检索结果-内蒙古大学图书馆

Object measurement system based on Surveillance video-images 1

6th International Conference on Frontier computing, FC 2017

作者： Duan, Hui-Xian Wang, Jun Song, Lei Liu, Na Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai201204 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Shanghai International Technology and Trade United Co. Ltd Shanghai200031 China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Object measurement based on surveillance video-images is a critical task in the field of intelligent video surveillance. In this paper, we proposed an object measurement system based on surveillance video-images. Firstly, considering the scene of the urban video surveillance, a novel calibration tool is designed for such scene, which is portable, collapsible and operational. Next, based on the portable calibration tool, the intrinsic and extrinsic parameters of the surveillance camera can be estimated. Then, an object measurement system based on surveillance video-images is developed, which include the acquisition module of calibration data, the computation module of camera parameters, the selection module of measurement endpoint and the computation module of object. Finally, in the traffic checkpoint, experiments are carried out to verify the effectiveness of the object measurement system. The results show that as long as the surveillance camera is one-time calibrated based on the portable calibration tool, this system can measure the object on the surveillance video-images captured by the calibrated camera. What’s more, experimental results have shown that the object measurement system can be used to estimate the vehicle speed and measure the body height within the acceptable error range, and then have demonstrated the effectiveness of the object measurement system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Hybrid Filtrations Recommendation system based on Privacy Preserving in Edge computing

引用

Procedia Computer Science 2018年 129卷 407-409页

作者： Lina Ni Hongdi Lin Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

It is challenging to design a secure recommendation system on the Internet which can help users to select their favorite products as less privacy leaked as possible. In this paper, we present a hybrid filtrations recommendation system based on privacy preserving in edge computing (HFRS-PP), which can prevent the users’ privacy information from being leaked via the merits of edge computing in the process of computing and ensure the real-time, accuracy and stability of the query results. Particularly, we propose a privacy-preserving recommendation algorithm to obtain the desired results for the end users through hybrid filtrations. The filtration-rough set theory algorithm is given to distinguish the valid reviews from spam reviews for the next filtration.

关键词： Privacy protection edge computing recommendation system rough set theory

来源：评论

学校读者我要写书评

暂无评论

A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

引用

Procedia Computer Science 2018年 129卷 368-371页

作者： Lina Ni Yanfeng Yuan Xiao Wang Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the central anonymous location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location.

关键词： Privacy preserving mobile social network location-based service location privacy anonymous region repartition

来源：评论

学校读者我要写书评

暂无评论

A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

引用

7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Raptor: A Five-Safes Approach to a Secure, Cloud Native and Serverless Genomics Data Repository

SSRN

引用

SSRN 2022年

作者： Shih, Chih Chuan Chen, Jieqi Lee, Ai Shan Bertin, Nicolas Hebrard, Maxime Khor, Chiea Chuen Li, Zheng Tan, Joanna Hui Juan Meah, Wee Yang Peh, Su Qin Mok, Shi Qi Sim, Kar Seng Liu, Jianjun Wang, Ling Wong, Eleanor Li, Jingmei Tin, Aung Cheng, Ching-Yu Heng, Chew-Kiat Yuan, Jian-Min Koh, Woon-Puay Saw, Seang Mei Friedlander, Yechiel Sim, Xueling Chai, Jin Fang Chong, Yap Seng Davila, Sonia Goh, Liuh Ling Lee, Eng Sing Wong, Tien Yin Karnani, Neerja Leong, Khai Pang Yeo, Khung Keong Chambers, John C. Lim, Su Chi Goh, Rick Siow Mong Tan, Patrick Dorajoo, Rajkumar Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore Eye Research institute Singapore National Eye Centre Singapore Yong Loo Lin School of Medicine National University of Singapore Singapore Duke-NUS Medical School Singapore Singapore National Eye Centre Singapore Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Khoo Teck Puat - National University Children's Medical Institute National University Health System Singapore Department of Epidemiology Graduate School of Public Health University of Pittsburgh PittsburghPA United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA United States Healthy Longevity Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute for Clinical Sciences Agency for Science Technology and Research Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Braun School of Public Health Hebrew University of Jerusalem Jerusalem Israel Precision Health Research Singapore SingHealth Duke-NUS Institute of Precision Medicine Singapore Molecular Diagnostic Laboratory Tan Tock Seng Hospital Singapore Clinical Research Unit National Healthcare Group Polyclinics Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Bioinformatics Institute Agency for Science Technology and Research Singapore Personalised Medicine Service Tan Tock Seng Hospital Singapore Department of Cardiology National Heart Centre Singapore Singapore School of Public Health Imperial College London United Kingdom Clinical Research Unit Khoo Teck Puat Hospital Singapore Diabetes Centre Admiralty Medical Centre Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Cancer Science Institute of Singapore National University of Singapore Singapore

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a "five-safes" framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets © 2022, The Authors. All rights reserved.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Multi-task variational information bottleneck

arXiv

引用

arXiv 2020年

作者： Qian, Weizhu Chen, Bowei Zhang, Yichao Wen, Guanghui Gechter, Franck Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté France Adam Smith Business School University of Glasgow United Kingdom Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai200092 China Department of Systems Science School of Mathematics Southeast University Nanjing210016 China School of Engineering RMIT University MelbourneVIC3000 Australia Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté and Mosel LORIA UMR CNRS Université de Lorraine 7503 France

Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks. © 2020, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks

A Multi-label Classifier for Human Protein Subcellular Local...

引用

2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

来源：评论

学校读者我要写书评

暂无评论

A Novel Mutual Information based Ant Colony Classifier 5

A Novel Mutual Information based Ant Colony Classifier

引用

2017 International Conference on Progress in Informatics and computing(PIC)

作者： Hang Yu Xiaoxiao Qian Yang Yu Jiujun Cheng Ying Yu Shangce Gao College of Computer Science and Technology Taizhou University Faculty of Engineering University of Toyama Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

ISBN: (纸本)9781538619797;9781538619780

By constructing a list of IF-THEN rules, the traditional ant colony optimization(ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for *** paper proposes a novel hybrid mutual information based ant colony algorithm(mrAM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier(i.e., AM+)to perform the classification. Experimental results show that the proposed mrAM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.

关键词： ant colony optimization mutual information classification data mining feature selection

来源：评论

学校读者我要写书评

暂无评论

Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm

引用

IEEE Transactions on systems, Man, and Cybernetics: systems 2017年第6期47卷 1007-1018页

作者： Li, Jinzhong Liu, Guanjun Yan, Chungang Jiang, Changjun Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Watershed Ecology and Geographical Environment Monitoring NASG Department of Computer Science and Technology Jinggangshan University Ji'an343009 China

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches. © 2016 IEEE.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

A Recommender system for Ordering Platform Based on an Improved Collaborative Filtering Algorithm

A Recommender System for Ordering Platform Based on an Impro...

引用

International Conference on Audio, Language and Image Processing, ICALIP

作者： Chengchao Yu Qingshi Tang Zheng Liu Bin Dong Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Shanghai Zhuoji Information Technology ltd. Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved Collaborative Filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： Recommender systems Collaboration Training Measurement Filtering algorithms Training data Learning systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：