2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献_第10页

Architecture of an FPGA accelerator for LDA-based inference 基于lda推理的FPGA加速器结构

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022746

Taisuke Ono, H. M. Waidyasooriya, M. Hariyama, Tsukasa Ishigaki

引用次数: 0

Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms 不同人脸识别算法下面部表情和视点变化对人脸识别精度的影响

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022727

M. Phankokkruad, Phichaya Jaturawat

{"title":"Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms","authors":"M. Phankokkruad, Phichaya Jaturawat","doi":"10.1109/SNPD.2017.8022727","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022727","url":null,"abstract":"Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123843442","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: 9

Hybrid possibilistic-genetic technique for assessment of brain tissues volume: Case study for Alzheimer patients images clustering 混合可能性-遗传技术评估脑组织体积:阿尔茨海默病患者图像聚类的案例研究

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022714

L. Lazli, M. Boukadoum, O. Mohamed

引用次数: 3

Nature-inspired optimization method: Hydrozoan algorithm for solving continuous problems 自然启发的优化方法:求解连续问题的Hydrozoan算法

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022695

Daranat Tansui, A. Thammano

引用次数: 3

Effect of the reality of pictures in a horror game on feelings of fear 恐怖游戏中画面的真实感对恐怖感的影响

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022776

Kotoko Yamaguchi, T. Hochin, Hiroki Nomiya, Yukiko Nishizaki

引用次数: 1

Decision model for identity management product selection using fuzzy AHP 基于模糊层次分析法的身份管理产品选择决策模型

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022732

Noraset Noradachanon, T. Senivongse

{"title":"Decision model for identity management product selection using fuzzy AHP","authors":"Noraset Noradachanon, T. Senivongse","doi":"10.1109/SNPD.2017.8022732","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022732","url":null,"abstract":"Identity management (IDM) refers to a security discipline that employs technologies to manage information about the identity of users and control their access to organization resources. Various IDM products are in the market to offer corporate customers productivity and security while lowering costs of identity management. System integrators who implement the IDM solutions for their corporate customers often face problems when choosing the right IDM products for integrating with the customers' enterprise systems. This paper proposes a decision model for IDM product selection which is used in evaluating and ranking IDM products based on the proposed customer requirements questionnaire. The model is comprehensive in that its decision criteria comprise not only the technical specifications of the products but also other important aspects including price and accountability of system integrators and product vendors. Based on the decision model, product selection is conducted using a multi-criteria decision making technique called fuzzy analytic hierarchy process (fuzzy AHP). In an evaluation, a leading system integrator company in Thailand applies the proposed approach to IDM product selection for its customer and the result is quite satisfactory.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126285932","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

A parallelized GPU-based simulating annealing algorithm for intensity modulated radiation therapy optimization 基于并行gpu的调强放疗优化模拟退火算法

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022744

P. Galanakou, T. Leventouri, A. Georgakilas, G. Kalantzis

{"title":"A parallelized GPU-based simulating annealing algorithm for intensity modulated radiation therapy optimization","authors":"P. Galanakou, T. Leventouri, A. Georgakilas, G. Kalantzis","doi":"10.1109/SNPD.2017.8022744","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022744","url":null,"abstract":"Intensity modulated radiation therapy (IMRT) exhibits the ability to deliver the prescribed dose to the planning target volume (PTV), while minimizing the delivered dose to the organs at risk (OARs). Metaheuristic algorithms, among them the simulating annealing algorithm (SAA), have been proposed in the past for optimization of IMRT. Despite the advantage of the SAA to be a global optimizer, IMRT optimization is an extensive computational task due to the large scale of the optimization variables. Therefore stochastic algorithms, such as the SAA, require significant computational resources. In an effort to elucidate the performance improvement of the SAA in highly dimensional optimization tasks, such as the IMRT optimization, we introduce for the first time to our best knowledge a parallel graphic processing unit (GPU)-based SAA developed in MATLAB platform and compliant with the computational environment for radiotherapy research (CERR) for IMRT treatment planning. Our strategy was firstly to identify the major “bottlenecks” of our code and secondly to parallelize those on the GPU accordingly. Performance tests were conducted on four different GPU cards in comparison to a serial version of the algorithm executed on a CPU. Our studies have shown a gradual increase of the speedup factor as a function of the number of beamlets for all four GPUs. Particularly, a maximum speedup factor of ∼33 was achieved when the K40m card was utilized.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116540111","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

An autonomous configuration scheme of storage tiers for distributed file system 分布式文件系统存储层的自治配置方案

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022761

S. Shimano, Atsushi Nunome, Y. Yokoi, Kiyoshi Shibayama, Hiroaki Hirata

引用次数: 1

Retrieval and synchronized playback methods considering temporal harmony of music and video clips 考虑音乐和视频剪辑时间和谐的检索和同步播放方法

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022772

H. Kumagai, T. Hochin, Hiroki Nomiya

引用次数: 0

Alleviating adversarial attacks via convolutional autoencoder 通过卷积自编码器减轻对抗性攻击

2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2017-06-01 DOI: 10.1109/SNPD.2017.8022700

Wenjun Bai, Changqin Quan, Zhiwei Luo

{"title":"Alleviating adversarial attacks via convolutional autoencoder","authors":"Wenjun Bai, Changqin Quan, Zhiwei Luo","doi":"10.1109/SNPD.2017.8022700","DOIUrl":"https://doi.org/10.1109/SNPD.2017.8022700","url":null,"abstract":"In order to defend adversarial attacks in computer vision models, the conventional approach arises on actively incorporate such samples into the training datasets. Nonetheless, the manual production of adversarial samples is painful and labor intensive. Here we propose a novel generative model: Convolutional Autoencoder Model to add unsupervised adversarial training, i.e., the production of adversarial images from the encoded feature representation, on conventional supervised convolutional neural network training. To accomplish such objective, we first provide a novel statistical understanding of convolutional neural network to translate convolution and pooling computations equivalently as a hierarchy of encoders, and sampling tricks, respectively. Then, we derive our proposed Convolutional Autoencoder Model with the ‘adversarial decoders’ to automate the generation of adversarial samples. We validated our proposed Convolutional Autoencoder Model on MNIST dataset, and achieved the clear-cut performance improvement over the normal Convolutional Neural Network.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123842083","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: 15