Developed index — Computer science

Part hub for systems, theory, security, and learning. Long archive chapters (Unix, Cybersecurity, Architecture, …) are left structurally intact; this index supplies the field map, definitions, and paper spine.

At a glance

Theory: computability + complexity + information.
Systems: EE → architecture → Unix-like OS.
Trust: cryptography + cybersecurity (threat models, zero trust).
Learning: classical ML → LLMs; KM as organisational learning stack.

1. Field map

Figure 1. Field map of Computer science: theory and hardware feed systems and security, which lead to machine learning, knowledge management and the applied chapters.

2. Leaf chapters & what the archive holds

Theoretical CS

Extract these ideas: Turing machines, P vs NP, automata
Full papers / classics: Turing (1936); Sipser Intro to ToC; Arora–Barak Computational Complexity

Information theory

Extract these ideas: Entropy, channels, coding; IEML emptiness notes
Full papers / classics: Shannon (1948); Cover & Thomas Elements of Information Theory

Electrical engineering

Extract these ideas: Circuits, Maxwell, signals, semiconductors
Full papers / classics: Horowitz & Hill Art of Electronics; Maxwell (1865)

Computer architecture

Extract these ideas: ISA, pipeline, cache hierarchy
Full papers / classics: Hennessy & Patterson Computer Architecture; Amdahl (1967)

Unix-like systems

Extract these ideas: Process, file, pipe, POSIX
Full papers / classics: Ritchie & Thompson (1974); Bach Design of the UNIX OS; POSIX.1

Cryptography

Extract these ideas: Conf/int/auth; public-key; hash
Full papers / classics: Diffie–Hellman (1976); Rivest–Shamir–Adleman (1978); Katz–Lindell textbook

Cybersecurity

Extract these ideas: Threat model, zero trust, defence in depth
Full papers / classics: Anderson Security Engineering; NIST SP 800 series; Zero Trust (NIST SP 800-207)

General ML

Extract these ideas: Risk, generalization, optimization
Full papers / classics: Vapnik statistical learning; Bishop PRML; Goodfellow et al. Deep Learning

LLMs

Extract these ideas: Transformers, pretraining, alignment
Full papers / classics: Vaswani et al. (2017) Attention Is All You Need; Brown et al. GPT-3 (2020)

Crypto currencies

Extract these ideas: Consensus, incentives, ledgers
Full papers / classics: Nakamoto (2008) Bitcoin whitepaper; Boneh–Shoup crypto notes

HMI

Extract these ideas: Affordance, usability, HRI
Full papers / classics: Norman Design of Everyday Things; Nielsen heuristics; ISO 9241

Knowledge management

Extract these ideas: Explicit/tacit, SECI, ontology, RAG
Full papers / classics: Nonaka & Takeuchi (1995); Polanyi Tacit Dimension; Gruber ontology (1993)

Concepts governance

Extract these ideas: Plan, certify, reject forged, apply; conservativity before insert; two clocks
Full papers / classics: Open Ontologies README; OWL 2 Profiles (OWL 2 RL); RDF 1.1; SPARQL 1.1

Agent memory & world-model repos

Extract these ideas: Temporal KG memory (bi-temporal edges, episodes); entity resolution (exact → MinHash/LSH → LLM); swarm simulation; computer-use agents; graph of agents
Full papers / classics: Graphiti / Zep (arXiv:2501.13956); OASIS; MCP spec; repos pinned at commit

IEML × RDF × Situation Monitor

Extract these ideas: RDF 1.2 tagging (language tags, base direction), triple terms and reifiers for edge metadata, SHACL grounding check on LLM briefs, OWL 2 RL conservativity, propositional view of the argument map
Full papers / classics: RDF 1.2 Concepts (CR), SPARQL 1.2 (WD), SHACL, OWL 2 Profiles; arXiv:2607.05970

IEML / WWW Platform

Extract these ideas: See Engineering index
Full papers / classics: Lévy (2023); W3C RDF/OWL/SHACL

Immutable and image-based operating systems

Extract these ideas: Image identity, atomic updates, four verification modes
Full papers / classics: Engineering §4; OSTree; bootc; NixOS; Fedora Silverblue

3. Developed cores (equations & claims)

3.1 Computability

Definition (decidable language). A language (L) is decidable if some Turing machine halts on every input and accepts iff the input $\in L$ .

Classic. Turing, A. M. (1936). On Computable Numbers… Proc. London Math. Soc.

3.2 Complexity

Definition (class P). Languages decidable in deterministic polynomial time.
Definition (class NP). Languages verifiable in deterministic polynomial time (or decided by nondeterministic poly-time TM).

Open problem. $\mathrm{P}\overset{?}{=}\mathrm{NP}$ .

3.3 Information

H(X)=-\sum_x p(x)\log p(x),\quad I(X;Y)=H(X)-H(X\mid Y)

Classic. Shannon (1948); Cover & Thomas.

3.4 Learning

Empirical risk minimization. Given sample $S=\{(x_i,y_i)\}$ , minimise $\hat R_S(h)=\frac1n\sum \ell(h(x_i),y_i)$ over hypothesis class $\mathcal H$ , hoping true risk (R(h)) stays close (generalization).

Deep learning practice. Overparameterised models + SGD; theory still catching up (see Neyshabur, Zhang et al. lines of work).

3.5 Transformers (LLMs)

Claim (Vaswani et al., 2017). Self-attention yields parallelisable sequence transduction without recurrence as the primary computational primitive.

Reference. Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.

3.6 Security

Definition (threat model). Explicit adversary capabilities, assets, and success conditions.
Zero trust (NIST SP 800-207). Never trust, always verify; assume breach.

4. Full paper checklist (CS spine)

Paper / book	Year	Topic
Turing — Computable Numbers	1936	Computability
Shannon — Mathematical Theory of Communication	1948	Information
Diffie & Hellman — New Directions in Cryptography	1976	Public-key
Rivest, Shamir, Adleman — RSA	1978	Public-key
Nakamoto — Bitcoin	2008	Consensus currency
Vaswani et al. — Attention Is All You Need	2017	Transformers
Hennessy & Patterson — Computer Architecture	book	Systems
Anderson — Security Engineering	book	Security
Cover & Thomas — Elements of IT	book	Information
Sipser — Introduction to the Theory of Computation	book	Theory