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A Neocognitron Based on Multi-objective Optimization for Few-Shot Learning 1

6th EAI International Conference on Robotics and Networks, EAI ROSENET 2022

作者： Pan, Hao Guo, Guiyu Shi, Daming College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

In recent years, with the continuous development of deep learning, more and more network models have been proposed to solve practical problems. However, most models often need a large number of labeled samples to train the network to achieve better recognition results. Because a large number of samples need to be labeled, it is labor-intensive and prone to human error labels. For this reason, a large number of researchers have created the concept of few-shot learning, which entails that the model acquires new information from a limited amount of labeled samples in order to get improved recognition outcomes. In this chapter, we learn from the structure of the visual cortex of the human brain and propose new shape templates to improve the traditional neural network. First, we extract the global characteristics of the image to form a 0-1 mask. As the network hierarchy increases and the field of perception expands, the mask is cut to form shape templates. Then evolutionary computation selects some excellent shape templates, which provide a non-linear constraint for the convolution layer. Moreover, the convolution layer introduces inhibition signals for training, and a new function is proposed to make it more in line with the biomimetic principles of the human visual cortex. The results show that the proposed method can improve the accuracy of few-shot learning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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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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Multi-information Self-attention Autoencoder Sequential recommendation 5

Multi-information Self-attention Autoencoder Sequential reco...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Wang, Nan Liu, Yang University of Science and Technology Liaoning School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350309034

The field of sequential recommendation plays a crucial role in personalized recommendation systems, aiming to model users' past interactions and predict their future interactions with items or behaviors. Traditional methods in sequential recommendation typically rely on user behavior history and item attributes for making recommendations, but they overlook the internal relationships and contextual information among items within a sequence. Moreover, existing autoencoder models face limitations in capturing long-term dependencies and effectively modeling contextual information for sequential recommendation tasks. To address these issues, we propose a novel framework called miSAASRec that leverages a multi-information autoencoder with a self-attention module to capture internal relevance and contextual features of the data. This enables miSAASRec to achieve more accurate and comprehensive data encoding, thereby enhancing the performance of the autoencoder. We conducted several experiments to demonstrate the superior performance of our miSAASRec model compared to existing methods, achieving improvements ranging from 7.92% and 32.20% in MRR (Mean Reciprocal Rank) and 9.00% and 28.90% in Recall@10. © 2023 IEEE.

关键词： Multi-information autoencoder Self-attention Sequential recommendation

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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 New End-to-End Image Contrastive Clustering Based on Mining Latent Positive Samples 7

A New End-to-End Image Contrastive Clustering Based on Minin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Feng, Bo Yan, Xi Yan, Li Luo, Bing Pei, Zheng School of Science Xihua University Chengdu China School of Computer Science and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350354973

The integration of the contrastive learning paradigm into deep clustering has led to enhanced performance in image clustering. However, in existing researches, the samples in the class of the target may be still treated as negative samples, this means that more semantic sample pairs cannot be constructed and the intra-class compactness may be compromised. In this paper, we propose a new contrastive clustering method based on mining latent positive samples (CCLPS), which utilizes the nearest neighbor relationship between samples to mine semantic positive samples in the same class. In more detail, by mining latent positive samples in both the feature and cluster spaces, CCLPS can obtain more cluster-friendly feature representation of samples to enhance clustering performance. Samples mined from the feature space fuel the cluster-level contrastive learning task, while samples mined from the cluster space fuel the instance-level task. Three image datasets CIFAR-10, CIFAR-100 and Tiny-ImageNet are employed to experimentally demonstrate usefulness and effectiveness of the proposed method in image clustering, and transfer learning based on mining latent positive samples is also discussed and comparisons are made with 19 representative clustering methods on the datasets. Experimental results indicate that the proposed method can serve as a pre-training model for transfer learning and exhibits commendable feature modeling capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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Entropy-based Selective Homomorphic Encryption for Smart Metering Systems 28

Entropy-based Selective Homomorphic Encryption for Smart Met...

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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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Multichannel Seismic Deconvolution via 2D K-SVD and Convolutional Sparse Coding 3

Multichannel Seismic Deconvolution via 2D K-SVD and Convolut...

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3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023

作者： Zhang, Guiqian Gao, Xiayu Zou, Bangli Wang, Yaojun Chen, Yingzhu China College of Computer Science and Software Engineering Shenzhen University China

Seismic deconvolution encounters problems of multiplicity and instability. The regularization technique based on various prior knowledge is used to constrain the range of solutions and improve the stability of deconvolution results. We use dictionary learning as a technical means to consider both vertical resolution and lateral continuity issues in reflectivity inversion problem. Considering the drawbacks of patch-based operations in 1D dictionary learning, we adopt 2D K-SVD and convolutional sparse coding (CSC) based on global feature extraction as the dictionary learning methods. We divide the seismic profile into low-frequency component and high-frequency component, and reconstruct their corresponding reflectivity with 2D K-SVD and CSC, respectively. Finally, the seismic deconvolution with high resolution and strong continuity is obtained by summing the results of each component deconvolution. The effectiveness of the proposed method is verified by Marmousi model data. © 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Reflection

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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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A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

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computers, Materials & Continua 2024年第8期80卷 1831-1850页

作者： Hina Gul Sana Ullah Ki-Il Kim Farman Ali Department of Computer and Software Technology University of SwatMingora19130Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Applied AI Sungkyunkwan UniversitySeoul03063Republic of Korea

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart *** and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of *** paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such *** proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual *** protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base *** performance of the proposed protocol is evaluated through the network simulator *** different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network *** 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

关键词： Internet of Things wireless sensor networks traffic load clustering routing energy efficiency

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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge Computing 22

Container-Aware Service Function Chains Placement and Optimi...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Kaixin Hu, Shihong Qu, Zhihao Ye, Baoliu Hohai University School of Computer Science and Software Engineering China Nanjing University National Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798331509712

In the domain of Vehicular Edge Computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms. © 2024 IEEE.

关键词： service function chain SFC placement vehicle edge computing virtual network function

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