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Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

computer Systems Science & engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

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Data-driven energy demand forecasting for electric vehicle charging infrastructure

Data-driven energy demand forecasting for electric vehicle c...

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2023 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2023

作者： Meddi, Silvia Cavaglion, Sara Cerquitelli, Tania Manfredi, Emanuele Regalia, Andrea Menolascino, Raffaele Zardo, Guido Accenture S.p.A. Italy Department of Control and Computer Engineering Politecnico di Torino Italy F2M eSolutions Italy Politecnico di Torino Italy

In the era of Big Data and electric vehicles growth by market, data-driven methodologies assume a crucial role to create valuable information. The focus is on supporting the decision-making process for the development of an accurate charging infrastructure. Forecast analysis allows prediction of energy demand over the network. This supports growing trends with a consequent increase in customer satisfaction. By anticipating potential breakdowns due to infrastructure overloads, maintenance costs are reduced. In this paper, we focus on analyzing charging sessions data together with external data (weather and population information and energy/fuel prices) collected from different sources. The proposed methodology, named GEORGE (enerGy dEmand fOrecasting foR charGing infrastructurE), offers a building-blocks based approach for the monthly energy demand forecasting. The approach is both generalisable and data-specific. We discuss the results of a classification learning approach to predict a belonging range of kwh for a charge point. In particular the most promising model has good performances in predicting high utilization and is more advantageous to support the company's decision-making process. Many possible developments are discussed to improve the prediction. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Data mining

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A Scoring Model Considering the Variability of Subjects' Characteristics in Subjective Experiments

A Scoring Model Considering the Variability of Subjects' Cha...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Lohic Fotio Tiotsop Antonio Servetti Enrico Masala Control and Computer Engineering Department Politecnico di Torino Turin Italy

Many authors argued that the scoring behavior of a subject in a subjective quality evaluation experiment can be modeled by two main characteristics, i.e., the subject's bias and the subject's inconsistency. However, for simplicity's sake, they disregarded the fact that subjects are usually less inconsistent when evaluating stimuli with very low or very high quality. This work addresses this shortcoming by providing an analytical formulation about how to link subjects' bias and inconsistency to the ground truth subjective quality of the stimulus under evaluation. By integrating this formulation into a state-of-the-art subject scoring model we obtain a more realistic model to recover the ground truth subjective quality of each stimulus. An iterative algorithm able to estimate the model parameters is also provided. Computational experiments show that our proposed model yields more realistic confidence intervals for the recovered ground truth subjective quality values and exhibits more robustness to synthetically added noise in several testing conditions.

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A Quantum Adaptation to Roll Truly Random Dice in Role Playing Games

A Quantum Adaptation to Roll Truly Random Dice in Role Playi...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Antonio Costantino Marceddu Nicola Dilillo Marco Russo Renato Ferrero Bartolomeo Montrucchio Department of Control and Computer Engineering Politecnico di Torino Turin Italy

In role-playing games (RPGs), players are called upon to assume the role of a character moving in an imaginary environment and facing several challenges. Their success or failure often depends on randomizers like cards or dice. Regarding the latter, the most commonly used in RPGs are the Platonic solids with the addition of the ten-sided die. They are commonly simulated through classical computers, however, since true randomness is not in their nature, they can only generate pseudorandom numbers. On the contrary, quantum computers exploit the nondeterministic nature of quantum mechanics, so they are perfect candidates for truly random simulations in games of chance. For this reason, this paper proposes and tests various quantum circuits for sampling uniformly distributed discrete values within a fixed range, corresponding to the number of faces of the dice. The simulations reveal the pure randomness of the output of the implemented circuits. They were then used to generate random numbers within a three-dimensional dice-rolling game.

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GINN: Towards Gender InclusioNeural Network

GINN: Towards Gender InclusioNeural Network

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IEEE International Conference on Big Data

作者： Matteo Berta Bartolomeo Vacchetti Tania Cerquitelli Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Today’s data-driven systems and official statistics often oversimplify the concept of gender, reducing it to binary data, with far-reaching implications for policy development and equitable access to services. This simplification can lead to misclassification and discrimination against individuals who identify as *** are working to advance our research in this area to develop new, more equitable approaches that can avoid discrimination based on gender identity. Within this research framework, our primary focus is on mitigating the problem of underrepresentation and, in some cases, the complete absence of non-binary individuals in data *** this goal in mind, we present the GINN Gender InclusioNeural Network. This is our first attempt to develop an equitable neural network that accurately identifies gender in a multiclass context and includes individuals whose gender identity does not fall on the binary spectrum. To achieve this goal, we conducted a comprehensive comparative analysis of several fine-tuned neural network models. Our goal was to gain a deep understanding of the crucial distinguishing features in gender identify classification and to highlight the limitation of current methods using explainable AI *** initial results are promising and demonstrate the effectiveness of a fine-tuned EfficientNetB0 model in accurately categorizing images of individuals into their self-reported gender, but we are skeptical about the application in a real-world scenario because of the amount of data available about non-binary people at the moment.

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High-Dimensional controller Tuning through Latent Representations

High-Dimensional Controller Tuning through Latent Representa...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alireza Sarmadi Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high-dimensional controller parameter space to a lower dimensional space using a machine learning-based algorithm. This mapping is then utilized in an actor-critic framework using Bayesian optimization (BO). The proposed approach is applicable to complex systems (such as quadruped robots). In addition, the proposed approach also enables efficient generalization to different control tasks while also reducing the number of evaluations required while tuning the controller parameters. We evaluate our method on a legged locomotion application. We show the efficacy of the algorithm in tuning the high-dimensional controller parameters and also reducing the number of evaluations required for the tuning. Moreover, it is shown that the method is successful in generalizing to new tasks and is also transferable to other robot dynamics.

关键词： Legged locomotion Machine learning algorithms Heuristic algorithms Dynamics Aerospace electronics Vectors Quadrupedal robots

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

GRAIGH: Gene Regulation accessibility integrating GeneHancer...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lorenzo Martini Roberta Bardini Alessandro Savino Stefano Di Carlo Control and Computer Engineering Department Politecnico di Torino Torino Italy

Single-cell assays for transposase-accessible chromatin sequencing data represent a potent tool for exploring the epigenetic heterogeneity within cell populations. Despite their power, understanding the chromatin accessibility landscape poses challenges. This study introduces Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to interpreting genome accessibility by integrating information from the GeneHancer database, detailing genome-wide enhancer-to-gene associations. Initially, we outline the methods for integrating GeneHancer with scATAC-seq data. This involves creating a new matrix where GeneHancer element IDs replace traditional accessibility peaks as features. Subsequently, the paper assesses the method’s ability to analyze data and detect cellular heterogeneity. Notably, our findings demonstrate the selective accessibility of GeneHancer elements for distinct cell types, with connected genes serving as precise marker genes. Furthermore, we explore the specificity of GeneHancer element accessibility, highlighting their high selectivity against gene activity. This investigation underscores the potential of Gene Regulation Accessibility Integrating GeneHancer in unraveling the complexities of chromatin accessibility, offering insights into the nuanced relationship between accessibility and cellular heterogeneity.

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An analysis of widget layout attributes to support Android GUI-based testing

An analysis of widget layout attributes to support Android G...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Tommaso Fulcini Riccardo Coppola Marco Torchiano Luca Ardito Department of Control and Computer Engineering Politecnico di Torino Turin Italy

In the context of GUI testing, identifying robust locators (i.e., attributes to unambiguously identify on-screen widgets to be used in test sequences) is still considered an unsolved challenge by the researchers’ community. The frequent variation of attributes between different releases of the System Under Test (SUT) leads in fact to testing fragility, i.e., test case failing because of invalidated locators. Recent studies have highlighted the benefits of adopting multi-locator approach, i.e., the combination of multiple locators to enhance the robustness of widget *** objective of this work is to provide insights into the composition of Android applications, assessing the characteristics of different layout-based properties and their suitability to be used as locators for widgets in the context of GUI-based *** investigated the state of the practice by analysing the distribution of widget values within 30 real apps selected from the Google Play Store. For those apps, we selected two different versions to examine how they evolved over time from both visual and structural *** results of our analysis showed that providing robust GUI testing over multiple releases of mobile SUTs is difficult, as identifying a single attribute or technique (either coordinate-, property-, or visual-based) capable of locating visual elements is often not sufficient due to missing values, variability, and instability of attributes.

关键词： Software testing Visualization Conferences Layout Robustness Internet Object recognition

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Toward a Realistic Benchmark for Out-of-Distribution Detection

Toward a Realistic Benchmark for Out-of-Distribution Detecti...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Pietro Recalcati Fabio Garcea Luca Piano Fabrizio Lamberti Lia Morra Department of Control and Computer Engineering Politecnico di Torino Torino Italy

Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on farOOD samples drawn from very different distributions, and thus lack the complexity needed to capture the nuances of real-world scenarios. In this work, we introduce a comprehensive benchmark for OOD detection, based on ImageNet and Places365, that assigns individual classes as in-distribution or out-of-distribution depending on the semantic similarity with the training set. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties. Experimental results on different OOD detection techniques show how their measured efficacy depends on the selected benchmark and how confidence-based techniques may outperform classifier-based ones on near-OOD samples.

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation 3

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Zhang, Jiusi Tian, Jilun Jiang, Yuchen Luo, Hao Huang, Cong-Sheng Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Norwegian University of Science and Technology Faculty of Engineering Trondheim7034 Norway

ISBN: (纸本)9798350324754

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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