2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior 模拟人类用户行为的最优进化优化超参数

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002958

S. Saha, Thiago Rios, Leandro L. Minku, X. Yao, Zhao Xu, B. Sendhoff, S. Menzel

{"title":"Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior","authors":"S. Saha, Thiago Rios, Leandro L. Minku, X. Yao, Zhao Xu, B. Sendhoff, S. Menzel","doi":"10.1109/SSCI44817.2019.9002958","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002958","url":null,"abstract":"Shape morphing methods are a key representation in human user-centered design as well as computational optimization of engineering applications in the automotive domain.3D digital objects are modified using deformation algorithms to alter the shape for optimal product performance or design aesthetics. We imagine a system which can learn from historic user deformation sequences and support the user in present design tasks by predicting potential design variations based on currently observed design changes carried out by the user. Towards a practical realization, a large amount of human user deformation sequence data is required which is practically not available. To overcome this limitation, we propose to use a computational target shape matching optimization whose hyper-parameters are tuned to exemplary human user sequence data and that allows us to afterwards generate large data-sets of human-like shape modification data in an automated fashion. In addition, we classified the user sequences to experience levels based on their variance. These user experience-tuned evolutionary optimizers allow us in future to mimic different user behavior and generate a large number of potential design variations in an automated fashion.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"12 1","pages":"858-866"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75120040","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

Cigarette Detection Algorithm Based on Improved Faster R-CNN 基于改进更快R-CNN的卷烟检测算法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/ssci44817.2019.9002702

Guijin Han, Qian Li, You Zhou, Yue He

引用次数: 3

Application of Wearable Devices in Crime Scene Investigation and Virtual Reality 可穿戴设备在犯罪现场调查和虚拟现实中的应用

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002700

Cheng-Lung Lee, Yuan Fang, Yi-Hsin Huang, Szu-Hao Lee, W. Yeh

{"title":"Application of Wearable Devices in Crime Scene Investigation and Virtual Reality","authors":"Cheng-Lung Lee, Yuan Fang, Yi-Hsin Huang, Szu-Hao Lee, W. Yeh","doi":"10.1109/SSCI44817.2019.9002700","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002700","url":null,"abstract":"In 2009, the United States National Academy of Sciences (NAS) proposed a list of suggestions to forensic-related issues, with emphasis on the correct use of modern technology to improve on-the-scene investigation. To ensure that the scene is fully detailed, a team of personnel including but not limited to photographer, videographer, evidence collector, and those who estimate and mark the scene are involved. However, traditional way of investigation not only utilizes too much manpower, but the destruction of crime scene is also an unavoidable problem we faced. Hence, the urgent tasks to be improved now are to protect, record and efficiently investigate the crime scene, to instantly deliver evidences for data matching, as well to provide an option to setup a video conference with experts in Forensics fields to assist officers on scene.The research integrates the concepts of \"Wearable Devices\" and \"Forensic Cloud Computing\", to showcase the benefits of Forensic database. Through cloud sharing, it is now possible to use portable devices for all on-the-scene records, video taking, and on-the-spot graphic detailing. Forensic Cloud is used with wireless and 3G/4G to deliver evidences and information. The user friendly design and mobile computing can be integrated together with instant communication and on-line forensic database. The research not only utilizes the latest technology to protect the integrity of crime scene, but will make a breakthrough in Crime Scene Investigation (CSI)—cost reduction in manpower, as well as enhancing the efficiency and effectiveness of investigation. Finally, with this research, we hope to strengthen scientific evidence, to push forward judiciary reformation, and to reduce the possibility of erroneous conviction.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"65 1","pages":"206-210"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74408817","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

Population-based Search Relying on Spatial and/or Temporal Scale-free Behaviors of Individuals 基于个体空间和/或时间无标度行为的种群搜索

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002848

Zaixiang Zhang, Yunhao Zhu, A. Fujiwara, K. Ohnishi

{"title":"Population-based Search Relying on Spatial and/or Temporal Scale-free Behaviors of Individuals","authors":"Zaixiang Zhang, Yunhao Zhu, A. Fujiwara, K. Ohnishi","doi":"10.1109/SSCI44817.2019.9002848","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002848","url":null,"abstract":"We develop two types of simple population-based search algorithms that model two types of scale-free behaviors of individuals. The scale-free behavior is a particular behavior of individuals of species that search for food not cooperatively but independently. One type of the scale-free behaviors is that a moving distance of an individual from the present food source follows a power low distribution, which is called the spatial scale-free behavior. The other is that a staying duration of an individual at the preset food source follows a power low distribution, which is called the temporal scale-free behavior. We assume static and dynamic problems in which a position of the best food source (the global optimum) is not changed and changed, respectively. In addition, we assume a special event that individuals near the best food source are probabilistically eliminated. We compare the two search algorithms and show that they are complementary with respect to suitable problems. Therefore, we develop a search algorithm that initially includes both types of individuals in a population and evolutionarily adaptively increases an appropriate type of individuals in it. The algorithm is shown to be not the best but work quite well for any problems used.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"106 1","pages":"2327-2334"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74813457","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

Identifying Variables Interaction for Black-box Continuous Optimization with Mutual Information of Multiple Local Optima 多局部最优互信息下黑盒连续优化的变量识别交互

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003021

Yapei Wu, Xingguang Peng, Demin Xu

引用次数: 2

Towards Contradiction Detection in German: a Translation-Driven Approach 翻译驱动的德语矛盾检测方法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003090

R. Sifa, Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, L. Hillebrand, C. Bauckhage

{"title":"Towards Contradiction Detection in German: a Translation-Driven Approach","authors":"R. Sifa, Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, L. Hillebrand, C. Bauckhage","doi":"10.1109/SSCI44817.2019.9003090","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003090","url":null,"abstract":"With the recent advancements in Machine Learning based Natural Language Processing (NLP), language dependency has always been a limiting factor for a majority of NLP applications. Typically, models are trained for the English language due to the availability of very large labeled and unlabeled datasets, which also allow to fine tune models for that language. Contradiction Detection is one such problem that has found many practical applications in NLP and up to this point has only been studied in the context of English language. The scope of this paper is to examine a set of baseline methods for the Contradiction Detection task on German text. For this purpose, the well-known Stanford Natural Language Inference (SNLI) data set (110,000 sentence pairs) is machine-translated from English to German. We train and evaluate four classifiers on both the original and the translated data, using state-of-the-art textual data representations. Our main contribution is the first large-scale assessment for this problem in German, and a validation of machine translation as a data generation method. We also present a novel approach to learn sentence embeddings by exploiting the hidden states of an encoder-decoder Sequence-To-Sequence RNN trained for autoencoding or translation.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"46 1","pages":"2497-2505"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74130441","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: 12

Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric 基于相关熵诱导度量的快速拓扑自适应共振理论

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003098

Naoki Masuyama, Narito Amako, Y. Nojima, Yiping Liu, C. Loo, H. Ishibuchi

引用次数: 11

Distributed Average Tracking with Event-Triggered Algorithms of Multi-Agent Systems 基于事件触发算法的多智能体系统分布式平均跟踪

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002838

Chengxin Xian, Yu Zhao

引用次数: 0

Design of the Controlling System of a Six-DoF Manipulator 六自由度机械手控制系统的设计

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003087

Qiuyue Wei, Yufan Liu, Guangyuan Zhao, Bo Shang, Yun Yan, Yue He

引用次数: 0

A Hidden Feature Selection Method based on l2,0-Norm Regularization for Training Single-hidden-layer Neural Networks 基于l2,0范数正则化的单隐层神经网络隐特征选择方法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002808

Zhiwei Liu, Yuanlong Yu, Zhenzhen Sun

{"title":"A Hidden Feature Selection Method based on l2,0-Norm Regularization for Training Single-hidden-layer Neural Networks","authors":"Zhiwei Liu, Yuanlong Yu, Zhenzhen Sun","doi":"10.1109/SSCI44817.2019.9002808","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002808","url":null,"abstract":"Feature selection is an important data preprocessing for machine learning. It can improve the performance of machine learning algorithms by removing redundant and noisy features. Among all the methods, those based on l1-norms or l2,1-norms have received considerable attention due to their good performance. However, these methods cannot produce exact row sparsity to the weight matrix, so the number of selected features cannot be determined automatically without using a threshold. To this end, this paper proposes a feature selection method incorporating the l2,0-norm, which can guarantee exact row sparsity of weight matrix. A method based on iterative hard thresholding (IHT) algorithm is also proposed to solve the l2,0- norm regularized least square problem. For fully using the role of row-sparsity induced by the l2,0-norm, this method acts as network pruning for single-hidden-layer neural networks. This method is conducted on the hidden features and it can achieve node-level pruning rather than the connection-level pruning. The experimental results in several public data sets and three image recognition data sets have shown that this method can not only effectively prune the useless hidden nodes, but also obtain better performance.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"50 1","pages":"1810-1817"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76112972","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