2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第7页

On the Emergence of Abstract Sciences and Breakthroughs in Machine Knowledge Learning 论抽象科学的出现和机器知识学习的突破

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146058

Yingxu Wang

引用次数: 4

Experience-based analysis and modeling for cognitive vehicle data 基于经验的认知车辆数据分析与建模

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146043

H. Hiraishi

引用次数: 1

Visualizing the Temporal Similarity Between Clusters of Dynamic Graphs 动态图簇间时间相似性的可视化

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146098

Yunzhe Wang, G. Baciu, Chenhui Li

{"title":"Visualizing the Temporal Similarity Between Clusters of Dynamic Graphs","authors":"Yunzhe Wang, G. Baciu, Chenhui Li","doi":"10.1109/ICCICC46617.2019.9146098","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146098","url":null,"abstract":"The evolution of graph structures in large time-varying graphs is often difficult to visualize and interpret due to excessive clutter from overlapping nodes and edges. With limited display area, visual clutter often increases and makes it difficult to recognize developing patterns in embedded sub-graphs. In such situations viewers are often hampered in observing and exploring significant changes of the graph components. This poses a cognitive barrier in the visual analytics of large dynamic structures. Another important problem in visualizing dynamic graphs is capturing the difference between graph states. Their state changes often become intractable. In this paper we propose to construct cognitive templates for grouping closely related entities using community detection techniques. The induced subgraphs are collapsed into meta-nodes in order to simplify the representation of large graphs and induce similarities between communities. In order to compute the new structures, we introduce the GCN, or Graph Convolution Network, that learns the representations of sub-graphs induced by communities. The pair-wise similarities can then be calculated by graph-based cluster search algorithms. Furthermore, the proximity state might change temporally. We need to extract the matched communities between consecutive snapshots. Using multi-dimensional scaling and color mappings, we reveal the evolution of graphs at the community level. We evaluate the effectiveness of our method by applying it to the Wikipedia edit history data set.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115363742","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

Arbitrary Multiscale Explainable Decision-Making for Symbiotic Autonomous Systems 共生自治系统的任意多尺度可解释决策

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146071

R. Fiorini

{"title":"Arbitrary Multiscale Explainable Decision-Making for Symbiotic Autonomous Systems","authors":"R. Fiorini","doi":"10.1109/ICCICC46617.2019.9146071","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146071","url":null,"abstract":"In the near future to solve complex, arbitrary multiscale system problems, we need a unified, integrated framework that can offer an effective and convenient, universal mathematical approach, by considering information not only on the statistical manifold of model states, but also on the combinatorial manifold of low-level discrete, directed energy generators and empirical measures of noise sources, related to experimental high-level overall perturbation. To overcome past modeling limitations in dynamic cooperative multi-agent system, we propose the modeling of agent as purposive subject modeled by the Elementary Pragmatic Model (EPM) approach. In this context, predicative competence and natural language processing can play a fundamental role for developing a new generation of user-friendly, more autonomous, but still colloquial systems to offer explainable decision-making processes. In order to achieve this goal, a deep layer of “machine thought” is vital for developing highly competitive, reliable and effective symbiotic autonomous systems. Based on it, a new approach to computational predicative competence will be presented.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123624830","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

Early Experience in Forecasting Blood Glucose Levels Using a Delayed and Auto-Regressive Jump Neural Network 使用延迟和自回归跳跃神经网络预测血糖水平的早期经验

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146049

Federico D'Antoni, M. Merone, V. Piemonte, P. Pozzilli, G. Iannello, P. Soda

{"title":"Early Experience in Forecasting Blood Glucose Levels Using a Delayed and Auto-Regressive Jump Neural Network","authors":"Federico D'Antoni, M. Merone, V. Piemonte, P. Pozzilli, G. Iannello, P. Soda","doi":"10.1109/ICCICC46617.2019.9146049","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146049","url":null,"abstract":"Type 1 diabetes mellitus is a widespread chronic disease that, if not properly treated, can lead to short- and longterm complications. In recent years, continuous glucose monitoring has become very popular among patients since it allows to keep track of glucose levels for 24 hours. Nevertheless, hypo- and hyper-glycemic events are still widely reported, motivating the development of methods that forecast blood glucose levels at given prediction horizons, which usually range from 15 to 30 minutes. However their application, regardless of the approaches adopted, is limited in practice by the fact that they usually need a long training time and other information gathered from the patients. To overcome these issues in this work we present a new neural network that extends the jump network model by introducing time delays from the input to the hidden layer and auto-regressive feedback from the output to the hidden layer. The proposed neural network shows promising results at 15, 20 and 30-minute prediction horizons, which are comparable to or slightly better than the results reported in the literature, although the training period used in this work is considerably shorter.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131761783","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

Study of motor imagery for multiclass brain system interface with a special focus in the same limb movement 以同一肢体运动为重点的多脑系统界面运动意象研究

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146105

Mohit Patil, Nikhil Garg, L. Kanungo, V. Baths

{"title":"Study of motor imagery for multiclass brain system interface with a special focus in the same limb movement","authors":"Mohit Patil, Nikhil Garg, L. Kanungo, V. Baths","doi":"10.1109/ICCICC46617.2019.9146105","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146105","url":null,"abstract":"The presented study focuses on simple upper limb movements; lifting and clenching for use in Motor Imagery (MI) based Brain System Interface (BSI). Furthermore, we analyzed the same limb movement imagery and its validity in developing multi-class BSI. 15 subject datasets were analyzed using feature extraction and classification methods from regularized common spatial pattern toolbox. The results for offline analysis are presented in this study. The multiclass analysis was done using One-Versus-One (OVO) and One-Versus-Rest (OVR) approaches using pre-selected feature extraction and classification. OVO and OVR binary classifiers were converted into 4 class classifiers using a novel score based method. Neither lifting nor clenching actions showed any substantial advantage over the other for binary classification. Furthermore, within the same limb, two kinesthetically close actions (lifting vs. clenching) show good differentiability even with the same shared neural pathway. However, further analysis of the performance of the same limb actions in the 4 class environment is required. The same limb movements, if successfully incorporated, shows potential in increasing the number of differentiable classes in a multiclass BSI.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130166832","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

Copyright 版权

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/iccicc46617.2019.9146053

引用次数: 0

Challenges to New Economic Theory: Climate Change, the Fourth Industrial Revolution, Technology, and Global Values 新经济理论面临的挑战:气候变化、第四次工业革命、技术和全球价值观

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146056

W. Nagan, Samantha R. Manausa

引用次数: 0

An Effective Approach for Selecting Cluster Centroids for the k-means Algorithm using IABC Approach 基于IABC方法的k-means聚类质心选择方法

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146077

M. Batchanaboyina, Nagaraju Devarakonda

{"title":"An Effective Approach for Selecting Cluster Centroids for the k-means Algorithm using IABC Approach","authors":"M. Batchanaboyina, Nagaraju Devarakonda","doi":"10.1109/ICCICC46617.2019.9146077","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146077","url":null,"abstract":"K-means is a popular grouping technique for unsupervised data. Though the technique is simple and potential it suffers from the ambiguity in the selection of k (number of clusters). The selection or initialization of k-cluster centroids significantly impacts the performance of the k-means. The distribution of data with the possibility of outliers may degrade the clustering strength. If the initialization of clustering is best fitted then the rest of the k-means process will save the execution time and can offer better clustering. A searching procedure is needed that learn the data under hand and decide the cluster initialization accordingly. ABC is a good technique for optimized search. In this paper, a study of different variants of ABC is undertaken in order to propose a new methodology named IABC. The methodology made use of bee intelligence for search optimization along with k-means and k-nearest neighborhood algorithms. The proposed approach adds the strength to the k-means approach in terms of optimum centroid initialization. Experiments are done on two popular datasets to compare the proposed approach with the existing techniques. The comparisons are made between the existing and proposed methods. Better and encouraging results are found. The proposed process is able to save a significant amount of time and can offer better and accurate clustering.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"421 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132502590","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

Using Distance Landscape Strategy for RGV Dynamic Scheduling Problem 基于距离景观策略的RGV动态调度问题

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146042

Wei Li, Ke Li, Xiang Meng, Feng Wang, Yan Chen, Kangshun Li

{"title":"Using Distance Landscape Strategy for RGV Dynamic Scheduling Problem","authors":"Wei Li, Ke Li, Xiang Meng, Feng Wang, Yan Chen, Kangshun Li","doi":"10.1109/ICCICC46617.2019.9146042","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146042","url":null,"abstract":"Rail guide vehicle (RGV) dynamic scheduling problems have attracted increasing attention in recent years, which determines a great impact on the working efficiency of the entire scheduling system. However, the relative intelligent optimization study of RGV dynamic scheduling problems are insufficient scheduling of different working components in the previous works, it is easy to appear idle waiting, resulting in reduced operating efficiency during operation. Analysis of the fitness landscape is essential to understand the behavior of evolutionary algorithms for solving dynamic optimization problems in the evolutionary dynamics of biological evolution. With the continuous advancement of evolutionary algorithm optimization, the fitness landscape can present more abundant feature information around the fitness value, including autocorrelation, fitness distance correlation, landscape walks, local optima, and landscape roughness. This paper proposes a new distance landscape strategy for the RGV dynamic scheduling problems. The combination of the fitness landscape and dynamic search strategy are established according to the operating principle of the RGV system. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with one procedure programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solve the considered RGV dynamic scheduling problems effectively.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440308","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