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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Optimizing Skin Cancer Detection Through Hybrid Convolutional and Ensemble Learning Methods

Optimizing Skin Cancer Detection Through Hybrid Convolutiona...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Sahithi, Chalamala Teja Basha Shaik, Arif Pandala, Madhavi Latha Dept. of Information Technology Vijayawada India Vit Bhopal School of Computing Science and Engineering Kothrikalan India

ISBN: (纸本)9798331529833

One of the most prevalent types of cancer is skin cancer and millions of people suffer from it across the globe with melanoma being the most dangerous type if not treated at an early stage. Accurate and timely diagnosis is crucial in the management of skin cancer but traditional diagnosis methods like dermoscopy, is largely dependent on the skills of qualified dermatologists which makes it subjective and susceptible to variation among observers. In this sense, deep learning and machine learning approaches have a lot of potential in automating skin cancer classification and enhancing its accuracy for better early intervention and patient outcomes. This paper presents the analysis of the performance of several models, i.e., CNN + Random Forest, DenseNet201, MobileNetV2 and ConvoMixer + CatBoost on HAM10000, ISIC and PAD-UFES-20 datasets. The experimental results indicate that DenseNet201 achieved an accuracy of 91.8%, MobileNetV2 reached 87%, and the combination of ConvoMixer and CatBoost obtained 66% However, the CNN + RF model outperformed all of the other models achieving 96% accuracy revealing the effectiveness of hybrid approaches for skin cancer classification on a reliable and scalable manner. Future work could include investigating transformer-based models, such as Vision Transformers (Suns et al.) and Swin Transformers (Liao et al.), which are able to allow better global context within the data. Also federated learning may be used to enable the use of multi institutional databases without compromising the privacy of patients. In addition, self supervised learning approaches could also help alleviate the reliance in a substantial amount of annotated datasets thereby increasing accuracy and robustness in practical and clinical situations. © 2025 IEEE.

关键词： Federated learning

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Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

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Computer Modeling in Engineering & sciences 2024年第4期139卷 633-659页

作者： Nadeem Ahmed Khalid Mohammadani Ali Kashif Bashir Marwan Omar Angel Jones Fayaz Hassan School of Electronic Science Beijing University of Post and TelecommunicationBeijing100086China Department of Computer Science Huanggang Normal UniversityHuanggangChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabad502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Information Technology and Management Illinois Institute of TechnologyChicagoUSA College of Computing Capital Technology UniversityLaurelUSA

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes *** the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against *** additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network *** testing of our solution has revealed its remarkable ability to enhance communication in ***,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and *** results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

关键词： Black hole attack IoV vehicular ad hoc network AODV routing protocol

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Typicality-and instance-dependent label noise-combating:a novel framework for simulating and combating real-world noisy labels for endoscopic polyp classification

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 300-315页

作者： Yun Gao Junhu Fu Yuanyuan Wang Yi Guo School of Information Science and Technology Fudan UniversityShanghai 200433China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai 200433China

Learning with noisy labels aims to train neural networks with noisy *** models handle instance-inde-pendent label noise(IIN)well;however,they fall short with real-world *** medical image classification,atypical samples frequently receive incorrect labels,rendering instance-dependent label noise(IDN)an accurate representa-tion of real-world ***,the current IDN approaches fail to consider the typicality of samples,which hampers their ability to address real-world label noise *** alleviate the issues,we introduce typicality-and instance-dependent label noise(TIDN)to simulate real-world noise and establish a TIDN-combating framework to combat label ***,we use the sample’s distance to decision boundaries in the feature space to repre-sent *** TIDN is then generated according to *** establish a TIDN-attention module to combat label noise and learn the transition matrix from latent ground truth to the observed noisy labels.A recursive algorithm that enables the network to make correct predictions with corrections from the learned transition matrix is *** experiments demonstrate that the TIDN simulates real-world noise more closely than the existing IIN and ***,the TIDN-combating framework demonstrates superior classification performance when training with simulated TIDN and actual real-world noise.

关键词： Noisy label Instance-dependent label noise Noisy label simulation Real-world label noise Polyp classification

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A Game Theoretic Link Scheduler for Wireless Networks with Heterogeneous Energy Sources

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China Communications 2024年第12期21卷 108-125页

作者： He Tengjiao Kwanwu Chin Wang Yishun Sieteng Soh College of Information Science and Technology Jinan UniversityGuangzhouChina School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongNSWAustralia School of Electrical Engineering Computing and Mathematical SciencesCurtin UniversityWAAustralia

This paper considers link scheduling in a wireless network comprising of two types of nodes:(i)hybrid access points(HAPs)that harvest solar en-ergy,and(ii)devices that harvest radio frequency(RF)energy whenever HAPs *** aim is to de-rive the shortest possible link schedule that determines the transmission time of inter-HAPs links,and uplinks from devices to *** first outline a mixed in-teger linear program(MILP),which can be run by a central node to determine the optimal schedule and transmit power of HAPs and *** then out-line a game theory based protocol called Distributed Schedule Minimization Protocol(DSMP)that is run by HAPs and ***,it does not require causal energy arrivals and channel gains *** results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.

关键词： channel access finite battery mathemati-cal program renewable energy wireless power trans-fer

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs 6th

Route Privacy-Preserving Authentication Scheme Based on PUF...

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6th International Conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Deng, Hanwen Liu, Yining Wang, Dong School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031736988

Due to the exposure of transmitted messages in public channels within Vehicular Ad-Hoc Network (VANET), many authentication schemes have been proposed to safeguard security and privacy. And in existing schemes, to expedite the authentication process for Vehicle-to-Infrastructure (V2I) communication, vehicles requesting authentication information from Certification Authority (CA) regarding the RSUs they are expected to encounter before the journey is recommended. However, the viability of the aforementioned schemes rely on two challenging prerequisites: complete trustworthiness of CA and the assurance that the Onboard Units (OBUs), which store secret keys permanently, remain impervious to physical attacks and cloning attempts. Hence in this paper, we propose a route privacy-preserving authentication scheme based on PUF in VANETs. Basing on Oblivious Transfer (OT) method, we let CA complete authentication keys distribution without knowing the route plan of a vehicle. We utilize Physically Unclonable Function (PUF) to minimize the exposure of private key. And as the output of a PUF is easily affected by objective factors, Fuzzy Extractor (FE) is used in our scheme to correct the challenge-response pair of it. In addition, the detailed security analysis proves that our scheme can meet the security requirements. Finally, the experimental analysis suggests that our scheme is lower than the other schemes in terms of time complexity. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Vehicular ad hoc networks

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RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models 41

RoboMP2: A Robotic Multimodal Perception-Planning Framework ...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Qi Li, Hao Deng, Xiang Shao, Rui Wang, Michael Yu Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Engineering Great Bay University China School of Computing and Information Technology Great Bay University China

Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for embodied agents. However, these methods exhibit limited generalization capabilities on unseen tasks or scenarios, and overlook the multimodal environment information which is critical for robots to make decisions. In this paper, we introduce a novel Robotic Multimodal Perception-Planning (RoboMP2) framework for robotic manipulation which consists of a Goal-Conditioned Multimodal Preceptor (GCMP) and a Retrieval-Augmented Multimodal Planner (RAMP). Specially, GCMP captures environment states by employing a tailored MLLMs for embodied agents with the abilities of semantic reasoning and localization. RAMP utilizes coarse-to-fine retrieval method to find the k most-relevant policies as in-context demonstrations to enhance the planner. Extensive experiments demonstrate the superiority of RoboMP2 on both VIMA benchmark and real-world tasks, with around 10% improvement over the baselines. Copyright 2024 by the author(s)

关键词： Semantics

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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