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2. Evaluating the dynamics of games in a blockchain-based marketplace : final project paperGjore Janevski, 2025, diplomsko delo Ključne besede: blockchain, centralized/decentralized marketplace, games, marketing, liquidity, NFT, player retention, game engagement
Objavljeno v RUP: 04.10.2025; Ogledov: 411; Prenosov: 4
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4. Is open source the future of AI? : a data-driven approachDomen Vake, Bogdan Šinik, Jernej Vičič, Aleksandar Tošić, 2025, izvirni znanstveni članek Opis: Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means to enhance trust and transparency. Yet, open-sourcing comes with its own challenges, such as risks of illicit applications, limited financial incentives, and intellectual property concerns. Positioned between these extremes are hybrid approaches—including partially open models and licensing restrictions—that aim to balance openness with control. In this paper, we adopt a data-driven approach to examine the open-source development of LLMs. By analyzing contributions in model improvements, modifications, and methodologies, we assess how community efforts impact model performance. Our findings indicate that the open-source community can significantly enhance models, demonstrating that community-driven modifications can yield efficiency gains without compromising performance. Moreover, our analysis reveals distinct trends in community growth and highlights which architectures benefit disproportionately from open-source engagement. These insights provide an empirical foundation to inform balanced discussions among industry experts and policymakers on the future direction of AI development.
Ključne besede: large language models, artificial intelligence, open source, data science, HuggingFace
Objavljeno v RUP: 25.09.2025; Ogledov: 596; Prenosov: 4
Celotno besedilo (606,12 KB)
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7. Bridging the question–answer gap in retrieval-augmented generation : hypothetical prompt embeddingsDomen Vake, Jernej Vičič, Aleksandar Tošić, 2025, izvirni znanstveni članek Opis: Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text remains a persistent challenge in retrieval-augmented systems, often addressed by runtime solutions (e.g., Hypothetical Document Embeddings (HyDE)) that attempt to improve alignment but introduce extra computational overhead at query time. To address these challenges, we propose Hypothetical Prompt Embeddings (HyPE), a framework that shifts the generation of hypothetical content from query time to the indexing phase. By precomputing multiple hypothetical prompts for each data chunk and embedding the chunk in place of the prompt, HyPE transforms retrieval into a question-question matching task, bypassing the need for runtime synthetic answer generation. This approach does not introduce latency but also strengthens the alignment between queries and relevant context. Our experimental results on six common datasets show that HyPE can improve retrieval context precision by up to 42 percentage points and claim recall by up to 45 percentage points, compared to standard approaches, while remaining compatible with re-ranking, multi-vector retrieval, query decomposition, and other RAG advancements.
Ključne besede: LLM, hypothetical prompt embedding, Retrieval-Augmented Generation (RAG)
Objavljeno v RUP: 04.08.2025; Ogledov: 620; Prenosov: 4
Celotno besedilo (1,41 MB)
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8. Occupancy estimation using indoor air quality data : opportunities and privacy implicationsDomen Vake, Niki Hrovatin, Jernej Vičič, Aleksandar Tošić, 2025, izvirni znanstveni članek Opis: Indoor Air Quality (IAQ) has long been a significant concern due to its health-related risks and potential benefits. Readily available air quality sensors are now affordable and have been installed in many buildings with public buildings taking center stage. The dynamics of IAQ are commonly studied in relation to different materials used in construction, building design, room utility and effects on occupants. However, besides what the sensors were designed to measure, it is possible to infer other information. In this paper, we present a Machine Learning (ML) model that predicts the presence of people in the room with an accuracy as high as 93 % and the exact number of occupants with 2.17 MAE. We validate our proposed approach in the use-case of an elementary school in Slovenia. In collaboration with the elementary school in Ajdovščina, 8 air quality sensors were placed in classrooms and air quality parameters (VOC, CO, Temperature, and Humidity) were monitored for 6 months. During the monitoring period, school staff collected anonymous data about classroom occupancy. The indoor air quality data was paired with external weather data as well as occupancy to train the model. Moreover, we compare our approach with other commonly used ML approaches and provide results related to our use case. Finally, these results highlight the privacy concerns related to structural monitoring due to the established ability to infer potentially sensitive information.
Ključne besede: indoor air quality, occupancy estimation, machine learning, sensor networks, privacy, building monitoring
Objavljeno v RUP: 02.06.2025; Ogledov: 1110; Prenosov: 7
Celotno besedilo (3,66 MB)
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