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arXiv 2025年

作者： Tian, Yonglin Lin, Fei Li, Yiduo Zhang, Tengchao Zhang, Qiyao Fu, Xuan Huang, Jun Dai, Xingyuan Wang, Yutong Tian, Chunwei Li, Bai Lv, Yisheng Kovács, Levente Wang, Fei-Yue The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Automation Beijing Institute of Technology Beijing100081 China School of Software Northwestern Polytechnical University Xi’an710129 China College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China John von Neumann Faculty of Informatics Obuda University BudapestH-1034 Hungary

Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems’ perception and action capabilities, garnering widespread attention from academia and industry. However, current UAV operations primarily depend on human control, with only limited autonomy in simple scenarios, and lack the intelligence and adaptability needed for more complex environments and tasks. The emergence of large language models (LLMs) demonstrates remarkable problem-solving and generalization capabilities, offering a promising pathway for advancing UAV intelligence. This paper explores the integration of LLMs and UAVs, beginning with an overview of UAV systems’ fundamental components and functionalities, followed by an overview of the state-of-the-art LLM technology. Subsequently, it systematically highlights the multimodal data resources available for UAVs, which provide critical support for training and evaluation. Furthermore, key tasks and application scenarios where UAVs and LLMs converge are categorized and analyzed. Finally, a reference roadmap towards agentic UAVs is proposed to enable UAVs to achieve agentic intelligence through autonomous perception, memory, reasoning, and tool utilization. Related resources are available at https://***/Hub-Tian/UAVs Meet LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

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IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

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The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data (vol 13, 2365, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-11页

作者： Reiff, Sarah B. Schroeder, Andrew J. Kirli, Koray Cosolo, Andrea Bakker, Clara Mercado, Luisa Lee, Soohyun Veit, Alexander D. Balashov, Alexander K. Vitzthum, Carl Ronchetti, William Pitman, Kent M. Johnson, Jeremy Ehmsen, Shannon R. Kerpedjiev, Peter Abdennur, Nezar Imakaev, Maxim Ozturk, Serkan Utku Camoglu, Ugur Mirny, Leonid A. Gehlenborg, Nils Alver, Burak H. Park, Peter J. Department of Biomedical Informatics Harvard Medical School Boston MA 02115 USA Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA Karya SMD Software Solutions İzmir 35040 Turkey Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA

The 4D Nucleome (4DN) Network aims to elucidate the complex structure and organization of chromosomes in the nucleus and the impact of their disruption in disease biology. We present the 4DN Data Portal (https://***/), a repository for datasets generated in the 4DN network and relevant external datasets. Datasets were generated with a wide range of experiments, including chromosome conformation capture assays such as Hi-C and other innovative sequencing and microscopy-based assays probing chromosome architecture. All together, the 4DN data portal hosts more than 1800 experiment sets and 36000 files. Results of sequencing-based assays from different laboratories are uniformly processed and quality-controlled. The portal interface allows easy browsing, filtering, and bulk downloads, and the integrated HiGlass genome browser allows interactive visualization and comparison of multiple datasets. The 4DN data portal represents a primary resource for chromosome contact and other nuclear architecture data for the scientific community.

关键词： Computational platforms and environments Genetic databases Genomics

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An Optimized Approach to Hourly Temperature and Humidity Setpoint Generation for Reducing Tomato Disease and Saving Power Cost in Greenhouses

SSRN

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SSRN 2024年

作者： Sánchez-Molina, Jorge Antonio Wang, Hui Laktionov, Ivan Díaz, Francisco Rodríguez Li, Ming BIPP Institute of Plant Protection Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Department of Computer Systems Software Dnipro University of Technology DniproUA49005 Ukraine Department of Informatics University of Almería ceiA3 CIESOL Almería04120 Spain Beijing Research Center for Information Technology in Agriculture National Engineering Research Centre for Information Technology in Agriculture National Engineering Laboratory for Agri-product Quality Traceability Key Laboratory of Agri-informatics Ministry of Agriculture Beijing100097 China

Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops. The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop. Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems. The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day-1 in warm weather condition, as well as 3.74 €*day-1 in cold weather condition, the uninfected LAI (m2[Leaves](m-2[soils]*day-1)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose. The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth. © 2024, The Authors. All rights reserved.

关键词： Crops

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic 3

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Song, Jingping Prajapat, Shaligram Mishra, Durgesh Yang, Longzhi Boongoen, Tossapon Iam-On, Natthakan School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Northumbria University Department of Computer and Information Sciences United Kingdom Aberystwyth University Department of Computer Science United Kingdom

ISBN: (纸本)9798350343274

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks. © 2023 IEEE.

关键词： Decision making

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HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses

arXiv

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arXiv 2023年

作者： Ma, Caoyuan Liu, Yu-Lun Wang, Zhixiang Liu, Wu Liu, Xinchen Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China National Yang Ming Chiao Tung University Taiwan The University of Tokyo Japan National Institute of Informatics Japan School of Information Science and Technology University of Science and Technology of China China JD Explore Academy

We present HumanNeRF-SE, a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable parameters to fit the human images. Instead, we reload these approaches by combining explicit and implicit human representations to design both generalized rigid deformation and specific non-rigid deformation. Our key insight is that explicit shape can reduce the sampling points used to fit implicit representation, and frozen blending weights from SMPL constructing a generalized rigid deformation can effectively avoid overfitting and improve pose generalization performance. Our architecture involving both explicit and implicit representation is simple yet effective. Experiments demonstrate our model can synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies, HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Through the Sci-Fi Lens: Exploring Social Media’s Influence on Body Image and Bullying Among Adolescents in Boarding Schools

Through the Sci-Fi Lens: Exploring Social Media’s Influence...

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Learning and technology Conference (L&T)

作者： Jawahir Che Mustapha Muhammad Azmin Mohamed Ghazali Munaisyah Abdullah Nor Intan Saniah Sulaiman Hezlin Aryani Abd Rahman Informatics & Analytics Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Computer Engineering Tech. Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia College of Arts and Science School of Quantitative Sciences Universiti Utara Malaysia Kedah Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools.

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Extracting Relations between Organizational Patterns Using Association Mining 20

Extracting Relations between Organizational Patterns Using A...

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2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Waseeb, Shakirullah Khail, Waheedullah Sulaiman Wahaj, Haji Gul Vranić, Valentino Software Engineering Department Nangarhar University Afghanistan Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Slovakia Database and Information System Department Nangarhar University Afghanistan

ISBN: (纸本)9781450377690

Patterns are powerful when used in combinations. Identifying relationships between patterns is challenging. The existing approaches and pattern formats reflect the relationships with other patterns in a very informal and traditional way. We are proposing an automatic approach which discover such relationships from the patterns descriptive text using text mining and natural language processing techniques. In this work, we demonstrate how it contributes in inference of relationships and its strength among patterns. © 2020 ACM.

关键词： Natural language processing systems

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Modeling and Analysis of the Kinetics of the Pyrolysis Process of Biomass with Influence Raw Material Composition on Comsol Multiphysics

Modeling and Analysis of the Kinetics of the Pyrolysis Proce...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Mamatkulova Sayyora Gulyamovna Uzakov Gulom Norboyevich Qodirov Farrux Ergash Ug'Ii Ulasheva Shaxlo Tagayevna PhD Student of the Department Alternative Energy Sources Karshi Engineering Economic Institute Karshi City Uzbekistan Professor of the Department Alternative Energy Sources Karshi Engineering Economic Institute Karshi City Uzbekistan Head of the Department of Informatics and its Teaching Methodology Shakhrisabz State Pedagogical Institute Kashkadarya Uzbekistan Senior Lecturer of the Department of Information Technology Software Karshi Branch of the Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In this article, the modeling and analysis of the kinetics of the pyrolysis process is carried out, which allows us to determine the rate of chemical reactions occurring during the decomposition of the material, as well as to identify key factors affecting the speed and efficiency of pyrolysis. The simulation was performed using Comsol Multiphysics software based on the kinetic mechanisms of the reaction of biomass of corn cob particles at a temperature of $500^{\mathrm{o}}\mathrm{C}$ . The pyrolysis process of plant biomass was analyzed at a residence time of 0 to 10 minutes. The resulting models describe temperature changes well, especially in the center of the biomass. It was found that pyrolysis of corncob biomass occurred in the temperature range from 190 to $43 2^{0}\mathrm{C}$ , at $19 0^{0}\mathrm{C}$ mass loss began, which ended at 432 $\circ \mathrm{c}$ . The kinetic parameters were calculated using the Kissinger method, and the least squares method and correlation analysis were used to calculate the kinetic constants. The study results will be useful for optimizing the conditions of the biomass pyrolysis process in the future.

关键词： Temperature sensors Temperature distribution Analytical models Correlation Biological system modeling software Biomass Kinetic theory Surface treatment Least mean squares methods

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

arXiv

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arXiv 2023年

作者： Wen, Xin-Cheng Chen, Yupan Gao, Cuiyun Zhang, Hongyu Zhang, Jie M. Liao, Qing School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Big Data and Software Engineering Chongqing University China Department of Informatics King’s College London United Kingdom Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Peng Cheng Laboratory China

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs;2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection. © 2023, CC BY.

关键词： Deep learning

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