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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Pengyan Dong Jiahong Liu Hang Tao Rukhsana Ruby Muwei Jian Hanjiang Luo College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science and Technology Shandong University of Finance and Economics Jinan China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

The collaboration between unmanned aerial vehicles (UAV s) and unmanned surface vehicles (USV s) is critical in mar-itime search scenarios, in which UAV s can leverage high altitude and wide field of view providing real-time target information, while USV s have longer endurance capability of performing precise operations for surface targets. However, UAV s are limited by their battery capacity, which reduces their search duration and range. Furthermore, the traditional fixed charging stations necessitate UAV s to return for recharging, resulting in mission interruptions and decreased search efficiency. To address this problem, we propose an optimized scheduling scheme for UAV-USV cooperative search, in which USV s act as mobile charging stations to provide wireless charging services for UAV s to increase cooperative search mission duration with uninterrupted search execution. Firstly, the USV trajectory optimization problem under the target search constraint is formulated to minimize its energy consumption and maximize the UAV energy utilization. Then, we model the problem as a partially observable Markov decision process (POMDP) and design a scheduling algorithm leveraging the multi-agent deep deterministic policy gradient (MADDPG) method for long endurance UAV-USV collaborative search mission under energy constraints. Numerical simulations confirm the effectiveness of the proposed scheduling scheme.

关键词： Scheduling algorithms Markov decision processes Inductive charging Collaboration Reinforcement learning Charging stations Surface charging Autonomous aerial vehicles Search problems Trajectory optimization

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Measuring Influencing Factors of API Usability

Measuring Influencing Factors of API Usability

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Tian, Jeff Alanazy, Sultan Bokhary, Abdullah Alharthi, Sultan Ghanem, Sakher Southern Methodist University Dept of Computer Science DallasTX United States College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Software Engineering Jeddah Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Computer Science and Artificial Intelligence Jeddah Saudi Arabia

ISBN: (纸本)9798350320282

In this paper, we identify the influencing factors of API usability by extending a comprehensive framework for measuring API usability we proposed earlier, through a systematic examination of entities involved and artifacts produced in API development and usage. Existing research in software metrics are adapted to define metrics capturing relevant metrics dimensions and at the appropriate levels. Our current set of identified influencing factors includes metrics defined on API documentation and other entities/artifacts, in addition to the code metrics. Some preliminary results from actual measurement of such influencing factors are included to demonstrate the viability of our approach. © 2022 IEEE.

关键词： Application programs

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Secure Scheme for Locating Disease-Causing Genes Based on Multi-Key Homomorphic Encryption

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Tsinghua science and Technology 2022年第2期27卷 333-343页

作者： Tanping Zhou Wenchao Liu Ningbo Li Xiaoyuan Yang Yiliang Han Shangwen Zheng Tanping Zhou is with the College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an 710086China TCA Laboratory State Key Laboratory of Computer ScienceInstitute of SoftwareChinese Academy of SciencesBeijing 100080China the College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an 710086China the People’s Armed Police Command College China Tianjin 300100China

Genes have great significance for the prevention and treatment of some diseases.A vital consideration is the need to find a way to locate pathogenic genes by analyzing the genetic data obtained from different medical institutions while protecting the privacy of patients’genetic *** this paper,we present a secure scheme for locating disease-causing genes based on Multi-Key Homomorphic Encryption(MKHE),which reduces the risk of leaking genetic ***,we combine MKHE with a frequency-based pathogenic gene location *** medical institutions use MKHE to encrypt their genetic *** cloud then homomorphically evaluates specific gene-locating circuits on the encrypted genetic ***,whereas most location circuits are designed only for locating monogenic diseases,we propose two location circuits(TH-intersection and Top-q)that can locate the disease-causing genes of polygenic ***,we construct a directed decryption protocol in which the users involved in the homomorphic evaluation can appoint a target user who can obtain the final decryption *** experimental results show that compared to the JWB+17 scheme published in the journal science,our scheme can be used to diagnose polygenic diseases,and the participants only need to upload their encrypted genetic data once,which reduces the communication traffic by a few hundred-fold.

关键词： public key encryption Multi-Key Homomorphic Encryption(MKHE) fully homomorphic encryption disease-causing genes secure location of disease-causing genes

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PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning

arXiv

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arXiv 2024年

作者： Lin, Fangze He, Ying Yu, Fei College of Computer Science and Software Engineering Shenzhen University China Carleton University Canada

Personalized motion planning holds significant importance within urban automated driving, catering to the unique requirements of individual users. Nevertheless, prior endeavors have frequently encountered difficulties in simultaneously addressing two crucial aspects: personalized planning within intricate urban settings and enhancing planning performance through data utilization. The challenge arises from the expensive and limited nature of user data, coupled with the scene state space tending towards infinity. These factors contribute to overfitting and poor generalization problems during model training. Henceforth, we propose an instance-based transfer imitation learning approach. This method facilitates knowledge transfer from extensive expert domain data to the user domain, presenting a resolution to these issues. We initially train a pre-trained model using large-scale expert data. Subsequently, during the fine-tuning phase, we feed the batch data, which comprises expert and user data. Employing the inverse reinforcement learning technique, we extract the style feature distribution from user demonstrations, constructing the regularization term for the approximation of user style. In our experiments, we conducted extensive evaluations of the proposed method. Compared to the baseline methods, our approach mitigates the overfitting issue caused by sparse user data. Furthermore, we discovered that integrating the driving model with a differentiable nonlinear optimizer as a safety protection layer for end-to-end personalized fine-tuning results in superior planning performance. The code will be available at https://***/LinFunster/PP-TIL. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs 31

Idea23D: Collaborative LMM Agents Enable 3D Model Generation...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

ISBN: (纸本)9798891761964

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don't fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Principal component self-attention mechanism for melanoma hyperspectral image recognition 22

Principal component self-attention mechanism for melanoma hy...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Hong Liang Nanying Li Jiaqi Xue Yaqian Long Sen Jia College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Early detection of melanoma and prompt treatment are key approaches to reducing melanoma-related deaths. In order to improve the ability of early detection of melanoma, this paper introduces a set of hyperspectral images (HSIs) data captured by dermoscopy using hyperspectral technology, and based on this data, proposes a principal component self-attention mechanism (PCSAM) method for the classification of dysplastic nevus and melanoma. The proposed method uses principal component analysis technology to amplify the differences in spectral features of the lesions and extract some new features that are convenient for classification. In addition, under the action of the attention mechanism, the spectral features of melanoma are fully paid attention to, and the contextual spatial information between each HSI block can also be utilized. Finally, a comparison experiment is carried out using RGB images and HSIs. Experimental results demonstrate that the spectral features of melanoma can significantly improve the classification accuracy, and it also shows that the participation of hyperspectral technology can effectively improve the recognition accuracy of dysplastic nevus and melanoma, which reflects the advantages of HSI compared with the traditional image.

关键词： self-attention hyperspectral medical image melanoma principal component analysis

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3D Multi-Modal Feature Interaction Fusion Network for Early Alzheimer's Disease Diagnosis 22

3D Multi-Modal Feature Interaction Fusion Network for Early ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Cai, Gege Guan, Zhenghua Cai, Xiangwen Cheng, Nina Hu, Wenzheng Zhang, Tengda Liu, Yi Lei, Baiying College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging China

ISBN: (纸本)9798331520526

Positron Emission Tomography (PET) and Structural Magnetic Resonance Imaging (sMRI) are widely used for early Alzheimer's disease (AD) diagnosis, providing anatomical and metabolic insights. However, current fusion methods, like simple addition or concatenation, fail to leverage their complementary information fully. To address it, we proposed a 3D multi-modal feature interaction fusion network for AD diagnosis. Specifically, we leverage a robust 3D ResNet backbone network as the architecture for feature extraction. To enable the model to dynamically focus on the areas of interest, we added a local attention module at each layer of the network. We further introduced a collaborative attention module to facilitate the interaction and fusion of features between PET and sMRI modalities, thereby enhancing the overall capability of the model to utilize complementary information from both modalities. Finally, an enhanced feature fusion module based on Transformer is integrated into the network to further strengthen the representation of the fused features. The experiment shows that our model performs better than other representative models and validates the effectiveness of the proposed method on the ADNI dataset. © 2025 IEEE.

关键词： Positron emission tomography

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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GNN4FR: A Lossless GNN-based Federated Recommendation Framework

arXiv

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arXiv 2023年

作者： Wu, Guowei Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University China

Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data between a user and the corresponding items and then model them in a central server, which would break the privacy laws such as GDPR. So far, no existing work can construct a global graph without leaking each user's private interaction data (i.e., his or her subgraph). In this paper, we are the first to design a novel lossless federated recommendation framework based on GNN, which achieves full-graph training with complete high-order structure information, enabling the training process to be equivalent to the corresponding un-federated counterpart. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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