Proceedings of the 2019 8th International Conference on Software and Computer Applications最新文献_第10页

Model-Based Book Recommender Systems using Naïve Bayes enhanced with Optimal Feature Selection 基于模型的图书推荐系统，使用Naïve贝叶斯增强最优特征选择

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316727

Thi Thanh Sang Nguyen

引用次数: 12

Implicit Recommendation with Interest Change and User Influence 兴趣变化和用户影响下的隐性推荐

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316680

Qiaoqiao Tan, Fang’ai Liu, Shuning Xing

引用次数: 0

Verification of Verifiability of Voting Protocols by Strand Space Analysis 基于链空间分析的投票协议可验证性验证

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316629

Shigeki Hagihara, Masaya Shimakawa, N. Yonezaki

引用次数: 0

Risk Management in Projects Based on Open-Source Software 基于开源软件的项目风险管理

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316648

Nguyen Duc Linh, P. D. Hung, V. Diep, Ta Duc Tung

引用次数: 13

Development of Assessment System for Spine Curvature Angle Measurement 脊柱曲率角测量评估系统的开发

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316647

Chua Shanyu, L. C. Chin, S. Basah, A. F. Azizan

引用次数: 2

A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning 使用雾计算和机器学习减少医疗保健物联网网络延迟的三层体系结构

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3318222

Saurabh Shukla, M. Hassan, L. T. Jung, A. Awang, Muhammad Khalid Khan

{"title":"A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning","authors":"Saurabh Shukla, M. Hassan, L. T. Jung, A. Awang, Muhammad Khalid Khan","doi":"10.1145/3316615.3318222","DOIUrl":"https://doi.org/10.1145/3316615.3318222","url":null,"abstract":"Healthcare Internet-of-things comprises a huge number of wearable sensors and interconnected computers. The high volume of IoT data is transacted over servers leading to servers overloading with high traffic causing network congestion. These cloud servers are typically for analyzing, retrieving and storing the large data generated from IoT devices. There exist challenges regarding sending real-time healthcare data from cloud servers to end-users. These challenges include the high computational latency, high communication latency, and high network latency. Due to these challenges, IoTs may not be able to send data in real-time to end-users. Fog nodes can be used to play a major role in reducing the high delay and high traffic. It can be a solution to increase system performance. In this paper, we proposed a 3-tier architecture, an analytical model for healthcare IoT using a hybrid approach consisting of fuzzy logic and reinforcement learning in a fog computing environment. The aim is to minimize network latency. The proposed model and 3-tier architecture are simulated using iFogSim simulator.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133192908","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

An Information Source Identification Algorithm Based on Shortest Arborescence of Network 一种基于网络最短树围的信息源识别算法

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316686

Zhong Li, Chunhe Xia, Tianbo Wang, Xiaochen Liu

引用次数: 0

Cooperative Hierarchical Framework for Group Activity Recognition: From Group Detection to Multi-activity Recognition 群体活动识别的合作层次框架:从群体检测到多活动识别

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316722

Mohammed Al-habib, Dong-jun Huang, Majjed Al-Qatf, Kamal Al-Sabahi

{"title":"Cooperative Hierarchical Framework for Group Activity Recognition: From Group Detection to Multi-activity Recognition","authors":"Mohammed Al-habib, Dong-jun Huang, Majjed Al-Qatf, Kamal Al-Sabahi","doi":"10.1145/3316615.3316722","DOIUrl":"https://doi.org/10.1145/3316615.3316722","url":null,"abstract":"Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133905923","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

Personalized Ranking Point of Interest Recommendation Based on Spatial-Temporal Distance Metric in LBSNs 基于时空距离度量的LBSNs个性化兴趣点排序推荐

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316715

Chang Su, Hao Li, Xianzhong Xie

{"title":"Personalized Ranking Point of Interest Recommendation Based on Spatial-Temporal Distance Metric in LBSNs","authors":"Chang Su, Hao Li, Xianzhong Xie","doi":"10.1145/3316615.3316715","DOIUrl":"https://doi.org/10.1145/3316615.3316715","url":null,"abstract":"Nowadays, with the improvement of social network check-in and positioning technology, the positioning information is more accurate, and a large amount of network check-in data is generated. The recommendation research of interest points based on social networks is also increasing. Most of the points of interest refer to rely on geography, time, space, and textual information. In spatial-temporal, most studies consider the check-in rules from the geographical distance and time series. This paper introduces a geographic spatial-temporal distance measurement model to map temporal space information into a three-dimensional elliptical spherical coordinate system. The spatial-temporal distance is measured under the same reference standard. Helps alleviate the problems caused by cold start and data sparseness for location recommendation accuracy. Based on the Bayesian personalized ranking, this paper measures the temporal and spatial distance by using a Gaussian kernel function to weight the spatial-temporal distance, and proposes a personalized ranking recommendation algorithm based on the spatial-temporal distance metric. And it performs well on both datasets and is superior to the benchmark method.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"933 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418435","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

Reconstruct the Back of 3D Face Model Using 2D Gradient Based Interpolation 基于二维梯度插值的三维人脸模型背部重建

Proceedings of the 2019 8th International Conference on Software and Computer Applications Pub Date : 2019-02-19 DOI: 10.1145/3316615.3316660

W. Luo

引用次数: 0