The Importance of Optimal Design in Outdoor Light Source Positioning

In this work we underline the importance of the optimal design for outdoor light systems such that light posts and sources are placed correctly. We present a case study where we considered a city road section equipped with LED sources. On this section, we measured technical luminous coefficients, which were then compared with values listed in standard and normative documentation. Based on this we did a new design that had to follow certain requirements on street and lighting post configuration. Comparing the two designs we conclude that, to have an optimal street light system, the engineer must aim for the best possible design and consider this when deciding on the position of the light sources

A Multimodal Approach for Semantic Patent Image Retrieval

Patent images such as technical drawings contain valuable information and are frequently used by experts to compare patents. However, current approaches to patent information retrieval are largely focused on textual information. Consequently, we review previous work on patent retrieval with a focus on illustrations in figures. In this paper, we report on work in progress for a novel approach for patent image retrieval that uses deep multimodal features. Scene text spotting and optical character recognition are employed to extract numerals from an image to subsequently identify references to corresponding sentences in the patent document. Furthermore, we use a neural state-of-the-art CLIP model to extract structural features from illustrations and additionally derive textual features from the related patent text using a sentence transformer model. To fuse our multimodal features for similarity search we apply re-ranking according to averaged or maximum scores. In our experiments, we compare the impact of different modalities on the task of similarity search for patent images. The experimental results suggest that patent image retrieval can be successfully performed using the proposed feature sets, while the best results are achieved when combining the features of both modalities

Extended overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance

We describe the second edition of the LongEval CLEF 2024 shared task. This lab evaluates the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. Task 1 requires IR systems to run on corpora acquired at several timestamps, and evaluates the drop in system quality (NDCG) along these timestamps. Task 2 tackles binary sentiment classification at different points in time, and evaluates the performance drop for different temporal gaps. Overall, 37 teams registered for Task 1 and 25 for Task 2. Ultimately, 14 and 4 teams participated in Task 1 and Task 2, respectively.</p

Extended overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance

We describe the second edition of the LongEval CLEF 2024 shared task. This lab evaluates the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. Task 1 requires IR systems to run on corpora acquired at several timestamps, and evaluates the drop in system quality (NDCG) along these timestamps. Task 2 tackles binary sentiment classification at different points in time, and evaluates the performance drop for different temporal gaps. Overall, 37 teams registered for Task 1 and 25 for Task 2. Ultimately, 14 and 4 teams participated in Task 1 and Task 2, respectively.</p

LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

In this paper, we describe the plans for the first LongEval CLEF 2023 shared task dedicated to evaluating the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant, with respect to time, from the training data. LongEval differs from traditional shared IR and classification tasks by giving special consideration to evaluating models aiming to mitigate performance drop over time. We envisage that this task will draw attention from the IR community and NLP researchers to the problem of temporal persistence of models, what enables or prevents it, potential solutions and their limitations.</p

Data Stewardship – Austrian National Strategy and Alignment

Within the FAIR Data Austria project, supported by the Federal Ministry for Education, Science, and Research (BMBWF), a national strategy has been established to advance the creation of tailored Data Stewardship solutions for the Austrian context. The strategy, formalized as a toolbox, delineates various Data Steward models, corresponding competencies, and accessible training resources. Despite the crucial role of Data Stewardship in supporting data-driven scientific research, Austrian universities encounter challenges in its implementation. Issues include lack of consensus on the skills, roles, and responsibilities of Data Stewards, coupled with insufficient funding for these positions. This article explores these challenges and emphasizes the importance of addressing them to promote effective Data Stewardship within the Austrian academic landscape

Extended Overview of the CLEF-2023 LongEval Lab on Longitudinal Evaluation of Model Performance

We describe the first edition of the LongEval CLEF 2023 shared task. This lab evaluates the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. Task 1 requires IR systems to run on corpora acquired at several timestamps, and evaluates the drop in system quality (NDCG) along these timestamps. Task 2 tackles binary sentiment classification at different points in time, and evaluates the performance drop for different temporal gaps. Overall, 37 teams registered for Task 1 and 25 for Task 2. Ultimately, 14 and 4 teams participated in Task 1 and Task 2, respectively.</p

User interface features in Theorema: A summary

Abstract. This paper presents the main features of Theorema’s user interface. We briefly describe how mathematical knowledge can be expressed in the Theorema Formal Text Language and how the knowledge can be used for proving, solving, computing. We illustrate how the system presents the proofs it generated and how the user can influence the proof search process interactively.