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共 20045 条记录，以下是51-60 订阅

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Rethinking Early-Fusion Strategies for Improved Multimodal Image Segmentation

Rethinking Early-Fusion Strategies for Improved Multimodal I...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Zhengwen Li, Yulian Zhang, Han Weng, Yuchen Wang, Jun School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China

ISBN: (纸本)9798350368741

RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for multimodal semantic segmentation. However, these methods require massive parameter updates and computational effort during the feature extraction and fusion. To address this issue, we propose a novel multimodal fusion network (EFNet) based on an early fusion strategy and a simple but effective feature clustering for training efficient RGB-T semantic segmentation. In addition, we also propose a lightweight and efficient multiscale feature aggregation decoder based on Euclidean distance. We validate the effectiveness of our method on different datasets and outperform previous state-of-the-art methods with lower parameters and computation. © 2025 IEEE.

关键词： feature aggregation feature clustering RGB-T semantic segmentation

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Kolmogorov-Arnold Networks Based Signal Detection for OTFS Systems in LEO Satellite Communications 14th

Kolmogorov-Arnold Networks Based Signal Detection for OTFS ...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： Zhang, Yakai He, Jiayi Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031861956

In numerous studies, orthogonal time-frequency space (OTFS) has been utilized in satellite-terrestrial communication systems with high mobility due to its strong adaptability to Doppler shifts and delays. Signal detection, a crucial technology influencing OTFS performance, is often hindered by high complexity or suboptimal detection capabilities. To address this, we propose a Kolmogorov-Arnold Networks (KANs)-based OTFS signal detection method for low Earth orbit satellite (LEO-Sat) communication systems. KANs employ learnable activation functions instead of traditional learnable linear weights between network nodes, enabling dynamic activation functions to enhance model accuracy. We integrate KANs, trained offline, into the LEO-Sat system to recover distorted signals at the receiver, facilitating effective signal detection. Compared to conventional detection algorithms, the proposed method demonstrates superior Bit Error Rate (BER) performance. Additionally, KANs are more parameter-efficient than DNNs, using significantly fewer parameters. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Bit error rate

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Integrating Systems Thinking into Software engineering Education: A Teaching Experience 17

Integrating Systems Thinking into Software Engineering Educa...

引用

17th international conference on computer Supported Education, CSEDU 2025

作者： Correa, Rodrigo Lima, Márcia Conte, Tayana Instituto de Computação Universidade Federal do Amazonas Manaus Brazil Universidade do Estado do Amazonas Manaus Brazil

ISBN: (纸本)9789897587467

Context: The rapid evolution of software engineering in response to the complex demands of the modern digital society has led to increased pressure on developers to adapt quickly. However, a pragmatic approach often overlooks the deeper theoretical foundations, which can result in inefficient software development practices. Systems Thinking (ST), particularly through Critical Systems Heuristics (CSH), offers a reflective and holistic approach to address these challenges, especially in the software requirements elicitation phase. Goal: This research aims to relate an experience of introducing Systems Thinking to undergraduate students using Critical Systems Heuristics as a support tool in a requirement elicitation process. Method: A mixed-methods educational experience was conducted with 36 undergraduate software engineering students. The students applied the CSH framework during an assignment on software requirements elicitation. The effectiveness of CSH was assessed through both quantitative measures (number and categorization of elicited requirements) and qualitative feedback (students’ perceptions and reflections). At the end of the project, we collected the students’ reflections about the application experience to gather students’ feedback Results: The application of CSH led to the elicitation of 372 total requirements, of which 25 were derived using the CSH framework. Students reported a positive impact on their overall understanding of the system but also highlighted challenges related to the complexity and time-consuming nature of the framework. Conclusion: The study demonstrates that the CSH framework can be a valuable tool in software requirements elicitation, aiding in the understanding the problem’s context and in the confirmation of requirements. While students acknowledged its benefits, they also recognized its limitations, suggesting that further refinement is needed for practical use. This experience contributes to the integration of Systems Thinking

关键词： Students

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Minimizing Average Job Completion Time for Multi-resource Queuing Computing Systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon EC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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A New Method for One-Pedal Driving With Fuzzy-Based Efficiency-Optimization Algorithm

A New Method for One-Pedal Driving With Fuzzy-Based Efficien...

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2025 IEEE international conference on Mechatronics, ICM 2025

作者： Heydrich, Marius Hammer, Tim Mitsching, Thomas Ivanov, Valentin Technische Universitä t Ilmenau Dept. of Computer Science and Automation Ilmenau Germany Technische Universitä t Ilmenau Dept. of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9798331533892

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller. © 2025 IEEE.

关键词： Vehicle wheels

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A survey of data-driven insights into air quality trends and prediction in India 4th

A survey of data-driven insights into air quality trends and...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Kabilan, K. Jebathangam, J. Department of Computer Science VISTAS Chennai India

ISBN: (纸本)9781032911571

Air pollution is a major health and environmental issue in increasingly urbanizing countries like India. This study examines ML and DL implementations on Indian Air Quality Index (AQI) datasets from 2015 to 2020. The research seeks to identify patterns, anticipate trends, and guide air pollution mitigation decisions. The study carefully analyses AQI parameters, explores data, and uses powerful ML and DL algorithms to forecast AQI and identify pollution hotspots. Technical features include missing value imputation, feature engineering, model selection criteria, hyper parameter tuning procedures, and model performance assessment measures. Key results show that ML and DL models can accurately forecast air quality, identify pollution sources, and capture temporal and geographical fluctuations. The article suggests integrating more data sources, exploring advanced deep learning methods, and developing dynamic air quality control systems. This study uses data-driven insights and multidisciplinary techniques to improve our knowledge of air quality dynamics in India and create the framework for proactive air pollution mitigation and public health policies. © 2025 the Author(s).

关键词： Data quality

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Leveraging Large Language Models for Usability Testing: a Preliminary Study 25

Leveraging Large Language Models for Usability Testing: a Pr...

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30th international conference on intelligent User Interfaces Companion, IUI 2025

作者： Calvano, Miriana Curci, Antonio Lanzilotti, Rosa Piccinno, Antonio Ragone, Azzurra Department of Computer Science University of Bari Aldo Moro BA Bari Italy Department of Computer Science University of Bari Aldo Moro Bari Italy University of Pisa Pisa Italy Department of Computer Science University of Bari Bari Italy

ISBN: (纸本)9798400714092

Despite growing efforts to prioritize user experience in product development, software organizations often perform little or no usability engineering activities. Therefore, it is crucial to develop strategies to integrate them effectively into software development processes. The rapid advances in Artificial Intelligence have significantly influenced various aspects of daily life, particularly with the emergence of Large Language Models (LLMs), which can serve as promising tools to support activities to enhance the usability of software products. This paper presents a study investigating the potential of LLMs to assist practitioners in conducting usability tests. Specifically, we conducted an experiment where LLMs generate usability test tasks. Our goal is to assess whether AI can effectively support evaluators by comparing tasks generated by LLMs to those defined by usability experts. The findings indicate that while LLMs can provide valuable support, effective usability testing still requires human oversight and expert intervention. © 2025 Copyright held by the owner/author(s).

关键词： Software design

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UAV-Aided Multi-antenna Relay Systems with Wireless Power Transfer for Maritime Communications 14th

UAV-Aided Multi-antenna Relay Systems with Wireless Power T...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： Li, Yuheng Zhou, Zhiquan Wang, Jinlong Wang, Lina Wang, Chenxu School of Information Science and Engineering Harbin Institute of Technology Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

With the increase in maritime activities, traditional communication methods can no longer meet the needs of today’s maritime work, and the use of unmanned aerial vehicle(UAV) relay communications in marine scenarios has attracted more and more attention. In this paper, A long-distance three-node two-hop UAV relay system for maritime users is investigated. The UAV aerial relay node first transmits an energy-carrying radio frequency (RF) signal to the maritime user node, which then harvests energy and sends data information back to the UAV aerial relay node, which in turn forwards it to the land node. Considering the peculiarities of maritime communication channels, a novel land-air-maritime communication channel model is proposed. Utilizing Zero-Forcing/Zero-Transmission (ZFR/ZFT) technology, a precoding matrix for the aerial relay node is designed, followed by simulation and emulation, the performance of the channel is tested in the case of different transmission power of relay nodes or different number of antennas of relay nodes. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Unmanned aerial vehicles (UAV)

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Maximum Entropy Deep Reinforcement Learning Based Power Allocation for NOMA Maritime Network 14th

Maximum Entropy Deep Reinforcement Learning Based Power Allo...

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14th EAI international conference on Wireless and Satellite Systems, WiSATS 2024

作者： He, Jiayi Zhang, Yakai Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

In order to address the challenges of high propagation delays and limited service capabilities in maritime satellite communications, unmanned aerial vehicles have been proposed as an airborne backhaul solution to enhance communications between satellites and maritime base stations. The non-orthogonal multiple access (NOMA) framework can solve the user sparsity problem in maritime networks. In this paper, a deep reinforcement learning algorithm is used to solve the nonconvex power allocation problem under NOMA. In order to mitigate the risk of overestimation of Q values and local optimal convergence of Deep Q Network (DQN) algorithm, we propose an algorithm called Soft Agent Critical Ocean Satellite Communication Power Allocation (SAC-OSCPA) based on the idea of maximum entropy and compare it with the traditional DQN algorithm. The main goal of this research is to maximize network throughput in scenarios with randomly distributed users. Simulation results show that the average system throughput is improved by 13.18% with the SAC-OSCPA algorithm, and the average throughput of the worst performing user is significantly improved by 41.59%. These results demonstrate the efficacy of the proposed algorithm in optimizing the communication performance of maritime satellite networks. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Deep reinforcement learning

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