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2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献

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Fault diagnosis of rod pumping system based on deep conditional domain adaption network 基于深度条件域自适应网络的有杆抽油系统故障诊断
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811334
Xiaohua Gu, Fei Lu, Dedong Tang, Guang Yang, Wei Zhou, Jun Peng
{"title":"Fault diagnosis of rod pumping system based on deep conditional domain adaption network","authors":"Xiaohua Gu, Fei Lu, Dedong Tang, Guang Yang, Wei Zhou, Jun Peng","doi":"10.1109/ICCICC53683.2021.9811334","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811334","url":null,"abstract":"The generalization ability of traditional fault diagnosis methods is insufficient. This paper presents a fault diagnosis method of sucker rod pumping system based on deep condition domain adaption network (DCDAN). Firstly, the convolution neural network is used for feature extraction. Then, the weighted maximum mean discrepancy (WMMD) is used to adjust the characteristic distribution of relevant subclasses in different domains to realize the fine-grained domain adaptation of subclasses. At the same time, the fault classification ability of the model is guaranteed by optimizing the classification loss. The results show that this method can improve the generalization performance of the model in the target domain.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A LSTM-based Approach for Gait Emotion Recognition 基于lstm的步态情感识别方法
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811330
Yajurv Bhatia, A. Bari, Marina L. Gavrilova
{"title":"A LSTM-based Approach for Gait Emotion Recognition","authors":"Yajurv Bhatia, A. Bari, Marina L. Gavrilova","doi":"10.1109/ICCICC53683.2021.9811330","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811330","url":null,"abstract":"Gait Emotion Recognition (GER) is a popular problem which has applications in a variety of fields, including smart home design, cognitive systems, border security, robotics, virtual reality, and gaming. In the recent years, several Deep Learning (DL) based approaches for GER have been adopted. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with significant number of model parameters which are neither robust, nor efficient. This paper contributes to the domain of knowledge by presenting a novel light architecture for inferring human emotions through gait. It outperforms all recent deep learning methods, while having the lowest inference time for each gait sample.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116430753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Classification-assisted Deep Sparse Image Recognition 分类辅助深度稀疏图像识别
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811293
Fu-quan Zhu, Wen-Xin Chen, Liang Chen
{"title":"Classification-assisted Deep Sparse Image Recognition","authors":"Fu-quan Zhu, Wen-Xin Chen, Liang Chen","doi":"10.1109/ICCICC53683.2021.9811293","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811293","url":null,"abstract":"This paper proposes a classification-assisted deep sparse network (CDSRC) model to achieve the purpose of image classification. The proposed algorithm consists of four parts: Encoder, Self-representer, Decoder and Classifier. The Encoder part can extract the high-level feature map of the input image, and Self-representer can establish the representational relationship between the test set and the training set image, so as to reconstruct the image of the test set. The Decoder can restore the reconstructed sample to the original image in the form of deconvolution, which is used to supervise the Self-representer. Next, the Encoder can effectively extract the feature map of the original image and the reconstruction of the test set sample. In addition, in order to increase the robustness of image recognition, a Classifier part is added after the Encoder. The Classifier is mainly used to classify training samples while extracting features in the training phase. This will increase the feature similarity of images of the same category, increase the difference of image features of different categories, and reduce the noise interference formed by individual samples. After the algorithm training is completed, the test sample is imported into the Encoder to extract the feature map, the feature map is combined with the sparse matrix of the Self-representer part, and then the test sample category is predicted. Experiments show that the algorithm(CDSRC) in this paper has better results than the SRC-related algorithms that have been proposed.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detecting DDoS Attacks Using a New Polyscale Convolutional Neural Network for Policy Gradient Based DRL 基于策略梯度DRL的多尺度卷积神经网络检测DDoS攻击
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/iccicc53683.2021.9811301
M. Ghanbari, W. Kinsner
{"title":"Detecting DDoS Attacks Using a New Polyscale Convolutional Neural Network for Policy Gradient Based DRL","authors":"M. Ghanbari, W. Kinsner","doi":"10.1109/iccicc53683.2021.9811301","DOIUrl":"https://doi.org/10.1109/iccicc53683.2021.9811301","url":null,"abstract":"This paper presents a new architecture of a policy gradient based deep reinforcement learning (PGDRL) for detecting Internet traffic data (ITD) with distributed denial of service (DDoS) attacks (DDoS ITD) as unlabelled data. In this application, the main procedure in designing an intrusion detection system agent (IDSA) is policy approximation. Furthermore, a polyscale convolutional neural network (PCNN) is presented as a novel structure regarding the policy approximation. The IDSA aims to maximize its expected long-term rewards. Finally, the IDSA classification efficiency is assessed to find the detection rate of the proposed architecture. The PGDRL method detects the DDoS attack with almost 93% accuracy.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Alternative Method of Backward Fuzzy Interpolation based on Areas of Fuzzy Sets 一种基于模糊集面积的反向模糊插值方法
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811316
Kun Du, Shangzhu Jin, Jun Peng
{"title":"An Alternative Method of Backward Fuzzy Interpolation based on Areas of Fuzzy Sets","authors":"Kun Du, Shangzhu Jin, Jun Peng","doi":"10.1109/ICCICC53683.2021.9811316","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811316","url":null,"abstract":"Fuzzy rule interpolation techniques can reduce the complexity of fuzzy systems and make inferences of conclusions in sparse rule-based systems. However, when certain crucial antecedents are missing in the observations and the subsequent interpolation inference process involves these missing antecedents, conventional fuzzy interpolation methods cannot obtain inferred conclusions. To tackle this problem, Jin et al. proposed the method of backward fuzzy rule interpolation, which allows the missing antecedents to be deduced or interpolated from the known antecedents and given conclusion, extending the research field of fuzzy interpolation techniques. In order to extend the generality of backward fuzzy rule interpolation inference, this paper proposes a backward fuzzy rule interpolation approach based on the CCL algorithm, which utilizes the triangular fuzzy membership functions and verifies the effectiveness of the approach by examples.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards a Computational Cognitive Neuroscience Model of Creativity 迈向创造力的计算认知神经科学模型
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811309
Hugo Chateau-Laurent, F. Alexandre
{"title":"Towards a Computational Cognitive Neuroscience Model of Creativity","authors":"Hugo Chateau-Laurent, F. Alexandre","doi":"10.1109/ICCICC53683.2021.9811309","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811309","url":null,"abstract":"Recent progress in AI has expanded the boundaries of the cognitive functions that can be simulated, but creativity remains a challenge. Neuroscience sheds light on its mechanisms and its tight relationship with episodic memory and cognitive control, while machine learning provides preliminary models of these mechanisms. We present these lines of research and explain how they can be exploited in the domain of computational creativity in order to further expand the capabilities of AI.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126510477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Pest Identification System based on YOLOv5 基于YOLOv5的害虫识别系统
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811296
Sheng Yu, Shangzhu Jin, Jun Peng, Zhishu Zhao, Ming Yang Hou, Wenjun Cheng
{"title":"Pest Identification System based on YOLOv5","authors":"Sheng Yu, Shangzhu Jin, Jun Peng, Zhishu Zhao, Ming Yang Hou, Wenjun Cheng","doi":"10.1109/ICCICC53683.2021.9811296","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811296","url":null,"abstract":"Forest pests and diseases are a global problem. The key to control forestry diseases is to accurately identify the species and severity of pests. How to use artificial intelligence and image recognition technology to detect forestry pests is an important challenge and opportunity. This paper presents a new method for forestry pest identification based on YOLOv5 algorithm. In addition, in order to unify the system and expand the flexibility of the future system, we adopted the B/S/S structure to develop the pest identification system. The system uses the camera to shoot images and transmits the data to the background recognition. The experimental results show that our system can detect the target pests more accurately and conveniently, which is helpful for the actual prevention and control of forestry pests.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133864301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Explainable AI for Car Crash Detection using Multivariate Time Series 基于多元时间序列的可解释AI汽车碰撞检测
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811335
L. Tronchin, R. Sicilia, E. Cordelli, L. R. Celsi, D. Maccagnola, Massimo Natale, P. Soda
{"title":"Explainable AI for Car Crash Detection using Multivariate Time Series","authors":"L. Tronchin, R. Sicilia, E. Cordelli, L. R. Celsi, D. Maccagnola, Massimo Natale, P. Soda","doi":"10.1109/ICCICC53683.2021.9811335","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811335","url":null,"abstract":"The pervasiveness of Artificial Intelligence approaches in effectively supporting the decision process in many applications has raised the need to explain their behaviour. In this context, we present the application and evaluation of three eXplainable Artificial Intelligence methods in a real-world multimodal task of anomaly detection on telematics data. We cope with the challenge of explaining Multivariate Time Series and of translating methods designed for images to this domain.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134274927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
An Interactive Approach to Bias Mitigation in Machine Learning 机器学习中消除偏见的交互式方法
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811333
Hao Wang, S. Mukhopadhyay, Yunyu Xiao, S. Fang
{"title":"An Interactive Approach to Bias Mitigation in Machine Learning","authors":"Hao Wang, S. Mukhopadhyay, Yunyu Xiao, S. Fang","doi":"10.1109/ICCICC53683.2021.9811333","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811333","url":null,"abstract":"Underrepresentation and misrepresentation of protected groups in the training data is a significant source of bias for Machine Learning (ML) algorithms, resulting in decreased confidence and trustworthiness of the generated ML models. Such bias can be mitigated by incorporating both objective as well as subjective (through human users) measures of bias, and compensating for them by means of a suitable selection algorithm over subgroups of training data. In this paper, we propose a methodology of integrating bias detection and mitigation strategies through interactive visualization of machine learning models in selected protected spaces. In this approach, a (partially generated) ML model performance is visualized and evaluated by a human user or a community of human users in terms of potential presence of bias using both objective and subjective criteria. Guided by such human feedback, the ML algorithm can implement a variety of remedial sampling strategies to mitigate the bias using an iterative human-in-the-loop approach. We also provide experimental results with a benchmark ML dataset to demonstrate that such an interactive ML approach holds considerable promise in detecting and mitigating bias in ML models.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"32 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Informatics and Cognitive Computing towards General AI (Plenary Panel Report-I) IEEE ICCI*CC系列会议第20届:面向通用人工智能的认知信息学和认知计算的最新进展(全体小组报告- 1)
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2021-10-29 DOI: 10.1109/ICCICC53683.2021.9811324
Guoyin Wang, B. Widrow, W. Pedrycz, R. Berwick, P. Soda, S. Kwong, O. Kaynak, C. Regazzoni, Christine W. Chan, Marina L. Gavrilova, Guoyin Wang
{"title":"IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Informatics and Cognitive Computing towards General AI (Plenary Panel Report-I)","authors":"Guoyin Wang, B. Widrow, W. Pedrycz, R. Berwick, P. Soda, S. Kwong, O. Kaynak, C. Regazzoni, Christine W. Chan, Marina L. Gavrilova, Guoyin Wang","doi":"10.1109/ICCICC53683.2021.9811324","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811324","url":null,"abstract":"Cognitive Informatics (CI) and Cognitive Computing (CC) are fundamental intelligence theories and general AI technologies triggered by the transdisciplinary advances in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the theoretical foundations of CI/CC as well recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, general AI, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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