2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第4页

Investigating Genetic Network Programming for Multiple Nest Foraging 多巢觅食的遗传网络规划研究

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659926

Fred D. Foss, Truls Stenrud, P. Haddow

引用次数: 0

Interaction-Based Trust Evaluation in a Team of Agents Using a Determination of Trust Model 基于交互的代理团队信任评估——基于信任决定模型

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659848

Shuo Yang, M. Barlow, E. Lakshika, Kathryn E. Kasmarik

{"title":"Interaction-Based Trust Evaluation in a Team of Agents Using a Determination of Trust Model","authors":"Shuo Yang, M. Barlow, E. Lakshika, Kathryn E. Kasmarik","doi":"10.1109/SSCI50451.2021.9659848","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659848","url":null,"abstract":"Trust has been widely recognized as one of the most important factors influencing team performance. The ability to accurately evaluate the trustworthiness of team members (agents) is crucial for effective team performance. Interaction data among agents are suitable sources of information determining each agent's trustworthiness. However, the existing interaction-based trust models are usually task specific and are only applicable to some well-defined domains (tasks). This paper addresses the problem of accurate trust evaluation in a team of agents by proposing an interaction-based trust evaluation model - the Determination of Trust Model (DoTM), which is applicable to various team tasks. The DoTM maps the relationships between interaction records and the trustworthiness of an agent through a supervised learning algorithm. To take full advantage of the interaction data, before being fed into the machine learner, interaction data are pre-processed by three data processing methods, i.e., combining data from multiple runs, involving indirect interaction records and calculating relative data across agents. A series of experiments are conducted on a simulation platform which performs a cooperative food foraging task. Different types of flawed agents are introduced to distinguish between agents with different trustworthiness. The experimental results demonstrate that the DoTM achieves high accuracy and consistency in scenarios involving different types of flawed agents. The DoTM is compared with an existing interaction-based trust model - LogitTrust and achieved significantly better evaluation accuracy in all considered scenarios. Moreover, the impact of each data processing method is demonstrated through experimental investigations.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"47 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132643851","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

Evolution of Approximate Functions for Image Thresholding 图像阈值分割近似函数的演化

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659876

Michal Bidlo

引用次数: 0

Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images 利用机器学习从二维图像的反射对称性中检测旋转对称性

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660120

Koen Ponse, Anna V. Kononova, Maria Loleyt, Bas van Stein

引用次数: 2

A Bi-Population Based Multi-Objective Evolutionary Algorithm Using Hybrid Identification Method for Finding Knee Points 基于混合识别的双种群多目标进化算法求膝关节点

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660167

Junfeng Tang, Handing Wang

{"title":"A Bi-Population Based Multi-Objective Evolutionary Algorithm Using Hybrid Identification Method for Finding Knee Points","authors":"Junfeng Tang, Handing Wang","doi":"10.1109/SSCI50451.2021.9660167","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660167","url":null,"abstract":"In the preference-based multi-objective optimization, decision makers may be interested in only a part of the representative solutions and hardly specify their preferences. In this case, knee points are considered as the naturally preferred trade-off solutions. Most research utilizes the trade-off information or certain properties to find knee points. However, little attention has been paid to combine them to further enhance the knee identification. This paper proposes a multi-objective evolutionary algorithm using a hybrid identification method and a bi-population structure to find knee points. The hybrid identification method is based on the localized α-dominance and the distance to the hyperplane. Firstly, two populations are partitioned by a set of predefined reference vectors and apply the localized α-dominance to guide the search towards potential knee regions. Then knee solutions are detected based on the distance to hyperplane constructed by the extreme points. Finally in the environmental selection, a niche-preserving operation is applied to take the knee solutions of all sub-populations into account. The first population is the main part of the search, and affects the offspring generation and environmental selection of the second population. The experiments demonstrate that the proposed method is effective and competitive in identifying knee solutions.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131208731","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

A Quantum-inspired Particle Swarm Optimization K-means++ Clustering Algorithm 量子启发粒子群优化k -means++聚类算法

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659549

Chun Hua

引用次数: 1

Connoisseur: Provenance Analysis in Paintings 鉴赏家:绘画的来源分析

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659547

L. David, Hélio Pedrini, Z. Dias, A. Rocha

{"title":"Connoisseur: Provenance Analysis in Paintings","authors":"L. David, Hélio Pedrini, Z. Dias, A. Rocha","doi":"10.1109/SSCI50451.2021.9659547","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659547","url":null,"abstract":"Authorship attribution and matching have become paramount activities in current digital art repositories and communities, which seek to efficiently catalog and authenticate the ever-growing number of digitized paintings, uploaded in professional and casual capturing setups, by their own authors or enthusiasts alike. In this work, we employ convolutional network-based strategies to identify and classify art-related digital artifacts over the Painter by Numbers dataset. Firstly, we propose to exploit the authorship, style and genre annotated information in a multi-task setup, in which patches of paintings are encoded through a multiple outputs network and, in a second stage, used in an Siamese discriminating network to solve the authorship matching problem. Secondly, we combine the available annotated information in a more efficient manner, by posing the Painter by Numbers challenge as a multi-label problem. Empirical results show a substantial increase in class-balanced accuracy and ROC AUC score for both multi-task solutions, compared with their simpler counterparts trained using only authorship annotation. Furthermore, a slight increase in ROC AUC score is observed in the multi-label setup, indicating that this simple combination strategy is beneficial to training convergence.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133705385","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

Causal Analysis of On-line Math Tutoring Impact on Low-income High School Students Using Bayesian Logistic and Beta Regressions 基于贝叶斯Logistic和Beta回归的在线数学辅导对低收入高中学生影响的因果分析

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660176

Maher A. Alhossaini, Mohammed Aloqeely

{"title":"Causal Analysis of On-line Math Tutoring Impact on Low-income High School Students Using Bayesian Logistic and Beta Regressions","authors":"Maher A. Alhossaini, Mohammed Aloqeely","doi":"10.1109/SSCI50451.2021.9660176","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660176","url":null,"abstract":"The use of on-line tutoring, especially after the COVID-19 pandemic, has increased dramatically. It has become clear that measuring the effectiveness of on-line tutoring, especially on low-income students, is much needed in such difficult times. This paper, which is based on observational data collected before the COVID-19 era, is targeting measuring the impact of a web-based math tutoring program, Noon Academy, on the academic achievement of low-income high school students (grades 10 to 12) in Saudi Arabia. We use a large amount of data collected in a student registration process and two Bayesian generalized linear models (GLM) to measure the tutoring causal effects. Model 1 uses a binomial logistic regression to predict the impact of enrolling in the tutoring program on the rate of passing in a number of students. Model 2 uses a multi-level Beta regression to measure the impact of the number of minutes on the total mark. Model 1 results show that giving math tutoring to higher-failing-risk students significantly improves the rate of passing by +5 %, reaching a maximum of + 17.15 % in some classes of students. Model 2 shows a significant positive impact of the number of tutoring minutes on the yearly math mark (max of 100), reaching an average of +3.52 marks for the highest number of minutes taken. The paper presents an application of a causal analysis approaches on a real-life social problem. It demonstrates how the model is used to obtain a measure of the impact with quantifiable uncertainty that can be used in practice.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133453034","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

Machine Learning based Prediction of Situational Awareness in Pilots using ECG Signals 基于机器学习的心电信号飞行员态势感知预测

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660076

Anushri Rajendran, P. Kebria, N. Mohajer, A. Khosravi, S. Nahavandi

{"title":"Machine Learning based Prediction of Situational Awareness in Pilots using ECG Signals","authors":"Anushri Rajendran, P. Kebria, N. Mohajer, A. Khosravi, S. Nahavandi","doi":"10.1109/SSCI50451.2021.9660076","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660076","url":null,"abstract":"Reducing aviation fatalities requires a high level of reliable real-time monitoring so that events can be predicted and prevented before they can occur. Situational awareness is essential in the cockpit where manual and autonomous operations co-exist. Many interventions and countermeasures have been designed into cockpits to enhance pilot awareness and performance. This study aims to analyse pilot and copilot teams' awareness by using physiological data which was collected in a flight simulator to train models to predict when pilots are in a state of Channelised Attention (CA), Diverted Attention (DA), and Startle/Surprise (SS). Electrocardiogram (ECG) signals collected for 18 subjects were processed in preparation to develop a comprehensive tool which utilises active Line Oriented Flight Training (LOFT) data to evaluate machine learning tools which are capable of predicting pilot awareness response. A combination of linear, non-linear, binary and multi-class classification were applied to this data. The results indicate that while all classifiers produced stable results, Decision Tree(DT) far outperformed the others. Further analyses revealed that the maximum value for ECG was the most important feature used by all classifiers evaluated for importance in training the classification models. However, for DT which was the best performing classifier both maximum and minimum ECG values were the most important features in predictions made by this model.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132152394","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

Website Privacy Notification for the Visually Impaired 视障人士网站私隐通知

2021 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659986

Karen Schnell, K. Roy

引用次数: 1