1194 research outputs found
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Secure Device Provisioning Using SZTP
Get PDFInitial Situation
Managing many network devices takes a lot of effort and poses risks of inconsistency in configuration. Furthermore, time is needed to plug the device in, attach your computer and connect to the console for configuration. With automation, this process can be made much more efficient and reliable. The classical approach for this is called Zero Touch Provisioning (ZTP), meaning the device doesn’t have to be touched to configure it. Instead, it is registered in an inventory and can load a predefined configuration automatically.
Objective
The objective of this term project is to lay the groundworks for a network controller that handles zero touch provisioning of newly installed devices, as well as transferring configuration in case of 1-to-1 device replacement. The scope of the controller is limited to Cisco devices for this project, but it should be extendable to support various other devices. Furthermore, the controller should also be open for future extension, providing more functionality like ongoing configuration after the provisioning process.
Result
The authors developed a controller that bridges between inventory management and network device. On one end Netbox is used to manage device parameters and context-dependent configuration. Both are rendered into the target devices configuration using a template. On the other end the devices use DHCP to get SZTP redirect information for the controller. The provisioning controller provides an endpoint for SZTP-compliant devices to securely get the data needed to bootstrap themselves via HTTPS. The bootstrapping data includes firmware target version, download source and integrity hash as well as the configuration itself. During the devices lifetime our backup controller is used to automatically retrieve configuration backups. Nornir and Napalm are used to run the backup task on all devices registered in Netbox. In the event of a hardware failure, a replacement device can quickly be set up. By simply setting the configuration source device in Netbox the provisioning controller will automatically load the backup configuration
Post Quantum P2P Chat
No full textIntroduction
Our application, qChat, is a decentralized, peer-topeer chat application meant for future-proof privacy. It uses the latest encryption algorithms to ensure secret message exchange in the post-quantum era.
Quantum computing poses a significant threat to current encryption methods, particularly due to advancements like Shor's Algorithm. This algorithm, in theory, allows quantum computers to break widelyused encryption schemes such as RSA and ECC much faster than conventional computers.
Result
The solution involves developing a peer-to-peer chat application using post-quantum cryptography, eliminating reliance on external servers for data storage. The project has successfully created a prototype demonstrating peer-to-peer functionality with integrated post-quantum cryptography.
Conclusion
These developments are significant as they provide a practical approach to secure communication, setting a precedent in the field of quantum-resistant digital communication.
The insights and technologies developed in qChat lay the groundwork for future advancements in secure communications. This also marks a significant advancement in protecting the private sphere in an increasingly interconnected post-quantum world
Swisscom Design System - Server-Side-Rendering
Get PDFIntroduction
Swisscom has its own design system, the Swisscom Digital Experience (SDX). It consists of a component library, UX principles, design guidelines, documentation, and rules. In this design system, Swisscom offers web components, reusable styled building blocks, build using StencilJS. This research project investigates the integration of Server-Side rendering (SSR) with these components. When built with StencilJS and combined with SSR, web components are not interactive and do incorporate incorrect CSS styling. The project focuses on addressing these issues. Additionally, it seeks solutions for integrating these web components within the frameworks Angular and Next.js.
Approach
The methodology involved a series of experiments to tackle the challenges in implementing SSR with StencilJS. These experiments included resolving interactivity deficits and rendering inconsistencies within Stencil’s SSR framework, as well as exploring the integration of StencilJS web components in Angular and Next.js frameworks. The performance aspects of SSR were inspected in a simple Node.js environment using Express.js as a server and within an Angular application. The aim was to comprehensively evaluate the efficiency and responsiveness of SSR in different contexts.
Result
The findings revealed that manually adding ESM scripts post rendering resolved the interactivity issues. Incorrect CSS styling, linked to bugs in StencilJS, was addressed through a CSS-grid based workaround. The integration of StencilJS web components with Angular was achieved by defining Angular output targets and pre-rendering the components. Integrating these components into Next.js presented significant challenges. The investigation revealed that, in its current state, Next.js is not compatible with StencilJS. This necessitates the development of react wrapper generator aimed at encapsulating Stencil components within React components, enabling seamless integration into the Next.js framework. Regarding performance, SSR generally demonstrated a faster response than clientside rendering, particularly in the first contentful paint. However, this performance advantage was not consistent across all scenarios.
The outcome of this project showed that SSR is possible with StencilJS web components. However, we identified a number of remaining issues with the current tooling for Stencil SSR support, necessitating the development of custom solutions and workarounds. While the integration of StencilJS web components within Angular and Next.js is achievable, it demands significant setup and configuration, especially for Next.js. Nevertheless, as framework compatibility improves, the efficiency and user experience offered by SSR with StencilJS web components are expected to become more pronounced, highlighting the promising future of this technology in web development
Study Buddy
Get PDFThe emergence of large language models (LLM) changes the way we search for information. LLMs allow us to ask questions directly and receive answers in natural language. However, the knowledge of LLMs is limited to the information they have been trained on and is therefore often outdated. This limitation can be overcome by using the retrieval augmented generation (RAG) technique. This technique combines the user’s prompt with contextual information from a custom knowledge base before asking the LLM to generate an answer. The technique relies on semantic search using embeddings to find relevant content related to the user’s prompt in the knowledge base. RAG significantly improves the quality of the answers received from the LLM, especially when specific knowledge beyond what the LLM has been trained on is required.
The goal of this project was to implement a chatbot in Python and React that uses the RAG technique to answer a student's questions about lecture-related content, such as PDF lecture notes. In addition to providing correct answers, the bot should also list the sources used to generate the answers, allowing the student to verify the answer.
A chatbot was implemented using open- source components. The focus was on the LLama2 LLM family and LLamaIndex, a data framework in Python for connecting LLM. The chatbot was tested using slides from the C++ and OOP lectures at OST. We found that the RAG technique works well for answering questions based on text-based notes. However, we encountered difficulties in retrieving relevant context when dealing with bullet points and images in lecture slides, resulting in the LLM generating inaccurate answers. To reduce the impact of these limitations, we conducted tests to evaluate an embedding model that best fits our use case. During our testing we could not find any model, including Llama2, that performed adequate with languages other than English. This problem can only be addressed by fine tuning a model. So we focused our evaluation on English texts. We have also provided a guide for lecturers and students on how to use chatbots like this one efficiently
Bluetooth LE Audio Test Infrastructure
Get PDFThe introduction of Bluetooth® Core Specification 5.2 has paved the way for new audio transmission features, like LE Audio and Auracast™, revolutionizing the audio technology landscape. This bachelor thesis, conducted in collaboration with Sonova Holding AG, aims to develop a test infrastructure to facilitate the testing of Auracast™ features in Bluetooth® LE Audio devices.
Given the limited adoption and support for Auracast™, there were not many existing solutions for testing available at the start of this thesis. The main goal of this thesis is to extend the existing test infrastructure of Sonova with a controlled environment for testing broadcast receivers, ensuring they can effectively receive and process Auracast™ broadcasts.
The test setup comprises two Nordic nRF5340 Audio Development Kits, configured as broadcast transmitter and assistant. A .NET-based library manages communication between the boards and the existing test infrastructure, allowing to set up an Auracast™ environment. Existing sample applications for the Nordic boards were translated from C to C++ and customized to meet the project’s requirements. Additionally, a small console application was developed to test the setup locally.
The successful implementation of this project will contribute to the advancement of audio technologies, enhancing the use of end-user devices and enabling the development of innovative audio products. This thesis serves as a first step in adding Auracast™ capabilities to the test environment, which Sonova will continue to adjust to meet their evolving needs.
The future of Auracast™ appears promising, with many companies quickly adopting the trend. Some earbuds and smartphones already support the new features, and we are hopeful to see more widespread adoption in the near future
Development of a Deep Learning Model for Object Detection
No full textAutonomous cleaning robots are increasingly vital in keeping large-scale environments like warehouses and factories clean. This project presents the development and evaluation of a machine learning-based object detection system for KEMARO’s industrial cleaning robots. The primary objective was to address the robot’s current limitations in detecting small, valuable, or dangerous objects, such as keys or cables, which are overlooked by the current computer vision algorithms. The project explored state-of-the-art sensor technologies, including depth cameras, alongside advanced deep learning models such as YOLO and FastSAM.
High-quality real-world data was recorded using the Intel RealSense D435 with various configurations, including color recording and different resolutions, while maintaining the same approximate height and tilt to the camera placement of KEMARO’s K900 cleaning robot.
A key innovation of the study was the adaptation of YOLO for RGB-D input, allowing depth information to complement color image data. Data scarcity was addressed through a combination of real-world data collection and synthetic dataset generation using Blender. Various training configurations were tested to evaluate the generalization of the object detection models in real-world scenarios.
The results demonstrate that synthetic data can improve training outcomes when combined with real-world data, but challenges remain in model stability and the quality of depth information. Feeding RGB instead of grayscale images to the model achieved better results in object detection. While incorporating depth (RGB-D) increased model object detection performance when using perfect depth-maps, it proofed to be disadvantageous with imperfect and noisy real-world depth maps.
A roadmap for deep learning based object detection was also developed for KEMARO, providing recommendations for data governance, sensor improvements, and iterative hardware upgrades
Hate Speech Detection with LLM
Get PDFThe increasing acceptance of hate expression, particularly through the internet and social media, has significantly amplified the presence of online hate speech. This phenomenon negatively impacts psychological health and can incite violence, necessitating effective detection methods to prevent such content from being posted or shared. A recent trend involves leveraging the large context windows of new Large Language Models (LLMs) to enable In-Context Learning (ICL) where correct examples showcased in the prompt window. Studies have demonstrated the ability of LLMs in hate speech detection using few-shot strategies, comparing their performance to fine-tuned models. However, bias is a prevalent issue in pre-trained LLMs, requiring identification and mitigation specific to the task or dataset.
In this study, an experiment is conducted on text-based hate speech detection using ICL for two LLMs (GPT-4.o mini and Llama 3.1 8B) alongside a fine-tuned BERT model, forming a voting ensemble. The objective of this study is to assess whether bagging with majority voting can balance the strengths and weaknesses of individual models, thereby mitigating specific biases and achieving fairer, more accurate categorization of hate speech, offensive language and normal language.
The study results demonstrate that, while all LLMs exhibit different biases toward various labels (e.g., normal language being marked as offensive), majority voting effectively reduces bias and improves accuracy in categories where the participating models were close in performance. However, in scenarios where the performance of the participating models varies drastically, for example in the category offensive in this study, majority voting does not outperform the best single model. Furthermore, the voting ensemble encountered cases where a draw occurred, comprising about 5.4% of the total 13’229 data entries. Separate considerations were made for these ambiguous cases - either excluding them from the evaluation or replacing them with the best model’s decision. Excluding ambiguous data entries helped improve accuracy and reduce bias in misclassification but did not fully achieve the task's purpose of reaching a comprehensive result. This study underscores the potential of ensemble approaches in enhancing the fairness and accuracy of hate speech detection systems
Monad Torrent
Get PDFIntroduction:
The BitTorrent protocol revolutionized peer-to-peer file sharing by enabling efficient and decentralized data distribution. This project, developed by Fadil Smajilbasic and Davor Lucic, explores the implementation of a simple BitTorrent client using the Haskell programming language. By using Haskell's robust functional programming paradigms, this project aims to provide educational approach to understanding how BitTorrent clients can be used to download files. Alongside the implementation, the project includes a simple, step-by-step guide to empower others to build their own BitTorrent clients, demystifying the technical complexities of this widely used protocol.
This project aims to shed some light on Haskell's versatility and another practical use of the language outside of academics.
Objective:
The primary objectives of this project encompass a combination of technical development, organizational discipline, and educational outreach. Effective project management and time tracking are central to ensuring that the development process remains structured. A key focus is on delivering a functional and usable BitTorrent client, showcasing a practical application of the Haskell programming language in network programming. Additionally, the project aims to produce a comprehensive and accessible guide that simplifies the process of building a BitTorrent client, enabling others to replicate and expand upon the work. These objectives collectively ensure a well-rounded project that balances technical achievement with educational value and professional practice.
Approach:
For the development of we decided to take inspiration on a pre-existing guide developed by a team at codecrafters.io and the Brick library for the terminal UI. This guide helped us to comprehend the challenge and more easily use Haskell to solve the objective of this project. Further details about the technologies used can be found in our documentation
Design of a locomotive UI for system testing (BA)
No full textThis project presents a novel UI prototype for a locomotive testing system that specifically targets the driver's cab.
Starting from the cluttered and unstructured LabVIEW application, our goal was to design and develop a new application that was user-friendly and structured.
By aligning the structure with the real locomotive, the prototype aims to provide an intuitive user experience.
The resulting prototype takes a component-based approach and focuses on design, extensibility and modularization.
The design was developed with Figma and implemented using web technologies; more specifically an Electron application combined with React was implemented.
A key feature is the customization capability through a configuration file in CSV format, allowing flexible configuration of modules and variants.
To simulate the prospective interface connection, a mock-backend was created, as the actual backend implementation is pending.
This project provides a solid foundation for further development and demonstrates the possibilities of extending the application with additional views and functionality, ultimately serving as a valuable platform for ongoing work in this area
Barrierelos
Get PDFIn December 2023, a revision of regulations was announced by the Swiss Federal Council, also mandating private companies to enhance the accessibility of their digital services. Therefore, a far greater number of actors will now have to adhere to these rules. Despite existing regulations, accessibility issues persist, particularly in government services at the cantonal and municipal levels.
The project aims to address this gap by recording and scoring accessibility issues on websites, categorizing them by region and other factors, and making the data publicly available. The project also aids website maintainers in resolving these issues by providing a list of issues found in an easy-to-use format.
This thesis presents a software system, incorporating crowdsourcing, scanning for accessibility issues using the axe-core engine, and a scoring algorithm named Barrierelos-Score. The system comprises a React-based web application frontend, a Spring Boot-based backend with a RESTful web API, and a scanner implemented in TypeScript running on Node.js.
Containerization and temporal decoupling ensure scalability, while a modularized architecture facilitates easy implementation of new requirements.
The project embraces an agile workflow, with automated testing and deployment, enabling continuous integration and continuous deployment. The documentation is made available online through GitLab Pages. The result is a comprehensive approach to enhance web accessibility awareness and facilitate issue resolution in Switzerland and Liechtenstein