intial commit

app.py

@@ -1,11 +1,29 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# RAG imports
+import os
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.schema import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# We'll load the existing FAISS index at the start
+INDEX_FOLDER = "faiss_index"
+_vectorstore = None
+
+def load_vectorstore():
+    """Loads FAISS index from local folder."""
+    global _vectorstore
+    if _vectorstore is None:
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        _vectorstore = FAISS.load_local(INDEX_FOLDER, embeddings, allow_dangerous_deserialization=True)
+    return _vectorstore
 
 def respond(
     message,
@@ -15,7 +33,26 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    """
+    Called on each user message. We'll do a retrieval step (RAG)
+    to get relevant context, then feed it into the system message
+    before calling the InferenceClient.
+    """
+    # 1. Retrieve top documents from FAISS
+    vectorstore = load_vectorstore()
+    top_docs = vectorstore.similarity_search(message, k=3)
+
+    # Build context string from the docs
+    context_texts = []
+    for doc in top_docs:
+        context_texts.append(doc.page_content)
+    context_str = "\n".join(context_texts)
+
+    # 2. Augment the original system message with retrieved context
+    augmented_system_message = system_message + "\n\n" + f"Relevant context:\n{context_str}"
+
+    # 3. Convert (history) into messages
+    messages = [{"role": "system", "content": augmented_system_message }]
 
     for val in history:
         if val[0]:
@@ -23,8 +60,10 @@ def respond(
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
+    # Finally, add the new user message
     messages.append({"role": "user", "content": message})
 
+    # 4. Stream from the InferenceClient
     response = ""
 
     for message in client.chat_completion(

build_index.py

@@ -0,0 +1,98 @@
+# build_index.py
+import os
+import requests
+from langchain_community.document_loaders import TextLoader, PyPDFLoader, UnstructuredURLLoader, UnstructuredHTMLLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import Document
+from langchain_community.docstore.document import Document as LCDocument
+
+DOCS_PATH = "docs"
+INDEX_PATH = "faiss_index"
+
+def fetch_html_with_timeout(url: str, timeout=5) -> list[Document]:
+    """
+    Downloads the page content with a timeout, then parses it using UnstructuredHTMLLoader.
+    Returns a list of Documents (can be 1 or multiple if you want to split further).
+    """
+    try:
+        response = requests.get(url, timeout=timeout)
+        response.raise_for_status()  # raise HTTPError if not 200
+    except Exception as e:
+        print(f"[Timeout/Fetch Error] Skipping {url}: {e}")
+        return []
+
+    # Write the HTML to a temporary file so we can load it with UnstructuredHTMLLoader
+    # (unstructured requires a file-like, we can do in-memory, but let's keep it simple)
+    temp_filename = "temp_html_file.html"
+    with open(temp_filename, "w", encoding="utf-8") as f:
+        f.write(response.text)
+    
+    loader = UnstructuredHTMLLoader(temp_filename)
+    docs = loader.load()  # returns a list of Document objects
+    for doc in docs:
+        doc.metadata["source"] = url
+    return docs
+
+def load_web_docs(urls: list[str], timeout=5) -> list[Document]:
+    all_docs = []
+    for url in urls:
+        print(f"Fetching: {url}")
+        docs_from_url = fetch_html_with_timeout(url, timeout=timeout)
+        all_docs.extend(docs_from_url)
+    return all_docs
+
+def load_documents(docs_path=DOCS_PATH):
+    all_docs = []
+
+    for file_name in os.listdir(docs_path):
+        file_path = os.path.join(docs_path, file_name)
+        print(f"Processing file: {file_name}")  # Debug log
+
+        # 1) Text files
+        if file_name.lower().endswith(".txt"):
+            print(" -> Loading as .txt")
+            loader = TextLoader(file_path, encoding="utf-8")
+            loaded_docs = loader.load()
+            all_docs.extend(loaded_docs)
+            print(f" -> Loaded {len(loaded_docs)} docs from {file_name}")
+
+        # 2) PDF
+        elif file_name.lower().endswith(".pdf"):
+            print(" -> Loading as .pdf")
+            loader = PyPDFLoader(file_path)
+            pdf_docs = loader.load_and_split()
+            all_docs.extend(pdf_docs)
+            print(f" -> Loaded {len(pdf_docs)} docs from {file_name}")
+
+        # 3) URLs
+        elif file_name.lower().endswith(".urls"):
+            print(" -> Loading as .urls")
+            with open(file_path, "r", encoding="utf-8") as f:
+                urls = [line.strip() for line in f if line.strip()]
+            print(f" -> Found {len(urls)} URLs in {file_name}")
+            if urls:
+                web_docs = load_web_docs(urls, timeout=5)
+                print(f" -> Loaded {len(web_docs)} web docs from URLs")
+                all_docs.extend(web_docs)
+
+        else:
+            print(" -> Skipped: unrecognized file type.")
+
+    return all_docs
+
+def build_faiss_index():
+    documents = load_documents()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    splitted_docs = text_splitter.split_documents(documents)
+
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cuda"})
+    vectorstore = FAISS.from_documents(splitted_docs, embeddings)
+
+    os.makedirs(INDEX_PATH, exist_ok=True)
+    vectorstore.save_local(INDEX_PATH)
+    print(f"Vector index saved to {INDEX_PATH}")
+
+if __name__ == "__main__":
+    build_faiss_index()

docs/biography.txt

@@ -0,0 +1,143 @@
+Prakash Naikade
+Computer Vision & Machine Learning Engineer
+
+PROFILE
+I am passionate about Machine Learning, especially Computer Vision and Generative AI. I have hands-on experience
+from academia and industry. My research interests span in the broad areas of 3D-Reconstruction, Scene Understanding, 
+Neural Rendering, Radiance Field, Motion Capture, Digital Twins, LLMs, AR/VR, and generally Computer Vision,
+Computer Graphics, GenAI, Human Computer Interaction, Deep Learning, Machine Learning, and Data Science, to
+solve real-world problems with impactful AI aided solutions.
+
+EDUCATION
+MS Media Informatics Saarland University, Germany
+Oct 2020 – Present
+Grade: 1.6/5.0 (1.0 being the best possible score) 
+Thesis: Novel View Synthesis of Structural Color Objects Created by Laser Markings. (1.3)
+Relevant Courses: Computer Graphics, Image Processing & Computer Vision, Neural Networks: Theory & Implementation, 
+High-Level Computer Vision, Statistics with R, Adversarial Reinforcement Learning, Human Computer Interaction,
+Games & Interactive Media. 
+[Audited]: Geometric Modeling, Machine Learning, AI, Ethics for Nerds
+
+BEng Computer Engineering Pune University, India
+June 2011 – May 2015
+Grade: 65% (First Class) 
+Thesis: Secure Data Storage on Multi-Cloud Using DNA Based Cryptography.
+Relevant Courses: Data Structures and Algorithms, Design & Analysis of Algorithms, Software Architecture, Software
+Engineering, Software Testing & Quality Assurance, Microprocessors & Microcontrollers
+
+PROFESSIONAL EXPERIENCE
+Junior Researcher (HiWi) Saarbrücken, Germany
+August-Wilhelm Scheer Institute 
+Sept 2023 – Dec 2024
+• Worked on several applied research projects, including MediHopps, iperMö, FläKI and VuLCAn.
+• Implemented advanced deep learning methods for human action recognition (HAR) and body pose estimation (HPE),
+and delivered detailed performance evaluations of these models, along with a trained HAR model (ST-GCN++) for
+custom rehabilitation exercise data captured in the lab.
+• Contributed significantly to the feature extraction, generation, and visualization of furniture functionalities in the
+Python codebase for the iperMö project, developing an AR application to turn individual furniture wishes into reality.
+• Systematic Literature Research and Reviews, Project Proposals and Scientific Literature Writing.
+• Generally worked on computer vision, computer graphics, and machine/deep learning tasks like human pose esti-
+mation, human action recognition, and some XR tasks.
+
+Research Assistant Saarbrücken, Germany
+AIDAM, Max Planck Institute for Informatics Advisor: Dr Vahid Babaei 
+July 2023 – Aug 2024
+• Worked on Radiance Field methods for Novel View Synthesis of structural color objects created by laser markings.
+• Benchmarked SOTA radiance methods for synthetic scene involving Structural Color Object created in Blender.
+• Developed capture setup to capture highly reflective and shiny structural color paintings on metal substrates.
+• Improved the scene optimization using geometric prior and Anisotropy Regularizer in 3D Gaussian-Splatting method.
+• Presented comprehensive experiments to demonstrate methods for simulating structural color objects before printing
+them using only captured images of laser-printed primaries.
+• Facilitated interactive visualization of view-dependent structural color objects in web viewer.
+
+Computer Vision Intern Münster, Germany
+BASF-Coatings GmbH 
+March 2023 – May 2023
+• Developed dataset for adhesive test and corrosion detection on images of test panels of metal substrates.
+• Developed framework and trained YOLOv8 model for adhesive tests’ detection and UNet for corrosion detection
+using created dataset for automation project.
+
+Computer Vision Intern Aachen, Germany
+Fenris GmbH 
+May 2022 – Sept 2023
+• Contributed to markerless motion capture solutions using single and multiple cameras for athlete motion tracking
+and analysis.
+• Conducted a comprehensive literature research and review focused on deep learning approaches for human pose
+estimation and benchmarked SOTA approaches for domain specific video data.
+• Worked on different tasks such as camera calibration, deep learning based human pose estimation & golf sequence
+detection, estimating joint angles from 3D body poses, comparing two pose sequences and visualization of results
+in Blender and Unity.
+
+Indian Civil Services Exam Preparation 
+Jun 2015 – July 2019
+During the preparation of this exam, I gained Under-Graduate level knowledge of Anthropology, Polity, Governance,
+Indian Constitution, Social Justice, International Relations, Economics (Macro), Indian & World Geography, Indian &
+World History, Indian Culture & Society, Environment, and Ethics. (Overall pass percentage of candidates ≈ 0.1%)
+
+SKILLS
+• Programming: Python, C#, C++, R, SQL, Matlab
+• Frameworks: PyTorch, TensorFlow, NumPy, Pandas, SKLearn, OpenCV, Open3D, Matplotlib, HuggingFace
+• Tools: Conda, Jupyter Notebook, Git, Unity, Blender, Metashape, Colmap, Meshlab, Docker, Slurm/HPC, DevOps
+• OS: Linux, Windows, Shell/Dos Scripting
+• Concepts: Regression, k-NN, k-Means Clustering, PCA, SVM, Neural Networks, CNN, RNN, LSTM, Transformers,
+ViT, CLIP, Autoencoders, VAE, GAN, Diffusion Models, LLMs, NLP, GPT, Prompt Engineering, LangChain, 2D/3D
+Image Processing, Object Detection, Classification, Localization, Segmentation, NeRF, 3DGS, 3D Reconstruction,
+Scene Understanding, Scene Interaction, HCI, XR, Reinforcement Learning
+
+PROJECTS
+Learn-LLMs GenAI, Information Retrieval
+Getting a hands-on experience of using different LLM models and tools, to understand the finetuning, data preparation,
+evaluation & other techniques related to LLMs such as RAG.
+
+Diffusion Models Computer Vision, GenAI
+This Project is a basic implementation of Diffusion Model to understand how diffusion works.
+
+Human Action Recognition (HAR) Computer Vision
+Investigating the performance of different deep learning models and their ensembles used for HAR in still images.
+
+Image Segmentation on PASCAL VOC and Cityscapes Datasets Computer Vision
+Training and Evaluation of CNNs like UNet, RU-Net and R2U-Net for Image Segmentation.
+
+COVID-19 Detection Computer Vision
+TensorFlow implementation of model based on ResNet50 architecture for COVID-19 detection on Chest X-rays using
+dataset sourced from Kaggle.
+
+Object Detection Computer Vision
+Training an object detection model on custom dataset (Oxford Pets dataset) using TensorFlow Object Detection API 2.
+
+Easy Flappy Bird Game Development
+An simple implementation of Flappy Bird game using Unity and C#.
+
+Roman Villa Nennig Bot - Your virtual guide to Roman Villa Nennig NLP
+This chatbot helps the user throughout their journey of visiting a museum of the Roman Villa Nennig, developed using
+Google Cloud, Dialogflow Essentials and Telegram.
+
+Ludwig Palette - an AR painting game AR/VR
+App developed in Unity and C# allows visitors of Ludwigskirche to explore its architecture by painting on its surfaces
+and understand the intricacies of sculptures inside the church.
+
+Mini-RayTracer Computer Graphics
+Simple ray tracing engine developed in C++.
+
+Synthetic Dataset Computer Graphics
+Generate simple 3D rendered datasets in Blender and Unity.
+
+PUBLICATIONS
+• Secure Data Storage on Multi-Cloud Using DNA Based Cryptography. D Zingade, S Dhuri, P Naikade, N Gade,
+A Teke, International Journal of Advance Engineering and Research Development March 2015
+
+CERTIFICATIONS
+• Kaggle: Python, ML, Pandas, Feature Engineering, Data Visualization, Data Cleaning, SQL, Reinforcement Learning
+& Game AI, Time Series
+• Udacity: C++, AWS ML Foundations
+• Coursera: Mathematics for Machine Learning and Data Science, Structuring ML Project, Neural Network and Deep
+Learning, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
+• Udemy: Foundations of MR, XR, VR Development on Quest headsets with Meta’s Presence Platform and Unity.
+• DataCamp: Intermediate R, Data in R
+• Memgraph: Graph Analytics
+
+LANGUAGES
+English (Fluent), Hindi (Fluent), Marathi (Native), German (Elementary)
+
+HOBBIES
+Biking, Running, Hiking, Movies, Music

docs/websites.urls

@@ -0,0 +1,3 @@
+https://www.linkedin.com/in/prakashknaikade/
+https://prakashknaikade.github.io/
+

faiss_index/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37c0a27bc965c078f14aa6d8bd9d7680624df804249cae9a5ed62867e1f9cb14
+size 25988

temp_html_file.html

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+<span class=avatar-emoji>📽</span></div><div class="portrait-title dark:text-white"><div class="text-3xl font-bold mb-2 mt-6">Prakash Naikade</div><h3 class="font-semibold mb-1">Computer Vision and Machine Learning<br>Research Engineer</h3><div class=mb-2><a href=https://www.mpi-inf.mpg.de/home target=_blank rel=noopener><div>Max Planck Institute for Informatics</div></a></div><div class=mb-2><a href=https://saarland-informatics-campus.de/en/ target=_blank rel=noopener><div>Saarland University</div></a></div></div><ul class="network-icon dark:text-zinc-100"><li><a href=mailto:[email protected] aria-label=at-symbol data-toggle=tooltip data-placement=top title="E-mail Me"><svg style="height:1.5rem" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-width="1.5" d="M16.5 12a4.5 4.5.0 11-9 0 4.5 4.5.0 019 0zm0 0c0 1.657 1.007 3 2.25 3S21 13.657 21 12a9 9 0 10-2.636 6.364M16.5 12V8.25"/></svg></a></li><li><a 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Computer Vision and Machine Learning Research Engineer. He is passionate about Machine Learning, especially Computer Vision & Generative AI. He has hands on experience from academia and industry. His research interests are in Computer Vision, Computer Graphics, LLMs, Human Computer Interaction, Deep/Machine Learning & Data Science, to solve real world problems with impactful AI aided solutions.<br>In his free time, he likes to run, bike, hike, watch movies, and listen to music.</p></div></div><a href=uploads/resume.pdf target=_blank class="inline-flex items-center px-4 py-2 text-sm font-medium text-gray-900 bg-white border border-gray-200 rounded-lg hover:bg-gray-100 hover:text-primary-700 focus:z-10 focus:ring-4 focus:outline-none focus:ring-gray-200 focus:text-primary-700 dark:bg-gray-800 dark:text-gray-400 dark:border-gray-600 dark:hover:text-white dark:hover:bg-gray-700 dark:focus:ring-gray-700"><svg class="w-3.5 h-3.5 me-2.5" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" fill="currentcolor" viewBox="0 0 20 20"><path d="M14.707 7.793a1 1 0 00-1.414.0L11 10.086V1.5a1 1 0 00-2 0v8.586L6.707 7.793A1 1 0 105.293 9.207l4 4a1 1 0 001.416.0l4-4a1 1 0 00-.002-1.414z"/><path d="M18 12h-2.55l-2.975 2.975a3.5 3.5.0 01-4.95.0L4.55 12H2a2 2 0 00-2 2v4a2 2 0 002 2h16a2 2 0 002-2v-4a2 2 0 00-2-2zm-3 5a1 1 0 110-2 1 1 0 010 2z"/></svg> Download CV</a><div class="grid grid-cols-2 gap-4 justify-between mt-6 dark:text-gray-300"><div><div class="section-subheading mb-3">Interests</div><ul class="list-disc list-inside space-y-1 pl-5"><li>Computer Vision</li><li>Computer Graphics</li><li>LLMs</li><li>Deep/Machine Learning</li><li>AR/VR, HCI</li><li>Data Science, AI</li></ul></div><div><div class="section-subheading mb-3">Education</div><ul><li class="flex items-start gap-3"><svg style="" class="flex-shrink-0 w-5 h-5 me-2 mt-1" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M4.26 10.147a60.436 60.436.0 00-.491 6.347A48.627 48.627.0 0112 20.904a48.627 48.627.0 018.232-4.41 60.46 60.46.0 00-.491-6.347m-15.482.0a50.57 50.57.0 00-2.658-.813A59.905 59.905.0 0112 3.493a59.902 59.902.0 0110.399 5.84 51.39 51.39.0 00-2.658.814m-15.482.0A50.697 50.697.0 0112 13.489a50.702 50.702.0 017.74-3.342M6.75 15a.75.75.0 100-1.5.75.75.0 000 1.5m0 0v-3.675A55.378 55.378.0 0112 8.443m-7.007 11.55A5.981 5.981.0 006.75 15.75v-1.5"/></svg><div class=description><p class=course>Master Media Informatics</p><p class=text-sm>Saarland University</p></div></li><li class="flex items-start gap-3"><svg style="" class="flex-shrink-0 w-5 h-5 me-2 mt-1" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M4.26 10.147a60.436 60.436.0 00-.491 6.347A48.627 48.627.0 0112 20.904a48.627 48.627.0 018.232-4.41 60.46 60.46.0 00-.491-6.347m-15.482.0a50.57 50.57.0 00-2.658-.813A59.905 59.905.0 0112 3.493a59.902 59.902.0 0110.399 5.84 51.39 51.39.0 00-2.658.814m-15.482.0A50.697 50.697.0 0112 13.489a50.702 50.702.0 017.74-3.342M6.75 15a.75.75.0 100-1.5.75.75.0 000 1.5m0 0v-3.675A55.378 55.378.0 0112 8.443m-7.007 11.55A5.981 5.981.0 006.75 15.75v-1.5"/></svg><div class=description><p class=course>BEng Computer Engineering</p><p class=text-sm>Pune University</p></div></li></ul></div></div></div></div></section><section id=section-markdown class="relative hbb-section blox-markdown" style="padding:2rem 0"><div class=home-section-bg></div><div class="flex flex-col items-center max-w-prose mx-auto gap-3 justify-center"><div class="mb-6 text-3xl font-bold text-gray-900 dark:text-white">📚 My Research</div><div class="prose prose-slate lg:prose-xl dark:prose-invert max-w-prose"><p>I am junior researcher at August-Wilhelm Scheer Institute. I worked on my master thesis at Max Planck Institute for Informatics.</p><p>My research interests span in the broad areas of Neural Rendering, Radiance Field Methods, Novel View Synthesis, Motion Capture, 3D-Reconstruction, Scene Understanding, Scene Interaction, Digital Twins, AR/VR, GenAI, LLMs, and generally Computer Vision, Computer Graphics, HCI, Deep/Machine Learning & Data Science, to solve the real world problems with impactful AI aided solutions.</p><p>Please reach out to collaborate 😃</p></div></div></section><section id=papers class="relative hbb-section blox-collection" style="padding:2rem 0"><div class=home-section-bg></div><div class="flex flex-col items-center max-w-prose mx-auto gap-3 justify-center"><div class="mb-6 text-3xl font-bold text-gray-900 dark:text-white">📌 Featured Publications</div></div><div class="flex flex-col items-center"><div class="container px-8 mx-auto xl:px-5 py-5 lg:py-8 max-w-screen-lg"><div class="grid gap-10 md:grid-cols-2 lg:gap-10"><div class="group cursor-pointer"><div class="overflow-hidden rounded-md bg-gray-100 transition-all hover:scale-105 dark:bg-gray-800"><a class="relative block aspect-video" href=/publications/nvs_structural_color_object/><img alt="Novel View Synthesis of Structural Color Objects Created by Laser Markings" class="object-fill transition-all" data-nimg=fill decoding=async fetchpriority=high height=540 loading=lazy src=/publications/nvs_structural_color_object/pipeline_gsplat_hu96489e5fcef0c77704c1632d7669e979_114204_1fc6cd3f9e680b12ea1b5c744a1490c9.webp style=position:absolute;height:100%;width:100%;inset:0;color:transparent width=960></a></div><div><div><div class="flex gap-3"><a href=/tags/novel-view-synthesis/><span class="inline-block text-xs font-medium tracking-wider uppercase mt-5 text-primary-700 dark:text-primary-300">Novel View Synthesis</span></a></div><h2 class="text-lg font-semibold leading-snug tracking-tight mt-2 dark:text-white"><a href=/publications/nvs_structural_color_object/><span class="bg-gradient-to-r from-primary-200 to-primary-100 bg-[length:0px_10px] bg-left-bottom bg-no-repeat transition-[background-size] duration-500 hover:bg-[length:100%_3px] group-hover:bg-[length:100%_10px] dark:from-primary-800 dark:to-primary-900">Novel View Synthesis of Structural Color Objects Created by Laser Markings</span></a></h2><div class=grow><p class="mt-2 line-clamp-3 text-sm text-gray-500 dark:text-gray-400"><a href=/publications/nvs_structural_color_object/>This work marks the beginning of novel view synthesis for structural color objects and opens up potential avenues for various future research directions and endeavors. Moreover, this thesis will act as a foundational guide for novice researchers interested in exploring the novel view synthesis of specular and shiny objects with high view-dependent colors, commonly referred to as Structural Colors and Pearlescent Colors.</a></p></div><div class=flex-none><div class="mt-3 flex items-center space-x-3 text-gray-500 dark:text-gray-400 cursor-default"><time class="truncate text-sm" datetime=2024-08-08>Aug 8, 2024</time></div></div></div></div></div><div class="group cursor-pointer"><div class="overflow-hidden rounded-md bg-gray-100 transition-all hover:scale-105 dark:bg-gray-800"><a class="relative block aspect-video" href=/publications/dna_based_cryptography/><img alt="Secure Data Storage on Multi-Cloud Using DNA Based Cryptography" class="object-fill transition-all" data-nimg=fill decoding=async fetchpriority=high height=540 loading=lazy src=/publications/dna_based_cryptography/featured_hu039fe7ded9e1ba0553f85e9e483f7848_223565_18c0a9ddd37a9a74074a1a294b75c01d.webp style=position:absolute;height:100%;width:100%;inset:0;color:transparent width=960></a></div><div><div><div class="flex gap-3"><a href=/tags/dna-based-cryptography/><span class="inline-block text-xs font-medium tracking-wider uppercase mt-5 text-primary-700 dark:text-primary-300">DNA Based Cryptography</span></a></div><h2 class="text-lg font-semibold leading-snug tracking-tight mt-2 dark:text-white"><a href=/publications/dna_based_cryptography/><span class="bg-gradient-to-r from-primary-200 to-primary-100 bg-[length:0px_10px] bg-left-bottom bg-no-repeat transition-[background-size] duration-500 hover:bg-[length:100%_3px] group-hover:bg-[length:100%_10px] dark:from-primary-800 dark:to-primary-900">Secure Data Storage on Multi-Cloud Using DNA Based Cryptography</span></a></h2><div class=grow><p class="mt-2 line-clamp-3 text-sm text-gray-500 dark:text-gray-400"><a href=/publications/dna_based_cryptography/>This paper demonstrates use of DNA based cryptography to ensure secure data storage on multi-cloud to address service availability failure risks and possibilities of malicious attack.</a></p></div><div class=flex-none><div class="mt-3 flex items-center space-x-3 text-gray-500 dark:text-gray-400 cursor-default"><time class="truncate text-sm" datetime=2015-03-01>Mar 1, 2015</time></div></div></div></div></div></div></div></div></section><section id=section-collection class="relative hbb-section blox-collection" style="padding:2rem 0"><div class=home-section-bg></div><div class="flex flex-col items-center max-w-prose mx-auto gap-3 justify-center"><div class="mb-6 text-3xl font-bold text-gray-900 dark:text-white">📜 Recent Publications</div></div><div class="flex flex-col items-center"><div class="mt-16 sm:mt-20 container max-w-3xl w-full"><div class="flex flex-col space-y-3"><div class="pub-list-item view-citation" style=margin-bottom:1rem><i class="far fa-file-alt pub-icon" aria-hidden=true></i>
+<span class="article-metadata li-cite-author"><span class=font-bold>Prakash Naikade</span>
+</span>(2024).
+<a href=/publications/nvs_structural_color_object/ class=underline>Novel View Synthesis of Structural Color Objects Created by Laser Markings</a>.
+(MASTER THESIS).<div class="flex space-x-3"><a class="hb-attachment-link hb-attachment-small" href=/publications/nvs_structural_color_object/NVS_Structural_Color_Objects_compressed.pdf target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M19.5 14.25v-2.625A3.375 3.375.0 0016.125 8.25h-1.5A1.125 1.125.0 0113.5 7.125v-1.5A3.375 3.375.0 0010.125 2.25H8.25m0 12.75h7.5m-7.5 3H12M10.5 2.25H5.625c-.621.0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621.0 1.125-.504 1.125-1.125V11.25a9 9 0 00-9-9"/></svg>
+PDF
+</a><a class="hb-attachment-link hb-attachment-small" href="https://www.dropbox.com/scl/fo/9btslfn5y71lb6ct4jy62/AJ-6C-8QmycAND85arMPBDQ?rlkey=tqq444p2k608el58aoq5tzbvf&amp;e=1&amp;st=qcq9xn97&amp;dl=0" target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M20.25 6.375c0 2.278-3.694 4.125-8.25 4.125S3.75 8.653 3.75 6.375m16.5.0c0-2.278-3.694-4.125-8.25-4.125S3.75 4.097 3.75 6.375m16.5.0v11.25c0 2.278-3.694 4.125-8.25 4.125s-8.25-1.847-8.25-4.125V6.375m16.5.0v3.75m-16.5-3.75v3.75m16.5.0v3.75C20.25 16.153 16.556 18 12 18s-8.25-1.847-8.25-4.125v-3.75m16.5.0c0 2.278-3.694 4.125-8.25 4.125s-8.25-1.847-8.25-4.125"/></svg>
+Dataset
+</a><a class="hb-attachment-link hb-attachment-small" href=/publications/nvs_structural_color_object/taylor_simulation_2.mp4 target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-width="1.5" d="m15.75 10.5 4.72-4.72a.75.75.0 011.28.53v11.38a.75.75.0 01-1.28.53l-4.72-4.72M4.5 18.75h9a2.25 2.25.0 002.25-2.25v-9A2.25 2.25.0 0013.5 5.25h-9A2.25 2.25.0 002.25 7.5v9a2.25 2.25.0 002.25 2.25z"/></svg>
+Video
+</a><a class="hb-attachment-link hb-attachment-small" href=https://prakashknaikade.github.io/StructColorPaintingViewer/ target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M13.19 8.688a4.5 4.5.0 011.242 7.244l-4.5 4.5a4.5 4.5.0 01-6.364-6.364l1.757-1.757m13.35-.622 1.757-1.757a4.5 4.5.0 00-6.364-6.364l-4.5 4.5a4.5 4.5.0 001.242 7.244"/></svg>
+Demo (3D Web Viewer)</a>
+<a class="hb-attachment-link hb-attachment-small" href="https://www.dropbox.com/scl/fi/in79kjuyn2h8b2mmd847x/NVS_Structural_Color_Objects.pdf?rlkey=sdv7lxi3rymelltdv8u7sgl1d&amp;st=25ixlxzf&amp;dl=0" target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M13.19 8.688a4.5 4.5.0 011.242 7.244l-4.5 4.5a4.5 4.5.0 01-6.364-6.364l1.757-1.757m13.35-.622 1.757-1.757a4.5 4.5.0 00-6.364-6.364l-4.5 4.5a4.5 4.5.0 001.242 7.244"/></svg>
+PDF (HQ)</a></div></div><div class="pub-list-item view-citation" style=margin-bottom:1rem><i class="far fa-file-alt pub-icon" aria-hidden=true></i>
+<span class="article-metadata li-cite-author"><span>D Zingade</span>, <span>S Dhuri</span>, <span class=font-bold>Prakash Naikade</span>, <span>N Gade</span>, <span>A Teke</span>
+</span>(2015).
+<a href=/publications/dna_based_cryptography/ class=underline>Secure Data Storage on Multi-Cloud Using DNA Based Cryptography</a>.
+IJAERD.<div class="flex space-x-3"><a class="hb-attachment-link hb-attachment-small" href=https://github.com/prakashknaikade/Bachelor-Thesis/blob/main/2.%20Paper-Secure%20Data%20Storage%20On%20Multi-cloud%20Using%20DNA%20Based%20Cryptography.pdf target=_blank rel=noopener><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M19.5 14.25v-2.625A3.375 3.375.0 0016.125 8.25h-1.5A1.125 1.125.0 0113.5 7.125v-1.5A3.375 3.375.0 0010.125 2.25H8.25m0 12.75h7.5m-7.5 3H12M10.5 2.25H5.625c-.621.0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621.0 1.125-.504 1.125-1.125V11.25a9 9 0 00-9-9"/></svg>
+PDF
+</a><a class="hb-attachment-link hb-attachment-small" href=/publications/dna_based_cryptography/cite.bib target=_blank data-filename=/publications/dna_based_cryptography/cite.bib><svg style="height:1em" class="inline-block" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path fill="none" stroke="currentcolor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M15.75 17.25v3.375c0 .621-.504 1.125-1.125 1.125h-9.75A1.125 1.125.0 013.75 20.625V7.875c0-.621.504-1.125 1.125-1.125H6.75a9.06 9.06.0 011.5.124m7.5 10.376h3.375c.621.0 1.125-.504 1.125-1.125V11.25c0-4.46-3.243-8.161-7.5-8.876a9.06 9.06.0 00-1.5-.124H9.375c-.621.0-1.125.504-1.125 1.125v3.5m7.5 10.375H9.375A1.125 1.125.0 018.25 16.125v-9.25m12 6.625v-1.875A3.375 3.375.0 0016.875 8.25h-1.5A1.125 1.125.0 0114.25 7.125v-1.5A3.375 3.375.0 0010.875 2.25H9.75"/></svg>
+Cite</a></div></div></div></div></div></section><section id=ama class="relative hbb-section blox-markdown" style="padding:2rem 0"><div class=home-section-bg></div><div class="flex flex-col items-center max-w-prose mx-auto gap-3 justify-center"><div class="mb-6 text-3xl font-bold text-gray-900 dark:text-white">💬 Ask Me Anything</div><div class="prose prose-slate lg:prose-xl dark:prose-invert max-w-prose">Click on the 👉
+to ask questions about me.</div></div></section><section id=news class="relative hbb-section blox-collection" style="padding:48 114 101 109"><div class=home-section-bg></div><div class="flex flex-col items-center max-w-prose mx-auto gap-3 justify-center"><div class="mb-6 text-3xl font-bold text-gray-900 dark:text-white">📰 Recent News</div></div><div class="flex flex-col items-center"><div class="mt-16 sm:mt-20 w-fit"><div class="flex max-w-3xl flex-col space-y-16"><article class="md:grid md:grid-cols-4 md:items-baseline"><div class="md:col-span-3 group relative flex flex-col items-start"><h2 class="text-base font-semibold tracking-tight text-zinc-800 dark:text-zinc-100"><div class="absolute -inset-x-4 -inset-y-6 z-0 scale-95 bg-zinc-50 opacity-0 transition group-hover:scale-100 group-hover:opacity-100 dark:bg-zinc-800/50 sm:-inset-x-6 sm:rounded-2xl"></div><a href=/post/job/><span class="absolute -inset-x-4 -inset-y-6 z-20 sm:-inset-x-6 sm:rounded-2xl"></span><span class="relative z-10">🧠 Open to work</span></a></h2><time class="md:hidden relative z-10 order-first mb-3 flex items-center text-sm text-zinc-400 dark:text-zinc-500 pl-1" datetime=2024-11-01>Nov 1, 2024</time><p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">Looking for Computer Vision and Machine Learning Research Engineer Roles</p><div aria-hidden=true class="relative z-10 mt-4 flex items-center text-sm font-medium text-primary-500">Read more<svg aria-hidden="true" class="ml-1 h-4 w-4 stroke-current" fill="none" viewBox="0 0 16 16"><path d="M6.75 5.75 9.25 8l-2.5 2.25" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5"/></svg></div></div><time class="mt-1 hidden md:block relative z-10 order-first mb-3 flex items-center text-sm text-zinc-400 dark:text-zinc-500" datetime=2024-11-01>Nov 1, 2024</time></article></div></div></div></section></div><div class=page-footer><footer class="container mx-auto flex flex-col justify-items-center text-sm leading-6 mt-24 mb-4 text-slate-700 dark:text-slate-200"><p class="powered-by text-center">© 2024 Prakash. This work is licensed under <a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank>CC BY NC ND 4.0</a></p><p class="powered-by footer-license-icons"><a href=https://creativecommons.org/licenses/by-nc-nd/4.0 rel="noopener noreferrer" target=_blank aria-label="Creative Commons"><i class="fab fa-creative-commons fa-2x" aria-hidden=true></i>
+<i class="fab fa-creative-commons-by fa-2x" aria-hidden=true></i>
+<i class="fab fa-creative-commons-nc fa-2x" aria-hidden=true></i>
+<i class="fab fa-creative-commons-nd fa-2x" aria-hidden=true></i></a></p><p class="powered-by text-center">Published with <a href="https://hugoblox.com/?utm_campaign=poweredby" target=_blank rel=noopener>Hugo Blox Builder</a> — the free, <a href=https://github.com/HugoBlox/hugo-blox-builder target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></body></html>

websites.urls

@@ -0,0 +1,17 @@
+https://www.linkedin.com/in/prakashknaikade/
+https://prakashknaikade.github.io/
+https://prakashknaikade.github.io/project/ludwig-palette/
+https://prakashknaikade.github.io/project/diffusion-models/
+https://prakashknaikade.github.io/publications/nvs_structural_color_object/
+https://prakashknaikade.github.io/project/mini-ray-tracer/
+https://prakashknaikade.github.io/project/
+https://prakashknaikade.github.io/publication_types/
+https://prakashknaikade.github.io/project/easy-flappy-bird/
+https://prakashknaikade.github.io/event/
+https://prakashknaikade.github.io/experience/
+https://prakashknaikade.github.io/project/llms/
+https://prakashknaikade.github.io/project/har/
+https://prakashknaikade.github.io/event/example/
+https://prakashknaikade.github.io/publications/dna_based_cryptography/
+https://prakashknaikade.github.io/project/chatbot/
+https://prakashknaikade.github.io/teaching/