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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

arXiv

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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Vision Language Model Empowered Surgical Planning

Vision Language Model Empowered Surgical Planning

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Yihe Chen Runsheng Yu Xin Wang Wensheng Wang Ning Tan Youzhi Zhang School of Artificial Intelligence Nanjing University Nanjing China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

The integration of a flexible endoscope with a surgical manipulator is crucial in minimally invasive surgery (MIS), facilitating detailed visualization of the operative field within the patient’s body. During MIS, the remote center of motion (RCM) constraints are essential for achieving visual servoing control and ensuring accurate tracking control of the robotic endoscope. Existing work requires the exact trajectory for the tracking control and does not connect both tasks with the RCM constraints. In this paper, we exploit GPT-V to develop Vision Language Model Empowered surgical Planning (VLM-EP), which uses environmental observations and task description to finish the tracking task without the exact trajectory and connect both tasks through the exploration procedure in vivo safety range. Our simulated experiments show that our VLM-EP significantly outperforms the state-of-the-art control-based baseline. We demonstrate a practical implementation of VLM-EP in real-world scenarios, which shows that VLM-EP effectively handles the tracking control task and the visual servoing control task.

关键词： Visualization In vivo Minimally invasive surgery Tracking Endoscopes Manipulators Visual servoing Planning Trajectory Safety

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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit 22

Effect of Environment Temperature and Relative Humidity on T...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Sirisomboon, Panmanas Sripinyowanich Jongyingcharoen, Jiraporn Junto, Apiwat Phanomsophon, Thitima Dachoupakan Sirisomboon, Cheewanun Rerngsamran, Panan Pokhrel, Dharma Raj Sharma, Sneha Posom, Jetsada Pornchaloempong, Pimpen Terdwongwarakul, Anupun Chongstitvatana, Prabhas School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Agricultural Engineering Bangkok Thailand School of Engineering and Innovation Rajamangala University of Technology Tawan-Ok Department of Agricultural Engineering Chon Buri Thailand School of Agricultural Technology King Mongkut's Institute of Technology Ladkrabang Office of Administrative Interdisciplinary Program on Agricultural Technology Bangkok Thailand Chulalongkorn University Faculty of Science Department of Microbiology Bangkok Thailand University of Melbourne Department of Infrastructure Engineering Victoria Australia Khon Kaen University Faculty of Engineering Department of Agricultural Engineering Khon Kaen Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Food Engineering Bangkok Thailand Kasetsart University Faculty of Engineering at Kanpangsan Department of Agricultural Engineering Thailand Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350385601

This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained. © 2024 IEEE.

关键词： Fruits

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Privacy Preserving Charge Location Prediction for Electric Vehicles

arXiv

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arXiv 2025年

作者： Marlin, Robert Jurdak, Raja Abuadbba, Alsharif Miller, Dimity School of Computer Science Australia School of Electrical Engineering and Robotics Queensland University of Technology Australia CSIRO’s Data61 Cyber Security Cooperative Research Centre Australia CSIRO’s Data61 Australia

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs’ next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN’s ability to forecast energy demands over extended periods. We present a privacy focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

Outram: One-shot Global Localization via Triangulated Scene ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Pengyu Yin Haozhi Cao Thien-Minh Nguyen Shenghai Yuan Shuyang Zhang Kangcheng Liu Lihua Xie Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://***/Pamphlett/Outram.

关键词： Location awareness Point cloud compression Three-dimensional displays Laser radar Costs Pose estimation Feature extraction

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DECIDER: Difference-aware Contrastive Diffusion Model with Adversarial Perturbations for Image Change Captioning 39

DECIDER: Difference-aware Contrastive Diffusion Model with A...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhong, Guojin Hu, Jinhong Chen, Jiajun Yuan, Jin Pan, Wenbo College of Computer Science and Electronic Engineering Hunan University China GuangDong Engineering Technology Research Center of Intelligent Service of Urban and Rural Planning and Construction China School of Robotics Hunan University China CRRC Zhuzhou Institute China

ISBN: (纸本)157735897X

Image change captioning (ICC) poses great challenges stemming from describing subtle differences between two similar images in natural language, significantly increasing the complexity of feature extraction and cross-modal learning compared to the image captioning task. Existing ICC methods often suffer from two key challenges: 1) Massive irrelevant information of uni-image features leads to suboptimal visual difference representations;2) Imprecise inter-modality correspondence degrades the quality of generated captions. This paper proposes a Difference-aware Contrastive Diffusion Model with Adversarial Perturbations (DECIDER) for ICC due to the excellent performance of diffusion models in image/text generation. Technically, difference-aware cross-modal learning is developed to suppress irrelevant information and learn compact yet robust visual difference representations. This is achieved by optimizing a novel objective mathematically derived from the information bottleneck principle that excels in filtering redundant features and highlighting differences. Furthermore, we propose to dynamically generate “hard” positive and negative samples via adversarial perturbations, which are involved in contrastive diffusion training with a tighter variational bound. This design encourages our DECIDER to excavate and construct complex correspondences between visual differences and captions, thereby improving generalization performance. Extensive experiments on four datasets demonstrate that DECIDER significantly exceeds state-of-the-art performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Semantic-guided Image Feature Matching for Agricultural Robots 21

Semantic-guided Image Feature Matching for Agricultural Robo...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Zhenghong Yu Xiuli Zhu Huabing Zhou College of Robotics Guangdong Polytechnic of Science and Technology China School of Computer Science and Engineering Wuhan Institute of Technology China

ISBN: (纸本)9781450384087

Image feature matching is one of the most important basic techniques for agricultural robots. This task has been tried, but because of the complexity of the image, especially the non-rigid transformations, the performance is still far from satisfactory. This paper proposed a semantic-guided feature matching method for both agricultural mobile robot and UAV via semantic segmentation and probabilistic inference. Given an original image pair, a learning-based method, which is very active to the challenging variation of outdoor luminosity, is introduced firstly to produce a sematic image pair. This step shifts the robot's focus to the task itself, while improving matching efficiency at the same time. Then, based on the characteristic similarity of the ROI region of the semantic image pair, a series of putative correspondences are generated. Next, the focuses on eliminating outliers from that series while deducting the transformation, where this procedure was developed on a maximum likelihood Bayesian model. The paper has also applied three effective regularization techniques to the correspondence, which helped to come up with the best solution. Finally, as the problem was solved by EM algorithm and extensive experiments on those images taken from the real farmland, it is safe to say that the accurate results of this method are superior to other state-of-the-art methods.

关键词： Agricultural mobile robot Image feature matching Probabilistic inference Semantic segmentation Unmanned aerial vehicle

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