Maritime 4.0: Innovation Driven by AI, Data, and Cyber Security

I would like to organize and document the learning curriculum and key research papers of interest for the Recommender System course that I am taking in the final semester of my Ph.D. program. Goals of this Course Collaborative Filtering (CF) A recommendation approach based on user behavior . Neighborhood-based Collaborative Filtering (CF) A method that recommends items based on similarities between users or items . User-based Collaborative Filtering (User-based CF) Recommends items based on similarities between users . Users with similar preferences or behaviors receive similar recommendations. Item-based Collaborative Filtering (Item-based CF) Recommends items based on similarities between items . Items that have been interacted with in a similar way are recommended together

 I have come to As the first step, I am recording my personal conclusions on the issues I have contemplated over the past month. [Relational Aspects] I do not strive to become someone else in order to be remembered as a good person in the organization. I am who I am. Work-related matters should be discussed in meetings where all relevant team members (or stakeholders) are present. Even if someone feels uncomfortable or is being held accountable, I will not avoid the situation. Instead, I will share the facts accurately and discuss solutions together to foster future growth and stronger relationships. When I recognize actions that go against basic common sense, I will focus on identifying the fundamental issue rather than getting caught up in others’ emotions. I do not indiscriminately offer consideration, comfort, or reprimands to others. I focus solely on facts and do not convey my emotions in judgment. The driving force of my 30s— “An eye for an eye, a tooth for a tooth” ...

Shifting Perceptions and Expectations of AI from Companies The way companies perceive and expect from AI is evolving. Until now, aside from cutting-edge research and product development , the role of AI engineers and data scientists in enterprises can generally be categorized as follows: Image Processing : Companies have widely utilized CNN-based models for object recognition and comparison (performance improvement). To overcome data scarcity, techniques like few-shot learning have been applied in areas such as product search, similar image/product recommendation, and design generation . Text Processing : Enterprises have leveraged various Transformer-based models trained on proprietary datasets for customer intent recognition and sentiment analysis . Data-Driven Decision Support : Companies often assume they possess sufficient data, but in many cases, they lack the necessary datasets. AI teams analyze available data, form hypotheses , and generate insightful reports to support an...

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks EfficientNet is a highly influential paper that has gained significant attention in the field of image classification due to its outstanding performance . For projects requiring extensive training time or computational resources , EfficientNet serves as a valuable approach to enhancing ConvNet performance . It provides an efficient and scalable method for training convolutional neural networks while optimizing accuracy and computational cost, making it highly applicable for real-world AI deployment . 🔗 Research Paper: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 📌 Key Resources & Reviews 📖 Paper Review Summaries: Bellzero’s Review Laonple Blog Review 💻 Source Code (PyTorch Implementation): GitHub: EfficientNet-PyTorch  

Installing Required Libraries to Convert SVG Files to PNG The goal was to apply NBP OCR to SVG images and record the output as JSON (text, coordinates) . To achieve this, the first step was to convert the ".svg" file into ".png" . Initially, I thought that simply installing cairo via pip would suffice, but I soon realized that additional steps were necessary . To avoid wasting time solving issues when I attempted it again later, I decided to document the process and share it . If you need code references or encounter any setup issues, please feel free to leave a message here: https://github.com/shipjobs/HAND2TEXT/issues . Environment Language: Python 3.8.3 64bit Operating System: Windows 10 Development Environment: Visual Studio Code To convert an ".svg" file to ".png" , if you import the libraries as shown below, you will naturally encounter a reference error : import  cairo  from  svglib.svglib  import  svg2rlg  import  cairosvg For those...

CLOVA OCR (optical character reader) Service review 문서를 인식하고, 사용자가 지정한 영역의 텍스트와 데이터를 정확하게 추출 CLOVA OCR (광학문자인식) 을 한번 사용해 본다면, NCP가 제공 중에 있는 AI Service 들 에 대하여 보다 쉬운 접근이 가능해질 거라 생각 하게 되어 review 를 해보고자 합니다. [접근] Products & service : https://console.ncloud.com/dashboard OCR Service 경로 : Classic / CLOVA OCR / Domain [이용 방식] 서비스 타입 General / Template / Document 선택에 따라 Text OCR / 템플릿 빌더 / Document 버튼이 노출되는 형식으로 서비스를 설정함  Text OCR (텍스트만 추출)  과 Template 빌더 형태 (판독 영역 직접 지정을 통해 인식 값 추출 후 테스트 및 결과 전송이 가능) 는 서비스 타입에 따라 아래의 2가지 방식이 있으며 1. General OCR : 우리가 일반적으로 생각하는 png, jpg이미지 혹은 pdf 에 존재하는 text 들을 모두 읽어 오고자 하는 방식 2. Template OCR : 운전 면허증, 신용 카드, 주민 등록 등본 이미지 등 이미지내 정해진 특정 영역을 기준으로 text 들을 읽어 오는 방식 Document 방식은 머신러닝 기반으로 문서의 의미적 구조를 이해하는 특화 모델 엔진을 탑재하여 입력 정보(key-value)를 자동 추출하는 방식 인식 모델로는 미리 정해진 사업자 등록증, 신용카드,영수증, 신분증, 명함이 제공 되며 이를 선택할 수 있게 되어 있습니다. [이러한 Type 별 서비스를 이용하는 방법 또한 2가지로 구분 될 수 있습니다.] 1. NCP OCR 사이트에 접속해서 제공되는 UI 화면으로 접근 하는 방식으로 원하는 이미지 파일을 drag and drop으로 등록하고 텍...

  StyleGAN — Official TensorFlow Implementation Material related to our paper is available via the following links: Paper:  https://arxiv.org/abs/1812.04948 Video:  https://youtu.be/kSLJriaOumA Code:  https://github.com/NVlabs/stylegan FFHQ:  https://github.com/NVlabs/ffhq-dataset Additional material can be found on Google Drive:

Dynamic allocation of limited memory resources in reinforcement learning

 Unsupervised Learning of Probably Symmetric Deformable 3D Objects  from Images in the Wild Shangzhe Wu Christian Rupprecht Andrea Vedaldi Visual Geometry Group, University of Oxford {szwu, chrisr, vedaldi}@robots.ox.ac.uk

[Open Source + Research Paper + Jetson Nano] Blinker Project This project aims to implement a blink detection system using dlib and OpenCV , following an existing open-source implementation. It builds upon facial recognition techniques to develop a system capable of detecting eye blinks in real-time. According to the contributor's description , this system can be applied in various scenarios, such as: 🚗 Drowsy driving detection – Alerting drivers when signs of fatigue are detected. 📚 Student monitoring – Analyzing drowsiness and focus levels in study environments. By leveraging Jetson Nano , this project explores the integration of edge AI for real-time blink detection, opening possibilities for applications in safety, education, and human-computer interaction . 🚀 asily Implementable with a Camera and Software Development Setup If you have a camera and a properly configured software development environment , this project is relatively easy to implement. In this project, we...

Let's explore the Face Recognition Project ! 😊 In this project, we will maximize the use of open-source resources with the following objectives: 1️⃣ Understand key libraries and source code 2️⃣ Set up the required development environment 3️⃣ Optimize performance for better accuracy and efficiency 4️⃣ Share results and discuss potential improvements The goal is to go beyond simple implementation , actively improving the system and exploring ways to enhance its capabilities. 🚀

ABSTRACT  1. Introduction  2. Scope and objective  3. Previous Works On NLP (Brief History)  4. Natural Language Processing Overview  5. Applications of NLP  6. Challenges and failures  7. Current and Future progress of NLP  8. Conclusions  References 

THE BEST ARTIFICIAL INTELLIGENCE  JOURNALS 안녕하세요?  연구 활동 및 학위 취득의 목적, 그리고 기업에 소속되어 논문 등재를 시작해 보려는 이들에게 도움이 될 만한 내용을 정리해 보고자 합니다.. 인공지능 학문에 대하여 연구자 및 엔지니어들이 모이는 학회는 어떤것들이 있으며 각 학회의 역사나 특징, 동향이 궁금해 졌기 때문 입니다. 원하는 논문의 탐색은 구글검색이나 "http://www.arxiv-sanity.com/" 을 통하면 쉽게 얻을 수있겠지만, 각 학회별 특징이나 동향을 아는 것이 우리의 논문 등재를 포함한 연구 활동에 도움이 될것이라 생각 하기에 그 내용을 정리함과 동시에 과정을 지속적으로 공유해 나가고자 합니다. 1. NeurIPS   (NIPS)     : 연구자 및 엔지니어가 모이는 최대 규모의 연례 학회로 , 새로운 발견을 공유하고 협업하며 함께 AI 산업 발전을 도모하는 장     : 역사 : 1987 년 , 분야 : 인지 과학과 머신러닝 응용 분야 등 폭 넓음     ; 바로가기:    https://papers.nips.cc/  2. ICML : (International Conference on Machine Learning)     : 머신 러닝에 집중,  NeurIPS  및  ICLR  과 함께  기계 학습  및  인공 지능  연구  에 큰 영향을 미치는 세          가지 주요 컨퍼런스 중 하나 , 정확한 날짜는 해마다 다르지만 일반적으로 논문 제출 마감일은 1 월 말          이며 회의는 일반...