Opera Academica & Pensa
Hic ea opera continentvr, qvae in cvrsibvs vniversitariis svmma cvra perfeci.
Discentia Profvnda
Agnitio Ciborvm & Compvtatio Caloriarvm
Systema ad Diaetam Inquirendam per Exemplaria Levia Opus in cvrsv “Discendi Profvndi” (Deep Learning) confectvm. Est systema integrvm ad diaetam percipiendam et consilia de valetvdine danda, qvod in machinis mobilibvs sine interrete operari potest vt intimitatem vsorvm tveatvr.
Technologiae Praecipuae:
- Visio Computatralis et Validatio: Rete neuronale YOLO cibos in imaginibus detegit et eorum magnitudinem per theorema “Shoelace” aestimat. Deinde, exemplar CLIP has detectiones confirmat (sicut ostiarius vel Gatekeeper) ne errores fiant.
- Requisitio Datorum (Retrieval): Algorithmi TF-IDF et Word2Vec ad thecas datorum maximas (“RecipeNLG” et “USDA Food Central”) in disco locali perscrutandas adhibentur.
- Ratiocinatio per LLM: Exemplar linguae leve SmolLM2-360M (cum quantisatione 4-bit et adaptatore LoRA) adhibetur ad calorias aestimandas et consilia personalia in forma JSON danda[cite: 215, 218]. Tota memoria adhibita tantum circiter 2.75 GB est, ergo machinis mobilibus valde apta est.
Python YOLOv5 CLIP SmolLM2 NLP
Programmatvra Obiectis Directa (OOP)
Lvdvs “MineMatching” in JavaFX Lvdvs “MineMatching” est ratiocinativvs qvidem, qvi qvasi Minecraft stilo fvtis est. Architectvra programmatis principia obiectis directa (vti svnt elvctatio et polymorphisvms) fvlcris fvndamentis demonstrat. Classis abstracta
Pieceper classes derivatas (vti svntEliminatorvelWall) extenditvr, vt omnia elementa in vna matrice bidimensionali (2D array) contineantvr.
- Tabvla et Elementa: Qvinqve genera elementorvm insvnt: Ordinaria, Eliminatores, Specialia (vti gladivs adamantinvs ad colvmnas delendas), Mvri (in gradv sexto, qvi moveri non possvnt), et vacva (Nulls).
- Instrvmenta et Gradvs: Sex gradvs praesto svnt. Instrvmento “malleo” vti licet ad vnvm elementvm delendvm (qvod obtinetvr vbi qvinqve elementa simvl delentvr), et “potione restitvtionis” ad vltimam actionem revocandam.
- Interficies: Qvattvor interficies GVI (Initivm, Lvdvs, Menvs, Finis) habet, vbi vtentes mvre trahendo vel cliccando lvdvnt.
Potes codicem descriptionemqve (Sinice) legere:
Imago: Tabvla lvdi MineMatching cvm elementis variis et instrvmentis.
Visio Compvtatralis
TriStreamNet: Exemplar ad Imagines ab AI Generatas Discernendas Propositvm hoc classificationem binariam perficit (vervm an generatvm). Architectvra TriStreamNet vtitvr tribvs flvminibvs:
- Flvmen Spatiale RGB: Reti ResNet18 vtitvr ad colores et textvras percipiendas.
- Flvmen Freqventiae: Transformata Fovrieriana Rapida (FFT) vtitvr ad artificia periodica invenienda.
- Flvmen Strepitvs (Noise): Filtra SRM adhibet ad anomalias in distribvtione strepitvs extrahendas. Accvratio finalis in imaginibvs probandis 75.90% fvit.
Imago: Architectvra TriStreamNet cvm tribvs flvminibvs et attentione CBAM.
Cryptographia & Secvritas Informationis
PlayfairCracker: Systema ad Cryptographiam Playfair Solvendam Hoc propositvm solvtionem cryptographiae Playfair per algorithmos varios demonstrat. In primis, algorithmvs “Simulated Annealing” (in lingva C) adhibetvr ad claves fortvitas explorandas et ad textvm clarvm paulatim inveniendvm. Deinde, algorithmvs provectior (in lingva Rust) vtitvr analysi freqventiae (bigrams & quadgrams) et inqvisitione DFS ad claves celeriter ervendas.
- Simulated Annealing (C): Vtitvr mvtationibvs clavivm et ratiocinatione probabilistica (Metropolis criterion) ad optima solvta reperienda sine in foveis localibvs manendo.
- Inqvisitio DFS & Parallelismvs (Rust): Algorithmos mltithreaded (Rayon) adhibet ad milia pensorvm simvl solvenda, vbi regulae Playfair ad spativm inqvisitionis coercendvm (pruning) valent.
- Interficies Graphica (GUI): Systema vtitvr interficie graphica ad progressvm vsvrvm et reconstrvctionem matricis Playfair in tempore reali ostendendvm.
C Rust Cryptography Simulated Annealing GUI
Imago: Interficies graphica PlayfairCracker matricem reconstrvctionis ostendens.
Principia Lingvarvm Programmatvrae
KanbunSE: Lingva Programmatvrae S-Expressionis Lingva KanbunSE Machina Interpretandi ▶ Extensio VS Code → (Kanbun S-Expression) a me creata est vt pontem faciat inter litteras Sinicas classicas et compvtationem hodiernam. Haec lingva syntaxim lingvae Sinicae classicae cvm strvctvra S-Expressionis eleganter conivngit.
Proprietates Praecipvvae:
- Systema Obiectis Directvm (OOP): Classes absolvtas et hereditarias, polymorphisvvm, et vocabvlvm
此(hoc) pro avtoreferentia in methodis plene svstinet.- Nvmeri et Genera Datorvm: Nvmeros Arabicos in nvmeros Sinicos traditionales vel formales (e.g., 三百一十四點一五 pro 314.15) avtomatice convertit per integrationem cvm lingva Python. Valores booleani vt 陽 (Yang, vervm) et 陰 (Yin, falsvm) repraesentantvr.
- Tractatio Errorvm: Errores contextuales plene in lingva classica Sinica nvntiantvr. Exempli gratia, error indicis extra fines sic ostenditvr: “索驥圖外、其轍亂矣”.
- Architectvra Interna: Interpretes in lingva Racket aedificatvs est. Analysim lexicalem, parsationem descensvs recvrsivi (recvrsive descent parser), et catenam ambitvs (Environment Chain) pro spatio lexicali (lexical scoping) comprehendit.
Potes codicem descriptionemqve (Anglice) legere:
Imago: Strvctvra interpretis et flvxvs execvtionis lingvae KanbunSE.
Designatio Thecarvm Datorvm
Systema Administrationis Refectionis Cvrruvm Systema datorvm designatvm ad negotia cvrruvm reficiendorvm. Tres partes habet: Vtentes, Mechanici, et Administratores. Vtitvr transactionibvs SQL ad integritatem datorvm servandam, et machinis (triggers) ad deletionem continvatam (cascade delete).
Ingeniaria Programmatvrae
Sitvs Interretialis ad Cavponas Aestimandas Applicationem interretialem aedificavi vbi vtentes cavponas inqvirere, locare, et de eis commentarios relinqvere possvnt. Systema plene operatvr cvm designatione robvsta.