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Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

Biomimetic Intelligence & Robotics 2024年第4期4卷 76-91页

作者： Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Department of Electrical and Computer Engineering Mississippi State UniversityStarkville 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA Department of Computer Science Asia UniversityTaichung 413305TaiwanChina

In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

关键词： Human autonomy teaming(HAT) Robot path planning Generalized Voronoi diagram(GVD) Spatio-temporal graphs Bio-inspired algorithms

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Comparing HAProxy Scheduling Algorithms during the DDoS Attacks

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 139-142页

作者： Kumar, Anmol Somani, Gaurav Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science and Engineering Patna800013 India Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financial and reputation losses. Load balancing software such as HAProxy (high availability proxy) is an important building block of the Web service delivery today. Load balancing software provides incoming request distribution among Web-servers and admission control which may even help with combating DDoS attacks. In this letter, we study and explore the performance of different load distribution or scheduling strategies in the presence of DDoS attacks. With the help of attack experiments, we find that First, HDR, Source, and URI scheduling algorithms performs best for both static and Poisson arrivals. © 2019 IEEE.

关键词： Scheduling algorithms

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A Single-Pass Noise Covariance Estimation Algorithm in Nonswitching Multiple-Model Adaptive Kalman Filters for Nonstationary Systems

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Journal of Advances in Information Fusion 2024年第1期19卷 3-19页

作者： Kim, Hee-Seung Bienkowski, Adam Zhang, Lingyi Pattipati, Krishna R. Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States

This paper presents a single-pass stochastic gradient descent (SGD) algorithm for estimating unknown noise covariances. The proposed algorithm is designed for nonswitching multiple-model adaptive Kalman filters, where the noise covariances can occasionally jump up or down by an unknown magnitude. Compared to our previous batch estimation or multipass decision-directed estimation methods, the proposed algorithm has the advantage of reading measurement data exactly once, leading to a significant improvement in computational efficiency and practicality. Moreover, the algorithm achieves an acceptable level of root mean square error (RMSE) in state estimates, making it suitable for real-time industrial applications. The proposed algorithm utilizes recursive fading memory estimates of the sample cross-correlations of the innovations and employs the root mean square propagation (RMSprop) accelerated SGD algorithm. The combination of these techniques enables the algorithm to achieve high accuracy in estimating the unknown noise covariances while maintaining superior computational efficiency over iterative batch methods. The paper presents a comparative evaluation of the proposed method on several test cases, which demonstrate its effectiveness in terms of computational efficiency and estimation accuracy. Overall, the proposed algorithm offers a promising approach for real-time noise covariance estimation in multiple-model adaptive Kalman filters. © 2024 JAIF.

关键词： Adaptive filters

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Exploration, Collaboration, and Applications in Multi-Agent Reinforcement Learning

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Performance Evaluation Review 2025年第3期52卷 31-34页

作者： Chen, Jingdi Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

In recent years, the field of humancentric decision-making has emerged as a critical area of research, driven by its potential to fundamentally reshape how decisions are made across a variety of complex systems. Human-centric decision-making is characterized by considerable variability, which can be both advantageous and problematic. Variability is beneficial in contexts like brainstorming or problem-solving, where diverse perspectives lead to innovation. However, in situations where consistency is crucial- such as public health, safety, and fairness-this variability can have negative consequences. For instance, when radiologists interpret the same mammogram differently, it can result in unnecessary medical procedures. Similarly, in military operations, inconsistent judgments by sonar technicians regarding potential threats can jeopardize both crew safety and mission success. This highlights the critical need for a deeper understanding of decisions where the stakes are high. Thus, it is important to deepen our understanding of the decision-making process in completed and dynamic scenarios that involve multiple decisionmakers. This should guide the creation of validated methods to identify, measure, model, and adjust variability in judgments to improve decision-making in multi-agent environments. © 2025 Copyright is held by the owner/author(s).

关键词： Reinforcement learning

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Classification of plant diseases in images using dense-inception architecture with attention modules

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Multimedia Tools and Applications 2024年 1-26页

作者： Khan, Sultan Daud Basalamah, Saleh Naseer, Atif Department of Computer Science National University of Technology Islamabad Pakistan Department of Computer Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Umm Al-Qura University Makkah Saudi Arabia

Agriculture plays a vital role in providing food to a growing world population. However, plant diseases and pests result in 50% reductions in crop yields, which exacerbates poverty and threatens a sustainable food system. Currently, plant disease detection mainly relies on manual visual inspection and analysis of the field, which is a time-consuming and unreliable method. Modern technology, such as smart agriculture, can leverage machine learning, computer vision, and remote sensing technologies to overcome the limitations of traditional methods. Several machine learning algorithms have been used for plant disease classification and detection. However, these models suffer from limitations such as dependence on complex hand-crafted features for classification, and limited adaptability to new data. Deep learning models, on the other hand, have the ability to automatically learn hierarchical representations from raw data, making them inherently more informative and adaptable to changing data patterns. While existing deep learning approaches for plant disease detection have achieved some degree of success, these models still face challenges in achieving superior performance due to similar visual appearances of different plant diseases, intra-class differences, and variation in scale and resolution. To address these challenges, we proposed a framework that consists of hybrid network and attention module. Our hybrid module combines the benefits of DenseNet and InceptionNet to capture multi-scale features, while the attention module addresses the challenges of inter-class similarity and intra-class variability by isolating the most discriminative feature and disregarding extraneous and insignificant information. We performed experiments on publicly available challenging dataset. From Experimental results, we demonstrate the effectiveness of the proposed framework in achieving high accuracy in plant disease detection. © The Author(s), under exclusive licence to Springer Sci

关键词： Agriculture

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Building Bridges: A Holistic Approach to Web Development Education Through Community Engaged Learning and Social Justice Initiatives 54

Building Bridges: A Holistic Approach to Web Development Edu...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Rusu, Amalia Rusu, Adrian Vijay, Aarushi Fairfield University Department of Computer Science and Engineering Fairfield United States University of New Haven Department of Electrical & Computer Engineering and Computer Science New Haven United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes an innovative pedagogical framework that integrates community engaged learning and social justice principles into a traditional web development course, fostering a holistic approach to education that goes beyond programming languages and algorithms, and aiming to redefine the educational landscape by encouraging socially responsible and community-driven web development practices. The community engaged learning component of the course is structured around partnerships with local non-profit organizations, community groups, or socially impactful initiatives. Through these partnerships, students engaged in real-world projects, applying their technical expertise to develop impactful solutions that contribute to social justice causes. The web development course framework is presented and sample projects are provided with student and community partner reflections and benefits. We explored the effect of the community engaged learning and social justice initiatives on technical proficiency, critical thinking, teamwork, and ethical reasoning. Results indicate a positive correlation between community engaged learning experiences and increased student motivation, teamwork, and a heightened awareness of the societal implications of their work. Furthermore, the paper discusses the challenges encountered during the implementation process and proposes strategies for overcoming potential barriers. The findings of this research contribute to the ongoing discourse on the evolution of computer science and engineering education, advocating for a holistic approach that not only equips students with technical skills but also instills a sense of social responsibility. © 2024 IEEE.

关键词： Web Design

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IBAttack: Being Cautious about Data Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1484-1493页

作者： Agarwal, Akshay Singh, Richa Vatsa, Mayank Ratha, Nalini IISER Bhopal Department of Data Science and Engineering Bhopal462066 India IIT Jodhpur Department of Computer Science and Engineering Rajasthan Jodhpur342037 India University at Buffalo Department of Computer Science and Electrical Engineering BuffaloNY14260 United States

Traditional backdoor attacks insert a trigger patch in the training images and associate the trigger with the targeted class label. Backdoor attacks are one of the rapidly evolving types of attack which can have a significant impact. On the other hand, adversarial perturbations have a significantly different attack mechanism from the traditional backdoor corruptions, where an imperceptible noise is learned to fool the deep learning models. In this research, we amalgamate these two concepts and propose a novel imperceptible backdoor attack, termed as the IBAttack, where the adversarial images are associated with the desired target classes. A significant advantage of the adversarial-based proposed backdoor attack is the imperceptibility as compared to the traditional trigger-based mechanism. The proposed adversarial dynamic attack, in contrast to existing attacks, is agnostic to classifiers and trigger patterns. The extensive evaluation using multiple databases and networks illustrates the effectiveness of the proposed attack. © 2020 IEEE.

关键词： computer vision

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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Conditional-GAN-Based Face Inpainting Approaches With Symmetry and View-Degree Utilization

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IEEE Access 2024年 12卷 87467-87478页

作者： Hong, Tzung-Pei Wu, Jin-Hang Su, Ja-Hwung Yin, Tang-Kai National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung811726 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

Recently, image inpainting has been proposed as a solution for restoring the polluted image in the field of computer vision. Further, face inpainting is a subfield of image inpainting, which refers to a set of image editing algorithms re-conducting the missing regions in face smoothly. Actually, face inpainting is more challenging than general image inpainting because it needs more face structure information. Although a number of past studies were proposed for face inpainting by using face segmentation, face edge and face topology, there is some important information ignored, such as geometric and symmetric properties. Based on such concepts, in this paper, we propose a two-stage face inpainting method called CGAN (Conditional Generative Adversarial Network) which integrates face landmarks and Generative Adversarial Network (called GAN). In the first stage, the face landmark is predicted as the condition, providing GAN with important information of geometry and symmetry. The main idea in this stage is to dynamically adjust the loss by the proposed view degree. Accordingly, the masked face image and the corresponding face landmark are used as conditions input to the GAN in the second stage. Finally, the missing-regions are inpainted by the proposed CGAN. To reveal the effectiveness of proposed method, a number of evaluations were conducted on real datasets. The experimental results show that, the proposed method predicts a better face landmark by information of geometric structures and symmetric outlooks, and thereupon the proposed CGAN reconstructs the missing regions superior to the compared methods. © 2013 IEEE.

关键词： Image reconstruction

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Dynamic Quantum Federated Learning for UAV-Based Autonomous Surveillance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University School of Electrical Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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