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SSRN 2025年

作者： Zhu, Xian Zeng, Xiaoqin Cong, Yuhua Ma, Wei Liu, Qikai Wang, Qiuling Hohai University College of Computer and Software Engineering Nanjing China Nanjing University of Science and Technology Zijin College Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China Nanjing Institute of Tourism and Hospitality Nanjing China

Saliency detection in UAV low-altitude outdoor scenes is critical for applications in environmental monitoring, disaster management, and autonomous navigation. However, challenges such as cluttered backgrounds, small or indistinct objects, and limited annotated datasets hinder the effectiveness of existing methods. To address these issues, we propose an innovative approach that integrates depth-activated feature modeling, multi-source pseudo-label generation, and uncertainty-aware optimization. The first component, the Depth-Activated Deformable Transformer (DADT), integrates global contextual modeling and depth-activated feature extraction to enhance foreground-background separation and improve saliency detection in complex environments. The second component employs a multi-source pseudo-label generation strategy, incorporating synthetic data and depth-guided refinement to enhance label diversity and improve small-object detection accuracy. Finally, an uncertainty-aware optimization strategy dynamically balances pseudo-label contributions, refining high-confidence regions while suppressing noise, thereby further improving boundary precision. Experimental results on UAV low-altitude datasets demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance and offering a robust solution for saliency detection in challenging outdoor scenarios. © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Extracting GHZ states from linear cluster states

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Physical Review Research 2024年第1期6卷 013330-013330页

作者： J. de Jong F. Hahn N. Tcholtchev M. Hauswirth A. Pappa Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Fraunhofer Institute for Open Communication Systems - FOKUS 10589 Berlin Germany

Quantum information processing architectures typically only allow for nearest-neighbor entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to n=19 qubits.

关键词： Measurement-based quantum computing Quantum communication Quantum entanglement Quantum networks Superconducting qubits

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention

Optimizing Load and Routes in Supply Chain Management with T...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Rajasekar P M Shabeena Begam Shouvik Chattopadhyay S. Chand Basha Shanker Chandre Vijay Kumar Dwivedi Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of MBA SNS College of Technology Coimbatore India Department of MBA Institute of Engineering & Management Kolkata India University of Engineering & Management Kolkata India Department of Business Administration St Ann's College of Engineering and Technology Chirala India Department of Computer Science & Artificial Intelligence School of Computer Science & Artificial Intelligence SR University Warangal India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results.

关键词： Training Surveys Supply chain management Accuracy Profitability Time series analysis Feature extraction Integrated circuit modeling Surges Principal component analysis

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Job Shop Scheduling for Dynamic Environments Using a Q-Learning Approach

Job Shop Scheduling for Dynamic Environments Using a Q-Learn...

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International Conference on Knowledge and Smart Technology (KST)

作者： Pakkaporn Saophan Pakorn Uttayopas Warut Pannakkong Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Department of Mechanical Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Efficient scheduling in dynamic production environments with frequent disruptions presents a significant challenge, particularly in the context of the Job Shop Scheduling Problem (JSSP). Traditional solutions, constrained by static assumptions, often fail to adapt to such dynamic environments. To address this issue, this study proposed a Q-learning-based framework for the JSSP, utilizing the adaptability of Reinforcement Learning (RL) to dynamically select scheduling strategies. The numerical experiment using Taillard's JSSP dataset demonstrated that the proposed method achieves a mean makespan of $1594.70 \pm 81.23$ , outperforming conventional dispatching rules such as First In, First Out (FIFO), Longest Processing Time (LPT), Shortest Processing Time (SPT), and Most Remaining Time (MRT). It also surpasses the Random Strategy (RS), which yields significantly higher makespan values. These findings illustrated the potential of the Q-learning-based method as a robust alternative to traditional heuristic methods, paving the way for advancements in real-time production scheduling strategies.

关键词： Job shop scheduling Q-learning Production Dynamic scheduling Dispatching Real-time systems

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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DomainAdaptor: A Novel Approach to Test-time Adaptation

arXiv

引用

arXiv 2023年

作者： Zhang, Jian Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University China

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture coefficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://***/koncle/DomainAdaptor. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Live Demonstration: A fully embedded adaptive real-time hand gesture classifier leveraging HD-sEMG and deep learning

Live Demonstration: A fully embedded adaptive real-time hand...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Xavier Isabel Thomas Labbé Félix Chamberland Étienne Buteau Evan Campbell Ulysse Côté-Allard Simon Tam Paul Fortier Alexandre Campeau-Lecours Mounir Boukadoum Erik Scheme Benoit Gosselin Electrical Engineering and Computer Engineering Department Laval University Institute of Biomedical Engineering University of New-Brunswick Department off Technology Systems University of Oslo Mechanical Engineering Department Laval University Computer Science Department University of Quebec at Montreal

In a fully immersive virtual reality setting, visitors will actively participate in our demonstration, engaging in an interactive videogame utilizing our cutting-edge HD-sEMG Bracelet. Prior to this, they will undergo state-of-the-art training to personalize their machine learning gesture recognition model. The gameplay will be broadcast on a computer monitor, allowing onlookers to observe while the demonstrator explains the core concept of our work. By wearing the bracelet, the player gains control over their limb in a game inspired by Simon Says. Throughout the demonstration, visitors will gain firsthand experience and knowledge about the forthcoming advantages of HD-sEMG for control applications, such as prosthesis control. They will also discover how the EMaGer HD-sEMG Bracelet capitalizes on its uniform and high electrode density to enhance the reliability of hand gesture recognition models utilized in myoelectric prosthesis control.

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An Efficient Medium Access Control Mechanism for Flying Ad-hoc Networks

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computer systems science & engineering 2021年第7期38卷 47-63页

作者： Muhammad Asghar Khan Fazal Noor Insaf Ullah Sajjad ur Rehman Shibli Nisar Mohaira Ahmad Department of Electronic Engineering ISRA UniversityIslamabad44000Pakistan Department of Computer Science and Information Systems Islamic University of MadinahMadinah400411Saudi Arabia Hamdard Institute of Engineering&Technology Islamabad44000Pakistan Department of Electrical Engineering Namal InstituteMianwali42200Pakistan Department of Electrical Engineering National University of Sciences and Technology(NUST)Islamabad44000Pakistan

The Flying Ad-hoc Networks(FANETs)is characterized by the transition from a single large Unmanned Aerial Vehicle(UAV)to multiple small UAVs connected in an ad-hoc *** high mobility is the core feature of such networks,they are prone to route breaks within the *** issue of connectivity loss can be coped with,to some extent,by making use of omnidirectional *** modification,however,curtails Quality-of-Service(QoS)requirements of networks in terms of bandwidth,media access delay,coverage and ***,directional antennas have advantages over omnidirectional antennas such as improved transmission range,spatial reuse and high ***,its introduction raises location-dependent issues to the Medium Access Control(MAC)*** calls for an efficient MAC protocol that can cater to new directional antenna models and,at the same time,can counter the constraints associated with the dynamic ***,in this article,we consider a UAV interconnection mechanism that lets the UAVs execute the communication tasks using the directional MAC *** technique is advantageous as compared to the approach of utilizing the MAC protocol using omnidirectional *** scheme is being implemented as a case study for Industry 4.0 inventory and traceability applications in the *** modeling and simulation purposes,we use the Optimized Network engineering Tool(OPNET)and aim to seek an evaluation with respect to throughput,media access delay,retransmission attempts and data *** results obtained demonstrate the effectiveness of the proposed scheme.

关键词： FANETs QoS Industry 4.0 IEEE 802.11 MAC OPNET mobility models directional antenna

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Solving the Quadratic Assignment Problem using Deep Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： Bagga, Puneet S. Delarue, Arthur School of Computer Science Georgia Institute of Technology United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States

The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven particularly challenging to solve: unlike other combinatorial problems like the traveling salesman problem (TSP), which can be solved to optimality for instances with hundreds or even thousands of locations using advanced integer programming techniques, no methods are known to exactly solve QAP instances of size greater than 30. Solving the QAP is nevertheless important because of its many critical applications, such as electronic wiring design and facility layout selection. We propose a method to solve the original Koopmans-Beckman formulation of the QAP using deep reinforcement learning. Our approach relies on a novel double pointer network, which alternates between selecting a location in which to place the next facility and a facility to place in the previous location. We train our model using A2C on a large dataset of synthetic instances, producing solutions with no instance-specific retraining necessary. Out of sample, our solutions are on average within 7.5% of a high-quality local search baseline, and even outperform it on 1.2% of instances. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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