International Conference on Crowd Science and Engineering最新文献_第4页

Towards Long-term Tracking and Detection of Early Dementia: A Computerized Cognitive Test Battery with Gamification 迈向早期痴呆的长期跟踪和检测:一个带有游戏化的计算机化认知测试电池

International Conference on Crowd Science and Engineering Pub Date : 2018-07-28 DOI: 10.1145/3265689.3265719

Zhiwei Zeng, Simon Fauvel, Benny Toh Hsiang Tan, D. Wang, Yang Qiu, Pamela Chew Oi Khuan, Cyril Leung, Zhiqi Shen, J. Chin

{"title":"Towards Long-term Tracking and Detection of Early Dementia: A Computerized Cognitive Test Battery with Gamification","authors":"Zhiwei Zeng, Simon Fauvel, Benny Toh Hsiang Tan, D. Wang, Yang Qiu, Pamela Chew Oi Khuan, Cyril Leung, Zhiqi Shen, J. Chin","doi":"10.1145/3265689.3265719","DOIUrl":"https://doi.org/10.1145/3265689.3265719","url":null,"abstract":"Effective diagnosis of mild cognitive impairment (MCI), in many cases preceding dementia, is important in determining the efficacy of dementia treatments. Inherent in the transition from normal ageing to MCI to and then to dementia is cognitive decline, which can be detected using multiple assessments over an extended period of time. Computerized cognitive tests arise as a promising way of long-term cognitive monitoring in home environment and supplementing clinical evaluation. Compared to conventional paper-and-pencil tests, they are cheaper, more repeatable, and easier to distribute and administer. Over the years, research efforts have been devoted to improve the validity, reliability and comprehensiveness of the computerized cognitive tests. However, it has long been omitted that the usability and entertainment aspects are also crucial to their overall effectiveness and user experience.\u0000 To reduce dropout rates and improve effectiveness of long-term cognitive monitoring, we present a first-of-its-kind gamified computerized cognitive test battery, called Virtual ADL+ House. It consists of a series of mini-games, each embedded a cognitive test and featured one of the daily living activities in the Lawton IADL. Virtual ADL+ House can be used to monitor cognitive functions in long-term, alert signs of cognitive decline and provide longitudinal data to facilitate clinical diagnosis. In focus group studies conducted with doctors and older adults, we received positive feedback on the usability and entertainment value of Virtual ADL+ House. We plan to evaluate the validity and reliability of it in subsequent studies.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656987","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: 13

LBTask: A Benchmark for Spatial Crowdsourcing Platforms LBTask:空间众包平台的基准

International Conference on Crowd Science and Engineering Pub Date : 2018-07-28 DOI: 10.1145/3265689.3265716

Qian Yang, Li-zhen Cui, Miao Zheng, Shijun Liu, Wei Guo, Xudong Lu, Yongqing Zheng, Qingzhong Li

{"title":"LBTask: A Benchmark for Spatial Crowdsourcing Platforms","authors":"Qian Yang, Li-zhen Cui, Miao Zheng, Shijun Liu, Wei Guo, Xudong Lu, Yongqing Zheng, Qingzhong Li","doi":"10.1145/3265689.3265716","DOIUrl":"https://doi.org/10.1145/3265689.3265716","url":null,"abstract":"The popularity of smart phones has made rapid development of crowdsourcing. The emergence of these crowdsourcing software has brought great convenience to our life. Traditional crowdsourcing platforms, such as Amazon Mechanical Turk and Crowdflower, publish some tasks on the site, Workers choose the tasks that are of interest and submit the answers to the tasks by browsing the tasks on the platform. And spatial crowdsourcing platforms (like gMission) are used to assign crowdsourcing tasks related to location. However, most crowdsourcing platforms support a small number of assignment and quality control algorithms. In this paper, a benchmark for spatial crowdsourcing platforms, called LBTask, is designed in order to adapt to the emergence of spatial crowdsourcing tasks, which focuses on solving location aware crowdsourcing tasks. Compared with other crowdsourcing platforms, LBTask can support various assignment and quality control algorithms in the architecture according to different strategies. In the distribution and assignment of tasks, the position factors of tasks and workers are taken into consideration in addition to considering the time and other factors.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116825857","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

Deep Transfer Learning for Cross-domain Activity Recognition 跨域活动识别的深度迁移学习

International Conference on Crowd Science and Engineering Pub Date : 2018-07-20 DOI: 10.1145/3265689.3265705

Jindong Wang, V. Zheng, Yiqiang Chen, Meiyu Huang

{"title":"Deep Transfer Learning for Cross-domain Activity Recognition","authors":"Jindong Wang, V. Zheng, Yiqiang Chen, Meiyu Huang","doi":"10.1145/3265689.3265705","DOIUrl":"https://doi.org/10.1145/3265689.3265705","url":null,"abstract":"Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar K source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.","PeriodicalId":370356,"journal":{"name":"International Conference on Crowd Science and Engineering","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134335705","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: 103

A review of knowledge management and future research trend 知识管理综述及未来研究趋势

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126997

Tingwei Gao, Y. Chai, Yi Liu

引用次数: 6

Using Blockchain Technology to Build Trust in Sharing LoRaWAN IoT 使用区块链技术在共享LoRaWAN物联网中建立信任

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126980

Jun Lin, Zhiqi Shen, C. Miao

引用次数: 105

Learning Complex Crowdsourcing Task Allocation Strategies from Humans 从人类那里学习复杂众包任务分配策略

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126988

Li-zhen Cui, Xudong Zhao, Lei Liu, Han Yu, Yuan Miao

引用次数: 9

A MCIN-based Model of Crowd-designing Clothing Industry 基于mcin的服装行业群体设计模型

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126998

Yixuan Nan, Yi Liu, Jianping Shen, Y. Chai

引用次数: 1

Will the Monopolistic Market Structure Produce Market Power?: a direct measure of market power of Internet platform enterprises 垄断性的市场结构会产生市场力量吗?:直接衡量互联网平台企业市场力量的指标

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126983

Baowen Sun, Wenjun Jing, Xuankai Zhao, Yi He

引用次数: 0

Efficient Crowd-Powered Active Learning for Reliable Review Evaluation 有效的群体动力主动学习可靠的审查评估

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3129307

Xinping Min, Yuliang Shi, Li-zhen Cui, Han Yu, Yuan Miao

引用次数: 2

Extracting Temporal Information from Online Health Communities 从在线健康社区提取时间信息

International Conference on Crowd Science and Engineering Pub Date : 2017-07-06 DOI: 10.1145/3126973.3126975

Lichao Zhu, Hangzhou Yang, Zhijun Yan

引用次数: 4