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检索条件"任意字段=Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition"

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Explainable Hybrid CNN and FNN Approach Applied on Robotic Wall-Following Behaviour Learning 21

Explainable Hybrid CNN and FNN Approach Applied on Robotic W...

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proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Jakub Kwiatkowski Liang Ou Yu-Cheng Chang Chin-Teng Lin Institute of Computing Science Poznan University of Technology Poland School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781450384087

Fuzzy Neural Network (FNN) applied to robotic control tasks has proved to be effective by previous researchers. However, FNN has an inherent deficiency in dealing with inputs of large dimensions, such as images. therefore, this research utilizes a Convolutional Neural Network (CNN) model to convert image into distance values and delivers these values to FNN based robot controller as inputs. the proposed hybrid CNN+FNN are tested with both a regression model and a multi-task model. Results show that the multi-task method performs better with less information loss from input images. this paper also proved that the proposed hybrid approach can be generalized into an unknown robotic simulation environment and performs better than its FNN counterpart. By utilizing state of the art explainable analysis method, both the CNN part and the FNN part of the hybrid approach can be explained in a human-understandable way.

关键词： Additional Key Words and Phrases: Explainable AI Fuzzy System Robotic Navigation

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A Model robustness optimization method based on adversarial sample detection 22

A Model robustness optimization method based on adversarial ...

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proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Jiaze Sun Siyuan Long Xianyan Ma Yanmei Tang Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts & Telecommunications China Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. the detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments.

关键词： Adversarial samples Deep neural networks Integrated detection Model robustness

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An IoT-based Framework for an Optimal Monitoring and Control of Cyber-Physical Systems: Application on Biogas Production System 21

An IoT-based Framework for an Optimal Monitoring and Control...

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proceedings of the 11th international conference on the Internet of things

作者： Mohamed Anis Aguida Samir Ouchani Mourad Benmalek School of Computing Wichita State University United States Lineact CESI France Ecole nationale Supérieure d'Informatique Algeria

ISBN: (纸本)9781450385664

Among the possible conversion processes for biomass is Anaerobic Digestion (AD) that produces a valuable energy known as biogas. However, the instability of biogas production is challenging due to the complex process of the AD that requires a precise environmental conditions as well as different bacteria populations. Hence, monitoring and controlling these conditions and tracking the bacteria behaviour inside the digester is the way to overcame this challenge and enhance the biogas production. We address these challenges by proposing an IoT-based framework that aims to implement an End-to-End secure Cyber-Physical System (CPS) for biogas plants. the proposed framework encloses three layers: the perception layer which is mainly the industrial network, the network layer that develops the IT network inside the plant where the supervision and control is being conducted. In addition, the application layer which is defined by the IoT platform and software sensor to enable predictive analysis. To show the effectiveness of the proposed solution, we deployed the framework implementation on GNS3 that fully simulates the real conditions.

关键词： Internet of things Industrial network. Cyber-Physical Systems Biogas plant SCADA

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Development and Evaluation of Enhanced National Greening Program Monitoring and Document Archiving System using DeLone and McLean IS Success Model 22

Development and Evaluation of Enhanced National Greening Pro...

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proceedings of the 2022 11th international conference on Networks, Communication and computing

作者： Joana Victoria S Pitao Jenie P Nabas Junrie B Matias Jesterlyn Q Timosan Godwin G Rollorata College of Computing and Information Sciences Caraga State University Philippines

ISBN: (纸本)9781450398039

the information system was developed to aid the critical operations of the enhanced national greening program within the Caraga region, eliminate the risk of human error, and lift the tedious manual processing and progress monitoring. However, the sudden shift of processes from manual to automated information systems can affect the user adoption of the system. For this reason, a survey was conducted eight (8) months after the software development to assess and evaluate the development and implementation of the information system adopting the Delone and Mclean IS Success Model and hypothesizing nine (9) relationships to examine the information system success. this work used Fornell-Lacker's Criterion to measure the discriminant validity of the constructs - Information Quality, Intention to Use, Perceived Net Benefits, System Quality, Service Quality, and User Satisfaction. Results show that the intention to use positively affects Perceived Net Benefits. Moreover, Service Quality and User Satisfaction affect users’ Intention to Use; evidently, the user's perceptions of the quality of the services delivered and service performance are motivating factors for users’ continued use of the system. Lastly, user satisfaction, intention, system quality, and quality of service are contributory factors that affect the usage and adoption of the system.

关键词： Use Intention IS Implementation DeLone and McLean IS Success Model Net Benefits

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Development of Real-Time Traffic Monitoring and Visualization System Using Stationary Roadside Sensor 22

Development of Real-Time Traffic Monitoring and Visualizatio...

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proceedings of the 2022 11th international conference on Networks, Communication and computing

作者： Jundee Mark G. Molina Angelica S. Sotis Rogamestica C. Pascual James Earl D. Cubillas Junrie B. Matias College of Computing and Information Sciences Caraga State University Philippines

ISBN: (纸本)9781450398039

this study presented the development of a web-based system that visualizes real-time traffic by deploying lightweight and mobile monitoring devices at roadside intersections in the vicinity of Butuan City to assist commuters and drivers in making optimal decisions regarding efficient roadways for travel. the system can visualize the traffic situation at an intersection using IoT devices, wireless communication devices, and artificial intelligence. To analyze captured real-time traffic conditions and quantify traffic parameters using YOLOv5s and DeepSort Algorithm. DeepSort Algorithm is utilized for object tracking, whereas YOLOv5s is utilized for real-time object detection. the system has been successfully tested and is functional, providing a dashboard with real-time traffic information and a great platform for commuters and drivers in the vicinity of Butuan City to be aware of what is occurring on that particular roadway. this aids motorists, travelers, traffic and transportation offices, and organizations in obtaining traffic data for optimal decision-making, such as selecting the quickest route and boosting productivity and services. this would also aid in identifying specific transportation or traffic problems and generating cogent solutions.

关键词： YOLO Real-Time Traffic Monitoring DeepSort Algorithm

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Meta Actor-Critic Framework for Multi-Agent Reinforcement Learning 21

Meta Actor-Critic Framework for Multi-Agent Reinforcement Le...

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proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Jiateng Huang Wanrong Huang Dan Wu Long Lan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China Artifcial Intelligence Research Center National Innovation Institute of Defense Technology China College of Computer & Hefei Interdisciplinary Center National University of Defense Technology China

ISBN: (纸本)9781450384087

In recent years, multi-agent reinforcement learning has received sustained attention in the last few years. the typical Actor-Critic methods learn mappings directly from observation to action without understanding the tasks themselves. In this paper, we present a meta actor-critic framework for meta-actor critique based on observational learning processes and the additional loss of meta-learning actors to accelerate and improve multi-agent learning across agents' experiences. Within our framework, all agents are deliberately designed to share the same meta-critic loss to achieve the optimum actor learning progress. Meanwhile, by minimizing the loss of meta-actors, the meta actor learns the features of the meta-observation, leading to better actions. We implemented the MADDPG and MATD3 algorithms in our proposed framework and empirically demonstrated the superiority of our framework on two kinds of multi-agent tasks. In addition, the framework can be flexibly incorporated into various contemporary multi-agent Actor-Critic methods.

关键词： Reinforcement learning meta-learning multi-agent system

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Is it Nemo or Dory? Fast and accurate object detection for IoT and edge devices 21

Is it Nemo or Dory? Fast and accurate object detection for I...

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proceedings of the 11th international conference on the Internet of things

作者： Sudhanshu Agarwal Richard Vuduc College of Computing Georgia Institute of Technology United States School of Computational Science and Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781450385664

Current state-of-the-art object detection neural networks, such as YOLO and SSD, are trained and developed on server-class GPUs. these neural networks do not scale down well to resource-constrained devices, with both accuracy and precision taking a significant hit at the expense of speed. this is particularly concerning as object detection algorithms are often used for low-powered devices, such as surveillance and smart-home cameras, where accuracy is critical. therefore, these devices generally tend to forward data to servers for processing, which adds network latency. In other cases, algorithms developed for these devices shrink neural networks to reduce computation at the expense of accuracy, which is often not acceptable. We create an alternative object detection scheme for static-camera systems such as those used in surveillance and smart-home settings. Our model does not require positions of objects in training data, enabling us to retain only the relevant parts of a video frame for training data and reduce data storage cost while preserving privacy of other subjects in the video. When evaluated on static-camera video feeds using an NVIDIA Jetson Nano (a hybrid Arm and GPU IoT embedded device platform), our method increases throughput compared to Tiny YOLOv3. Further, it takes half of the time that Tiny YOLOv3 takes when run on OpenCV’s CUDA-optimized DNN framework per frame while having high accuracy of bounding box prediction and classification. the lessons learned from this work also suggest other strategies for tailoring the deployment of deep learning algorithms at the edge.

关键词： Background Modeling Object Detection Edge computing Parallel computing

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OpenAUC: towards AUC-oriented open-set recognition 22

OpenAUC: towards AUC-oriented open-set recognition

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proceedings of the 36th international conference on Neural Information Processing Systems

作者： Zitai Wang Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang SKLOIS Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University and SKLOIS Institute of Information Engineering CAS School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness.

关键词：

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Analysis of urban bus control strategies under the COVID-19 pandemic 23

Analysis of urban bus control strategies under the COVID-19 ...

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proceedings of the 2023 11th international conference on Information Technology: IoT and Smart City

作者： Liye Zhang Hao Shi Jukong Li Jie Song Shandong University of Science and Technology China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9798400709043

During the peak of the COVID-19 outbreak in Wuhan, there were distinct variations in bus control intensity among cities, influenced by factors such as population size and economic scale. In response to these differences, this study collected and compiled bus control strategies from more than 200 cities across the country. Daily data on the existing confirmed cases (confirmed cases per million population) from January 24, 2020, to March 23, 2020, were gathered as research samples. Statistical analysis using the Kruskal-Wallis One-Way ANOVA (k-sample) test was performed in SPSS, followed by pairwise comparisons of the samples. the study involved comparing epidemic data between cities with high control intensity (implementing complete shutdown) and those with lower control intensity (implementing partial shutdown or non- shutdown). the results of the analysis indicated that there were no significant differences in the existing confirmed COVID-19 cases between many pairs of cities. this finding suggests that the control intensity of bus strategy in these cities may be unreasonable. Pairwise comparison heatmaps also revealed a certain pattern, with the decline of the GDP volume, the proportion of the city pairs with no significant difference in COVID-19 diagnosis statistics continues to increase under the same population size, which furthermore proves that bus control strategies of some cities during COVID-19 is unnecessarily tight, especially in economically underdeveloped areas, which need to adapt to local conditions, develop reasonable control strategies based on actual travel needs. Research on public transportation strategies for COVID-19 can offer valuable insights for response strategies, prompting improvements and strengthening of healthcare infrastructure to better address future threats posed by similar infectious diseases.

关键词： Bus control intensity COVID-19 Kruskal-Wallis One-Way ANOVA (K samples) test ,Pairwise comparison

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Measurements of Cloud Base Height and Coverage using Elastic Multiangle Lidar Scans at the Pierre Auger Observatory 38

Measurements of Cloud Base Height and Coverage using Elastic...

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38th international Cosmic Ray conference, ICRC 2023

作者： Pallotta, Juan Vicente Rodriguez, Jorge Ruben Abdul Halim, A. Abreu, P. Aglietta, M. Allekotte, I. Almeida Cheminant, K. Almela, A. Aloisio, R. Alvarez-Muñiz, J. Ammerman Yebra, J. Anastasi, G.A. Anchordoqui, L. Andrada, B. Andringa, S. Aramo, C. Araújo Ferreira, P.R. Arnone, E. Arteaga Velázquez, J.C. Asorey, H. Assis, P. Avila, G. Avocone, E. Badescu, A.M. Bakalova, A. Balaceanu, A. Barbato, F. Bartz Mocellin, A. Bellido, J.A. Berat, C. Bertaina, M.E. Bhatta, G. Bianciotto, M. Biermann, P.L. Binet, V. Bismark, K. Bister, T. Biteau, J. Blazek, J. Bleve, C. Blümer, J. Boháčová, M. Boncioli, D. Bonifazi, C. Bonneau Arbeletche, L. Borodai, N. Brack, J. Brichetto Orchera, P.G. Briechle, F.L. Bueno, A. Buitink, S. Buscemi, M. Büsken, M. Bwembya, A. Caballero-Mora, K.S. Cabana-Freire, S. Caccianiga, L. Caracas, I. Caruso, R. Castellina, A. Catalani, F. Cataldi, G. Cazon, L. Cerda, M. Cermenati, A. Chinellato, J.A. Chudoba, J. Chytka, L. Clay, R.W. Cobos Cerutti, A.C. Colalillo, R. Coleman, A. Coluccia, M.R. Conceição, R. Condorelli, A. Consolati, G. Conte, M. Convenga, F. Correia dos Santos, D. Costa, P.J. Covault, C.E. Cristinziani, M. Cruz Sanchez, C.S. Dasso, S. Daumiller, K. Dawson, B.R. de Almeida, R.M. de Jesús, J. de Jong, S.J. de Mello Neto, J.R.T. De Mitri, I. de Oliveira, J. de Oliveira Franco, D. de Palma, F. de Souza, V. De Vito, E. Del Popolo, A. Deligny, O. Denner, N. Deval, L. di Matteo, A. Dobre, M. Dobrigkeit, C. D’Olivo, J.C. Domingues Mendes, L.M. dos Anjos, R.C. Ebr, J. Ellwanger, F. Emam, M. Engel, R. Epicoco, I. Erdmann, M. Etchegoyen, A. Evoli, C. Falcke, H. Farmer, J. Farrar, G. Fauth, A.C. Fazzini, N. Feldbusch, F. Fenu, F. Fernandes, A. Fick, B. Figueira, J.M. Filipčič, A. Fitoussi, T. Flaggs, B. Fodran, T. Fujii, T. Fuster, A. Galea, C. Galelli, C. García, B. Gaudu, C. Gemmeke, H. Gesualdi, F. Gherghel-Lascu, A. Ghia, P.L. Giaccari, U. Giammarchi, M. Glombitza, J. Gobbi, F. Gollan, F. Golup, G. Gómez Berisso, M. Buenos Aires Argentina Observatorio Pierre Auger Av. San Martín Norte 304 Malargüe5613 Argentina San Carlos de Bariloche Argentina Departamento de Física Departamento de Ciencias de la Atmósfera y los Océanos FCEyN Universidad de Buenos Aires and CONICET Buenos Aires Argentina IFLP Universidad Nacional de La Plata and CONICET La Plata Argentina Buenos Aires Argentina CONICET U.N.R. Facultad de Ciencias Bioquímicas y Farmacéuticas U.N.R. Rosario Argentina Mendoza Argentina Buenos Aires Argentina International Center of Advanced Studies Instituto de Ciencias Físicas ECyT-UNSAM CONICET Campus Miguelete – San Martín Buenos Aires Argentina Argentina Observatorio Pierre Auger and Comisión Nacional de Energía Atómica Malargüe Argentina Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina University of Adelaide AdelaideSA Australia Brussels Belgium Vrije Universiteit Brussels Brussels Belgium Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Petropolis Brazil Brazil Universidade de São Paulo Escola de Engenharia de Lorena SP Lorena Brazil Universidade de São Paulo Instituto de Física de São Carlos SP São Carlos Brazil Universidade de São Paulo Instituto de Física SP São Paulo Brazil Universidade Estadual de Campinas IFGW SP Campinas Brazil Universidade Estadual de Feira de Santana Feira de Santana Brazil Universidade Federal de Campina Grande Centro de Ciencias e Tecnologia Campina Grande Brazil Universidade Federal do ABC SP Santo André Brazil Universidade Federal do Paraná Setor Palotina Palotina Brazil Universidade Federal do Rio de Janeiro Instituto de Física RJ Rio de Janeiro Brazil Observatório do Valongo RJ Rio de Janeiro Brazil Universidade Federal Fluminense EEIMVR RJ Volta Redonda Brazil Universidad de Medellín Medellín Colombia Universidad Industrial de Santander Bucaramanga Colombia Charles University Faculty of Mathematics and Physics Institute of Particle and

Cloud features significantly affect the reconstruction of extensive air showers, and their characterization plays an important role in atmospheric monitoring. A multi-directional characterization of the cloud pattern is provided by a combination of several instruments of the atmospheric monitoring network at the Pierre Auger Observatory (Mendoza Province, Argentina). In this work, we present the results of an analysis of the cloud measurements using data taken from 2007 to 2022 by the elastic lidars positioned in the proximity of the fluorescence detector (FD) sites. these systems provide hourly measurements of cloud coverage and base height above FD. the ansatz of horizontal homogeneity of cloud structures is tested by comparing the hourly measurements of cloud base height and coverage done simultaneously at different lidar locations. these results allow a detailed description of cloud patterns observed above the array throughout the whole period. the variation of cloud parameters is shown and quantitative conclusions about cloud homogeneity across the array of the Pierre Auger Observatory are given. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Miocene

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