本)157735897X

Existing face forgery detection methods achieve promising performance when training and testing forgery data are from identical manipulation types, while they fail to generalize well to unseen samples. In this paper, we experimentally investigate and find that the poor generalization of the methods mainly arises from their overfitting on the known fake patterns. Excessively focused on seen fakes, those detectors fail to effectively learn image-intrinsic information and the distributional disparity between real and fake images. Then, to address this issue, we redefine fake learning as real-fake distributional disparity learning. We propose a novel deepfake detection framework learning distributional disparity based on the differentiated reconstruction on real and fake images for improved generalization. Specifically, distributional disparity learning on differentiated reconstruction of the real and fake images, enforces the model to learn image-invariant intrinsic representations. The reconstruction on real and fake images forces the decoders to learn the distribution of real and fake images, respectively. Moreover, to avoid the influence from the specificalization of the known fake patterns, we further propose the information interaction learning on the encoded intrinsic information and the pixel disparity between the input image and its reconstruction to distinguish face forgeries that are even unknown. Extensive experiments on large-scale benchmark datasets demonstrated the effectiveness of addressing the overfitting issue of the classification network, and verified the superior performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review 1

Using Blockchain to Overcome the Issues in Land Registry Man...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Riaz, Ayesha Ikram, Muhammad Jawad Asadullah, Najmussaba University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Jeddah International College Department of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

A Blockchain network contains a distributed ledger that is used to store a secure and permanent record of transactions among multiple parties. As the registries of land records are historically stored in the form of paper documentation where, there might arise the issue of losing, destroying, and modifying needed documents. This land registration process is complicated and time-consuming and required a lot of effort to transfer ownership of the land registry or change any information. For this purpose, various contributions have been made by different researchers to overcome the problem of the classical land registration process by using blockchain technology. In this survey, we shed light on some advantages (reduce time and effort, security, permanency, etc.) of blockchain to identify some early problems in the paper-based land registration process. © 2023 IEEE.

关键词： Blockchain

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QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Reinforcement Learning

QoSCAPE: QoS-Centric Adaptive Path Engineering with Blockcha...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karakus, Murat Guler, Evrim Ayaz, Furkan Uludag, Suleyman Department of Software Engineering Ankara University Ankara06830 Turkey Department of Computer Engineering Bartin University Bartin74110 Turkey Department of Computer Science University of Michigan-Flint FlintMI48502 United States

ISBN: (纸本)9798350379433

In the evolving landscape of effective network management, path engineering, also known as traffic engineering, optimization and selection remain a critical challenge. This paper presents an innovative framework that integrates software Defined Networking (SDN), Blockchain technology (BC), and Reinforcement Learning (RL) to enhance the efficiency and security of network path optimization. SDN's centralized control enables dynamic network traffic management, while BC ensures secure, transparent, and immutable logging of network transactions, fostering trust and accountability. Reinforcement learning, capable of learning and adapting from environmental interactions, is employed to dynamically optimize routing decisions. Our proposed framework, QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Multi-Agent Reinforcement Learning, leverages the programmability of SDN to collect real-time network state information and the decentralization of BC to secure this data. The numerical results underscore the superior efficiency of QoSCAPE, which consistently achieves near-total request success rates within milliseconds, significantly outperforming traditional HRA and DRA methods in rapidly fulfilling network service demands in terms of Path Setup Time (PST) and Requests Serviced (RS) metrics, yet notably surpasses all other approaches by minimizing Network Message Overhead (NMO). Its optimization in reducing message volume ensures efficient resource usage and preserves network scalability, distinguishing it as a superior choice for multi-ISP routing frameworks. © 2024 IEEE.

关键词： Efficiency

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： Data privacy distributed machine learning federated learning software frameworks

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A Comparison of the Performance of ResNet and DenseNet for Obstacle Avoidance and Path Following on a Mobile Robot Using Embedded GPUs

A Comparison of the Performance of ResNet and DenseNet for O...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Ma, Shengjun Karkal, Pawan Sudhir Ma, Jing Alam, Fakhrul Auckland University of Technology Electrical and Electronic Engineering Department Auckland New Zealand Auckland University of Technology Computer Science and Software Engineering Department Auckland New Zealand

ISBN: (纸本)9798331539917

In this study, we evaluated the performance of deep learning-based navigation models on an novel platform that combines the Jetson Xavier NX with an RC four-wheel-drive car for autonomous driving. Focusing on data collection and model training using a single-camera setup, obstacle avoidance and path navigation were prioritised. The study compares the effectiveness of ResNet18, ResNet34, and DenseNet121 models by analysing training time, accuracy, obstacle avoidance efficiency, and path navigation capabilities. The results highlight the computational advantages of the Jetson Xavier NX over the Jetson Nano, demonstrating enhanced real-time processing and decision-making capabilities. This integration of the Jetson Xavier NX with an RC vehicle not only improves overall performance but also introduces a novel approach to autonomous vehicle development. The findings offer valuable insights into optimising deep learning models and hardware configurations for improved autonomous navigation. © 2024 IEEE.

关键词： Mobile robots

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