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Exploring Text-Guided Single Image Editing for Remote Sensing Images

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arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

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arXiv 2024年

作者： Yu, Jun Dai, Yutong Liu, Xiaokang Huang, Jin Shen, Yishan Zhang, Ke Zhou, Rong Adhikarla, Eashan Ye, Wenxuan Liu, Yixin Kong, Zhaoming Zhang, Kai Yin, Yilong Namboodiri, Vinod Davison, Brian D. Moore, Jason H. Chen, Yong Department of Computer Science and Engineering Lehigh University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Department of Industrial and Systems Engineering Lehigh University United States Department of Statistics University of Missouri United States School of Software Shandong University China Department of Computer Science University of Hong Kong Hong Kong Department of Computer Science and Engineering South China University of Technology China Department of Community and Population Health Lehigh University United States Department of Computational Biomedicine Cedars-Sinai Medical Center United States

Multi-Task Learning (MTL) is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to Single-Task Learning (STL), MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including computer vision, natural language processing, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for zero-shot learning, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://***/junfish/Awesome-Multi

关键词： Learning systems

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

Privacy-Preserving Quantum Two-Party Geometric Intersection

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, James C.N. Yu, Wen-Bin Chi, Lian-Hua Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan Department of Computer Science and Information Technology La Trobe University VIC3086 Australia

Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB ) as the set of numbered grids SA (SB ), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB ) is firstly utilized to encode the private elements of SA (a0, a1,, aM1) (SB (b0,b1,bN1)) into the quantum states, and then the oracle operation Of is applied to obtain a new quantum state which includes the XOR results between each element of SA and SB . Finally, the quantum counting is introduced to get the amount (t ) of the states ai bj equaling to 0, and the intersection result can be obtained by judging t 0 or not. Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity. © 2023, CC BY.

关键词： Computational geometry

Discrete biological modeling for the immune response to dengue virus

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第1期13卷 214-234页

作者： Khaled A.Al-Utaibi M.Muzamil Ayesha Sohail Fatima Alam Alessandro Nutini Sadiq M.Sait Computer Science and Software Engineering Department University of Ha’il Ha’ilSaudi Arabia Quaid e Azam Medical College BahawalpurPakistan Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Biology and Biomechanics Department Centro Studi Attivita Motorie via di Tiglio 9455100 LuccaItaly Center for Communications and IT Research Research Institute King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–*** via the mosquito bite,the dengue virus infects Langerhans ***,B lymphocytes,and mast cells infected with dengue virus produce various cytokines although it is not clear which ones are predominant during DHF disease.A mathemat-ical model of the Dengue virus infection is developed according to complex dynamics determined by many *** from a state of equilibrium that we could define as“virus-free”asymptotically stable with a viral reproduction number lower than one which means a very effective action of the innate immune system:it stops the infectious process,the mathematical analysis of stability in the presence of the virus demonstrates that the proposed model is dynamically *** fever affects more than half of the world’s population,with 1 billion symptomatic cases and multiple genetic serotypes confirmed each year,which simulates a network of interactions between the various populations involved without considering the speeds of the processes in question which are indicated in a separate *** this research,a hybrid approach of petri nets is utilized to connect the discrete models of dengue.

关键词： Modeling scientific computing DENV cytokines petri nets infected cell

Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

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Empowering Donors: How Blockchain Technology Can Help Ensure...

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol Internet of Things Security

Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

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Mitigating Insecure Outputs in Large Language Models(LLMs): ...

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings

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arXiv 2025年

作者： Obioma, Peace Agbodike, Obinna Chen, Jenhui Wang, Lei Department of Computer Science University of Manchester M139PL United Kingdom Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Guishan Dist Taoyuan33375 Taiwan iTrustal Department of Computer Science and Information Engineering Chang Gung University Guishan Dist. Taoyuan33302 Taiwan Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system’s performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems. © 2025, CC BY.

关键词： Constrained optimization