Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification

NNOSE: Nearest Neighbor Occupational Skill Extraction

The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks---combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, Nearest Neighbor Occupational Skill Extraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction without additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30% span-F1 in cross-dataset settings

Re-purposing museum experiences::A design-after-design approach

This thesis explores the conflicts of incorporating playful installations that make use ofdigital technologies in an exhibition space of a cultural institutions. The research is situated inthe exhibition space of the Danish Architecture Center (DAC), a cultural institution about thedissemination of architecture. Museum experiences often employ digital technologies andsupport play to bring forward the qualities of exploration and free-choice, thus attractingvisitors and improving the quality of their visit. Including play in their space is valuable butalso controversial. Stakeholders seem to disagree on the value that play brings, and hadtrouble accepting some of its elements. The creative, personal, exploratory, and self-expressive qualities of play lead to visitors discovering new ways of interacting with theinstallations, based on their personal motives and interests; re-purposing them in the process.While established museum design processes often involve participation of visitors during thepre-deployment phase to inform the design of such experiences, those processes rarely re-design them once they are deployed; as a result, any use discovered by visitors during thepost-deployment phase does not inform their overall design. Those newly discoveredinteractions can create new engagement opportunities, as well as technical and curatorialchallenges. Because of that, there is a need for design approaches that can include that re-purposing by investigating and using it to inform the design of experiences.To address that need, this thesis conducts empirical research on the re-purposing whichhappened during real use in the exhibition space of DAC. I approached those researchendeavours by employing research-through-design while gathering in-the-wild data from theexhibition space of DAC, where I conducted three out of four of my studies. Initially, throughthe study of Bio-sonic Sense, I worked with elements of ambiguity to support exploration andfree-choice. Then, through the study of We Dare You I gathered ethnographic data, whichallowed me to build an understanding of the specific challenges DAC faces when deployinghybrid playful experience in their exhibition space. Finally, the subsequent studies of CityLights and Light House allowed me to try out a specific iterative design process which is builton the ideals of design-after-design. The process I tested aimed to use that re-purposing as atool to re-design the artefact. That process is divided into two stages. First, the initial phasefocus on creating undetermined artefacts by designing for flexible affordances that promoteopenness in the possible uses of the object. Second, during the post-deployment phase, thoseartefacts are followed and their re-purposing is used as a source for re-designing by throughspecific changes in their affordances. Specifically, the artefact is re-designed to highlight thediscovered uses for future visitors.This research contributes by using the results of that approach to identify three themes thatled to conflicting expectations from play. First, there is a disconnection between the bodily-sensorial experience of those playful installations by the stakeholders and their culturalunderstanding of play when they observed visitors using the same installations. Second, theireducational background seemed to affect their ideals on education, with some followingrealist ideals while others idealist ideals. Third, it was difficult for the ones following realistideals to accept the unproductive and frivolous aspects of play in the exhibition space.Overall, incorporating play in museum installations that use digital technologies haschallenges since it can lead to technical failure and it can be opposed by the stakeholder’sviews; however, since newer educational theories, visitor satisfaction, and contemporarymuseology ideals can benefit from including such installations into their space, there is valueto explore further how to properly support digital technologies and play to create museumexperiences. Further research can investigate potential design processes that can support thespecific needs of exhibition spaces to address the occurred re-purposing of those installations

How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, better bicycle infrastructure networks are required. Planning such networks requires high-quality infrastructure data, yet the quality of bicycle infrastructure data is understudied. Here, we compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data