Serafín Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domínguez
Abstract
Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.
{"title":"Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder1","authors":"Serafín Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domínguez","doi":"10.3233/ica-230728","DOIUrl":"https://doi.org/10.3233/ica-230728","url":null,"abstract":"<h4><span>Abstract</span></h4><p>Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"1 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.
{"title":"Highly compressed image representation for classification and content retrieval","authors":"Stanisław Łażewski, Bogusław Cyganek","doi":"10.3233/ica-230729","DOIUrl":"https://doi.org/10.3233/ica-230729","url":null,"abstract":"<h4><span>Abstract</span></h4><p>In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called <i>PCA-ResFeats</i>. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"21 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georg Novotny, Yuzhou Liu, Walter Morales-Alvarez, Wilfried Wöber, Cristina Olaverri-Monreal
Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle estimation that combines perception with a hybrid artificial neural network pushing the prediction horizon beyond existing approaches. We exploited the feature extraction capabilities of convolutional neural networks and the dynamic time series relationship learning capabilities of gated recurrent units and combined them with a motion model to estimate the side-slip angle. Subsequently, we evaluated the model using the 3DCoAutoSim simulation platform, where we designed a suitable simulation environment with snowfall, friction, and car tracks in snow. The results revealed that our approach outperforms the baseline model for prediction horizons ⩾ 2 seconds. This extended prediction horizon has practical implications, by providing drivers and autonomous systems with more time to make informed decisions, thereby enhancing road safety.
{"title":"Vehicle side-slip angle estimation under snowy conditions using machine learning","authors":"Georg Novotny, Yuzhou Liu, Walter Morales-Alvarez, Wilfried Wöber, Cristina Olaverri-Monreal","doi":"10.3233/ica-230727","DOIUrl":"https://doi.org/10.3233/ica-230727","url":null,"abstract":"Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle estimation that combines perception with a hybrid artificial neural network pushing the prediction horizon beyond existing approaches. We exploited the feature extraction capabilities of convolutional neural networks and the dynamic time series relationship learning capabilities of gated recurrent units and combined them with a motion model to estimate the side-slip angle. Subsequently, we evaluated the model using the 3DCoAutoSim simulation platform, where we designed a suitable simulation environment with snowfall, friction, and car tracks in snow. The results revealed that our approach outperforms the baseline model for prediction horizons ⩾ 2 seconds. This extended prediction horizon has practical implications, by providing drivers and autonomous systems with more time to make informed decisions, thereby enhancing road safety.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"64 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139410512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José L. Salazar-González, José María Luna-Romera, Manuel Carranza-García, Juan A. Álvarez-García, Luis M. Soria-Morillo
Abstract
The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of +0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.
{"title":"Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation","authors":"José L. Salazar-González, José María Luna-Romera, Manuel Carranza-García, Juan A. Álvarez-García, Luis M. Soria-Morillo","doi":"10.3233/ica-230726","DOIUrl":"https://doi.org/10.3233/ica-230726","url":null,"abstract":"<h4><span>Abstract</span></h4><p>The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of <span><mml:math alttext=\"+\" display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"><mml:mo>+</mml:mo></mml:math></span>0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"24 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139410513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller’s ability to accurately track foot placement on discrete terrains and the lack of consideration for safety concerns. In this study, we address these challenges by focusing on ensuring the overall system’s safety. To begin with, we tackle the inverse kinematics problem by introducing constraints to the damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges for joint angles, thus ensuring the stability and reliability of the system. Based on this, we propose the adoption of the constrained DDPG method to correct controller deviations. In constrained DDPG, we incorporate a constraint layer into the Actor network, incorporating joint deviations as state inputs. By conducting offline training within the range of safe angles, it serves as a deviation corrector. Lastly, we validate the effectiveness of our proposed approach by conducting dynamic simulations using the CRANE biped robot. Through comprehensive assessments, including singularity analysis, constraint effectiveness evaluation, and walking experiments on discrete terrains, we demonstrate the superiority and practicality of our approach in enhancing walking performance while ensuring safety. Overall, our research contributes to the advancement of biped robot locomotion by addressing gait optimisation from multiple perspectives, including singularity handling, safety constraints, and deviation learning.
在本文中，我们提出了一种用于机器人运动控制的新型强化学习（RL）算法，即有约束的深度确定性策略梯度（DDPG）偏差学习策略，以帮助双足机器人安全、准确地行走。以往关于这一主题的研究强调了控制器在离散地形上精确跟踪脚部位置能力的局限性，以及缺乏对安全问题的考虑。在本研究中，我们通过重点确保整个系统的安全性来应对这些挑战。首先，我们通过在阻尼最小二乘法中引入约束条件来解决逆运动学问题。这一改进不仅解决了奇异性问题，还保证了关节角度的安全范围，从而确保了系统的稳定性和可靠性。在此基础上，我们提出采用约束 DDPG 方法来修正控制器偏差。在受约束 DDPG 中，我们在 Actor 网络中加入了一个约束层，将关节偏差作为状态输入。通过在安全角度范围内进行离线训练，它可作为偏差校正器。最后，我们通过使用 CRANE 双足机器人进行动态模拟，验证了我们提出的方法的有效性。通过奇异性分析、约束有效性评估和离散地形行走实验等综合评估，我们证明了我们的方法在提高行走性能、确保安全方面的优越性和实用性。总之，我们的研究从奇异性处理、安全约束和偏差学习等多个角度解决了步态优化问题，为双足机器人运动的发展做出了贡献。
{"title":"Deep deterministic policy gradient with constraints for gait optimisation of biped robots","authors":"Xingyang Liu, Haina Rong, Ferrante Neri, Peng Yue, Gexiang Zhang","doi":"10.3233/ica-230724","DOIUrl":"https://doi.org/10.3233/ica-230724","url":null,"abstract":"In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller’s ability to accurately track foot placement on discrete terrains and the lack of consideration for safety concerns. In this study, we address these challenges by focusing on ensuring the overall system’s safety. To begin with, we tackle the inverse kinematics problem by introducing constraints to the damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges for joint angles, thus ensuring the stability and reliability of the system. Based on this, we propose the adoption of the constrained DDPG method to correct controller deviations. In constrained DDPG, we incorporate a constraint layer into the Actor network, incorporating joint deviations as state inputs. By conducting offline training within the range of safe angles, it serves as a deviation corrector. Lastly, we validate the effectiveness of our proposed approach by conducting dynamic simulations using the CRANE biped robot. Through comprehensive assessments, including singularity analysis, constraint effectiveness evaluation, and walking experiments on discrete terrains, we demonstrate the superiority and practicality of our approach in enhancing walking performance while ensuring safety. Overall, our research contributes to the advancement of biped robot locomotion by addressing gait optimisation from multiple perspectives, including singularity handling, safety constraints, and deviation learning.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"25 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138818438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.
{"title":"Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements","authors":"Borja Bordel, Ramón Alcarria, Joaquin Chung, Rajkumar Kettimuthu","doi":"10.3233/ica-230722","DOIUrl":"https://doi.org/10.3233/ica-230722","url":null,"abstract":"Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"26 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linkun Fan, Fazhi He, Yupeng Song, Huangxinxin Xu, Bing Li
The 3D point cloud deep neural network (3D DNN) has achieved remarkable success, but its black-box nature hinders its application in many safety-critical domains. The saliency map technique is a key method to look inside the black-box and determine where a 3D DNN focuses when recognizing a point cloud. Existing point-wise point cloud saliency methods are proposed to illustrate the point-wise saliency for a given 3D DNN. However, the above critical points are alternative and unreliable. The findings are grounded on our experimental results which show that a point becomes critical because it is responsible for representing one specific local structure. However, one local structure does not have to be represented by some specific points, conversely. As a result, discussing the saliency of the local structure (named patch-wise saliency) represented by critical points is more meaningful than discussing the saliency of some specific points. Based on the above motivations, this paper designs a black-box algorithm to generate patch-wise saliency map for point clouds. Our basic idea is to design the Mask Building-Dropping process, which adaptively matches the size of important/unimportant patches by clustering points with close saliency. Experimental results on several typical 3D DNNs show that our patch-wise saliency algorithm can provide better visual guidance, and can detect where a 3D DNN is focusing more efficiently than a point-wise saliency map. Finally, we apply our patch-wise saliency map to adversarial attacks and backdoor defenses. The results show that the improvement is significant.
{"title":"Look inside 3D point cloud deep neural network by patch-wise saliency map","authors":"Linkun Fan, Fazhi He, Yupeng Song, Huangxinxin Xu, Bing Li","doi":"10.3233/ica-230725","DOIUrl":"https://doi.org/10.3233/ica-230725","url":null,"abstract":"The 3D point cloud deep neural network (3D DNN) has achieved remarkable success, but its black-box nature hinders its application in many safety-critical domains. The saliency map technique is a key method to look inside the black-box and determine where a 3D DNN focuses when recognizing a point cloud. Existing point-wise point cloud saliency methods are proposed to illustrate the point-wise saliency for a given 3D DNN. However, the above critical points are alternative and unreliable. The findings are grounded on our experimental results which show that a point becomes critical because it is responsible for representing one specific local structure. However, one local structure does not have to be represented by some specific points, conversely. As a result, discussing the saliency of the local structure (named patch-wise saliency) represented by critical points is more meaningful than discussing the saliency of some specific points. Based on the above motivations, this paper designs a black-box algorithm to generate patch-wise saliency map for point clouds. Our basic idea is to design the Mask Building-Dropping process, which adaptively matches the size of important/unimportant patches by clustering points with close saliency. Experimental results on several typical 3D DNNs show that our patch-wise saliency algorithm can provide better visual guidance, and can detect where a 3D DNN is focusing more efficiently than a point-wise saliency map. Finally, we apply our patch-wise saliency map to adversarial attacks and backdoor defenses. The results show that the improvement is significant.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"5 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niccolò Cecchinato, Ivan Scagnetto, Andrea Toma, Carlo Drioli, Gian Luca Foresti
Nowadays, set of cooperative drones are commonly used as aerial sensors, in order to monitor areas and track objects of interest (think, e.g., of border and coastal security and surveillance, crime control, disaster management, emergency first responder, forest and wildlife, traffic monitoring). The drones generate a quite large and continuous in time multimodal (audio, video and telemetry) data stream towards a ground control station with enough computing power and resources to store and process it. Hence, due to the distributed nature of this setting, further complicated by the movement and varying distance among drones, and to possible interferences and obstacles compromising communications, a common clock between the nodes is of utmost importance to make feasible a correct reconstruction of the multimodal data stream from the single datagrams, which may be received out of order or with different delays. A framework architecture, using sub-GHz broadcasting communications, is proposed to ensure time synchronization for a set of drones, allowing one to recover even in difficult situations where the usual time sources, e.g. GPS, NTP etc., are not available for all the devices. Such architecture is then implemented and tested using LoRa radios and Raspberry Pi computers. However, other sub-GHz technologies can be used in the place of LoRa, and other kinds of single-board computers can substitute the Raspberry Pis, making the proposed solution easily customizable, according to specific needs. Moreover, the proposal is low cost, since it does not require expensive hardware like, e.g., onboard Rubidium based atomic clocks. Our experiments indicate a worst case skew of about 16 ms between drones clocks, using cheap components commonly available in the market. This is sufficient to deal with audio/video footage at 30 fps. Hence, it can be viewed as a useful and easy to implement architecture helping to maintain a decent synchronization even when traditional solutions are not available.
{"title":"A broadcast sub-GHz framework for unmanned aerial vehicles clock synchronization","authors":"Niccolò Cecchinato, Ivan Scagnetto, Andrea Toma, Carlo Drioli, Gian Luca Foresti","doi":"10.3233/ica-230723","DOIUrl":"https://doi.org/10.3233/ica-230723","url":null,"abstract":"Nowadays, set of cooperative drones are commonly used as aerial sensors, in order to monitor areas and track objects of interest (think, e.g., of border and coastal security and surveillance, crime control, disaster management, emergency first responder, forest and wildlife, traffic monitoring). The drones generate a quite large and continuous in time multimodal (audio, video and telemetry) data stream towards a ground control station with enough computing power and resources to store and process it. Hence, due to the distributed nature of this setting, further complicated by the movement and varying distance among drones, and to possible interferences and obstacles compromising communications, a common clock between the nodes is of utmost importance to make feasible a correct reconstruction of the multimodal data stream from the single datagrams, which may be received out of order or with different delays. A framework architecture, using sub-GHz broadcasting communications, is proposed to ensure time synchronization for a set of drones, allowing one to recover even in difficult situations where the usual time sources, e.g. GPS, NTP etc., are not available for all the devices. Such architecture is then implemented and tested using LoRa radios and Raspberry Pi computers. However, other sub-GHz technologies can be used in the place of LoRa, and other kinds of single-board computers can substitute the Raspberry Pis, making the proposed solution easily customizable, according to specific needs. Moreover, the proposal is low cost, since it does not require expensive hardware like, e.g., onboard Rubidium based atomic clocks. Our experiments indicate a worst case skew of about 16 ms between drones clocks, using cheap components commonly available in the market. This is sufficient to deal with audio/video footage at 30 fps. Hence, it can be viewed as a useful and easy to implement architecture helping to maintain a decent synchronization even when traditional solutions are not available.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"2 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136227744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francisco S. Marcondes, José João Almeida, Paulo Novais
Private and military troll factories (facilities used to spread rumours in online social media) are currently proliferating around the world. By their very nature, they are obscure companies whose internal workings are largely unknown, apart from leaks to the press. They are even more concealed when it comes to their underlying technology. At least in a broad sense, it is believed that there are two main tasks performed by a troll factory: sowing and spreading. The first is to create and, more importantly, maintain a social network that can be used for the spreading task. It is then a wicked long-term activity, subject to all sorts of problems. As an attempt to make this perspective a little clearer, this paper uses exploratory design science research to produce artefacts that could be applied to online rumour spreading in social media. Then, as a hypothesis: it is possible to design a fully automated social media agent capable of sowing a social network on microblogging platforms. The expectation is that it will be possible to identify common opportunities and difficulties in the development of such tools, which in turn will allow an evaluation of the technology, but above all the level of automation of these facilities. The research is based on a general domain Twitter corpus with 4M+ tokens and on ChatGPT, and discusses both knowledge-based and deep learning approaches for smooth tweet generation. These explorations suggest that for the current, widespread and publicly available NLP technology, troll factories work like a call centre; i.e. humans assisted by more or less sophisticated computing tools (often called cyborgs).
{"title":"An exploratory design science research on troll factories","authors":"Francisco S. Marcondes, José João Almeida, Paulo Novais","doi":"10.3233/ica-230720","DOIUrl":"https://doi.org/10.3233/ica-230720","url":null,"abstract":"Private and military troll factories (facilities used to spread rumours in online social media) are currently proliferating around the world. By their very nature, they are obscure companies whose internal workings are largely unknown, apart from leaks to the press. They are even more concealed when it comes to their underlying technology. At least in a broad sense, it is believed that there are two main tasks performed by a troll factory: sowing and spreading. The first is to create and, more importantly, maintain a social network that can be used for the spreading task. It is then a wicked long-term activity, subject to all sorts of problems. As an attempt to make this perspective a little clearer, this paper uses exploratory design science research to produce artefacts that could be applied to online rumour spreading in social media. Then, as a hypothesis: it is possible to design a fully automated social media agent capable of sowing a social network on microblogging platforms. The expectation is that it will be possible to identify common opportunities and difficulties in the development of such tools, which in turn will allow an evaluation of the technology, but above all the level of automation of these facilities. The research is based on a general domain Twitter corpus with 4M+ tokens and on ChatGPT, and discusses both knowledge-based and deep learning approaches for smooth tweet generation. These explorations suggest that for the current, widespread and publicly available NLP technology, troll factories work like a call centre; i.e. humans assisted by more or less sophisticated computing tools (often called cyborgs).","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":"57 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Beatriz Macas, Javier Garrigós, J. Martínez, J. M. Ferrández, M. P. Bonomini
Left bundle branch block is a cardiac conduction disorder that occurs when the electrical impulses that control the heartbeat are blocked or delayed as they travel through the left bundle branch of the cardiac conduction system providing a characteristic electrocardiogram (ECG) pattern. We use a reduced set of biologically inspired features extracted from ECG data is proposed and used to train a variety of machine learning models for the LBBB classification task. Then, different methods are used to evaluate the importance of the features in the classification process of each model and to further reduce the feature set while maintaining the classification performance of the models. The performances obtained by the models using different metrics improve those obtained by other authors in the literature on the same dataset. Finally, XAI techniques are used to verify that the predictions made by the models are consistent with the existing relationships between the data. This increases the reliability of the models and their usefulness in the diagnostic support process. These explanations can help clinicians to better understand the reasoning behind diagnostic decisions.
{"title":"An explainable machine learning system for left bundle branch block detection and classification","authors":"Beatriz Macas, Javier Garrigós, J. Martínez, J. M. Ferrández, M. P. Bonomini","doi":"10.3233/ica-230719","DOIUrl":"https://doi.org/10.3233/ica-230719","url":null,"abstract":"Left bundle branch block is a cardiac conduction disorder that occurs when the electrical impulses that control the heartbeat are blocked or delayed as they travel through the left bundle branch of the cardiac conduction system providing a characteristic electrocardiogram (ECG) pattern. We use a reduced set of biologically inspired features extracted from ECG data is proposed and used to train a variety of machine learning models for the LBBB classification task. Then, different methods are used to evaluate the importance of the features in the classification process of each model and to further reduce the feature set while maintaining the classification performance of the models. The performances obtained by the models using different metrics improve those obtained by other authors in the literature on the same dataset. Finally, XAI techniques are used to verify that the predictions made by the models are consistent with the existing relationships between the data. This increases the reliability of the models and their usefulness in the diagnostic support process. These explanations can help clinicians to better understand the reasoning behind diagnostic decisions.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44767202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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