9607 research outputs found
Sort by
NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems
No full textAutonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach
Circle of Life: Microworld Project at the End of CS1
No full textMicroworlds have been proposed as an educational approach for engaging students and increasing motivation. However, the use of microworlds has largely been studied in the initial phases of learning to program (or entire courses). We adopted a microworld project at the end of our CS1 course to investigate its use in teaching students composition and creation of larger programs rather than initial familiarity with programming constructs. We here compare the impact of using a scaffolded, microworld project over a (previously used) non-scaffolded, non-microworld project. Based on data from N=303 students exposed to the two kinds of projects (145 in 2022 vs 158 in 2023), we compare the two cohorts according to a test of learning (TOL) based on the final exam grades. In 2023, we studied the impact further by considering the feel of learning (FOL) based on a qualitative student survey. The results show that the student grade-point average (TOL) increased significantly when we adopted the microworld project. Further, the microworld project appears to produce a high FOL
Interorganizational preparedness in business-to-business relationships
No full textIn recent years, business-to-business firms have experienced increasing uncertainty, disruptive events, and major crises that have challenged their businesses. While these developments have triggered a focus on firms' preparedness to handle uncertainty, surprisingly little has been said about preparedness in an interorganizational context. This oversight is noteworthy, as interorganizational contexts are not only the dominant settings within business markets but also key drivers of the development of resilience and responsiveness. This conceptual paper outlines the concept of preparedness in business-to-business relationships and suggests a research agenda for interorganizational preparedness—an important concept in a fast-changing and uncertain business environment
Estrangement through Silence
No full textHow can we cultivate deeper attunement to one another, ourselves, and the environment that can, in turn, inform and enrich design? Over the course of four workshops conducted across 1.5 years - primarily outdoors - the authors engaged in prolonged periods of shared silence. This collective silence functioned as an estrangement method, revealing the porous and interdependent boundaries between people and things, mutually constituting one another. We unpack some of the experiential qualities emerging from these experiments and mobilize them for future design processes, including: cultivating multifaceted sensibilities, dynamic modes of noticing and interacting, such as coming together and dispersing, being alone together, and acting or playing in unison; the malleability of silence to specific, orchestrated design activities, such as cooking or designing; and reframing silence, not as an absence, but as a presence - rich with sounds, interactions, and possibilities for engagement. We discuss how to set up temporal and spatial boundaries, alongside boundaries within and between ourselves
Understanding joint range of motion development in robotic learning
No full textJoint Range of Motion (JROM) development has been shown to facilitate learning motor control in human beings. This developmental strategy has been applied in robotics to improve learning performance with different outcomes: sometimes it is favourable, others irrelevant, and others, even detrimental. The reasons that underpin this variability in the results are still not well understood. In this paper, we seek to better understand the principles underlying the application of JROM based morphological development to make its use more straightforward. To this end, empirical studies were carried out over two representative use cases: quadruped and bipedal robot morphologies learning to walk. Different parameters of the application of JROM development (morphological configuration, JROM developmental strategy, etc.) have been evaluated to elucidate their effects over learning. The results show that there are significant connections between the reduction of the motor space induced by JROM and the way the exploration and exploitation of the solution space is carried out by the learning algorithm, and the performance achieved. Through these connections, we have identified a set of conditions that must be satisfied for JROM development to be effective as a tool for learning improvement
Autonomous Regulation of Social Media Use: Implications for Self-control, Well-Being, and UX
No full textMuch work in HCI has investigated strategies for supporting au-tonomous self-regulation in social media use (SMU): helping usersto control their time online and ensure it serves personally valuedoutcomes. However, results suggest that the effectiveness and ac-ceptability of these strategies may vary based on individual needs.Recent work has attributed this variation to motivational factors,though we currently lack data to understand how these factorsinfluence self-regulation, user experience and well-being. We drawon Self-Determination Theory to analyse autonomous and non-autonomous patterns of motivation in 521 users of social media.Using latent profile analysis, we identify 4 “motivational profiles”associated with significant differences in need satisfaction, affect,and compulsive engagement. Our results clarify distinct aspectsof autonomy in SMU and identify opportunities to target and per-sonalise design interventions; they suggest autonomous regulationcan be associated with better experience and well-being, thoughnot necessarily less time online
Reminiscences on Influential Papers
No full textThis issue's contributions highlight the impact and educational value of the qualitative and quantitative analysis papers. Enjoy reading! While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested
Code Like Humans: A Multi-Agent Solution for Medical Coding
No full textIn medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. We introduce `Code Like Humans': a new agentic framework for medical coding with large language models. It implements official coding guidelines for human experts, and it is the first solution that can support the full ICD-10 coding system (+70K labels). It achieves the best performance to date on rare diagnosis codes. Fine-tuned discriminative classifiers retain an advantage for high-frequency codes, to which they are limited. Towards future work, we also contribute an analysis of system performance and identify its `blind spots' (codes that are systematically undercoded)
NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
No full textMatching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market analysis. Furthermore, aligning job titles to job skills can be considered an extension to this task, with similar relevance for the same downstream tasks. In this report, we outline NLPnorth's submission to TalentCLEF 2025, which includes both of these tasks: Multilingual Job Title Matching, and Job Title-Based Skill Prediction. For both tasks we compare (fine-tuned) classification-based, (fine-tuned) contrastive-based, and prompting methods. We observe that for Task A, our prompting approach performs best with an average of 0.492 mean average precision (MAP) on test data, averaged over English, Spanish, and German. For Task B, we obtain an MAP of 0.290 on test data with our fine-tuned classification-based approach. Additionally, we made use of extra data by pulling all the language-specific titles and corresponding \emph{descriptions} from ESCO for each job and skill. Overall, we find that the largest multilingual language models perform best for both tasks. Per the provisional results and only counting the unique teams, the ranking on Task A is 5/20 and for Task B 3/14