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--- | ||
date: 2024-10-22 | ||
publishDate: 2024-10-22 | ||
external_link: "" | ||
image: | ||
caption: Stealth-VPN | ||
focal_point: Smart | ||
slides: example | ||
summary: A stunnel based virtual private network service for unrestricted internet access | ||
tags: | ||
- Opensource | ||
title: Stealth-VPN for Unrestricted Network Access | ||
links: | ||
- icon_pack: fab | ||
icon: github | ||
name: Source | ||
url: 'https://github.com/prasenjit52282/docker-stealth-openvpn' | ||
--- | ||
The repository implements a scalable non-blockable VPN service to enable unrestricted internet within an organisation that restricts network via firewalls. The clients are oblivious to the interanl working process and only route traffic through the local socks5 proxy deployed within the organisation. The proxy host forward all packet via a stunnel to the open internet (VPN server). Moreover, the local openssh server allows outgoing ssh connections from the restricted network. | ||
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* VPN server must allows inbound connection to port: 443 | ||
* Local Proxy must allows outbound connection to port: 443, and inbound connection to ports: 2000 (socks5), 2002 (sshd) | ||
* End user must use appropiate proxy clients to connect to the local proxy server. Some tested clients are shown later |
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--- | ||
date: 2024-10-22 | ||
publishDate: 2024-10-22 | ||
external_link: "" | ||
image: | ||
caption: NPTEL Python Course | ||
focal_point: Smart | ||
slides: example | ||
summary: Organizing live interaction sessions for NPTEL course - The Joy of Computing using Python | ||
tags: | ||
- Teaching | ||
title: TA for The Joy of Computing using Python Course | ||
links: | ||
- icon_pack: fas | ||
icon: newspaper | ||
name: Recorded Sessions | ||
url: 'https://www.youtube.com/watch?v=gUtRrjyB4mw&list=PL4OzPVnKOQIPsrWrQvsXB_ACI0Qpaq9kP' | ||
--- | ||
Offering Teaching Assistanceship in [The Joy of Computing using Python](https://onlinecourses.nptel.ac.in/noc24_cs113/preview), NPTEL Course in Fall 2024 semester. The course will provide: | ||
* intermediate level knowledge of python programming language | ||
* hands-on problem solving experience (case-studies) with various open source libraries such as numpy, pandas, matplotlib etc. | ||
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Prerequisites: Basic concepts of Programming, beginner level C.<br> | ||
Mode: Online Every Tuesday, 6:00 PM - 8:00 PM (26 hours in total) |
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title: "Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India" | ||
date: 2024-09-16 | ||
publishDate: 2024-09-16 | ||
authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] | ||
publication_types: ["2"] | ||
abstract: "Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors such as traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues such as lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollution’s extent are missing. To fill this gap, we carried out a 6-months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of volatile organic compounds (VOCs) in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative Internet of Things (IoT) air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON), explicitly designed to meet the needs of these nations, considering factors such as cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot spots in low- and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring." | ||
featured: true | ||
publication: "ACM Journal on Computing and Sustainable Societies, Vol. 2, No. 3" | ||
links: | ||
- icon_pack: fas | ||
icon: scroll | ||
name: Link | ||
url: 'https://dl.acm.org/doi/full/10.1145/3685694' | ||
--- |
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title: "Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting" | ||
date: 2024-09-21 | ||
publishDate: 2024-09-21 | ||
authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] | ||
publication_types: ["1"] | ||
abstract: "Changing public perceptions and government regulations have led to the widespread use of low-cost air quality monitors in modern indoor spaces. Typically, these monitors detect air pollutants to augment the end user's understanding of her indoor environment. Studies have shown that having access to one's air quality context reinforces the user's urge to take necessary actions to improve the air over time. Thus, user's activities significantly influence the indoor air quality. Such correlation can be exploited to get hold of sensitive indoor activities from the side-channel air quality fluctuations. This study explores the odds of identifying eight indoor activities (i.e., enter, exit, fan on, fan off, AC on, AC off, gathering, eating) in a research lab with an in-house low-cost air quality monitoring platform named DALTON. Our extensive data collection and analysis over three months shows 97.7% classification accuracy in our dataset." | ||
featured: true | ||
publication: "ACM MobileHCI 2024" | ||
links: | ||
- icon_pack: fas | ||
icon: scroll | ||
name: Link | ||
url: 'https://dl.acm.org/doi/10.1145/3640471.3680243' | ||
--- |
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title: "Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities" | ||
date: 2024-10-21 | ||
publishDate: 2024-10-21 | ||
authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] | ||
publication_types: ["1"] | ||
abstract: "In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc." | ||
featured: true | ||
publication: "NeurIPS 2024" | ||
links: | ||
- icon_pack: fas | ||
icon: scroll | ||
name: Link | ||
url: 'https://arxiv.org/abs/2407.14501' | ||
- icon_pack: ai | ||
icon: open-data | ||
name: Open data | ||
url: 'https://github.com/prasenjit52282/dalton-dataset' | ||
--- |