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arXiv 2023年

作者： Chang, Shizhen Kopp, Michael Ghamisi, Pedram Du, Bo The Institute of Advanced Research in Artificial Intelligence Landstraßer Hauptstraße 5 Vienna1030 Austria The Institute of Advanced Research in Artificial Intelligence Landstraßer Hauptstraße 5 Vienna1030 Austria Machine Learning Group Chem-nitzer Str. 40 FreibergD-09599 Germany The National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China

Change detection, an essential application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. Due to the rapid increase in the quantity of high-resolution remote sensing data and the complexity of texture features, several quantitative deep learning-based methods have been proposed. These methods outperform traditional change detection methods by extracting deep features and combining spatial-temporal information. However, reasonable explanations for how deep features improve detection performance are still lacking. In our investigations, we found that modern Hopfield network layers significantly enhance semantic understanding. In this paper, we propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geographical information of the bitemporal images, we designed a feature retrieval module to extract difference features and leverage discriminative information in a deeply supervised manner. Additionally, we observed that the deeply supervised feature retrieval module provides explainable evidence of the semantic understanding of the proposed network in its deep layers. Finally, our end-to-end network establishes a novel framework by aggregating retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Compared to the best-performing DSAMNet, Dsfer-Net demonstrates significant improvements, with F1 scores increasing by 4.7%, 5.9%, and 2.3%. Furthermore, when compared to our previous FrNet, Dsfer-Net also achieves noteworthy enhancements, with F1 scores increasing by 2.0%, 1.4%, and 4.5% on three datasets. Code will be available online (http

关键词： Semantics

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KILOMETER-LEVEL COUPLED MODELING USING 40 MILLION CORES: AN EIGHT-YEAR JOURNEY OF MODEL DEVELOPMENT

arXiv

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arXiv 2024年

作者： Duan, Xiaohui Li, Yuxuan Liu, Zhao Yang, Bin Zheng, Juepeng Fu, Haohuan Zhang, Shaoqing Xu, Shiming Gao, Yang Xue, Wei Wei, Di Lv, Xiaojing Yan, Lifeng Huang, Haopeng Lu, Haitian Wan, Lingfeng Lin, Haoran Chang, Qixin Li, Chenlin He, Quanjie Song, Zeyu Wang, Xuantong Yu, Yangyang Fan, Xilong Qu, Zhaopeng Xu, Yankun Guo, Xiuwen Fei, Yunlong Wang, Zhaoying Li, Mingkui Jiang, Yingjing Lu, Lv Su, Liang Fu, Jiayu Yu, Peinan Liu, Weiguo Wu, Lixin Wang, Lanning Liu, Xin Chen, Dexun Yang, Guangwen School of Software Shandong University China Tsinghua University China Ocean University of China China National Supercomputing Center Wuxi China Laoshan Laboratory China School of Artificial Intelligence Sun Yat-Sen University China China Ship Scientific Reseach Center China National Research Center of Parallel Computer Engineering and Technology China Beijing Normal University China Qingdao GROSCI Technology Group China

With current and future leading systems adopting heterogeneous architectures, adapting existing models for heterogeneous supercomputers is of urgent need for improving model resolution and reducing modeling uncertainty. This paper presents our three-week effort on porting a complex earth system model, CESM 2.2, to a 40-million-core Sunway supercomputer. Taking a non-intrusive approach that tries to minimizes manual code modifications, our project tries to achieve both improvement of performance and consistency of the model code. By using a hierarchical grid system and an OpenMP-based offloading toolkit, our porting and parallelization effort covers over 80% of the code, and achieves a simulation speed of 340 SDPD (simulated days per day) for 5-km atmosphere, 265 SDPD for 3-km ocean, and 222 SDPD for a coupled model, thus making multi-year or even multi-decadal experiments at such high resolution possible. Copyright © 2024, The Authors. All rights reserved.

关键词： Supercomputers

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A cooperative heterogeneous vehicular clustering framework for

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Frontiers of Information Technology & Electronic engineering 2021年第9期22卷 1247-1259页

作者： Iftikhar AHMAD Rafidah Md NOOR Zaheed AHMED Umm-e-HABIBA Naveed AKRAM Fausto Pedro GARCIA MARQUEZ Department of Computer Science and Information Technology MirpurUniversity of Science and TechnologyMirpur-10250(AJK)Pakistan Facultyof Computer Science and Information Technology University of MalayaKualaLumpur 50603Malaysia Departmentof Software Engineering University of KotliAJK 11100Pakistan Department of Mechanical Engineering Mirpur University of Science andTechnologyMirpur-10250(AJK)Pakistan Department of MechanicalEngineering Faculty of Mechanical EngineeringUniversity of MalayaKualaLumpur 50603Malaysia Ingenium Research Group University of Castilla-LaManchaSpain

Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular *** popular form of heterogeneous network is the integration of long-term evolution(LTE)and dedicated short-range *** heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve.A vehicular clustering framework is one solution to these problems,but the framework should be formally verified and validated before being deployed in the real *** solve these issues,first,we present a het-erogeneous framework,named destination and interest-aware clustering,for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic ***,we specify a model system of the proposed *** model is formally verified to evaluate its performance at the functional level using a model checking *** evaluate the performance of the proposed framework at the micro-level,a heterogeneous simulation environment is created by integrating state-of-the-art *** comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.

关键词： Vehicular cluster Heterogeneity Cooperation Formal verification System model

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Vulnerability Name Prediction Based on Enhanced Multi-Source Domain Adaptation

Vulnerability Name Prediction Based on Enhanced Multi-Source...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Ying Xing Mengci Zhao Bin Yang Yuwei Zhang Wenjin Li Jiawei Gu Jun Yuan Lexi Xu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing China Yunnan Key Laboratory of Software Engineering Yunnan China China Unicom Research Institute Beijing China School of Computer Science Peking University Beijing China NSFOCUS Technologies Group Co. Ltd. Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

关键词： Training software maintenance Java Data privacy Correlation Training data Maintenance engineering

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An overview of domain-specific foundation model: key technologies, applications and challenges

arXiv

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arXiv 2024年

作者： Chen, Haolong Chen, Hanzhi Zhao, Zijian Han, Kaifeng Zhu, Guangxu Zhao, Yichen Du, Ying Xu, Wei Shi, Qingjiang Shenzhen Research Institute of Big Data Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China China Academy of Information and Communications Technology Beijing100191 China China Mobile Group Device Co. Ltd. Beijing100033 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Software Engineering Tongji University Shanghai201804 China

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models. Copyright © 2024, The Authors. All rights reserved.

关键词： Building Information Model

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Optimizing vibratory energy harvesters with differentiable data-driven control

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Chaos, Solitons and Fractals 2025年 198卷

作者： Al-zahrani, Mohammed S. Alsaade, Fawaz W. Alsaadi, Fuad E. Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

Nonlinear vibratory energy harvesters often exhibit multiple coexisting attractors, making their control challenging and energy-intensive. Ensuring an effective transition between these attractors while minimizing control effort is crucial for maximizing power generation and maintaining system stability. This paper introduces a differentiable data-driven control strategy that optimizes energy harvester performance by leveraging a neural network (NN)-based control framework. Unlike traditional fixed-gain or surrogate modeling approaches, the proposed method directly integrates system dynamics and control power consumption into the gain adaptation process, ensuring real-time adaptability. By exploiting the differentiability of neural networks, the controller employs gradient-based optimization to continuously refine control parameters in response to changes in operating conditions. The control framework consists of an offline training phase, where the neural network learns an energy-efficient control strategy through differentiable simulations. During this phase, the neural network is trained using a large dataset of system responses to different control inputs, allowing it to learn the most energy-efficient control strategy. This trained network is then used in the online deployment phase, where the trained controller dynamically adjusts control parameters in real-time. The proposed approach minimizes control energy consumption and efficiently guides the system toward the high-energy attractor, avoiding unnecessary control effort. Our approach significantly outperforms conventional PID-based sliding mode controllers. Simulation results confirm that the differentiable controller considerably enhances energy harvesting efficiency and reduces chattering, ensuring a smooth transition to the high-energy orbit while maintaining robust system stability. The study also highlights the necessity of an adaptive control strategy, stressing the urgency of its implementation for o

关键词： Lyapunov methods

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Event-driven bi-fidelity control for efficient and reliable robotic manipulation

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Results in engineering 2025年 26卷

作者： Alsaade, Fawaz W. Al-zahrani, Mohammed S. Alsaadi, Fuad E. Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper proposes a novel bi-fidelity control framework for robotic manipulators that integrates a high-fidelity model predictive control (MPC) scheme with a low-fidelity Long Short-Term Memory (LSTM) neural network surrogate. Unlike conventional fixed-schedule approaches, our method employs an event-triggered mechanism that dynamically selects the appropriate controller based on a real-time error metric. This mechanism ensures that the computationally intensive MPC is invoked only when the LSTM approximation deviates beyond acceptable bounds. We rigorously established the near-optimal performance and closed-loop stability of our approach via Lyapunov analysis under mild assumptions, instilling confidence in its reliability. The simulation results on a three-arm manipulator, subject to low-frequency sinusoidal and high-frequency chirp trajectories, demonstrate that the proposed approach achieves up to 80–90% reduction in MPC calls, significantly improving the accuracy of the follow-up and computational efficiency. These findings highlight the potential of integrating learning-based approximations with conventional optimization-based control to achieve reliable and time-efficient performance in complex robotic manipulation tasks. © 2025 The Authors

关键词： Predictive control systems

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Prediction of Parkinson's Disease by Analyzing fMRI Data and using Supervised Learning

Prediction of Parkinson's Disease by Analyzing fMRI Data and...

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IEEE Region 10 Symposium

作者： Ahmed Hasin Neehal Md. Nura Azam Md. Sazzadul Islam Md. Ishrak Hossain Mohammad Zavid Parvez Software Engineering and HCI Research Group Brac UniversityDepartment of Computer Science and EngineeringBangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Almost 10 million people are estimated to have the disorder of Parkinson's disease. However, Parkinson's symptoms appear gradually and get worse over time. Therefore, the detection of Parkinson's disease at an early stage might significantly improve lifestyle by giving proper treatment. In recent years, the use of Functional Imaging in neurodegenerative diseases has increased. As Functional Imaging seems very efficient in the case of brain disorders, we used Functional Magnetic Resonance Imaging (fMRI) data for conducting our research. Furthermore, SVM classifier was used for the classification and prediction of Parkinson's disease. Using our proposed method, we have achieved 100% sensitivity, specificity, and accuracy considering seven subjects. However, one subject was exceptional whereas we have achieved 99.76% accuracy, 100% specificity, and 99.53% sensitivity. Finally, this process is a well-structured model for predicting the early stages of PD. It may help the doctors for diagnosis of the disease at its early stages and the patients should receive better treatment.

关键词： Functional magnetic resonance imaging Feature extraction Time series analysis Diseases Support vector machines Time-frequency analysis Training

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DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication

arXiv

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arXiv 2023年

作者： Zhang, Wenyu Bai, Kaiyuan Zeadally, Sherali Zhang, Haijun Shao, Hua Ma, Hui Leung, Victor C.M. The School of Intelligence Science and Technology The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The China Telecom Research Institute Beijing100096 China The College of Communication and Information University of Kentucky LexingtonKY40506 United States Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes. © 2023, CC BY.

关键词： Signal to noise ratio

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Prediction of Epileptic Seizures using Support Vector Machine and Regularization

Prediction of Epileptic Seizures using Support Vector Machin...

引用

IEEE Region 10 Symposium

作者： Shaikh Rezwan Rafid Ahmad Samee Mohammad Sayeed Zaziba Ahmed Nusayer Masud Siddique Mohammad Zavid Parvez Software Engineering and HCI Research Group Department of Computer Science and Engineering Brac University Bangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field.

关键词： Support vector machines Electroencephalography Feature extraction Brain modeling Predictive models Epilepsy Training

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