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Ray Launching-Based Computation of Exact Paths with Noisy Dense Point Clouds

IEEE Transactions on Antennas and Propagation 2025年第5期73卷 3270-3283页

作者： Vaara, Niklas Sangi, Pekka Lopez, Miguel Bordallo Heikkila, Janne University of Oulu Faculty of Information Technology and Electrical Engineering Center for Machine Vision and Signal Analysis Finland

Point clouds have been a recent interest for ray tracing-based radio channel characterization, as sensors such as RGB-D cameras and laser scanners can be utilized to generate an accurate virtual copy of a physical environment. In this paper, a novel ray launching algorithm is presented, which operates directly on noisy point clouds acquired from sensor data. It produces coarse paths that are further refined to exact paths consisting of reflections and diffractions. A commercial ray tracing tool is utilized as the baseline for validating the simulated paths. A significant majority of the baseline paths is found. The robustness to noise is examined by artificially applying noise along the normal vector of each point. It is observed that the proposed method is capable of adapting to noise and finds similar paths compared to the baseline path trajectories with noisy point clouds. This is prevalent especially if the normal vectors of the points are estimated accurately. Lastly, a simulation is performed with a reconstructed point cloud and compared against channel measurements and the baseline paths. The resulting paths demonstrate similarity with the baseline path trajectories and exhibit an analogous pattern to the aggregated impulse response extracted from the measurements. Code available at https://***/nvaara/NimbusRT. © 1963-2012 IEEE.

关键词： Computer graphics equipment

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Privacy-preserving explainable AI: a survey

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Science China(information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Multimedia Tools and Applications 2025年第10期84卷 6873-6897页

作者： Zhang, Ruihu Kang, Xiaobing Lu, Qiao Miao, Yalin Chen, Yajun Department of Information Science Faculty of Printing Packaging Engineering and Digital Media Technology Xi’an University of Technology Shaanxi Xi’an710048 China

Image encryption based on chaotic systems is a hot research topic in recent decade. However, the existing methods have shortcomings in key generation techniques such as insufficient randomness and limited key space. For that reason, this paper presents a new substitution-scrambling-diffusion color image encryption scheme based on key generation using generative adversarial networks (GANs). In the key generation phase, a key image associated with an original plaintext image is produced by GANs trained on the hyperchaotic Chen system for increasing the key space. The image encryption phase involves three steps: pixel-level substitution, scrambling and diffusion, which all are dependent on the produced key image. In the first step, a pixel-substitution rule is performed on each color channel for increasing the difference between the plaintext and ciphertext images. In scrambling step, the plaintext image is confused globally for reducing the correlation between neighborhood pixels by means of the randomness of the produced key. The diffusion step of the XOR operation employs the diffusion matrix from the generated key for spreading the change of each plaintext bit over as many output ciphertext bits as possible. Experimental simulations and analysis demonstrate that GANs can significantly expand the key space, the proposed image encryption scheme has effective advantages in terms of key randomness, key sensitivity and strong robustness against common security attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-Based Approach

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1259-1275页

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing Hefei University of Technology School of Computer Science and Information Engineering China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Macau University of Science and Technology Faculty of Information Technology China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. © 2004-2012 IEEE.

关键词： Level measurement

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SGGAA: a robust adversarial generation based on synthesizing point cloud and mesh space

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Neural Computing and Applications 2025年 1-23页

作者： Hu, Ruihan Yang, Rui Faculty of Computing Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Tianjin University Tianjin China

3D vision recognition offers a significantly more robust tool for achieving machine cognition compared to traditional 2D vision techniques. However, similar to the vulnerabilities present in 2D vision, many 3D vision recognition applications, including point cloud classification models, are susceptible to degradation when confronted with imperceptible outliers. Outliers have a considerable impact on the performance and accuracy of models, compromising their overall effectiveness. Existing 3D point cloud attack and defense strategies neglect outliers and overlook the crucial impact of 3D printability in real-world scenarios. Neglecting this aspect hinders the consideration of potential manipulation or alteration of 3D objects in the physical realm, posing significant implications for the security and robustness of 3D vision systems. This manuscript presents a novel and effective targeted attack framework called synthesizing geometry and geodesic attack (SGGAA). It offers an alternative adversarial training algorithm that bridges the gap between the digital and physical domains. SGGAA introduces a novel generalized distance metric, facilitating the design of powerful and simple techniques for generating adversarial samples and improving the training process. It focuses on the regularizer loss object in synthesizing attack modes for mesh and point cloud spaces, utilizing Hausdorff distance, geometry distance, and geodesic distance losses. Comprehensive evaluations, including hyperparameter tuning, qualitative analysis, and quantitative experiments, were conducted to assess SGGAA’s performance. The results demonstrated its superiority, with high attack success rates, excellent geometric similarity scores, and outperformance of state-of-the-art attack strategies across various 3D defense techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mesh generation

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On Suitability of Renko Charts for Algorithmic Trading 15th

On Suitability of Renko Charts for Algorithmic Trading

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15th International Scientific Conference on Distance Learning in Applied Informatics, DiVAI 2024

作者： Drozda, Martin Cavojsky, Maros Sebes, Patrik Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9783031812606

Are Renko charts suitable for algorithmic trading? What are their advantages and disadvantages? How can they be represented for convolutional neural networks (CNNs)? We contrast a classical trading approach (Renko charts) with a machine learning approach (CNN) and then integrate these two approaches into a single method. We show that Renko charts offer an opportunity to train a CNN classifier with noise filtered out and demonstrate how they can be applied in algorithmic trading. We use Kibot as a source of historical market data and evaluate our approach with 50 stocks and exchange-traded funds (ETFs) that have the highest trading volume. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Comparisons of{100}texture improvement and formability in hot-rolled non-oriented electrical steel by austenite-ferrite phase transformation and shear deformation

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Journal of Iron and Steel Research International 2025年第1期32卷 171-185页

作者： Chi-hao Yu Hong-jiang Pan Yan-ping Zhao De-ting Tang Hai-jun Wang Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093YunnanChina School of Electronics and Information Engineering Anshun UniversityAnshun561000GuizhouChina School of Metallurgical Engineering Anhui University of TechnologyMa’anshan243032AnhuiChina

Over the years,the high magnetic induction of industrial Mn-added electrical steel is assumed to be the enhancement of{100}texture derived from its austenite-ferrite phase transformation during hot rolling(phase transformation(PT)method).However,it is still undetermined without straightforward experimental *** reason for{100}texture improvement of Mn-added electrical steel is experimentally confirmed due to the recrystallization induced by the austenite-ferrite phase transformation during hot ***,a more promising methodology to further improve{100}texture and formability of hot-rolled electrical steel is promoted by the control of hot rolling deformation condition(shear deformation(SD)method).The results show that the nucleation mechanisms of{100}oriented recrystallized grains are different in the samples by SD and PT methods,which are in-depth shear deformation and austenite-ferrite phase transformation,*** this case,coarse{100}oriented recrystallized grains and low residual stress are obtained in the sample by SD method,which is responsible for its superior{100}texture and *** contrast,the sample by PT method forms fine recrystallized grains with random orientations and accumulates severe residual stress.

关键词： Non-oriented electrical steel {100}texture Formability Austenite-ferrite phase transformation Shear deformation

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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (Science and information Organization). All rights reserved.

关键词： Logistic regression

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