Augmented reality supported work instructions for onsite facility maintenance

During the operation and maintenance phase of buildings operators need to perform on site maintenance activities to prevent functional failures of technical equipment. As this phase is the longest and most expensive one respective improvements can significantly reduce the overall lifecycle budget. Based on their previous work, in this paper the authors present an Augmented Reality (AR) based concept and implementation to support mobile and onsite maintenance activities by (1) preparing and generating AR work order instructions based on Product Lifecycle Management (PLM) information, (2) using these to aid the actual onsite maintenance job using hybrid 3D tracking, and (3) creating enhanced and context-related maintenance service reports to be fed back to the PLM system. Preliminary results reveal the potential of the proposed solution, but also leave room for future improvements

Eyes on the Street: Racialized Bodies and Surveillance in Urban Space

Senior Project submitted to The Division of Arts of Bard College

Equity Of Urban Neighborhood Infrastructure: A Data-Driven Assessment

Neighborhood infrastructure, such as sidewalks, medical facilities, public transit, community gathering places, and tree canopy, provides essential support for safe, healthy, and resilient communities. This thesis proposes, develops, and implements an innovative approach to thoroughly examine the presence and condition of neighborhood infrastructure. It demonstrates the necessity of considering multiple infrastructure types when studying neighborhood infrastructure and its equity. This thesis provides an automated assessment framework as well as case studies among four major metropolitan cities across the United States, which expands the research opportunities for future infrastructure-related research

Disparate sense of exclusion between young people of color living within variable social infrastructures.

I analyzed transcripts of listening sessions with youth/young adults of color in 2021-2022 for the purpose of addressing local racial inequity during COVID-19. I used inductive coding methods and found three themes on sense of exclusion to be most salient. These themes related to racial exclusion, exclusion of social infrastructures in the community, exclusion of young people of color by people working in schools and other public settings, and exclusion or disconnection of young people of color from opportunities for building community. I show how these themes vary across some dimensions of the local social infrastructure, and I discuss implications for developing more equitable policy solutions across local sites. This thesis concludes by presenting possible changes in local social infrastructure directly suggested and inspired by the suggestions of young people who participated in the study. This thesis adds to the many discussions of social infrastructure for this research context

Augmented and virtual reality in construction: Drivers and limitations for industry adoption

Augmented and virtual reality have the potential to provide a step-change in productivity in the construction sector; however, the level of adoption is very low. This paper presents a systematic study of the factors that limit and drive adoption in a construction sector-specific context. A mixed research method was employed, combining qualitative and quantitative data collection and analysis. Eight focus groups with 54 experts and an online questionnaire were conducted. Forty-two limiting and driving factors were identified and ranked. Principal component analysis was conducted to group the identified factors into a smaller number of factors based on correlations. Four types of limiting factors and four types of driving factors were identified. The main limitation of adoption is that AR and VR technologies are regarded as expensive and immature technologies that are not suitable for engineering and construction. The main drivers are that AR and VR enable improvements in project delivery and provision of new and better services. This study provides valuable insights to stakeholders to devise actions that mitigate the limiting factors and that boost the driving factors. This is one of the first systematic studies to present a detailed analysis of the factors that limit and drive adoption of AR and VR in the construction industry. The main contribution of this study is that it grouped and characterized myriad limiting and driving factors into easily understandable categories, so that the limiting factors can be effectively mitigated and the driving factors potentiated. A roadmap with specific short-term and medium-term actions for improving adoption was outlined

Discrimination-aware classification

Classifier construction is one of the most researched topics within the data mining and machine learning communities. Literally thousands of algorithms have been proposed. The quality of the learned models, however, depends critically on the quality of the training data. No matter which classifier inducer is applied, if the training data is incorrect, poor models will result. In this thesis, we study cases in which the input data is discriminatory and we are supposed to learn a classifier that optimizes accuracy, but does not discriminate in its predictions. Such situations occur naturally as artifacts of the data collection process when the training data is collected from different sources with different labeling criteria, when the data is generated by a biased decision process, or when the sensitive attribute, e.g., gender serves as a proxy for unobserved features. In many situations, a classifier that detects and uses the racial or gender discrimination is undesirable for legal reasons. The concept of discrimination is illustrated by the next example: Throughout the years, an employment bureau recorded various parameters of job candidates. Based on these parameters, the company wants to learn a model for partially automating the matchmaking between a job and a job candidate. A match is labeled as successful if the company hires the applicant. It turns out, however, that the historical data is biased; for higher board functions, Caucasian males are systematically being favored. A model learned directly on this data will learn this discriminatory behavior and apply it over future predictions. From an ethical and legal point of view it is of course unacceptable that a model discriminating in this way is deployed. Our proposed solutions to the discrimination problem fall into two broad categories. First, we propose pre-processing methods to remove the discrimination from the training dataset. Second, we propose solutions to the discrimination problem by directly pushing the non-discrimination constraints into classification models and post-processing of built models. We further studied the discrimination-aware classification paradigm in the presence of explanatory attributes that were correlated with the sensitive attribute, e.g., low income may be explained by the low education level. In such a case, as we show, not all discrimination can be considered bad. Therefore, we introduce a new way of measuring discrimination, by explicitly splitting it up into explainable and bad discrimination and propose methods to remove the bad discrimination only. We tried our discrimination-aware methods over real world data sets. We observed in our experiments that our methods show promising results and clearly outperform the traditional classification model w.r.t. accuracy discrimination trade-off. To conclude, we believe that discrimination-aware classification is a new and exciting area of research addressing a societally relevant problem

Shifts in Mapping: Maps as a Tool of Knowledge

Depicting the world, territory, and geopolitical realities involves a high degree of interpretation and imagination. It is never neutral. Cartography originated in ancient times to represent the world and to enable circulation, communication, and economic exchange. Today, IT companies are a driving force in this field and change our view of the world; how we communicate, navigate, and consume globally. Questions of privacy, authorship, and economic interests are highly relevant to cartography's practices. So how to deal with such powers and what is the critical role of cartography in it? How might a bottom-up perspective (and actions) in map-making change the conception of a geopolitical space