Aleksandar Tošić

Domen Vake, Bogdan Šinik, Jernej Vičič et al.

Large Language Models (LLMs) have become central in academia and industry, raising concerns about privacy, transparency, and misuse. A key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution. However, open-sourcing presents challenges, including potential misuse, financial disincentives, and intellectual property concerns. Proprietary models, backed by private sector resources, are better positioned for return on investment. There are also other approaches that lie somewhere on the spectrum between completely open-source and proprietary. These can largely be categorised into open-source usage limitations protected by licensing, partially open-source (open weights) models, hybrid approaches where obsolete model versions are open-sourced, while competitive versions with market value remain proprietary. Currently, discussions on where on the spectrum future models should fall on remains unbacked and mostly opinionated where industry leaders are weighing in on the discussion. In this paper, we present a data-driven approach by compiling data on open-source development of LLMs, and their contributions in terms of improvements, modifications, and methods. Our goal is to avoid supporting either extreme but rather present data that will support future discussions both by industry experts as well as policy makers. Our findings indicate that open-source contributions can enhance model performance, with trends such as reduced model size and manageable accuracy loss. We also identify positive community engagement patterns and architectures that benefit most from open contributions.

Daniil Baldouski, Aleksandar Tošić

In this paper, we present a plugin for visualising vote based consensus mechanisms primarily aimed to help engineers understand and debug blockchain and distributed ledger protocols. Both tools are built as Grafana plugins and make no assumptions on the data storage implementation. The plugins can be configured via Grafana plugin configuration interface to fit the specifics of the protocol implementation.

Niki Hrovatin, Aleksandar Tošić, Michael Mrissa et al.

Wireless Sensor Networks (WSNs) are composed of a large number of spatially distributed devices equipped with sensing technology and interlinked via radio signaling. A WSN deployed for monitoring purposes can provide a ubiquitous view over the monitored environment. However, the management of collected data is very resource-consuming and raises security and privacy issues. In this paper, we propose a privacy preserving protocol for collecting aggregated data from WSNs. The protocol relies on the Onion Routing technique to provide uniformly distributed network traffic and confine the knowledge a foreign actor can gain from monitoring messages traveling the network. Our solution employs the computing power of nodes in the network by conveying them general-purpose computer code for in-situ processing and aggregation of data sourcing from multiple sensor nodes. We complement our work with a simulation of the proposed solution using the network simulator ns-3. Results of the simulation give an overview of the scalability of the solution and highlight potential constraints.