[论文收集] CHI 2011 Workshop on Crowdsourcing and Human Computation

本次会议汇集了来自不同领域的专家,共同探讨了人类计算、众包研究、协作技术和相关应用。内容涵盖从基础理论到实际案例,涉及众包在设计、社会媒体分析、政府2.0等多个领域的应用。

召开时间:May 8, 2011

主页:http://crowdresearch.org/chi2011-workshop/

 

Eytan Adar (University of Michigan)

Why I Hate Mechanical Turk Research (and Workshops)

Benjamin B. Bederson, Alex Quinn (University of Maryland)

Participation in Human Computation

Lukas Biewald, Mollie Allick (CrowdFlower)

Massive Multiplayer Human Computation for Fun, Money, and Survival

Jeffrey Bigham, Erin Brady, Samuel White (University of Rochester)

Human-Backed Access Technology

Jenny J. Chen, Natala J. Menezes, Adam D. Bradley (Amazon)

Opportunities for Crowdsourcing Research on Amazon Mechanical Turk

Kuan-Ta Chen (Academia Sinica)

Human Computation: Experience and Thoughts

Parmit K. Chilana, Andrew J. Ko, Jacob O. Wobbrock (University of Washington)

Using Crowdsourcing in the Design of Context-Sensitive Help for Web Applications

Nick DePalma (MIT Media Lab), Sonia Chernova (Worcester Polytechnic), Cynthia Breazeal (MIT Media Lab)

Leveraging Online Virtual Agents to Crowdsource Human-Robot Interaction

Mira Dontcheva (Adobe), Elizabeth Gerber, Sheena Lewis (Northwestern)

Crowdsourcing and Creativity

Steven P. Dow, Scott R. Klemmer (Stanford)

Shepherding the Crowd: An Approach to More Creative Crowd Work

Casey Dugan, Werner Geyer (IBM Research)

Harnessing Crowds as a Motivational Mechanism

Thomas Erickson (IBM Research)

Some Thoughts on a Framework for Crowdsourcing

Adam Fourney, Michael Terry (University of Waterloo)

Leveraging Crowdsourced Technical Documentation: Building a Command Thesaurus

Dan Goldman, Joel Brandt (Adobe)

Task Decomposition and Human Computation in Graphics and Vision

David Alan Grier (George Washington University)

Foundational Issues in Human Computation and Crowdsourcing

Gary Hsieh (Michigan State University)

Understanding and Designing for Cultural Differences on Crowdsourcing Marketplaces

Jessica R. Hullman (University of Michigan)

Not All HITs Are Created Equal: Controlling for Reasoning and Learning Processes in MTurk

Panagiotis G. Ipeirotis (NYU), John J. Horton (oDesk)

The Need for Standardization in Crowdsourcing

Anand Kulkarni (UC Berkeley)

The Complexity of Crowdsourcing: Theoretical Problems in Human Computation

Ben Lafreniere, Michael Terry (University of Waterloo)

Socially-Adaptable Interfaces: Crowdsourcing Customization

James Landay (University of Washington)

A New View on HCI and Crowdsourcing

Edith Law (Carnegie Mellon University)

Defining (Human) Computation

Alison Lee, Richard A. Hankins (Nokia Research)

Crowd Sourcing and Prediction Markets

Greg Little (MIT CSAIL) and Yu-An Sun (Xerox)

Human OCR: Insights from a Complex Human Computation Process

Kurt Luther (Georgia Tech)

Fast, Accurate, and Brilliant: Realizing the Potential of Crowdsourcing and Human Computation

Adam Marcus, Eugene Wu, David R. Karger, Samuel Madden, Robert C. Miller (MIT CSAIL)

Platform Considerations in Human Computation

David McDonald (University of Washington)

Task Dependency and the Organization of the Crowd

Robert Morris (MIT Media Lab)

The Emergence of Affective Crowdsourcing

Jeffrey Nichols, Jalal Mahmud (IBM Research)

Data Capture with the Crowd: Exploring the Continuum of Implicit to Explicit

Jeffrey V. Nickerson, Yasuaki Sakamoto, Lixiu Yu (Stevens Institute of Technology)

Structures for Creativity: The crowdsourcing of design

Gabriel Parent, Maxine Eskenazi (Carnegie Mellon University)

Sources of Variability and Adaptive Tasks

Sharoda A. Paul, Lichan Hong (Palo Alto Research Center), Ed H. Chi (Google)

What is a Question? Crowdsourcing Tweet Categorization

Reid Priedhorsky

Wiki, Absurd Yet Successful: A Position Paper for CHI 2011 Workshop on Crowdsourcing and Human Computation

Alexander J. Quinn, Benjamin B. Bederson (University of Maryland)

Human-Machine Hybrid Computation

Jakob Rogstadius, Vassilis Kostakos (University of Maderia), Jim Laredo, Maja Vukovic (IBM Research)

Towards Real-time Emergency Response using Crowd Supported Analysis of Social Media

Irene Ros, Yannick Assogba, Joan DiMicco (IBM Research)

Crowdsourcing and Gov 2.0

Jeffrey M. Rzeszotarski (Carnegie Mellon University)

Worker Collaboration in Crowdsourcing Markets

Kate Starbird (University of Colorado)

Digital Volunteerism During Disaster: Crowdsourcing Information Processing

William Thies, Aishwarya Ratan (Microsoft Research India), James Davis (UC Santa Cruz), Ed Cutrell (Microsoft Research India, participating on behalf of MSR India authors)

Paid Crowdsourcing as a Vehicle for Global Development

Brian E. Tidball, Pieter Jan Stappers (Delft TU)

Crowdsourcing Contextual User Insights for UCD

Anthony Tomasic, John Zimmerman, Aaron Steinfeld, Yun Huang, Daisy Yoo, Chaya Hiruncharoenvate, Ellen Ayoob (Carnegie Mellon University)

Design Uncertainty in Crowd-Sourcing Systems

Michael Toomim (University of Washington)

Economic Utility of Interaction in Crowdsourcing

Haoqi Zhang (Harvard University), Eric Horvitz (Microsoft Research), Robert C. Miller (MIT CSAIL), David C. Parkes (Harvard University)

Crowdsourcing General Computation

转载于:https://www.cnblogs.com/yuquanlaobo/archive/2012/06/05/2536539.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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