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Understanding how professionals use digital fabrication in production workflows is critical for future research in digital fabrication technologies. We interviewed thirteen professionals who use digital fabrication for the low-volume manufacturing of commercial products. From these interviews, we describe the workflows used for nine products created with a variety of materials and manufacturing methods. We show how digital fabrication professionals use software development to support physical production, how they rely on multiple partial representations in development, how they develop manufacturing processes, and how machine control is its own design space. We build from these findings to argue that future digital fabrication systems should support the exploration of material and machine behavior alongside geometry, that simulation is insufficient for understanding the design space, and that material constraints and resource management are meaningful design dimensions to support. By observing how professionals learn, we suggest ways digital fabrication systems can scaffold the mastery of new fabrication techniques. 

Procedural functionality enables visual creators to rapidly edit, explore alternatives, and fine-tune artwork in many domains including illustration, motion graphics, and interactive animation. Symbolic procedural tools, such as textual programming languages, are highly expressive but often limit directly manipulating concrete artwork; whereas direct manipulation tools support some procedural expression but limit creators to pre-defined behaviors and inputs. Inspired by visions of using geometric input to create procedural relationships, we identify an opportunity to use vector geometry from artwork to specify expressive user-defined procedural functions. We present Drawing Transforms (DTs), a technique that enables the use of any drawing to procedurally transform the stylistic, spatial, and temporal properties of target artwork. We apply DTs in a prototype motion graphics system to author continuous and discrete transformations, modify multiple elements in a composition simultaneously, create animations, and control fine-grained procedural instantiation. We discuss how DTs can unify procedural authoring through direct manipulation across visual media domains. 

Clay 3D printing provides the benefits of digital fabrication automation and reconfigurability through a method that evokes manual clay coiling. Existing design technologies for clay 3D printing reflect the general 3D printing workflow in which solid forms are designed in CAD and then converted into a toolpath. In contrast, in hand-coiling, form is determined by the actions taken by the artist’s hands through space in response to the material. We theorized that an action-oriented approach for clay 3D printing could allow creators to design digital fabrication toolpaths that reflect clay material properties. We present CoilCAM, a domain-specific CAM programming system that supports the integrated generation of parametric forms and surface textures through mathematically defined toolpath operations. We developed CoilCAM in collaboration with ceramics professionals and evaluated CoilCAM’s relevance to manual ceramics by reinterpreting hand-made ceramic vessels. This process revealed the importance of iterative variation and embodied experience in action-oriented workflows. 

New printing strategies have enabled 3D-printed materials that imitate traditional textiles. These filament-based textiles are easy to fabricate but lack the look and feel of fiber textiles. We seek to augment 3D-printed textiles with needlecraft to produce composite materials that integrate the programmability of additive fabrication with the richness of traditional textile craft. We present PunchPrint: a technique for integrating fiber and filament in a textile by combining punch needle embroidery and 3D printing. Using a toolpath that imitates textile weave structure, we print a flexible fabric that provides a substrate for punch needle production. We evaluate our material’s robustness through tensile strength and needle compatibility tests. We integrate our technique into a parametric design tool and produce functional artifacts that show how PunchPrint broadens punch needle craft by reducing labor in small, detailed artifacts, enabling the integration of openings and multiple yarn weights, and scaffolding soft 3D structures. 

While cross-disciplinary collaboration continues to be a cornerstone of inventive work in interactive design, the infrastructures of academia, as well as barriers to participation imposed by our professional organizations, make collaboration between particular groups difficult. In this workshop, we will focus specifically on how artist residencies are addressing (or not addressing) the challenges that artists, craftspeople, and/or independent designers face when collaborating with researchers affiliated with DIS. By focusing on the question “what is mutual benefit?”, this workshop seeks to combine the perspectives of artists and academic researchers who collaborate with artists (through residencies or other forms of sustained collaboration) to (1) reflect on benefits or deficiencies in what the residency research model is currently doing and (2) generate resources for our community to effectively structure and evaluate our methods of collaboration with artists. Our hope is to provide recognition of the research contributions of artists and pathways for equitable inclusion of artists as a first step towards broader infrastructural change. 

As machine learning methods become more powerful and capture more nuances of human behavior, biases in the dataset can shape what the model learns and is evaluated on. This paper explores and attempts to quantify the uncertainties and biases due to annotator demographics when creating sentiment analysis datasets. We ask >1000 crowdworkers to provide their demographic information and annotations for multimodal sentiment data and its component modalities. We show that demographic differences among annotators impute a significant effect on their ratings, and that these effects also occur in each component modality. We compare predictions of different state-of-the-art multimodal machine learning algorithms against annotations provided by different demographic groups, and find that changing annotator demographics can cause >4.5 in accuracy difference when determining positive versus negative sentiment. Our findings underscore the importance of accounting for crowdworker attributes, such as demographics, when building datasets, evaluating algorithms, and interpreting results for sentiment analysis.

 

Rural students, schools, and communities have unique challenges that hinder academic achievement, growth, and opportunities, compared to other locales. While there is a need to study this community more, there is also a pressing need to bring the local community members together to support the future generation of learners in developing pathways that lead them to future career opportunities. This article focuses on how a Research Practice Partnership (RPP) can be developed in rural communities to support STEM pathways for local middle-school youth. RPPs are often described as long-term collaborations between both researchers and practitioners in which the participating partners leverage research to address specific persistent problems of practice. We present findings from a developing design-based RPP focused on bringing community members and organizations together to co-design opportunities for underserved youth in rural mountain communities. 

“Talk moves” are specific discursive strategies used by teachers and students to facilitate conversations in which students share their thinking, and actively consider the ideas of others, and engage in rich discussions. Experts in instructional practices often rely on cues to identify and document these strategies, for example by annotating classroom transcripts. Prior efforts to develop automated systems to classify teacher talk moves using transformers achieved a performance of 76.32% F1. In this paper, we investigate the feasibility of using enriched contextual cues to improve model performance. We applied state-of-the-art deep learning approaches for Natural Language Processing (NLP), including Robustly optimized bidirectional encoder representations from transformers (Roberta) with a special input representation that supports previous and subsequent utterances as context for talk moves classification. We worked with the publically available TalkMoves dataset, which contains utterances sourced from real-world classroom sessions (human- transcribed and annotated). Through a series of experimentations, we found that a combination of previous and subsequent utterances improved the transformers’ ability to differentiate talk moves (by 2.6% F1). These results constitute a new state of the art over previously published results and provide actionable insights to those in the broader NLP community who are working to develop similar transformer-based classification models.