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Self-Schemas02:16

Self-Schemas

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Schemas01:42

Schemas

12.0K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
12.0K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.4K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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相关实验视频

Updated: Sep 18, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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通过集成异构数据源,通过自主知识图构建来增强网络安全.

Hatoon Alharbi1, Ali Hur2, Hasan Alkahtani1

  • 1Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, Eastern Province, Al-Ahsa, Saudi Arabia.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了网络安全知识图 (CKG),以提高网络威胁的检测. 通过整合各种数据和使用NLP,CKG提高了网络安全专业人员有效分析和减轻在线威胁的能力.

关键词:
网络安全知识图表扣除规则 扣除规则图形分析算法 图形分析算法自然语言处理.自然语言处理.

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Last Updated: Sep 18, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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科学领域:

  • 计算机科学 计算机科学
  • 信息安全 信息安全

背景情况:

  • 网络安全对于现代社会至关重要.
  • 知识图可以增强网络安全和隐私.
  • 组织需要积极的威胁检测和缓解.

研究的目的:

  • 构建一个高效的网络安全知识图 (CKG).
  • 为了创建CKG,自主整合异质数据源.
  • 通过逻辑规则和图形分析来增强CKG,以改进威胁分析.

主要方法:

  • 自主构建网络安全知识图 (CKG).
  • 不同质的结构化和非结构化数据的整合.
  • 使用自然语言处理 (NLP) 来提取文本数据.
  • 使用逻辑规则和图形分析算法进行增强.

主要成果:

  • 开发了一个功能性的网络安全知识图 (CKG).
  • 证明了网络安全专家对数据分析和威胁可见性的改进.
  • 通过查询制定和分析验证逻辑规则.

结论:

  • 该CKG授权网络安全专家拥有更好的数据分析能力.
  • 该方法增强了对网络威胁的积极检测和缓解.
  • 这项研究有助于保护网络空间免受恶意活动的影响.