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Conference on Lasers and Electro-Optics (CLEO)

作者： Hooman Barati Sedeh Yuruo Zheng Jiannan Gao Fangxing Lai Hao Li Shumin Xiao Natalia M. Litchinitser Department of Electrical and Computer Engineering Duke University Durham NC USA Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen P. R. China

We design and experimentally demonstrate the generation of blue and ultraviolet third-harmonic generation from a TiO 2 metasurface supporting quasi-bound state in the continuum with 0.2% conversion efficiency.

ISBN: (纸本)9798350369311

关键词： Lasers and electrooptics Metasurfaces Laser beams Titanium dioxide Electro-optical waveguides

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Fuzzy cognitive maps learning algorithm based on adaptive multi-factor particle swarm 25

Fuzzy cognitive maps learning algorithm based on adaptive mu...

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Proceedings of the 2025 International Conference on Big Data and Informatization Education

作者： Zongxi Li Jingping Wang Yingjun Zhang Frontiers Science Center for Smart High-speed Railway System School of Computer Science and Technology Beijing Jiaotong University Beijing China Center of Information & Network Technology Beijing Normal University Beijing China

ISBN: (纸本)9798400714405

Fuzzy Cognitive Maps (FCMs), as an effective soft computing method, has been widely applied in the modeling and analysis of complex systems. However, existing FCM learning algorithms do not fully consider the intricate causal relationships between nodes, making the learning process prone to local *** address this issue, this paper proposes an Adaptive Multi-Factor Particle Swarm Optimization-Based Fuzzy Cognitive Map Learning Algorithm (AMFPSO-FCM). First, a decomposition strategy is employed to partition the FCM into multiple local links, treating the learning of each local link as an independent task, thereby modeling FCM learning as a multi-task learning problem. Subsequently, an FCM learning approach is designed based on the Multi-Factor Particle Swarm Optimization (MFPSO) algorithm, incorporating multi-dimensional dynamic chaotic inertia weights to enhance search capability and utilizing an adaptive probabilistic transition matrix to improve knowledge transfer efficiency. Experiments conducted on three air quality datasets demonstrate that AMFPSO-FCM outperforms existing algorithms in both accuracy and generalization ability.

关键词： Fuzzy Cognitive Maps

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Sensor Infused Emperor Penguin Optimized Deep Maxout Network for Paralyzed Person Monitoring

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IEEE Sensors Journal 2024年

作者： Yang, Jing Hu, Ruoyang Wu, Chenwei Jiang, Gaozhe Alkanhel, Reem Ibrahim Elmannai, Hela Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Boston University Sargent College BostonMA02115 United States Evident Battery Inc MA02138 United States National University of Singapore Singapore Institute of Operations Research and Analytics Singapore119007 Singapore Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia

Paralyzed people must maintain proper posture to prevent respiratory problems, pressure sores, and muscular contractures. A paralyzed person may slip out of their wheelchair several times as a result of poor sitting position. To overcome these challenges, a novel Emperor Penguin optimized sensor Infused wheelChair (EPIC) framework has been proposed to constantly monitor the real-time posture of the paralyzed person's health state. The proposed model aims to monitor wheelchair posture and health in real-time, which can automatically detect posture issues, providing timely alerts and feedback to the user via a mobile application. The proposed framework utilizes the Emperor Penguin Optimizer (EPO) algorithm for feature selection to improve the accuracy of posture detection. A Deep Maxout Network (DMN) analyzes the features to predict the posture of the wheelchair-bound patient. The proposed EPIC framework has been assessed using a Python simulator. The effectiveness of the proposed EPIC framework has been determined using evaluation metrics such as precision, specificity, accuracy, and sensitivity. The proposed EPIC technique advances the overall accuracy by 10.1%, 7.73%, and 2.84% better than posture recognition, Graphic User Interface (GUI), and Independent Component Analysis-Kantorovich Distance (ICA-KD) respectively. © 2024 IEEE.

关键词： Graphical user interfaces

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Smart Agriculture Prediction system for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

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12th IEEE Annual information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Decision making

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Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

arXiv

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arXiv 2023年

作者： Su, Xingzhe Qiang, Wenwen Hu, Jie Zheng, Changwen Wu, Fengge Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Meituan Beijing China Science & Technology on Integrated Information System Laboratory Department of Computer Science and Technology Tsinghua University Beijing China

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the amount of training data for RS image generation than for natural image generation (Fig.1). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig.2). Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. Extensive experiments on eight RS datasets and three natural datasets show the effectiveness and versatility of our methods. The source code is available at https://***/rootSue/Causal-RSGAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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AI Based Monitoring system for Social Engineering 3

AI Based Monitoring System for Social Engineering

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Yapa, K. Udara, S.W.I. Wijayawardane, U.P.B. Kularatne, K.N.P. Navaratne, N.M.P.P. Dharmaphriya, W. G. V. U Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets;and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits. © 2021 IEEE.

关键词： Social networking (online)

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Evaluating the Privacy and Security Implications of Mobile Apps

Evaluating the Privacy and Security Implications of Mobile A...

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International Conference on Electronics, Materials Engineering and Nano-technology (IEMENTech)

作者： Shafiul Islam Mazumder Sazzadul Islam Shovon Md Tanvir Chowdhury Minhajul Islam Habiba Azad Raisa Habibur Rahman Monjurul Islam Sumon Md. Khalid Mahbub Khan Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Information System Central Michigan University Michigan United States Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering University of Information Technology & Sciences (UITS) Dhaka Bangladesh

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The rapid growth of mobile applications, combined with an increasing reliance on these apps for a variety of purposes, has prompted serious concerns about user privacy and data security. This study aims to assess the privacy practices, data handling methods, and security vulnerabilities of mobile apps in a variety of categories, including social media, e-commerce, health and fitness, and financial management. This study identifies major privacy and security issues by examining privacy policies and terms of service, as well as how these apps handle sensitive data. The study also examines free and premium apps' privacy procedures, taking into account the implications for user data protection. The findings illustrate differences in data handling and security standards across different app categories, highlighting the importance of transparent privacy policies, strong security mechanisms, and limited data sharing to protect user information. The research helps to define standards and best practices for promoting secure app activity, increasing user confidence, and guaranteeing a safer digital environment.

关键词： Privacy Social networking (online) Data security Ecosystems Data protection Mobile applications Electronic commerce Protection Standards Best practices

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel Spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the Spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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