Bibliographic record
Abstract
Many of the information technology products that we enjoy in our times are founded on theoretical tools of computing science. Some of these tools are presented in this concise textbook at an introductory level. In particular, we discuss basic concepts in the classical areas of formal languages, logic, and coding and information theory. We call these areas classical as they provided a lot of basic tools in the first few decades of the evolution of computing science. Of course in later stages of this evolution, people developed or utilized additional theoretical tools (such as fuzzy sets and fuzzy logic, neural networks and string distances) that are not covered here. However, the clas-sical tools are so basic that they continue to be of importance at present and most likely in the foreseeable future as well. Readers are expected to have some basic background in computer programming (in a high level language) and discrete mathematics (e.g., the concepts of set, function and relation, mathematical proof, etc.). This background knowledge is normally acquired after com-pleting a couple of first year related courses in a typical Canadian university. Then, completing a course based on the material of this textbook will provide one with a basic understanding of the following. • The existence of unsolvable computing problems. • The role of formal logic in representing and deducing knowl-edge. • The paradigm of declarative programming via the Prolog lan-guage. • The role of grammars in specifying the syntax of programming expressions. • The role of automata in recognizing programming expressions and communication languages. • The role of codes in communicating information. • The complexity involved in trying to solve certain important computing problems. The author invites any comments and corrections that could im-prove this work – see the author’s website for contact information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.405 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".