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Enhancing Pavement Condition Assessment: A Comprehensive Review of Affordable Sensing Technologies for Cycle Tracks 11

Enhancing Pavement Condition Assessment: A Comprehensive Rev...

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11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Garcia, Jeziel Antonio Ayala Shah, Syed Baig, Muhammad Hassam Qureshi, Waqar Shahid Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9789897587450

This paper explores the potential of low-cost sensing technologies for assessing the condition of cycling track pavement. As cycling gains popularity, the demand for efficient pavement maintenance solutions increases. machine survey-based pavement condition assessment often relies on expensive, specialized equipment, which is not always suitable for cycle tracks due to limited budgets and accessibility, hence the need for low-cost solutions. The integration of low-cost sensing technologies and data collection, such as inertial measurement units (IMUs), low-cost imaging sensors, and crowdsourced data collection, presents a promising alternative for cycle track pavement surface assessment. This paper highlights the advantages of employing out-of-the-shelf devices such as smartphones with GPS, cameras, and IMU, or action cameras with GPS and IMU to collect images, their location, and vehicle vibration in a specific orientation. This data is then used to estimate pavement roughness, detect surface distress, and compute pavement surface condition. Literature reviews reveal a significant gap in the utilization of these technologies for cycle tracks, suggesting a promising area for further research and application. Furthermore, it proposes a software framework for data collection and visualization to combine these technologies to enhance the efficiency and reliability of pavement assessment for cycling infrastructure at a lower cost than machine-based pavement assessment and faster and more quantitative than manual surveys. This paper emphasizes the need for more practical and scalable solutions that support the maintenance of sustainable transportation systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Budget control

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Graph Matching Networks Meet Optimum-Path Forest: How to Prune Ensembles Efficiently 27th

Graph Matching Networks Meet Optimum-Path Forest: How to Pr...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Jodas, Danilo Passos, Leandro A. Rodrigues, Douglas Costa, Kelton Paulo Papa, João School of Sciences Bauru Brazil

ISBN: (纸本)9783031781827

Ensemble pruning techniques are widely used to enhance a set of classifiers’ efficiency and predictive performance by selecting a subset of representative models, preventing redundancy, and ensuring diversity in classification tasks. The Optimum-Path Forest (OPF), a stable and efficient graph-based framework, offers versatile supervised and unsupervised capabilities in various machine-learning applications. The supervised version provides remarkable results with a simple graph-based structure produced by a training process conducted over a single dataset. However, one can notice little effort in OPF-based ensemble learning. This paper introduces an innovative approach to pruning OPF classifiers using meta-descriptions learned by Graph-Matching Networks, which are further employed to cluster similar OPF instances. The strategy selectively chooses representative models that excel in predictive tasks from groups generated by unsupervised OPF. Results demonstrate competitive performance to state-of-the-art pruning algorithms, with experiments conducted over fifteen public datasets, encouraging further exploration of Graph Matching Networks applied to ensemble pruning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Hierarchical learning of Generative Automaton Models from Sequential data 22nd

Hierarchical Learning of Generative Automaton Models from ...

引用

22nd international conference on Software Engineering and Formal Methods, SEFM 2024

作者： von Berg, Benjamin Aichernig, Bernhard K. Rindler, Maximilian Štern, Darko Tappler, Martin Institute of Software Technology Graz University of Technology Graz Austria TU Wien Vienna Austria AVL List GmbH Graz Austria

ISBN: (纸本)9783031773815

Passive automata learning is a method for inferring automaton models from a given collection of observations of system behavior (traces). It has been applied to reactive systems with probabilistic behavior. In particular, IOAlergia is a well known algorithm for inferring models in the form of deterministically labeled Markov decision processes from system traces. The quality of the resulting model depends heavily on the provided data set and suffers if data is scarce. However, in many cases additional knowledge about the system is available. This work aims to incorporate knowledge about system modes into the learning process in order to improve model quality for low-data scenarios. This is done by splitting the traces according to system modes, learning individual models for each mode and combining those models into one model with sub-regions corresponding to individual system modes. In our evaluation on artificial models, our method outperforms the baseline in at least 90 % of cases for all considered metrics. This method was developed to learn generative models of human driving behavior. data from recorded test drives on highways was used to learn a hierarchical stochastic model of typical acceleration behavior of human drivers. In the automotive industry, such models make the simulations of driving emissions more realistic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Predicting Malaria Cases in Mumbai: Insights from Statistical and machine learning Models 27th

Predicting Malaria Cases in Mumbai: Insights from Statisti...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Chougale, Praveen D. Somaraj, Adithya B. Ananthakumar, Usha Koita Centre for Digital Health Indian Institute of Technology Bombay Mumbai India Shailesh J. Mehta School of Management Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9783031876592

Malaria remains a persistent health challenge in India despite concerted control efforts. This study examines malaria data from 2012 to 2019 in Mumbai, employing Generalized Linear Models (GLMs), random forest and artificial neural network. All the models showed rainfall, mean duration of sunshine, and population density as significant variables in predicting malaria cases in Mumbai. We also observe that the GLMs outperform random forest and artificial neural network in terms of metrics such as RMSE and MAPE. This research underscores the importance of statistical modeling techniques in informing targeted interventions about the significant environmental factors influencing malaria and strengthening malaria control strategies in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Population statistics

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ADASYN: Oversampling Techniques for COVID-19 Prediction

ADASYN: Oversampling Techniques for COVID-19 Prediction

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mary, L. William Raj, S. Albert Antony SRM Institute of Science and Technology Department of Computer Applications Kattankulathur Chennai India

ISBN: (纸本)9798331512965

In 2019, the highly contagious COVID-19 virus emerged and rapidly spread, causing a global health crisis characterized by inflammatory respiratory symptoms. The primary target of this disease is the respiratory system. Research has identified bats as the reservoir for the virus, but the intermediate host remains unknown. There is ongoing concern about various coronavirus variants, with the Omicron variant previously dominating globally and now attention shifting to a new variant, BA.2.86, informally known as Pirola, which is being closely monitored worldwide. However, the inherent class imbalance in COVID-19 datasets poses a significant obstacle to the effectiveness of these models. This paper proposes a novel approach combining outlier detection and Adaptive Synthetic Minority Oversampling Technique (ADASYNA) to address this issue. The Outlier-ADASYNA method identifies outliers within the minority class (COVID-19 cases) and synthesizes informative samples to rebalance the dataset. The proposed approach is evaluated using data from 5,400 patient records for both training and testing. The study employs ADASYN to effectively mitigate dataset imbalances and outstanding traditional oversampling techniques. This paper presents a comparative analysis of machine learning techniques, evaluating their performance using diverse metrics. The classification algorithm Random Forest attains the highest accuracy percentage of 90%, and RF Oversampling 92% surpassing numerous classification algorithms in predictive performance. © 2025 IEEE.

关键词： COVID-19

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TADIL: Task-Agnostic Domain-Incremental learning Through Task-ID Inference Using Transformer Nearest-Centroid Embeddings 27th

TADIL: Task-Agnostic Domain-Incremental Learning Through Tas...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Bravo-Rocca, Gusseppe Liu, Peini Guitart, Jordi Dholakia, Ajay Ellison, David Barcelona Supercomputing Center Barcelona Spain Universitat Politècnica de Catalunya Barcelona Spain Lenovo Infrastructure Solutions Group MorrisvilleNC United States

ISBN: (纸本)9783031781094

Classical machine learning models have problems when faced with learning from data that changes over time or across domains due to factors such as noise, occlusion, illumination, or frequency variation, which humans can adapt to without being given independent and identically distributed data. Therefore, a Continual learning (CL) approach is essential, particularly in this case, the Domain-Incremental learning. This paper presents a novel pipeline for identifying tasks in domain-incremental learning scenarios without supervision. The pipeline consists of four steps. First, we obtain base embeddings from the raw data through a transformer-based existing model. Second, we group the embedding densities based on similarity and extract few nearest points to each cluster centroid. Third, we train an incremental task classifier using only the previous few points. Finally, we make the pipeline lightweight in terms of computational requirements and build an algorithm that decides when to learn a new task in an online way using the task classifier and drift detector. We experiment with the real-world driving dataset SODA10M and several CL strategies. We conclude that the performance of CL strategies with our pipeline is better not only when the task boundaries are given, but also in the more general practical case of task-agnostic strategies that demand identifying new tasks on-the-fly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Embeddings

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R-CONV: An Analytical Approach for Efficient data Reconstruction via Convolutional Gradients 25th

R-CONV: An Analytical Approach for Efficient Data Reconstru...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Eltaras, Tamer Ahmed Malluhi, Qutaibah Savino, Alessandro Carlo, Stefano Di Qayyum, Adnan Politecnico Di Torino Control and Computer Engineering Department Turin10129 Italy Qatar University Doha Qatar Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9789819605750

In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mechanism instead of exchanging raw data. However, recent studies show that private training data can be leaked through many gradient attacks. While previous analytical-based attacks have successfully reconstructed input data from fully connected layers, their effectiveness diminishes when applied to convolutional layers. This paper introduces an advanced data leakage method to efficiently exploit convolutional layers’ gradients. We present a surprising finding: even with non-fully invertible activation functions, such as ReLU, we can analytically reconstruct training samples from the gradients. To the best of our knowledge, this is the first analytical approach that successfully reconstructs convolutional layer inputs directly from the gradients, bypassing the need to reconstruct layers’ outputs. Prior research has mainly concentrated on the weight constraints of convolution layers, overlooking the significance of gradient constraints. Our findings demonstrate that existing analytical methods used to estimate the risk of gradient attacks lack accuracy. In some layers, attacks can be launched with less than 5 % of the reported constraints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Robust subspace learning via binary weights for face recognition

Robust subspace learning via binary weights for face recogni...

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2024 international conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Wang, Nan An, Jiayi Feng, Weijia College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9781510691773

In extreme noise environments, existing subspace learning algorithms applied to face recognition tasks exhibit limitations in feature extraction capabilities, robustness, and anomaly detection. To address these issues, this paper proposes a robust two-dimensional principal component analysis (2DPCA) method based on binary weights, aimed at effectively identifying and eliminating outliers, thereby enhancing the robustness and efficacy of face recognition systems in practical applications. By incorporating a binary weight strategy and optimal means, our model maintains a high capability for low-dimensional representation across varying noise levels. We conducted extensive experiments by artificially adding 40% and 60% occlusion noise into four publicly available facial datasets: Aberdeen, YALE, CaltechFaces, and GT. The experimental results demonstrate that the proposed algorithm achieves satisfactory outcomes even when handling severely corrupted data, particularly under conditions of 60% noise, where its reconstruction performance significantly surpasses that of the comparison algorithms, underscoring its superior robustness and adaptability. © 2025 SPIE.

关键词： Principal component analysis

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data Augmentation Method for Small-Sample Strip Steel Surface Defects Based on Improved ACGAN 24

Data Augmentation Method for Small-Sample Strip Steel Surfac...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wang, Chengzhi Lv, Donghao Yang, Chunlong Tian, Xu Inner Mongolia University of Science & Technology Inner Mongolia Autonomous Region China

ISBN: (纸本)9798400710681

This study introduces a novel data augmentation method aimed at addressing the limited availability of sample data for strip steel surface defects, utilizing an improved Auxiliary Classifier Generative Adversarial Network (ACGAN). Firstly, on the generator side, the improved ACGAN integrates an encoder that extracts essential features from images of strip steel surface defects and transforms them into latent vectors. These vectors serve as inputs for the generator, helping it learn the actual data distribution of strip steel defects more accurately. To enhance image resolution and reduce checkerboard artifacts common with transposed convolution, the generator uses a combination of transposed convolution and PixelShuffle for upsampling. Secondly, on the discriminator side, the convolutional network employs an alternating configuration of downsampling and standard convolutional layers, effectively reducing feature dimensions while preserving more local information. A Dropout regularization strategy is applied after each convolutional layer to boost the model’s robustness. Finally, the stability of the learning process is reinforced by employing Kaiming initialization for both the generator and discriminator’s convolutional and transposed convolution layers. Performance evaluations conducted using the Northeastern University surface defect database confirm the efficacy of the proposed method in generating accurate images of strip steel surface defects in small-sample scenarios. Notably, compared to the original ACGAN and other classical generative adversarial networks, the improved method demonstrated significant improvements, achieving reductions of 52.82% and 38.78% in FID and MMD metrics, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Auxiliary classifier generative adversarial network

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NAND-Tree: A 3D NAND Flash Based Processing In Memory Accelerator for Tree-Based Models on Large-Scale Tabular data 24

NAND-Tree: A 3D NAND Flash Based Processing In Memory Accele...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Zhong, Hongtao Li, Taixin Chen, Yiming Tang, Wenjun Wu, Juejian Yang, Huazhong Li, Xueqing Electronic Engineering BNRist Tsinghua University China

ISBN: (纸本)9798400710773

Tabular data are a widely used format in data science, and tree-based machine learning (ML) models are powerful tools and outperform Deep Neural Network (DNN) with higher accuracy for tasks on tabular data. However, computing multiple trees on massive tabular data via conventional von Neumann architectures suffers from irregular memory accesses. Prior work utilizes Analog Content Addressable Memories (ACAMs) to gain great speedup, but the analog matching method is vulnerable to device and voltage variations, and the limited density of 2D memory makes frequent data movement still inevitable for large-scale tabular data. In this work, we propose NAND-Tree: a 3D NAND flash based digital storage-level processing in memory (PIM) accelerator for Tree-based ML models on tabular data. Unlike prior 3D NAND flash based accelerators with limited memory density due to complex peripherals and specific array structure, NAND-Tree makes slight modifications to commercial 3D NAND flashes. The additional peripherals only increase slight hardware overhead, which enables NAND-Tree to achieve 2.5Gb/mm2 memory density (0.89×) compared with commercial 3D NAND flashes. Besides, to tackle the low performance of original storage-level PIM architecture, we propose two strategies, namely Block Grouping (BG) and In-cell Bit-parallel Comparison (IBC) taking advantage of the multi-bit property of 3D NAND flash, for higher parallelism and lower comparison latency, respectively. Results show that the proposed NAND-Tree achieves on average 9.4×, 2.0×, and 15.4× speedup and 218.3×, 50.0×, and 9.7× higher energy efficiency over CPU, GPU, the state-of-the-art SLC 3D memory based PIM solution, respectively. © 2024 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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