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检索条件"任意字段=Israel Conference on Computer Systems and Software Engineering"

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Revisiting DRUP-Based Interpolants with CaDiCaL 2.0 31th

Revisiting DRUP-Based Interpolants with CaDiCaL 2.0

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31st International conference on Tools and Algorithms for the Construction and Analysis of systems, TACAS 2025, which was held as part of the International Joint conferences on Theory and Practice of software, ETAPS 2025

作者： Khouri, Basel Vizel, Yakir Technion Computer Science Department Haifa Israel

ISBN: (纸本)9783031906527

We present our implementation of DRUP-based interpolants in CaDiCaL 2.0, and evaluate performance in the bit-level model checker Avy using the Hardware Model Checking Competition benchmarks. CaDiCaL is a state-of-the-art, open-source SAT solver known for its efficiency and flexibility. In its latest release, version 2.0, CaDiCaL introduces a new proof tracer API. This paper presents a tool that leverages this API to implement the DRUP-based algorithm for generating interpolants. By integrating this algorithm into CaDiCaL, we enable its use in model-checking workflows that require interpolants. Our experimental evaluation shows that integrating CaDiCaL with DRUP-based interpolants in Avy results in better performance (both runtime and number of solved instances) when compared to Avy with Glucose as the main SAT solver. Our implementation is publicly available and can be used by the formal methods community to further develop interpolation-based algorithms using the state-of-the-art SAT solver CaDiCaL. Since our implementation uses the Tracer API, it should be maintainable and applicable to future releases of CaDiCaL. © The Author(s) 2025.

关键词： Model checking

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Research on instrument fault detection based on internet of things and machine learning 4

Research on instrument fault detection based on internet of ...

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4th International conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Li, Shixi Zhao, Chenchen Yang, Benchen Peng, Zihang Bi, Jiayi School of Software Liaoning University of Technology Liaoning Huludao China Liaoning Provincial Second Women's Prison Liaoning Shenyang China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Modern industrial production, the industrial sector and daily life are inextricably linked to a plethora of chemical machinery and equipment, which serve as indispensable production apparatus. It is therefore paramount to guarantee their optimal functionality. It is therefore of great significance to the field of chemical machinery and equipment that fault monitoring is given due attention. The progressive advancement of computer and wireless communication technology has facilitated the maturation of expert diagnosis systems and the Internet of Things. Consequently, fault monitoring systems based on these technologies are poised for widespread adoption. In this work, the instrument operation data collected by IoT devices is used to undergo preliminary cleaning and normalization to ensure the consistency and accuracy of the data. This preprocessed data is then fed into a multi-layered deep neural network (DNN) model. In the model training stage, the backpropagation algorithm is used to calculate the gradient of each layer, and the weights and biases in the network are adjusted by the stochastic gradient descent (SGD) optimization algorithm to minimize the loss function. Through iterative training, the model gradually improves its ability to identify failure modes and predict accuracy. This approach enables deep neural networks to automatically learn complex fault signatures from large amounts of data and effectively detect and predict instrument failures in real-world applications. An experimental platform was set up in the LabView programming environment, equipped with 10 sensors, which were used to collect experimental data from the sensors and conduct fault monitoring experiments. The model shows high accuracy and robustness in different types of instrument fault detection, proving its effectiveness in practical industrial applications. © 2025 SPIE.

关键词： Deep neural networks

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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8th EAI International conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2024

8th EAI International Conference on Future Access Enablers o...

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8th EAI International conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2024

ISBN: (纸本)9783031723926

The proceedings contain 14 papers. The special focus in this conference is on Future Access Enablers of Ubiquitous and Intelligent Infrastructures. The topics include: Enhanced Relaxed Loop Free Updates in software Defined Network;an Empirical Analysis of Machine Learning Approaches for Phishing Detection;SARF: Stock Market Prediction with Sentiment-Augmented Random Forest;impact of Service Time Distributions and Server Utilization on Tandem Queueing System Performance;improvement of the Teaching Process Using the Genetic Algorithm;Sustainable Productivity Improvement in CPM Through Building Information Modeling in the Context of Circular Construction;integration of Electromobility into Public Transport systems: A Case Study;monitoring the Surface Treatment Effect on the Polyvinyl Butyral Samples in the Context of Industry 4.0;methods and Practices of Integrated Construction Process Management – Minimizing Environmental Impacts and Promoting Efficient Resource Management;CIO in the Organizational Hierarchy;use of Product Lifecycle Management in Preparation for Simulation of Logistic Processes;a Model of Cloud-Based System for Monitoring Air Quality in Urban Traffic Environment.

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An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

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3rd International conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models 2

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Nia, Nooshin Nooraei Shirzad, Nasratullah Osemeha, Nwabueze Kenneth Near East University Department of Computer Information Systems Nicosia99138 Cyprus Artificial Intelligence Near East University Nicosia Cyprus Near East University Software Engineering Department Nicosia Cyprus

ISBN: (纸本)9798331505745

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM. © 2025 IEEE.

关键词： Recurrent neural networks

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Machine Learning-assisted Test Records Analysis during the Real-Time Validation of Automotive software systems based on HIL Simulation 58

Machine Learning-assisted Test Records Analysis during the R...

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58th Hawaii International conference on System Sciences, HICSS 2025

作者： Abboush, Mohammad Knieke, Christoph Rausch, Andreas Technische Universität Clausthal Institute for Software and Systems Engineering Germany

ISBN: (纸本)9780998133188

In the automotive industry, according to ISO 26262, comprehensive testing is conducted to ensure software systems quality over various phases of the V-model. However, at the system integration and testing phase, a significant amount of time series data is analyzed manually based on expert knowledge to identify the nature of the failure occurring, which is a costly process in terms of time, effort, and difficulty. This study proposes a novel Machine Learning (ML)-assisted failure analysis approach for the real-time validation of automotive software systems (ASSs). Specifically, based on a representative critical faults dataset, intelligent data-driven ML-based Fault Detection and Diagnosis (FDD) models are developed, including LSTM and Denoising Autoencoder (DAE) + k-means techniques for known and unknown sensor-related faults, respectively. The novelty of these models lies in their ability to identify single and simultaneous faults in noisy conditions. To assess the efficacy of the developed model, a dataset from a virtual test drive platform with a high-fidelity entire vehicle model was employed. The evaluation outcomes illustrate the superiority of the proposed model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low MSE and DB, i.e., 0.044 and 0.68, respectively. Consequently, the proposed approach facilitates the real-time validation of the ASSs in an efficient manner enhancing the safety and reliability. © 2025 IEEE computer Society. All rights reserved.

关键词： automotive software systems development deep learning fault detection and diagnosis fault effect analysis HIL testing

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