2020 7th International Conference on Behavioural and Social Computing (BESC)最新文献_第4页

Emotional Disorders: “If you pinch him, he will squeak”. A new perspective on how machines can pass the Turing Test 情绪障碍:“如果你捏他，他会吱吱叫”。机器如何通过图灵测试的新视角

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348305

Saríah López-Fierro

引用次数: 0

Accuracy and Fairness in a Conditional Generative Adversarial Model of Crime Prediction 条件生成对抗犯罪预测模型的准确性和公平性

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348315

Christian Urcuqui, Juan Moreno, C. Montenegro, Alvaro J. Riascos, Mateo Dulce Rubio

{"title":"Accuracy and Fairness in a Conditional Generative Adversarial Model of Crime Prediction","authors":"Christian Urcuqui, Juan Moreno, C. Montenegro, Alvaro J. Riascos, Mateo Dulce Rubio","doi":"10.1109/BESC51023.2020.9348315","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348315","url":null,"abstract":"We propose a novel conditional GANs architecture for crime (robberies) prediction in Bogotá, capital city of Colombia. The model uses several layers of ConvLSTM neural nets in both the generative and the discriminatory networks. We further condition on past crime intensity maps, weekdays, and holidays. The trained network is able to capture spatiotemporal patterns and outperforms state-of-the-art predictive models such as spatiotemporal Poisson point process, as well as other models trained with the same dataset. Model's accuracy reaches an area under the Hit Rate - Percentage Area Covered by Hotspots curve of 0.86. However, our predictions suggest that there is a potential bias with heterogeneous effects on vulnerable populations. We address the fairness consequence of this model in low income vs. high income residents by estimating a calibration test conditional to these protected variables. Finally, we introduce a fairness - accuracy balancing technique that quantifies the tradeoffs between accuracy and fairness in this type of models. This technique notably reduces bias with a marginal effect on accuracy.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422393","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: 4

Collaborator Recommendation Based on Dynamic Attribute Network Representation Learning 基于动态属性网络表示学习的合作者推荐

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348323

Hansong Nie, Xiangtai Chen, Xinbei Chu, Wei Wang, Zhenzhen Xu, Feng Xia

{"title":"Collaborator Recommendation Based on Dynamic Attribute Network Representation Learning","authors":"Hansong Nie, Xiangtai Chen, Xinbei Chu, Wei Wang, Zhenzhen Xu, Feng Xia","doi":"10.1109/BESC51023.2020.9348323","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348323","url":null,"abstract":"Scientific collaboration plays an important role in modern academic research. Collaborations between scholars will bring about high-quality papers and improve the academic influence of scholars. However, it is more and more difficult to find a suitable collaborator due to the rapid growth of academic data. There are already some recommendation systems based on calculating the similarity between scholars. But most of them do not consider the dynamic nature of the scientific collaboration network. To this end, we propose a collaborator recommendation algorithm based on dynamic attribute network representation learning (DANRL). It takes advantage of the network topology, scholar attributes and the dynamic nature of the network to represent scholars as low-dimensional vectors. By calculating the cosine similarity between scholar vectors, we can recommend the most similar collaborators to target scholars. Moreover, at each time step of the dynamic network, our method only needs to train embedding vectors for some selected nodes instead of performing random walks and training embedding vectors for all nodes, which can significantly improve the recommendation efficiency. Experiments on two real-world datasets show that DANRL outperforms several baseline methods.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115621604","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

Heuristics-Based Process Mining on Extracted Philippine Public Procurement Event Logs 基于启发式的菲律宾公共采购事件日志挖掘

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348306

Maria Jihan G. Sangil

引用次数: 1

Designing Meta-choices in a Purpose Made Game to Explore Anti-social Choices 在目的导向型游戏中设计元选择以探索反社交选择

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348325

S. Hodge, Jacqui Taylor, J. McAlaney

引用次数: 0

Personality Traits and Coping Strategies of eSports Players 电子竞技选手的个性特征与应对策略

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348280

M. Semenova, Andrey Lange, Denis Koposov, A. Somov, E. Burnaev

引用次数: 2

Towards a Taxonomy for Evaluating Societal Concerns of Contact Tracing Apps 迈向评估接触者追踪应用的社会关注的分类

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348293

T. Welsh, Kaavya Rekanar, Manzar Abbas, Muslim Chochlov, Brian Fitzgerald, Liam Glynn, Kevin Johnson, J. Laffey, B. McNicholas, B. Nuseibeh, James O'Connell, Derek T. O'Keeffe, Ian R. O’Keeffe, Mike O'Callaghan, A. Razzaq, Ita Richardson, A. Simpkin, Cristiano Storni, Damyanka Tsvyatkova, J. Walsh, J. Buckley

{"title":"Towards a Taxonomy for Evaluating Societal Concerns of Contact Tracing Apps","authors":"T. Welsh, Kaavya Rekanar, Manzar Abbas, Muslim Chochlov, Brian Fitzgerald, Liam Glynn, Kevin Johnson, J. Laffey, B. McNicholas, B. Nuseibeh, James O'Connell, Derek T. O'Keeffe, Ian R. O’Keeffe, Mike O'Callaghan, A. Razzaq, Ita Richardson, A. Simpkin, Cristiano Storni, Damyanka Tsvyatkova, J. Walsh, J. Buckley","doi":"10.1109/BESC51023.2020.9348293","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348293","url":null,"abstract":"Contact Tracing (CT) is seen as a key tool in reducing the propagation of viruses, such as Covid-19. Given near ubiquitous societal usage of mobile devices, governments globally are choosing to augment manual CT with CT applications (CTAs) on smart phones. While a plethora of solutions have been spawned, their overall effectiveness is based on majority population uptake. Unfortunately, their rapid deployment and the nature of the information they gather has prompted a variety of user concerns such as information privacy and Data Protection (DP). Therefore selecting an optimal solution to maximise user trust and uptake is crucial. In this work, we present our initial deliberations towards a CTA evaluation taxonomy for societal concerns. This is a subset of a larger taxonomy which is being developed as part of the Science Foundation Ireland project - COVIGILANT, which will ultimately be utilized to evaluate and compare numerous CTAs to select the optimal solution for a given population. In this paper we present our preliminary CTAs with respect to the societal concerns of security, data protection and transparency. We then elaborate on these CTAs by means of two illustrative examples in order to promote discussion, evaluation and refinement.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"81 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128139772","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: 6

A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection 一种新的用于乳腺癌自动检测的深度卷积神经网络模型

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348322

Xujuan Zhou, Yuefeng Li, R. Gururajan, Ghazal Bargshady, Xiaohui Tao, R. Venkataraman, P. Barua, S. Kondalsamy-Chennakesavan

{"title":"A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection","authors":"Xujuan Zhou, Yuefeng Li, R. Gururajan, Ghazal Bargshady, Xiaohui Tao, R. Venkataraman, P. Barua, S. Kondalsamy-Chennakesavan","doi":"10.1109/BESC51023.2020.9348322","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348322","url":null,"abstract":"breast cancer is reported as one of most common malignancy amongst women in the world. Early detection of this cancer is critical to clinical and epidemiologic for aiding in informing subsequent treatments. This study investigates automated breast cancer prediction using deep learning techniques. A new 19-layer deep convolutional neural network (CNN) model for detecting the benign breast tumors from malignant cancers was proposed and implemented. The experiments on BreaKHis dataset was conducted and K-fold Cross Validation technique are used for the model evaluation. The proposed 19-layer deep CNN based classifiers compared with conventional machine learning classifier, namely Support Vector Machine (SVM) and a state-of-the-art deep learning model, namely GoogLeNet in terms of Accuracy, Area under the Receiver Operating Characteristic (ROC) Curve (AUC), the Classification Mean Absolute Error (MAE), Mean Squared Error (MSE) metrics. The results demonstrate that the proposed new model outperformed the other classifiers. The proposed model achieved an accuracy, AUC, MAE and MSE of 84.5%, 85.7%, 0.082, and 0.043, respectively.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948325","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

Analyses of Character Networks in Dramatic Works by Using Graphs 用图分析戏剧作品中的人物网络

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348328

Mehmet Can Yavuz

引用次数: 2

Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning 基于深度语言模型和迁移学习的抑郁和焦虑预测

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348290

T. Rutowski, Elizabeth Shriberg, A. Harati, Yang Lu, P. Chlebek, R. Oliveira

引用次数: 7