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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Social Loafing01:37

Social Loafing

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Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
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Robbers Cave04:49

Robbers Cave

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During the 1950s, the landmark Robbers Cave experiment demonstrated that when groups must compete with one another, intergroup conflict, hostility, and even violence may result. At the Oklahoman summer camp, two troops of boys—termed the Rattlers and the Eagles—took part in a week-long tournament. During this time, their negativity culminated in derogatory name-calling, fistfights, and even vandalism and destruction of property. However, this work also revealed that such tension...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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多目标隐私保护任务分配在空间众包中的空间众包

Yong-Feng Ge, Hua Wang, Elisa Bertino

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    此摘要是机器生成的。

    这项研究引入了一个新的隐私保护任务分配框架,用于空间众包. 它增强了员工的隐私,并优化了任务质量和激励,使用分布式合作共进化的多目标模拟算法.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 数据 隐私 数据 隐私 数据

    背景情况:

    • 空间众包依赖于位置数据来分配任务,但面临隐私挑战.
    • 现有的差异隐私 (DP) 方法缺乏个性化的隐私和众包的适当质量控制.

    研究的目的:

    • 解决当前DP空间众包框架中的局限性.
    • 制定一个多目标的隐私保护任务分配 (MP-TA) 问题.
    • 为了最大限度地提高员工激励和任务质量,同时确保个性化的隐私.

    主要方法:

    • 一个三个阶段的框架:工人提议,候选人选择和任务分配优化.
    • 开发一个分布式合作共进化的多目标记忆算法 (DCC-MMA).
    • 实现基于匹配的交叉,突变和修复操作以进行增强的搜索.

    主要成果:

    • 拟议的DCC-MMA算法为激励和质量目标提供了高质量的解决方案.
    • 在解决方案质量,融合速度和可扩展性方面表现出优越性,与现有算法相比.
    • 成功地将个性化的隐私保护与任务分配优化相结合.

    结论:

    • MP-TA 问题表述和 DCC-MMA 为保护隐私的空间众包提供了一个强大的解决方案.
    • 该框架有效地平衡了工人的隐私,激励和任务质量.
    • DCC-MMA显示了复杂的优化任务的效率和可扩展性的显著改善.