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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Mao, Jiayou Mo, Ziqian Fu, Xiyou Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Due to the limitations of current spectral imaging equipment in acquiring high-resolution hyperspectral images (HR-HSIs), a common approach is to fuse low-resolution hyperspectral images (LR-HSIs) with high-resolution multispectral images (HR-MSIs). However, most existing methods have not fully taken into account the correlation and discrepancy in modality between hyperspectral images (HSIs) and multispectral images (MSIs). To address this limitation, we propose an innovative spectral modality-aware interactive fusion network (SMIF-NET) for comprehensive extraction of spectral information and seamless feature fusion. First, we introduce the spectral modality-aware transformer (SMAT) with a dual-attention mechanism to compute spectral self-similarity and cross-spectral correlation. Second, we apply the interactive spatial-spectral feature fusion (IS2F2) to fuse the acquired high-level spectral and spatial features. This fusion technique combines spatial-wise and channel-wise squeeze and excitation to achieve seamless integration of spatial-spectral information. Finally, the extensive experiments on three datasets demonstrate the superior performance of SMIF-NET in both visual and quantitative assessments compared to eight state-of-the-art (SOTA) fusion-based methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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CLIP-Based Cross-Level Semantic Interaction and Recombination Network for Composed Image Retrieval 27

CLIP-Based Cross-Level Semantic Interaction and Recombinatio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Zhihang Huang, Qiang Zhu, Yingying College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781643685489

Composed image retrieval seeks to retrieve a target image that fulfills both modalities based on the user's query, which comprises a modified text and a reference image. Although existing studies introduce novel multi-modal feature fusion techniques at the global or local level, they ignore exploring the cross-level semantic correspondence and recombining the original and target features in the reference image and the modified text, and the problems of modal inconsistency. To address these issues, we propose a CLIP-based Cross-Level Semantic Interaction and Recombination Network (SeIR). Specifically, we first resort the image and text encoders of the CLIP pre-trained model to narrow the modal gap between image and text, and extract both global and local features. We also introduce a cross-modal attention mechanism to screen out original and target features by exploring the semantic correlation between the cross-level image and text features. Subsequently, to alleviate the modal difference between the generated composed query representation and the target image, we utilize an affine transformation technique to recombine the original and target features from the image and text. Extensive experiments on the FashionIQ and CIRR benchmark datasets demonstrate the competitive performance of the proposed SeIR compared to the state-of-the-art methods. © 2024 The Authors.

关键词： Affine transforms

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Expensive Optimization Based on Evolutionary Multi-Tasking and Hybrid Restart Strategy 13

Expensive Optimization Based on Evolutionary Multi-Tasking a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Zhenyuan Ma, Xiaoliang Zhu, Zexuan Li, Yueyue College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9798350308365

Evolutionary Algorithms (EAs) can not handle expensive optimization problems (EOPs) well due to the limited function evaluations in EOPs. To address this challenge, surrogate-assisted evolutionary algorithms (SAEAs) have been widely used and obtained good performance. With the problem dimension increases, SAEAs encounter some challenges in relatively high complexity on the training time and prediction time. To address this, this article proposes a novel expensive optimization algorithm with evolutionary multi-tasking and hybrid restart strategy (HRS-EMT). In the surrogate model construct, two radial basis function (RBF) models with different kernel functions are trained on all evaluated data to provide diversity and then are solved by a multi-tasking optimizer to a better optimization performance. In the surrogate model management, HRS-EMT combines multiple RBF models into an ensemble RBF (ERBF) model, strategically applied in the initial population pre-selection of surrogate model. Based on the prediction of ERBF surrogate model, HRS-EMT can obtain a better initial population in high dimensions. HRS-EMT is validated on twelve benchmark functions and compared with other state-of-the-art SAEAs. Experimental studies have shown the superior or comparable performance to other popular SAEAs in addressing EOPs. © 2024 IEEE.

关键词： evolutionary multitasking Expensive optimization infill criteria surrogate-assisted optimization

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Improving Code Summarization With Tree Transformer Enhanced by Position-Related Syntax Complement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4776-4786页

作者： Song, Jie Zhang, Zexin Tang, Zirui Feng, Shi Gu, Yu Northeastern University Software College Liaoning Shenyang110819 China Northeastern University College of Computer Science and Engineering Liaoning Shenyang110819 China

Code summarization aims to generate natural language (NL) summaries automatically given the source code snippet, which aids developers in understanding source code faster and improves software maintenance. Recent approaches using NL techniques in code summarization fall short of adequately capturing the syntactic characteristics of programming languages (PLs), particularly the position-related syntax, from which the semantics of the source code can be extracted. In this article, we present Syntax transforMer (SyMer) based on the transformer architecture where we enhance it with position-related syntax complement (PSC) to better capture syntactic characteristics. PSC takes advantage of unambiguous relations among code tokens in abstract syntax tree (AST), as well as the gathered attention on crucial code tokens indicated by its syntactic structure. The experimental results demonstrate that SyMer outperforms state-of-the-art models by at least 2.4% bilingual evaluation understudy (BLEU), 1.0% metric for evaluation of translation with explicit ORdering (METEOR) on Java benchmark, and 4.8% (BLEU), 5.1% (METEOR), and 3.2% recall-oriented understudy for gisting evaluation - longest common subsequence (ROUGE-L) on Python benchmark. © 2020 IEEE.

关键词： Semantics

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Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

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computer Modeling in engineering & sciences 2024年第12期141卷 2351-2373页

作者： Mohammed A.Mahdi Shahanawaj Ahamad Sawsan A.Saad Alaa Dafhalla Alawi Alqushaibi Rizwan Qureshi Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Department of Computer and Information Sciences Universiti Teknologi PetronasSeri Iskandar32610Malaysia Center for Research in Computer Vision(CRCV) University of Central FloridaOrlandoFL 32816USA

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the

关键词： PET/CT imaging tumor segmentation weighted fusion transformer multi-modal imaging deep learning neural networks clinical oncology

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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