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28th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2023

作者： Xu, Weiyan Zhang, Yinghao Cardell-Oliver, Rachel Mian, Ajmal Hong, Jin B. The University of Western Australia Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350358766

Smart metering systems (SMS) are popular in industrial and residential areas but can risk privacy by revealing user behaviors. Homomorphic encryption (HE) is a technique that protects data privacy by enabling calculations on encrypted data. However, the high computational costs of HE can hinder real-time or resource-constrained applications. Our paper presents a framework to encrypt only selected SMS data for improved efficiency without compromising privacy significantly. By encrypting data blocks with higher entropy values, we can mitigate the leakage of key information to adversaries who may conduct privacy attacks, such as membership inference attacks (MIA). We evaluate our framework using two real-world datasets (i.e., electricity and water) to assess privacy and performance trade-offs. The results indicate a 47% performance increase while still providing a sufficient level of privacy when adopting an encryption ratio of 0.4. They demonstrate the effectiveness of the proposed framework, considering the trade-off between privacy and performance, where the user can determine the appropriate security level for privacy protection and enhance the performance using HE in practical SMS settings. © 2023 IEEE.

关键词： Entropy

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Dirty page prediction by machine learning methods based on temporal and spatial locality 26

Dirty page prediction by machine learning methods based on t...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Lu, Yahui Jiang, Yu Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350331684

The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance. © 2023 IEEE.

关键词： Forecasting

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Performance Optimization of SAC-OCDMA Network Based on 2-D CS Code Utilizing Two Light Sources 6

Performance Optimization of SAC-OCDMA Network Based on 2-D C...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Alayedi, Mohanad Jaradat, Ahmad M. Elgammal, Ziad Malkawi, Malek Haliç University Department of Software Engineering Istanbul Turkey University of Detroit Mercy Department of Electrical and Computer Engineering and Computer Science MI United States Istanbul Medipol University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331529987

This study introduces a novel 2-dimensional (2D) spectral/temporal code, termed 2D-cyclic shift (2D-CS) code, optimized for non-coherent Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) systems. Computational results demonstrate that the 2D-CS code has fostered the SAC-OCDMA system's performance whence spectral efficiency (SE), phase induced intensity noise (PIIN), and cardinality. Specifically, the 2D-CS code can increase cardinality by up to 117%, 89%, and 61% compared to one-dimensional CS (1D-CS), 2D-multi service (2D-MS), and 2D-diluted perfect difference (2D-DPD) codes, respectively. The system's performance was evaluated using Optisystem software, showing significant improvements in quality factor (QF) and bit error rate (BER) with LED and laser light sources for four active users at 115 dBm transmitted power and 20,000 m distance. © 2024 IEEE.

关键词： Code division multiple access

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Real-time Pedestrian Detection Using Resource Constrained Embedded Platforms - A Review 9

Real-time Pedestrian Detection Using Resource Constrained Em...

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9th IEEE Smart World Congress, SWC 2023

作者： Cocks, Grace Magbag, Teresito Hemmati, Maryam Wang, Kevin I-Kai The University of Auckland Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350319804

Autonomous Vehicles (AV) are currently limited to the sensors fitted on the vehicle itself to detect pedestrians instead of working collaboratively with the road side units (RSUs). If Resource Constrained Embedded Platforms (RCEPs) were capable of achieving real-time pedestrian detection, they could be deployed at busy traffic intersections to assist AVs. Currently, high-end GPU platforms are required to achieve real-time performance, but their size and cost mean they are not viable for deployment at busy traffic intersections. Therefore, this study performs a comparative analysis as to the ability of RCEPs to achieve real-time pedestrian detection using Convolutional Neural Network (CNN) architectures. Several CNN architectures were trained on a custom pedestrian dataset, with Tiny Yolov4 achieving 62% mean average precision (mAP). When deployed, this analysis indicates that some RCEPs are capable of achieving real-time pedestrian detection, benchmarked at 30FPS, with Tiny YOLOv4 achieving 40.48FPS on the NVIDIA Jetson Nano. The Raspberry Pi 4 paired with the Google Edge TPU also exceeded the real-time threshold, achieving 51.22FPS using MobileNetV2-SSD. Both models were quantized at FP16 and UINT8, which inherently have lower accuracy in favour of faster inferencing. Our analysis evaluates the real-time performance of the selected embedded devices when hosting lightweight CNNs, as well as offering numerous future research directions to improve the pedestrian detection. © 2023 IEEE.

关键词： lightweight (CNN) real-time pedestrian detection resource constrained embedded platform

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Application of Lightweight DSC-NET Network Model in Histopathological Images of Lung Cancer and Colon Cancer

Application of Lightweight DSC-NET Network Model in Histopat...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： An, Wenlong Zong, Hui Zhou, Heng Wang, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Classification of histopathological images is a fundamental task in the workflow of pathological diagnosis. Due to the complexity of pathological images, it is particularly important to use deep learning to improve diagnostic efficiency. This paper designs a lightweight network model DSC-NET, which consists of multi-scale feature stitching Multi-Conv, coordinate attention CA. The improved selfcalibrated convolution MSC-Conv is composed of selfcalibrated convolution SC-Conv, coordinate attention CA and depthwise DW. In addition, the DSC-NET network model converts the 1×1 convolutional layer (Conv) in the Block module into a linear layer (Linear), which reduces the computational complexity of the model while maintaining the ability of the convolution operation to capture local features. The research in this paper adopts the lung cancer and colon cancer datasets and adds Gaussian noise to these datasets to simulate the equipment shooting situation and evaluate the lightweight DSC-NET network model. Through quantitative comparisons with previous state-of-the-art methods, our experimental results demonstrate that the proposed method achieves superior accuracy. Furthermore, our method stands out with a smaller parameter count and significantly lower FLOPs, highlighting its efficiency and computational advantages. It has important potential to assist pathologists in pathological diagnosis. © 2023 IEEE.

关键词： Convolution

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Knowledge Distillation with Active Exploration and Self-Attention Based Inter-Class Variation Transfer for Image Segmentation 48

Knowledge Distillation with Active Exploration and Self-Atte...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Yifan Li, Shaojie Yang, Xuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781728163277

Knowledge distillation (KD) aims to distill the knowledge from a more extensive deep neural network into a small net-work without losing validity. This paper proposes a novel approach with active exploration and passive transfer (AEPT) and self-attention-based inter-class feature variation (AIFV) distillation for the cardiac image segmentation task. The AEPT encourages the student model to learn undiscovered knowledge/features of the teacher model, aiming to explore new features outperforming the teacher. More specifically, we focus on where the teacher performs poorly and encourage the student model to explore complementary features different from the teacher model. To improve the distinguishability of the student for different classes, the student learns the self-attention-based feature variation (AIFV) between classes. Extensive experiments on medical and public image datasets demonstrate that our approach makes the student model learn better representations and outperforms state-of-art methods by combining two knowledge distillation strategies. © 2023 IEEE.

关键词： Distillation

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A Hybrid Machine Learning Enabled Tourism Recommender System Providing a Context-Aware Experience to Tourists in Ireland 1

A Hybrid Machine Learning Enabled Tourism Recommender System...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Lemma, Tommaso Byrne, Willie Tadisetti, Sivarama Technological University of the Shannon Department of Computer and Software Engineering Athlone Ireland

ISBN: (纸本)9798350306484

Weather conditions are among the most influential factors when determining what type of tourist attraction to recommend. This paper focuses on a machine learning architecture capable of analysing publicly available tourism data. Based on the collected data, Multi-Layer Perceptron models have been trained that provided promising results during evaluation. It is a multi-phase, extendable approach that can be incorporated into a larger Tourism Recommender System. © 2023 IEEE.

关键词： Collaborative filtering

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A survey on communication protocols and performance evaluations for Internet of Things

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Digital Communications and Networks 2022年第6期8卷 1094-1104页

作者： Cüneyt Bayılmış MAli Ebleme ÜnalÇavuşoğlu Kerem Küçük Abdullah Sevin Department of Computer Engineering Sakarya UniversitySerdivanSakaryaTurkey Institute of Natural Science Sakarya UniversitySakaryaTurkey Department of Software Engineering Sakarya UniversitySerdivanSakaryaTurkey Department of Software Engineering Kocaeli UniversityKocaeliTurkey

The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, IoT interacts with many application areas such as smart city, smart building, security, traffic, remote monitoring, health, energy, disaster, agriculture, industry. The IoT network in these scenarios comprises tiny devices, gateways, and cloud platforms. An IoT network is able to keep these fundamental components in transmission under many conditions with lightweight communication protocols taking into account the limited hardware features (memory, processor, energy, etc.) of tiny devices. These lightweight communication protocols affect the network traffic, reliability, bandwidth, and energy consumption of the IoT application. Therefore, determining the most proper communication protocol for application developers emerges as an important engineering problem. This paper presents a straightforward overview of the lightweight communication protocols, technological advancements in application layer for the IoT ecosystem. The survey then analyzes various recent lightweight communication protocols and reviews their strengths and limitations. In addition, the paper explains the experimental comparison of Constrained Applications Protocol (CoAP), Message Queuing Telemetry (MQTT), and WebSocket protocols, more convenient for tiny IoT devices. Finally, we discuss future research directions of communication protocols for IoT.

关键词： Internet of Things IoT application layer protocols IoT communicati on pr otocols Performance evaluation

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SOID: Towards an Efficient Incremental Deployment Scheme for Source Address Validation 29

SOID: Towards an Efficient Incremental Deployment Scheme for...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yang, Shu Song, Bingqian Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350330717

The current Internet makes forwarding decisions based on only destination addresses, leading to a prevalence of IP source address spoofing. To mitigate the risks posed by IP spoofing, Source Address Validation in Intra-domain and Interdomain Networks (SAVNET) has been recently proposed and become a hot topic in both academia and industry. While all network equipment can not be upgraded to support SAVNET during one night, incremental deployment is needed for network operators. However, SAVNET can not defend against all spoofing attacks under partial deployment. Thus, we need to carefully choose nodes to be deployed, to improve incentive benefits during incremental deployment. In this paper, we formulate the incremental problem and prove that the problem is NP-Complete. To efficiently solve the problem, we propose a heuristic deployment scheme named SOID (SAV protocol optimized incremental deployment). The intuitive idea of SOID is using the sink-tree to get the detectable flow sets of each router. Then, it selects the routers which have the maximum total weight during each iteration. To evaluate the performance of the proposed algorithm, we conduct comprehensive simulations with generated and real topologies. The simulation results show that SOID performs much better compared with traditional schemes, such as random deployment and minimum vertex cover algorithms, with a manageable overhead. © 2023 IEEE.

关键词： Incremental Deployment Algorithm Intra-domain Source Address Validation

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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