2021 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献

Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules 基于神经符号归纳规则的知识推理和知识补全方法

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00040

Won-Chul Shin, Hyun-Kyu Park, Youngtack Park

{"title":"Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules","authors":"Won-Chul Shin, Hyun-Kyu Park, Youngtack Park","doi":"10.1109/CSCI54926.2021.00040","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00040","url":null,"abstract":"In knowledge graph completion, a symbolic reasoning method establishes a human readable rule by analyzing an imperfect knowledge graph and infers knowledge omitted by an inference engine. However, the entire rules cannot be defined based on a large-scale knowledge graph. This study proposes a method, based on a knowledge graph, that can facilitate end-to-end learning and induce rules without several processing steps that require direct human involvement. The proposed method combines the concept of unification used in symbolic reasoning and deep learning for training vectors expressing symbols. It trains the vectors expressing relations of rule schemas defined to induce rules based on a given knowledge graph. Furthermore, the performance of the proposed method is evaluated against neural theorem prover and the greedy neural theorem prover, which are recently developed neuro-symbolic models, based on four benchmark datasets. The experimental results verify that the proposed method induces more significant rules in less training time. Furthermore, this study conducted an experiment on knowledge graph completion, implemented by an inference engine. Based on the experiment results, it was confirmed that the rules induced by the proposed model can indeed effectively complete missing knowledge.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114965216","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

Virtual Basketball Training Platform 虚拟篮球训练平台

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/csci54926.2021.00057

Tyson Howard, Z. Wang

引用次数: 2

Simultaneous analysis of fMRI and EEG biosignals: a multimodal fusion approach 功能磁共振成像和脑电图生物信号的同时分析:一种多模式融合方法

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00318

Luis M. Pereira, A. Salazar, L. Vergara

引用次数: 0

Multi-Task Deep Neural Networks for Multimodal Personality Trait Prediction 多任务深度神经网络多模态人格特质预测

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00089

Dena F. Mujtaba, N. Mahapatra

{"title":"Multi-Task Deep Neural Networks for Multimodal Personality Trait Prediction","authors":"Dena F. Mujtaba, N. Mahapatra","doi":"10.1109/CSCI54926.2021.00089","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00089","url":null,"abstract":"Artificial intelligence (AI) is being increasingly integrated into the hiring process. A prominent example is video interviews used by large organizations to quickly screen job candidates. The personality traits of job candidates, such as the Big Five characteristics, are predicted using computer vision and affective computing approaches. Past methods have used feature extraction, text analysis, and other multimodal methods to achieve a high prediction accuracy. We build upon past approaches by using a multi-task deep neural network (MTDNN) to predict personality traits and job interview scores of individuals. An MTDNN shares lower layers to learn features which apply across outputs, and contains task-specific layers to predict each individual trait, thereby providing an advantage over single-task approaches since personality traits are determined by features (e.g., emotion, gestures, and speech) shared across traits. Our model is trained using the CVPR 2017 First Impressions V2 competition dataset, containing 10,000 videos of individuals and their Big Five personality and interview scores. We also use scene, audio, and facial features from the state-of-the-art model from the competition. A 5-fold cross-validation approach is used to evaluate our results. We achieve a prediction accuracy for all traits on par with state-of-the-art models, while reducing training time and parameter tuning to a single network.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121002129","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

Modeling Clinical Practice Guidelines for Interactive Decision Support Exemplified by Primary Myelofibrosis and Immune Thrombocytopenia 以原发性骨髓纤维化和免疫性血小板减少症为例的交互式决策支持建模临床实践指南

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00244

Patrick Philipp, L. Hempel, D. Hempel, Jürgen Beyerer

引用次数: 0

Face Recognition Using MATLAB 基于MATLAB的人脸识别

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00331

I. Obagbuwa, Stefany Bam, Dineo Tiffany Moroka

引用次数: 0

Improving Gene Expression Prediction of Cancer Data Using Nature Inspired Optimization Algorithms 利用自然启发的优化算法改进癌症数据的基因表达预测

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00128

Payal Patel, K. Passi, Chakresh Kumar Jain

{"title":"Improving Gene Expression Prediction of Cancer Data Using Nature Inspired Optimization Algorithms","authors":"Payal Patel, K. Passi, Chakresh Kumar Jain","doi":"10.1109/CSCI54926.2021.00128","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00128","url":null,"abstract":"Cancer being one of the most vital diseases in the medical history needs adequate focus on its causes, symptoms and detection. Various algorithms and software have been designed so far to predict the cancer at cellular level. The most crucial aspect for sorting the cancerous tissues is the classification of such tissues based on the gene expression data. Gene expression data consists of high amount of genetic data as compared to the number of data samples. Thus, sample size and dimensions are a major challenge for researchers. In this work, four different types of cancer microarray datasets are analyzed viz., breast cancer, lung cancer, leukemia and colon cancer. The analysis of the cancer microarray datasets was done using various nature-inspired algorithms like Grasshopper Optimization (GOA), Particle Swarm Optimization (PSO), and Interval Value-based Particle Swarm Optimization (IVPSO). To study the accuracy of the prediction, five different classifiers were used: Random Forest, K-Nearest Neighborhood (KNN), Neural Network, Naïve Bayes and Support Vector Machine (SVM). The Grasshopper Optimization (GOA) outperforms in accuracy compared to the other two optimization algorithms with SVM classifier on leukemia, lung and breast cancer datasets selecting the best genes/attributes to correctly classify the dataset.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124909711","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

WPA3: The Greatest Security Protocol That May Never Be WPA3:可能永远不会有的最伟大的安全协议

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00273

Glen W. Sagers

引用次数: 2

Industry Connect Initiative: Partnering for Student Success 行业连接倡议:为学生的成功而合作

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00241

Achee Bonnie, Alkadi Ghassan, McNulty Matthew, Summers Sandy

引用次数: 3

Game Engine Based 2D Emotion Segmentation Generation Method 基于游戏引擎的2D情感分割生成方法

2021 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2021-12-01 DOI: 10.1109/CSCI54926.2021.00173

Shinjin Kang, Jong-In Choi, Hyunjeong Tae, Sookyun Kim

引用次数: 0