2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第3页

Complex Networks 复杂网络

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450254

L. Trajković

{"title":"Complex Networks","authors":"L. Trajković","doi":"10.1109/ICCICC50026.2020.9450254","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450254","url":null,"abstract":"The Internet, social networks, power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms are all examples of complex networks. Collection and analysis of data from these networks is essential for their understanding. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze network topologies, and classify network anomalies. Data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while spectral graph theory has been applied to analyze network topologies and capture historical trends in their development. Recent machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies in complex networks. Further applications of these tools will help improve our understanding of the underlying mechanisms that govern behavior, improve their performance, and enhance their security of social networks such as Facebook, LinkedIn, Twitter, Internet blogs, forums, and websites.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124360570","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

Early Detection Method for Subclinical Mastitis in Auto Milking Systems Using Machine Learning 基于机器学习的自动挤奶系统亚临床乳腺炎早期检测方法

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450258

Haruka Motohashi, H. Ohwada, C. Kubota

{"title":"Early Detection Method for Subclinical Mastitis in Auto Milking Systems Using Machine Learning","authors":"Haruka Motohashi, H. Ohwada, C. Kubota","doi":"10.1109/ICCICC50026.2020.9450258","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450258","url":null,"abstract":"Bovine mastitis is an inflammation of the udder or mammary gland and dairy farmers must control its occurrence to prevent economic losses. The introduction of auto milking systems makes management of farms and udder health more efficient and auto detection systems for common diseases in dairy farms, which are implemented auto milking systems and detect the diseases based on some measurements while milking, are needed. In this study, we propose a novel model for subclinical mastitis detection. Our dataset was collected from dairy farms in Japan and labeled using risk values calculated by a commercially available milk analyzer based on lactate dehydrogenase (LDH) in order to train our model. Several measurements that can be obtained from any auto milking system, such as electrical conductivity in milk, were used as time series features. The models were trained using machine learning (a support vector machine or random forest) and their performances were compared. Our model detects the onset of subclinical mastitis with an accuracy of 81% in terms of sensitivity and 46% precision. In addition, some cases of subclinical mastitis can be detected earlier than when using an alert system based on LDH. Our model can be expected to be improved and utilized in real dairy farms.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123633789","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

QPA*: Design of a searching and path planning algorithm for intelligent agents in two dimensions QPA*:一种二维智能体搜索与路径规划算法的设计

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450270

Brian García Sarmina, Georgii Khachaturov

{"title":"QPA*: Design of a searching and path planning algorithm for intelligent agents in two dimensions","authors":"Brian García Sarmina, Georgii Khachaturov","doi":"10.1109/ICCICC50026.2020.9450270","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450270","url":null,"abstract":"Searching algorithms and path planning algorithms are in a variety of applications, where the mobile robotics field is one of the most popular. QPA* algorithm attempts to perform this two strategies at the “same time”. The algorithm makes use of three strategies to generate a different solution to the exploration and path planning mainstream, using a “proposed version” of a modified A* (star) algorithm, the idea of the Potential Fields Algorithm (to deal with the collision avoidance) applied as a artificial binary field, and an exploration heuristic named “quadrant grid search”. The QPA* algorithm is designed to be applied in a search and rescue robot, where the problem of “localization” and “mapping” is consider to be independent of the exploring and path planning algorithm. The “exploration” part of QPA * is intended to overcome the main problem of path planning algorithms, that is the lack of a true “exploration factor” and preserve the efficiency of making a route using the path planning approach of A* algorithm. Finally, we test three different approximations (for the path planning part) in combination with the quadrant grid search, in order to identify the pros and cons of this design.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121030417","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

Data Augmentation Methods and their Effects on Long-Range Dependence 数据增强方法及其对远程依赖性的影响

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450221

M. Ghanbari, W. Kinsner

引用次数: 1

The Cognitive and Mathematical Foundations of Analytic Epidemiology 分析流行病学的认知和数学基础

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450250

Yingxu Wang, K. Plataniotis, Jane Z. Wang, Ming Hou, Mengchu Zhou, N. Howard, Jun Peng, Runhe Huang, Shushma Patel, Du Zhang

{"title":"The Cognitive and Mathematical Foundations of Analytic Epidemiology","authors":"Yingxu Wang, K. Plataniotis, Jane Z. Wang, Ming Hou, Mengchu Zhou, N. Howard, Jun Peng, Runhe Huang, Shushma Patel, Du Zhang","doi":"10.1109/ICCICC50026.2020.9450250","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450250","url":null,"abstract":"Analytic epidemiology is a transdisciplinary study on the cognitive, theoretical, and mathematical models of COVID-19 and other contagious diseases. It is recognized that analytic epidemiology may be better studied by big data explorations at the macro level rather than merely biological analyses at the micro level in order to not lose the forest for the trees. This paper presents a basic research on analytic epidemiology underpinned by sciences of cognition, computer, big data, information, AI, mathematics, epidemiology, and systems. It introduces a novel Causal Probability Theory (CPT) for explaining the Dynamic Pandemic Transmission Model (DPTM) of analytic epidemiology. It reveals how the fundamental reproductive rate $(R_{0})$ may be rigorously calibrated based on big data of COVID-19. A theoretical framework of analytic epidemiology is developed to elaborating the insights of pandemic mechanisms in general and COVID-19 in particular. Robust and accurate predictions on key attributes of COVID-19, including $R_{0}(t)$, forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122588376","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: 5

SAPCGAN: Self-Attention based Generative Adversarial Network for Point Clouds 基于自注意的点云生成对抗网络

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450255

Yushi Li, G. Baciu

{"title":"SAPCGAN: Self-Attention based Generative Adversarial Network for Point Clouds","authors":"Yushi Li, G. Baciu","doi":"10.1109/ICCICC50026.2020.9450255","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450255","url":null,"abstract":"The direct extension of 2D image learning to three-dimensional space is 3D point cloud learning. Recently, point cloud learning has shown significant results in 3D shape estimation and semantic segmentation. Despite these advancements, fundamental problems in point cloud learning still pose significant challenges. These problems include representation learning, shape generation, shape segmentation, and shape matching. In this paper, we propose a cognitive self-attention based learning approach to aggregate global representation of 3D shapes from point cloud data. We also integrate 3D point data with a binary tree structure to build a point cloud generator. We further design a novel Generative Adversarial Network (GAN) architecture to generate point clouds resembling the ground truth that could be used for unsupervised learning of 3D shapes. Relying on a self-attention mechanism, our framework, called SAPCGAN, aggregates the global graph features to correct the structural information of 3D shapes in the latent space. Finally, we compare the performance of two gradient penalty methods used in stabilizing the training of our GAN system. We show that our framework has high training efficiency and can provide state-of-the-art results in 3D point cloud generation. The performance of our is demonstrated with both quantitative and qualitative experimental evaluations. Furthermore, the generated 3D point clouds can be segmented into their natural semantic parts, such as, for example the four legs of a chair, the wings of an air plane, or the four wheels of a car.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122082627","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

Research on Iterative Receiving Algorithm for Aviation Communication System 航空通信系统迭代接收算法研究

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450266

Di Wang, Sheng Wu, Yuehong Gao, Lin Sang

{"title":"Research on Iterative Receiving Algorithm for Aviation Communication System","authors":"Di Wang, Sheng Wu, Yuehong Gao, Lin Sang","doi":"10.1109/ICCICC50026.2020.9450266","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450266","url":null,"abstract":"To cope with the growth of traffic and the need for high-speed transmission, the next-generation aviation communication technology considers deploying the L-bond digital aeronautical communication system (L-DACS1) based on orthogonal frequency division multiplexing (OFDM) in ground-to-air scenarios. But the aviation channel with strong multipath, long-distance, Doppler frequency shift and other characteristics is likely to cause severe channel fading, which affects the reliability of the system and requires channel equalization. At present, the traditional one-time equalization algorithms are generally used in the research of channel equalization in aviation communication systems, but the performance is not ideal. This paper firstly introduces the idea of iteration to the aviation communication system, proposes to use SISO (soft input soft output) equalizer and SISO decoder at the receiving end, and design the iterative receiving algorithm of joint equalization and decoding to realize signal reception. After an in-depth study of linear minimum mean square error (LMMSE) algorithm and Gaussian approximate message passing (AMP-G) algorithm, simulations were built to analyze system performance. The results show that both algorithms can significantly reduce the bit error rate, improve the system reliability, obtain 0.6 dB gain through iteration, save receiver power, and increase the flight radius. The iterative receiving algorithm can improve flight safety, optimize user experience in civil systems, and resist interference, increase the combat range in military systems, so it has broad applications.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617575","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

Specific Time Embedding for Temporal Knowledge Graph Completion 时间知识图补全的特定时间嵌入

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450214

Runyu Ni, Zhonggui Ma, Kaihang Yu, Xiaohan Xu

{"title":"Specific Time Embedding for Temporal Knowledge Graph Completion","authors":"Runyu Ni, Zhonggui Ma, Kaihang Yu, Xiaohan Xu","doi":"10.1109/ICCICC50026.2020.9450214","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450214","url":null,"abstract":"The knowledge graph can be used as a corpus of cognitive computing, in this paper we mainly focus on the temporal knowledge graph. Temporal knowledge graph(TKG), as an extension of static knowledge graph(KG), can be used to deal with dynamic and time-varying knowledge in the real scenario, because many relations are only valid for a certain period, so it can ensure time consistency. Therefore, TKG has received more and more attention. KG embedding (KGE) is an enabling technique for KG completion(KGC), it can complete missing entities in tuples by discovering latent relations between representations. The previous methods mainly focus on static KGC(SKGC), with the emergence of TKG, temporal KGC(TKGC) should be developed. Currently, existing methods for TKGC, either consider changing the representation by temporal information or directly using temporal information to complete. In this paper, we inspired by quantum theory in a sense to propose specific time transE. We note that entities and relations are not time-restricted, only when they are combined to form tuples, the validity of tuples relies on time. We assume that entities and relations can get a determined status after being observed by a specific time, i.e., we use temporal information to get the specific representation of entities and relations. Tuples composed of these specific representations must be related to a specific time, and we use distance model transE to quantify correlation. Finally, through extensive experiments on TKGC datasets, the experimental results verify the validity of our models.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155607","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

Practicable Strategy of High-Dimensional Multi-objective Coevolution 高维多目标协同进化的可行策略

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450219

Hongbo Wang, Wei Huang, Ke-Na Tian, Xuyan Tu

引用次数: 0

Optimized design pattern matrix of PDMA based on binary particle swarm optimization for 5G 基于二元粒子群优化的5G PDMA设计模式矩阵优化

2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2020-09-26 DOI: 10.1109/ICCICC50026.2020.9450225

Kun Lu, Sheng Wu, Hongwen Yang

引用次数: 1