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The concept of mind

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing the myth of the paperless office as the reading material.

The Myth of the Paperless Office

Formal Concept Analysis has been originally developed as a subfield of Applied Mathematics based on the mathematization of concept and concept hierarchy. Only after more than a decade of development, the connections to the philosophical logic of human thought became clearer and even later the connections to Piaget's cognitive structuralism which Thomas Bernhard Seiler convincingly elaborated to a comprehensive theory of concepts in his recent book [Se01]. It is the main concern of this paper to show the surprisingly rich correspondences between Seiler's multifarious aspects of concepts in the human mind and the structural properties and relationships of formal concepts in Formal Concept Analysis. These correspondences make understandable, what has been experienced in a great multitude of applications, that Formal Concept Analysis may function in the sense of transdisciplinary mathematics, i.e., it allows mathematical thought to aggregate with other ways of thinking and thereby to support human thought and action.

Formal concept analysis as mathematical theory of concepts and concept hierarchies

ionist strategies, 24 commitments, 181–182; normative, Accidentally true beliefs, 114 187–189, 197 Acknowledgments, 173–175, 177–179, 181 Assimilation, 2–3, 11 Acquired beliefs, 106–107, 121 Assimilationalism, 34 Action, 15, 24, 37–38, 79–96. See also Assimilation theories, 3, 6 Commitment(s); Practical Associationist strategies, 24 reasoningM Asymmetric substitution inferences, Adverbs, 206n14 135–136, 142–143, 144–146, 149, 151–154 Analysis of meanings, 31. See also Atomism, 15–16, 29, 31 Concepts Attributions, 168–169, 173–175, 178, Anscombe, G. E. M., 82 180–181 Articulation: inferential, 17, 22, 26, 28, Authority, 11, 31, 44, 76, 165 39, 44, 47, 52, 63, 108, 129, 161–166, 173, Awareness, 2, 7, 16–17, 20, 22, 24–25, 31, 178, 183, 193, 195, 220n4; conceptual, 35, 48, 157, 160 32; logical, 52; social, 158–159, 163 Ascriptions, 175–178, 183 Belief (s), 4–6, 12, 16–17, 37, 43, 53, 69, Assertibilism, 64, 185–189 82–84, 187, 210n2, 211n6; true, 39, Assertibility conditions, 196–198, 97–101, 106–108, 111–112, 114, 118–119; 201–202 and reliabilism, 97–121; acquired, Assertional commitments, 179 106–107, 121; perceptual, 108–109, 121; Assertional contents, 192, 194, 196 and reasons, 158–161, 165–166, 168, Assertional practices, 189, 193, 197 174–177, 180–182, 189, 191–192; Assertion(s): pragmatic nature of, collateral, 219n3. See also Commit11–12, 17, 40; and inferences, 15, 17, 43; ment(s) and singular terms, 125, 129, 145–147, Belnap, Nuel D., Jr., 68 152–153; and reasons, 161, 165, 189–195. Bennett, Jonathan, 70 See also Commitment(s); Language; Blindsight, super, 102–106 Copyright © 2000 The President and Fellows of Harvard College Blindspots, 39, 97–122 Boole, George, 58 Boolean logic, 58–59 Bottom-up semantic explanation, 12–15 Bradley, F. H., 140 Brandom, Robert B.: Making It Explicit, 10, 35–36, 80–81, 119, 205n7, 209n4 Carnap, Rudolph, 7, 23, 118; The Logical Syntax of Language, 50 Causal chains, 113–115. See also Reliabilism Causal functionalism, 95 Causes, 45–46, 53, 84, 94 Certainty, 210n1 Ceteris paribus clauses, 88–89 Charged words, 69–71 Chomsky, Noam, 126–127 Circumstances, 62–66, 69, 72, 74–75, 221n7. See also Assertion(s); Consequences Claims, 43–44, 48, 56–57, 64, 81–82, 86, 196–197; assertion of, 11–12, 125, 194; in semantic explanations, 12–15; expressive nature of, 16–19; conditional, 21–22, 86–87; inferentially articulated, 26, 28–29; knowledge, 39; and objects, 40; conceptual content of, 50; reliable, 107; counter-, 149; and reasons, 158–162, 164–169, 174–175, 180–183, 193; understanding, 189, 191; undertaking, 192; quantificational, 214n13 Classification, conceptual, 17, 48 Cognitive activity, 46, 157, 160 Cognitive responsibility, 105–106 Collateral beliefs, 219n3 Commitment-preserving inferences, 43–44, 195, 201. See also Inference(s) Commitment(s), 31, 73, 220nn5–6, 221n7; inferential, 30, 64, 69–79, 91, ◆ Index 223 147–149, 178; and reasons, 43, 163–168, 174–175, 177–178, 183, 189–196; incompatible, 44; explicit/implicit, 56–57; and practical reasoning, 80–85, 91–96; practical, 93–96; discursive, 94, 164–165; consequential, 108–109, 191–192; and reliabilism, 108–109, 118–120; SMSICs, 136–141; and singular terms, 151–153; substitutional, 178–182; assertional, 179; normative, 189–196, 198–203. See also Action; Assertion(s); Belief(s); Entitlement Committive inferences, 194 Communication: and representation, 165–169, 183; and beliefs, 180 Concepts, 1–22, 80, 108–109, 160 Conceptual articulation, 32 Conceptual assimilationalism, 34 Conceptual Blindspot, 39, 109, 117–118,

Articulating Reasons: An Introduction to Inferentialism

: A prior effort, State of Knowledge Relative to Intelligence Analysis, was initially motivated by the fact that seemingly little change had resulted from numerous studies of the intelligence community. Starting with Pearl Harbor, the U.S. intelligence community has often faced criticism for failing to predict or warn of future events. Though the criticisms have come from different groups, a certain commonality exists among the proposals for change in the intelligence community. The other noteworthy feature of these proposals is how little their content has changed over time. A thorough analysis of the intelligence literature was accomplished. This analysis provided an answer to the question of whether the existing literature on intelligence analysis contains the requisite knowledge to inform the development and application of both the mechanistic and cognitive activities to support doing intelligence analysis. A dominant finding was how little the practice of intelligence analysis had been informed by the findings in related scientific disciplines. The primary objective of this research effort was to understand the research findings in relevant scientific disciplines and to relate these findings to the practice of intelligence analysis. This research effort was based on the full text of over 5,800 documents consisting of nearly 172,000 pages. Our analysis of the current intelligence literature showed a continuation of previously observed trends. The number of publications dealing with intelligence analysis has decreased since 2007; in 2012 publication was at the level observed in 1996-1997. Also, we observed that the literature places less emphasis on the improvement of the quality of intelligence analysis. During the effort described in this report we conducted research syntheses for the topics of critical thinking, thinking dispositions, epistemological beliefs, practice based training, and various facets of cognition.

https://apps.dtic.mil/sti/pdfs/ADA595401.pdf

Advanced Analysis Cognition: Improving the Cognition of Intelligence Analysis

This version is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract. The development of ontologies for various purposes is now a relatively commonplace process. A number of different approaches towards this aim are evident; empirical methodologies, giving rise to data­driven procedures; self­ reflective (innate) methodologies, resulting in artifacts that are based on intellectual understanding; collaborative approaches, which result in the development of an artifact representing a consensus viewpoint. We compare and contrast these approaches through two parallel use cases, in work that is currently ongoing. The first explores a case study in creation of a knowledge base from raw, semi­struc­ tured information available on the Web. This makes use of text and data mining approaches from various sources of informa­ tion, including semi­formally structured metadata, interpreted using methods drawn from statistical analysis, and data drawn from crowd­sourced resources such as Wikipedia. The second explores ontology development in the area of phys­ ical computing, specifically, context­awareness in ubiquitous computing, and focuses on exploring the significant impact of an evidence­led approach. Both examples are chosen from domains in which automated extraction of information is a significant use case for the resulting ontology. In the first case, automated ex­ traction takes the form of indexing for search and browse of the archived data. In the second, the predominant use cases relate to context­awareness. Via these examples, we identify a core set of design principles for software platforms that bring together evidence from each of these processes, exploring participatory development of ontologies intended for use in domains in which empirical evidence and user judgment are allied.

/pdf/an-evidence-based-approach-to-collaborative-ontology-4g3fducxpf.pdf

An evidence-based approach to collaborative ontology development

Representation of knowledge is used to store and retrieve informational data in a machine. Since meaningcannot be directly stored in the computer; this work proposes a series of levels of representation. The meaning of the data is transformed to a format that the machine can use to store and retrieve knowledge. These levels are designed to transform the knowledge from an abstract definition to a machine representation without loosing any meaning.

/pdf/representation-levels-within-knowledge-representation-2seq2cq144.pdf

Representation Levels Within Knowledge Representation

The panel will discuss tagging of documents in the context of social networks. This discussion will include to what extent and in what manner users, consciously or unconsciously, take into account their communities when assigning tags. Likewise, the panel will investigate to what extent the community influences tagging behavior, and if this influence helps or hinders search and retrieval or affects the context of the tags generated. The topic areas proposed for this conference panel are: Context of Social Network Metadata: Do users tag for the group(s) of people they associate with? Metadata use: Do small communities have a stable use of terminology? Search and retrieval: How do tags relate to metadata searches and contextual retrievals? Social Network Architecture: To what extent, if any, does community membership influence tagging behavior? Questions that will be discussed among panelists and with the audience will focus on the social network part of the difficulty of dealing with tags. Within a community, is it possible to decide whether to interpret a tag or just treat it as a primary key or keyword?

Tagging and Social Networks: The Impact of Communities on User Centered Tagging

The difficulty of maintaining effective testbed partnerships, among users of computer-based tools and builders of those tools, has been an obstacle to the success of science collaboratories. Testbed partnerships are as essential for knowledge-tool advancement as that advancement is to the support of effective partnerships, because knowledge tools must be instituted in a context that truly augments (rather than merely tries to replicate) human inference in collaborative contexts. We propose a game framework for engaging content-tool users and context-tool developers in testbed partnerships, and an architecture for modular knowledge-tool integration, as initial steps in the effective advancement of knowledge capture and improvement of testbed partnerships.

/pdf/collaboratory-testbed-partnerships-as-a-knowledge-capture-14ovorm7ar.pdf

Collaboratory testbed partnerships as a knowledge capture challenge

MERCURY is an integrated software system for acquiring, analysing, and extrapolating meteorological data in real time for regions a few hundred square kilometers in size. MERCURY employs a variety of data structures, including real scalars and vectors, three-dimensional models, chords, grids, and qualitative descriptions to represent its input data, and both procedural and declarative methods to represent meteorological knowledge. MERCURY employs the extrapolation method that it is designed to improve upon, mesoscale objective analysis, when it is likely to be sufficient, and supplements this method with additional heuristic and analytic methods when necessary. Initial performance comparisons indicate that MERCURY's extrapolations are better than those obtained with objective analysis alone in regions of complex terrain and surface cover. MERCURY is implemented in C, with graphics in X-windows, and runs on a Sun 3 60 workstation under Unix 4·2 bsd.

Heather D. Pfeiffer

Papers

An evidence-based approach to collaborative ontology development

Representation Levels Within Knowledge Representation

Tagging and Social Networks: The Impact of Communities on User Centered Tagging

Collaboratory testbed partnerships as a knowledge capture challenge

MERCURY: a heterogeneous system for spatial extrapolation of mesoscale meteorological data