2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW)最新文献

Privacy Preserving in Blockchain Based on Partial Homomorphic Encryption System for Ai Applications 人工智能应用中基于部分同态加密系统的区块链隐私保护

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634280

Sharath Yaji, Kajal Bangera, B. Neelima

{"title":"Privacy Preserving in Blockchain Based on Partial Homomorphic Encryption System for Ai Applications","authors":"Sharath Yaji, Kajal Bangera, B. Neelima","doi":"10.1109/HIPCW.2018.8634280","DOIUrl":"https://doi.org/10.1109/HIPCW.2018.8634280","url":null,"abstract":"The synergy between artificial intelligence and blockchain is increasing in the computing environment. To realize this blockchain technology making its way into applications such as healthcare, financial services, Internet of Things and much more., that use artificial intelligence making it more defendable to attacks. The current blockchain technology uses different encryption algorithms such as SHA256, MD5 etc. The blockchain attacks such as collision attack, primage attack and attacks on wallet motivated us to experiment on partial homomorphic encryption to enhance the strength of blockchain technology. This article considers i) Goldwasser- Micali and ii) Paillier encryption schemes for the comparative evaluation study with a focus on data privacy techniques. We believed and proved that the above two encryption schemes that were considered have less processing time and provide more strength to the possible attacks. While we present our preliminary results in this study, we discuss the pros and cons of the Goldwasser-Micali, Paillier and non-homomorphic encryption schemes that are expected to add value to blockchain technology to be used in Artificial Intelligence (AI) applications.","PeriodicalId":401060,"journal":{"name":"2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125085462","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: 28

25th IEEE International Conference on High Performance Computing Workshops 第25届IEEE高性能计算国际会议研讨会

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/hipcw.2018.8634191

引用次数: 0

Blended Learning-Assimilating Authentic Data Into Deep Learning Models 混合学习-将真实数据吸收到深度学习模型中

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634015

Saichand Avrp, P. K. Baruah

引用次数: 3

25th IEEE International Conference on High Performance Computing Workshops, HiPCW 2018, Bengaluru, India, December 17-20, 2018 第25届IEEE国际高性能计算研讨会，HiPCW 2018，印度班加罗尔，2018年12月17日至20日

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/hipcw.2018.8634405

引用次数: 0

Effective Mapping of an SPH Algorithm on Massively Parallel GPU Architecture SPH算法在大规模并行GPU架构上的有效映射

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634051

Pravin Jagtap, R. Nasre, B. Patnaik

引用次数: 0

Computational Fluid Modeling to Understand the Role of Anatomy in Bifurcation Lesion Disease 计算流体模型理解解剖学在分叉病变疾病中的作用

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634225

M. Vardhan, Arpita Das, Jonn Gouruev, A. Randles

{"title":"Computational Fluid Modeling to Understand the Role of Anatomy in Bifurcation Lesion Disease","authors":"M. Vardhan, Arpita Das, Jonn Gouruev, A. Randles","doi":"10.1109/HIPCW.2018.8634225","DOIUrl":"https://doi.org/10.1109/HIPCW.2018.8634225","url":null,"abstract":"Background: Treatment of bifurcation lesion disease is complex with limited studies that describe the influence of lesion anatomy on clinical outcomes. Hypothesis: Computational simulations can be used to understand the interplay between morphological characteristics of lesion and clinical diagnostic metrics. Methods: Geometric modifications along the bifurcation in a patient-derived left coronary artery were made to incorporate unique combination of anatomic features: curvature, length and occlusion severity. The resulting geometries were used to perform CFD simulations using physiological flow parameters. Three diagnostic metrics, resting gradient, instantaneous wave free ratio (iFR) and diastolic-systolic velocity ratio (DSVR), were computed from the simulations. Results: We report occlusion severity to be an independent predictor for lower resting gradient and iFR values, whereas lesion length and curvature did not yield dramatic changes in iFR and resting gradient. Our results suggest that DSVR is more sensitive to nuanced flow disturbances; however, it may be complex to derive direct correspondence to disease severity relative to resting gradient and iFR. Conclusion: Spatial lesion characteristics can be used to determine diseased bifurcation cases that may lead to interventional complications.","PeriodicalId":401060,"journal":{"name":"2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133213751","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

HiPC 2018 WORKSHOP 2: Fourth Workshop on Computational Fluid Dynamics (CFD) HiPC 2018 WORKSHOP 2:第四次计算流体动力学(CFD)研讨会

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/hipcw.2018.8634134

引用次数: 0

Performance Analysis of Deep Learning Architectures for Recommendation Systems 推荐系统中深度学习架构的性能分析

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634192

D. Anil, Anagha Vembar, Srinidhi Hiriyannaiah, G. Siddesh, K. Srinivasa

{"title":"Performance Analysis of Deep Learning Architectures for Recommendation Systems","authors":"D. Anil, Anagha Vembar, Srinidhi Hiriyannaiah, G. Siddesh, K. Srinivasa","doi":"10.1109/HIPCW.2018.8634192","DOIUrl":"https://doi.org/10.1109/HIPCW.2018.8634192","url":null,"abstract":"Recommendation systems play an important role in the field of e-commerce applications since they provide suggestions to each and every customer based on the reviews and ratings given by the customers. These reviews and ratings allow customers to share their opinions and experiences about products they purchase. This enables companies to market to more people of a similar demographic and influence more purchases. Deep learning techniques with different neural network architectures can be applied to the recommendation systems to identify the different patterns and behaviours of the customers in e-commerce applications. The main aim of this paper is to study the effect of combining deep learning neural architectures and collaborative filtering to provide an effective recommendation system. A comparative study of natural language processing techniques is analysed using three different Recurrent Neural Network (RNN) models that convert reviews to ratings. The RNNs that are included are Long Short Term Memory (LSTM), Gated Recurrent unit (GRU) and lastly, a multilayer RNN that includes LSTM stacked with GRU to test the possible advantages of a deeper architecture. A Neighbourhood based Collaborative Filter Recommendation System is developed that gives recommendations to users based on item-item similarities. The performance of the three models is analysed to find the best model to perform Review Rating prediction in order to enhance the accuracy of the Recommendation system.","PeriodicalId":401060,"journal":{"name":"2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128404270","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: 8

A Comparative Study of Turbulence Models for Two-Phase Coaxial Swirling Jet Flows 两相同轴旋流射流湍流模型的比较研究

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/HIPCW.2018.8634062

A. Choudhary, V. Narasimhamurthy

引用次数: 0

HiPC 2018 WORKSHOP 3: Workshop on Artificial Intelligence Meets Blockchain (AIMB) HiPC 2018工作坊3:人工智能与区块链工作坊(AIMB)

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) Pub Date : 2018-12-01 DOI: 10.1109/hipcw.2018.8634217

Manisha K. Gupta

引用次数: 0