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

作者： Li, Mengzhen Luo, Hanjiang Li, Xiang Ruby, Rukhsana Tao, Hang Wu, Kaishun Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Hong Kong

ISBN: (纸本)9798350330717

To implement high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) technique is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, e.g., coordinated moving targets tracking. However, multi-hop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh marine environments. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on deep reinforcement learning (DRL) approach to realize robust and reliable optical communication under mobile target tracking scenarios. In this scheme, we first model the optical channel with optical noise and then analyze the link performance of multiple AUVs to meet the bit error rate (BER) requirements. Afterwards, we map the cooperative optical communication problem to a Markov decision process (MDP) by incorporating the extended Kalman filter (EKF) technique to enhance effective communication. Finally, we propose a cooperative control strategy for multiple mobile AUVs based on deep deterministic policy gradient (DDPG). Through extensive simulations, it is demonstrated that the proposed algorithm is effective in achieving reliable underwater optical communication under mobile scenarios. © 2023 IEEE.

关键词： Autonomous Underwater Vehicles Deep Deterministic Policy Gradient Extended Kalman Filter Underwater Wireless Optical Communication

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Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosis

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Neural Computing and Applications 2024年第34期36卷 21383-21413页

作者： Zakariah, Mohammed Al-Razgan, Muna Alfakih, Taha Department of Computer Sciences and Engineering College of Applied Science King Saud University Riyadh11543 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Skin cancer poses a significant health hazard, necessitating the utilization of advanced diagnostic methodologies to facilitate timely detection, owing to its escalating prevalence in recent years. This paper proposes a novel approach to tackle the issue by introducing a method for detecting skin cancer that uses MixNets to enhance diagnosis and leverages mobile network-based transfer learning. Skin cancer has diverse forms, each distinguishable by its structural attributes, morphological characteristics, texture, and coloration. The pressing demand for accurate and efficient diagnostic instruments has spurred the investigation of novel techniques. The present study utilizes the ISIC dataset, comprising a validation set of 660 images and a training set of 2637 images. Moreover, the research employs a combination of MixNets and mobile network-based transfer learning as its chosen approach. Transfer learning is a technique that leverages preexisting models to enhance the diagnostic capabilities of the proposed system. Integrating MobileNet and MixNets allows for utilizing their respective functionalities, resulting in a dual-model methodology that enhances the comprehensiveness of skin cancer diagnosis. The results demonstrate impressive performance metrics, with MobileNet and MixNets models, and the proposed approach achieves an outstanding accuracy rate of 99.58%. The above findings underscore the efficacy of the dual-model method in effectively discerning between benign and malignant skin lesions. Moreover, the present study aims to examine the potential integration of emerging technologies to enhance the accuracy and practicality of diagnostics within real-world healthcare settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Diseases

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Adaptive Selection of BERT Layer for Multi-Label Text Classification

Adaptive Selection of BERT Layer for Multi-Label Text Classi...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Chen, Zhichao Liu, Han Zhang, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Multi-label text classification is a special type of natural language processing tasks, which is more complex than traditional single-label classification. Moreover, due to the key strength of pre-trained language models such as BERT in feature embedding at both token and sentence levels, transfer learning driven text classification has become increasingly popular. In this context, it is a commonly used strategy to take the output of the last layer of BERT as sentence embedding for text classification. However, the above-mentioned way of obtaining sentence embedding may not always be suitable for multi-label text classification, i.e., the best layer that leads to the optimal performance of classification may be varied for different labels, especially when a multi-label classification task is transformed into several binary ones. In order to address the aforementioned issue, we set a validation data driven way in this paper to explore the performance of sentence embedding obtained from each layer and adaptively select the best layer to output the sentence vector for each specific label. The experimental results show that using a sentence vector obtained from an intermediate layer may lead to better performance in comparison with simply taking the output of the last layer as the adopted sentence embedding. The results also indicate the effectiveness of the above-mentioned adaptive selection of the best layer in improving the performance of multi-label text classification. © 2023 IEEE.

关键词： Embeddings

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De-Biasing Methods in Neural Networks: A Survey

De-Biasing Methods in Neural Networks: A Survey

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Yixin Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Bias is a common problem in both human cognition and machine learning tasks. However, machines struggle more than humans with bias reduction, mainly because most algorithms rely on the assumption that the training data represent a full population in the real world. This assumption does not hold in most cases, i.e., collecting unbiased or representative data is challenging due to various factors such as sampling errors, human prejudices, and cultural differences. Therefore, bias mitigation has become an essential task in artifical intelligence. In recent years, significant progress has been made in improving the effectivess of debiasing, especially in computer vision. This paper provides a comprehensive review of de-biasing methods for neural networks trained on image data. Firstly, we present a formal defination of the bias mitigation problem and discuss some relevant topics. Moreover, we categorize the existing methods into three main types, namely, data-level methods that aim to balance or augment the training data, model-level methods that aim to modify or regularize the learning algorithms, and adversarial and ensembling approaches that use additional models to capture the biases present in data and to guide the training of the primary model. Finally, we conclude this survey and suggest potential research directions for the future. © 2023 IEEE.

关键词： Learning algorithms

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Research advances on blockchain-as-a-service:architectures,applications and challenges

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Digital Communications and Networks 2022年第4期8卷 466-475页

作者： Jie Song Pengyi Zhang Mohammed Alkubati Yubin Bao Ge Yu Software College Northeastern University Shenyang110819China School of Computer Science and Engineering Northeastern UniversityShenyang110819China

Due to the complexity of blockchain technology,it usually costs too much effort to build,maintain and monitor a blockchain system that supports a targeted *** this end,the emerging“Blockchain as a Service”(BaaS)makes the blockchain and distributed ledgers more accessible,particularly for businesses,by reducing costs and *** combines the high computing power of cloud computing,the pervasiveness of IoT and the decentralization of blockchain,allowing people to build their own applications while ensuring the transparency and openness of the *** paper surveys the research outputs of both academia and ***,it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision,roles,container and virtualization,interfaces,customization and *** typical applications of BaaS in both academic and practical domains are also *** present,the research on the blockchain is abundant,but research on BaaS is still in its *** challenges of BaaS are concluded in this paper for further study directions.

关键词： Blockchain as a service Blockchain Service Internet of things Cloud computing

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Life Expectancy Prediction Based on Particle Swarm Optimization Support Vector Machine Regression 3

Life Expectancy Prediction Based on Particle Swarm Optimizat...

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3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Guo, Zhaodong Que, Yuanxi Li, Xiang College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798350380989

This study delves into the application of Particle Swarm Optimization (PSO) algorithms in the parameter optimization of Support Vector Regression (SVR) models to enhance the accuracy of predicted average life expectancy. An adaptive PSO algorithm augmented with random perturbations is introduced. Through a fitness-function-guided dynamic adjustment mechanism, this method circumvents the premature convergence issue typical of conventional PSO, locating the optimal penalty parameter C and kernel parameter for the SVR model. These parameters are then incorporated into the SVR model for training and regression prediction, achieving optimal data fitting and generalization effects. Comparative analysis reveals that the PSO-enhanced SVR model outperforms the traditional SVR across various performance metrics. Notably, there is a marked improvement in the coefficient of determination, a significant re-duction in root mean square error, and mean absolute percentage error, affirming the advancements of the improved PSO-SVR model in prediction accuracy and reliability. © 2024 IEEE.

关键词： Composite Model PSO PSO-SVR SVM

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Truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds 18th

Truthful Double Auction-Based Resource Allocation Mechanisms...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wu, Dongkuo Wang, Xueyi Wang, Xingwei Huang, Min Wang, Zhitong College of Computer Science and Engineering Northeastern University Liaoning Shenyang China College of Software Northeastern University Liaoning Shenyang China College of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9783031714696

With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading markets among mobile users (MUs) and edge clouds are emerging which need well-designed allocation and pricing mechanisms. In this paper, we propose a double auction-based multi-market resource trading framework (DAMRT), which integrates the dynamic programming and padding-based double auction methods, aiming to achieve approximate social welfare maximization and guarantee the properties of truthfulness and budget balance in the constrained edge computing system. To be specific, for admission control among different base station (BS) central markets, a dynamic programming-based method is proposed to obtain the optimal BS-MU association set. For each market, a truthful padding-based double auction resource allocation mechanism (TPDA) is proposed, which uses a linear programming-based padding method and a binary search algorithm to obtain the near-optimal allocation solution, and leverages a critical-value and a VCG-based pricing strategy for winning MUs and service providers. Our theoretical analysis proves that TPDA achieves truthfulness, individual rationality, and budget balance. Furthermore, simulation results verify the effectiveness of DAMRT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Communications Exploiting Collaborative Beamforming 26

Jamming-aided Maritime Physical Layer Encrypted Dual-UAVs Co...

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

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Jilin University College of Software Engineering College of Computer Science and Technology Changchun130012 China

ISBN: (纸本)9798350331684

Unmanned aerial vehicles (UAVs) are widely used as relay platforms to assist in maritime wireless communications. However, due to the open channel of UAV communications, security issue needs to be paid extra attention. This paper proposes a dual-UAVs jamming-aided system to implement physical layer encryption in maritime wireless communication. Specifically, one UAV acts as a relay to transmit data to the legitimate vessel and a set of UAVs can form a maritime UAV-enabled virtual antenna array (MUVAA) as a jammer to send jamming signals to the eavesdropper. In this system, an encrypted and energy-efficient maritime communication multi-objective optimization problem (EEMCMOP) is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the overall flight energy consumption of UAVs. Since the EEMCMOP is proven to be NP-hard, we propose an enhanced evolutionary computation algorithm with some upgraded characteristics to solve the problem. Simulation results indicate that the performance of the proposed algorithm is superior to other benchmark algorithms. © 2023 IEEE.

关键词： Multiobjective optimization

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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