There are thought-provoking networks of elites in network communities who can conduct interdisciplinary research and development alongside various branches of blockchain, decentralized networks, neural networks, and many other disciplines.
It is not limited to technical knowledge, but grounded theorizing with the power to understand the relationship between underlying knowledge and data.

These people that I want to call "thinkers" can be systems engineers who theorize by modeling and processing information based on "opposite collective intelligence" on various issues related to multi-level community strategies, project process and program.

• Description

Knowledge acquisition is a difficult problem in knowledge engineering. This refers to the process of finding rules, ontologies that can be used to build a knowledge-based system and serve the use of knowledge. It is a common view that the web, and especially social networks such as Twitter and Facebook, contain a great deal of knowledge. Advanced technologies for scanning text in posts on public forums have been developed. Sophisticated algorithms are used to filter out valuable signals and patterns of noise to determine what we are referring to and even with what emotional tone. These methods have been successful in data and text mining in absorbing explicit knowledge.

However, there is some kind of knowledge on the web that is often regarded as tacit knowledge. This is because important insights and valuable knowledge are not directly articulated. Interaction often emerges after a long process. To illustrate this long interactive process is meaningful and valuable, which can help to understand how a meaning is formed and how to reach a common agreement. This process is also called sensation. Making sense itself is a new kind of knowledge that includes not only the sense made, but also the process of making sense. The conventional method for capturing the sensitization process is to simulate a community of professionals and monitor their reactions to the environment and interact with each other. This approach requires huge resources and is impossible in many business organizations where computational resources are limited.

Advanced big data analytics methods solve this problem. Extensive distributed architecture with parallel computations makes valuable insights visible from huge volumes of data. Behaviors can now be monitored, signals and individual interactions gained among members of a large community, and social knowledge acquired that would otherwise be impossible. However, the lack of modeling of interactions on the web makes it difficult to capture.

The Contradictory Collective Intelligence Model (CCI) is one of the efforts to model and absorb tacit organizational knowledge in a closed university environment. Discourse of academic documents, documents are mainly academic articles. The writing of the article has a relatively fixed style and pattern. The most important and important sentences of an article are the claims that the author tries to convince the readers to accept. These claims are usually to express a concept, or to interpret an existing concept with one's own understanding, or to provide evidence to support or argue against a proposition. Interactions occurred implicitly among many publications in the community. The whole community maintains a common understanding by reading and writing many articles. At any given time, a society will always have a stable knowledge base in which publicly agreed knowledge is maintained. New knowledge can emerge and the whole field of research can evolve with these continuous interactions.

In a web environment, all web users together can be seen as a large community. Each web user, who encounters complex and even contradictory data and information, clarifies meanings with others based on personal knowledge, understanding, and experience, as well as through claims and counter-claims in posts, blogs, or even in lengthy reviews. Understanding insights and ultimately achieving or towards, interacting. Reach a neutral and common agreement. This mutual agreement emerged slowly, and the process that leads to the common agreement together is valuable knowledge that our goal is to absorb, store, and reuse.

We as a progressive society need to support and expand knowledge in our field because having it is possible and will be possible only for us.

The program seeks to provide a combination of several computational cognitive paradigms and to spread useful knowledge across its field with the help of community members.

#Think_Rate focuses on the decentralized network of QNBAI knowledge and the development of this paradigm.

• ThinkRate Platform

• Decentralized network of verbal reasoning

• Contested Collective Intelligence Model

Research on hostile collective intelligence seeks to create a conceptual framework that enhances our ability to understand it and builds socio-technical infrastructures that can access collective information by combining the contributions of many resources on the web. Brought.

The core of the CCI model is its three key elements: claims, the semantic process, and the agreed general knowledge called "collective intelligence in question." The claim, in the form of a text statement in which the true value is clearly stated by the claimant, is the main entity in the CCI, also known as the concept. The claim has attributes such as author, date, and source that determine where the backup document came from. The semantic process is the interaction between users. This is presented as a chain of claims related to specific relationships, such as the logic of discussion in the "structure of reasoning". This rhetorical relationship has a fixed label, type, polarity, weight and direction. Controversial collective intelligence is a label of agreed and accepted knowledge. Shown as a subset of larger knowledge networks.

• Knowledge Network Construction Framework

In our experimental framework both human and machine annotations (text extraction) are used for the sensemaking activity with the aim of reducing the cost of the process. The framework comprises four stages as illustrated in the Figure 4.

Stage 1 begins with documents that may in different format for the analyst such as academic publications, posts in a forum, blogs in a personal space, papers, reports, diagrams, charts, etc. The knowledge workers firstly read the documents and trying to identify and extract information and knowledge which can be relevant for the issue they have to investigate. To make a sense of the document they have to read the full documents and in a lot of cases take notes and marginalia. Their annotations mark up as key issues, or evidence to an argument, or extensions to a theory, which may be relevant to a problem, or may be surprising, or contradicting the reader’s expectations. Once the notes have been taken they may be used to reflect on the contents of the document, and on what they may imply for the contingent inquiry. Our model aims at assisting the analysts by proposing tools and computer software to carry out these tasks.

In Stage 2 automatic text analysis technologies are used to further retrieve from the document relevant passages conveying contested ideas in a form of claims to reflect thinking. Machine annotation produces two main kinds of output as visual artifacts: sentences and labels. Sentences represent salient contents extracted from the document, and the labels indicate the semantics of the link between the salient content and the document or part of the document.

Stage 3 is human annotation: analysts can validate some of the automatically suggested text snippets and add their interpretation, or they may highlight and comment on new snippets, and thus create further visual artifacts. If the documents are shared by a group of analysts, all the annotations can be used. Human and machine annotation can thus be combined to provide analysts with a view of the salient contents in the document.

Finally stage 4 is the process of encoding the retrieved claims to answer specific questions or making sense of a specific issue. This is a key activity to enable sensemaking or to obtain collective intelligence. It is necessary and supported by a number of specific actions in order to make connections. These actions include validation or verification, integration and duplication removal.

Computational methods in the CCI model are implemented and studied in the decentralized network of verbal reasoning. Both human and machine concepts, claims, and connections are used to test machine creation and build knowledge networks with defined knowledge calculations. Figure 6 shows the adopted method, performed by human-machine analysis independently to allow comparison of functions and thus confirm the theory.

There are thought-provoking networks of elites in network communities who can conduct interdisciplinary research and development alongside various branches of blockchain, decentralized networks, neural networks, and many other disciplines.
It is not limited to technical knowledge, but grounded theorizing with the power to understand the relationship between underlying knowledge and data.

These people that I want to call "thinkers" can be systems engineers who theorize by modeling and processing information based on "opposite collective intelligence" on various issues related to multi-level community strategies, project process and program.

• Description

Knowledge acquisition is a difficult problem in knowledge engineering. This refers to the process of finding rules, ontologies that can be used to build a knowledge-based system and serve the use of knowledge. It is a common view that the web, and especially social networks such as Twitter and Facebook, contain a great deal of knowledge. Advanced technologies for scanning text in posts on public forums have been developed. Sophisticated algorithms are used to filter out valuable signals and patterns of noise to determine what we are referring to and even with what emotional tone. These methods have been successful in data and text mining in absorbing explicit knowledge.

However, there is some kind of knowledge on the web that is often regarded as tacit knowledge. This is because important insights and valuable knowledge are not directly articulated. Interaction often emerges after a long process. To illustrate this long interactive process is meaningful and valuable, which can help to understand how a meaning is formed and how to reach a common agreement. This process is also called sensation. Making sense itself is a new kind of knowledge that includes not only the sense made, but also the process of making sense. The conventional method for capturing the sensitization process is to simulate a community of professionals and monitor their reactions to the environment and interact with each other. This approach requires huge resources and is impossible in many business organizations where computational resources are limited.

Advanced big data analytics methods solve this problem. Extensive distributed architecture with parallel computations makes valuable insights visible from huge volumes of data. Behaviors can now be monitored, signals and individual interactions gained among members of a large community, and social knowledge acquired that would otherwise be impossible. However, the lack of modeling of interactions on the web makes it difficult to capture.

The Contradictory Collective Intelligence Model (CCI) is one of the efforts to model and absorb tacit organizational knowledge in a closed university environment. Discourse of academic documents, documents are mainly academic articles. The writing of the article has a relatively fixed style and pattern. The most important and important sentences of an article are the claims that the author tries to convince the readers to accept. These claims are usually to express a concept, or to interpret an existing concept with one's own understanding, or to provide evidence to support or argue against a proposition. Interactions occurred implicitly among many publications in the community. The whole community maintains a common understanding by reading and writing many articles. At any given time, a society will always have a stable knowledge base in which publicly agreed knowledge is maintained. New knowledge can emerge and the whole field of research can evolve with these continuous interactions.

In a web environment, all web users together can be seen as a large community. Each web user, who encounters complex and even contradictory data and information, clarifies meanings with others based on personal knowledge, understanding, and experience, as well as through claims and counter-claims in posts, blogs, or even in lengthy reviews. Understanding insights and ultimately achieving or towards, interacting. Reach a neutral and common agreement. This mutual agreement emerged slowly, and the process that leads to the common agreement together is valuable knowledge that our goal is to absorb, store, and reuse.

We as a progressive society need to support and expand knowledge in our field because having it is possible and will be possible only for us.

The program seeks to provide a combination of several computational cognitive paradigms and to spread useful knowledge across its field with the help of community members.

#Think_Rate focuses on the decentralized network of QNBAI knowledge and the development of this paradigm.

• ThinkRate Platform

• Decentralized network of verbal reasoning

• Contested Collective Intelligence Model

Research on hostile collective intelligence seeks to create a conceptual framework that enhances our ability to understand it and builds socio-technical infrastructures that can access collective information by combining the contributions of many resources on the web. Brought.

The core of the CCI model is its three key elements: claims, the semantic process, and the agreed general knowledge called "collective intelligence in question." The claim, in the form of a text statement in which the true value is clearly stated by the claimant, is the main entity in the CCI, also known as the concept. The claim has attributes such as author, date, and source that determine where the backup document came from. The semantic process is the interaction between users. This is presented as a chain of claims related to specific relationships, such as the logic of discussion in the "structure of reasoning". This rhetorical relationship has a fixed label, type, polarity, weight and direction. Controversial collective intelligence is a label of agreed and accepted knowledge. Shown as a subset of larger knowledge networks.

• Knowledge Network Construction Framework

In our experimental framework both human and machine annotations (text extraction) are used for the sensemaking activity with the aim of reducing the cost of the process. The framework comprises four stages as illustrated in the Figure 4.

Stage 1 begins with documents that may in different format for the analyst such as academic publications, posts in a forum, blogs in a personal space, papers, reports, diagrams, charts, etc. The knowledge workers firstly read the documents and trying to identify and extract information and knowledge which can be relevant for the issue they have to investigate. To make a sense of the document they have to read the full documents and in a lot of cases take notes and marginalia. Their annotations mark up as key issues, or evidence to an argument, or extensions to a theory, which may be relevant to a problem, or may be surprising, or contradicting the reader’s expectations. Once the notes have been taken they may be used to reflect on the contents of the document, and on what they may imply for the contingent inquiry. Our model aims at assisting the analysts by proposing tools and computer software to carry out these tasks.

In Stage 2 automatic text analysis technologies are used to further retrieve from the document relevant passages conveying contested ideas in a form of claims to reflect thinking. Machine annotation produces two main kinds of output as visual artifacts: sentences and labels. Sentences represent salient contents extracted from the document, and the labels indicate the semantics of the link between the salient content and the document or part of the document.

Stage 3 is human annotation: analysts can validate some of the automatically suggested text snippets and add their interpretation, or they may highlight and comment on new snippets, and thus create further visual artifacts. If the documents are shared by a group of analysts, all the annotations can be used. Human and machine annotation can thus be combined to provide analysts with a view of the salient contents in the document.

Finally stage 4 is the process of encoding the retrieved claims to answer specific questions or making sense of a specific issue. This is a key activity to enable sensemaking or to obtain collective intelligence. It is necessary and supported by a number of specific actions in order to make connections. These actions include validation or verification, integration and duplication removal.

Computational methods in the CCI model are implemented and studied in the decentralized network of verbal reasoning. Both human and machine concepts, claims, and connections are used to test machine creation and build knowledge networks with defined knowledge calculations. Figure 6 shows the adopted method, performed by human-machine analysis independently to allow comparison of functions and thus confirm the theory.