Vodenje projektov na podlagi analize podatkov

The project management office has the task of monitoring the execution of ongoing projects in the organization. To make the process easier and more transparent throughout the organization, a project management information system is used to track the current status of projects. The project management information system contains a large amount of rich data that is presented to users through only relatively simple visualization methods. To solve this problem, we hypothesize that the organization can benefit from the insights gained by applying modern data analysis methods to its data. To test the validity of our hypothesis, we present three new reports focusing on the performance of employees and on completed and ongoing work in the organization. The reports use newly designed metrics that estimate the complexity of a task and the time spent on it. We confirm our hypothesis by facilitating expert knowledge from the industry. As a result of our analysis, the project management office was able to improve the process of project execution in the organization.Naloga urada za upravljanje projektov je spremljanje izvajanja tekočih projektov v organizaciji. Za lažje in preglednejše sledenje trenutnega stanja projektov, se v celotni organizaciji običajno uporablja informacijski sistem za vodenje projektov. Informacijski sistem za vodenje projektov vsebuje veliko količino podatkov, vendar so le-ti običajno predstavljeni zgolj z razmeroma prerostimi vizualizacijami. Ker pa so podatki bogati, domnevamo, da ima organizacija lahko koristi od znanja, pridobljenega z uporabo sodobnejših metod analize podatkov. Za preverjanje veljavnosti naše hipoteze predstavljamo tri poročila, ki se osredotočajo na uspešnost zaposlenih ter na zaključena in tekoča dela v organizaciji. Poročila uporabljajo na novo uvedene metrike, ki ocenjujejo zapletenost naloge in čas, porabljen za nalogo. Svojo hipotezo smo potrdili v sodelovanju s strokovnjaki iz industrije. Kot rezultat naše analize je uradu za upravljanje projektov v priznanem podjetju uspelo izboljšati proces vodenja projektov

Generalization analysis of semantic segmentation with deep filter banks

Mobile robotic systems capable of autonomous navigation in non-structured environments depend on their vision module in order to safely navigate through the environment. The vision module provides perception of the surrounding area and it is often required to identify particular objects of interest, which is done by classifying image segments into pre-learned semantic classes. There are many methods which provide remarkable semantic segmentation results, but unfortunately only on specific datasets, which are not necessarily correlated to the scenes observed by a mobile robot. To verify the dataset's capability of transferring knowledge to a new domain we explore how well it generalises its classes. We examine the transfer of knowledge on a specific semantic segmentation method, which we adjust to best fit our needs

PyTorch model for Slovenian Named Entity Recognition SloNER 1.0

The SloNER is a model for Slovenian Named Entity Recognition. It is is a PyTorch neural network model, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers). The model is based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397). The model was trained on the SUK 1.0 training corpus (http://hdl.handle.net/11356/1747).The source code of the model is available on GitHub repository https://github.com/clarinsi/SloNER