arXiv subjects

Kit Kuksenok

Publications and source records attributed to Kit Kuksenok.

5 recordsLinked to original sources

arXiv2026

Processing/p5 Defined through Practice and Learning

Processing/p5 libraries across different programming languages enact consistent priorities for creative coding as a designed experience. While different programming language ecosystems, like Java and JavaScript, are each associated with their own affordances, community norms, and patterns of use, Processing/p5 sketches across these languages share similarities. Based on case studies of building an implementation of Processing/p5 in two host languages, JavaScript and Lua, we propose a list of software decision-making guiding aspects that constitute Processing/p5, regardless of host language. We discuss this framework in the context of decisions in other exploratory and creative tools that demonstrate how each of the guiding aspects can be operationalized differently than in the case studies. The proposed list highlights opportunities for learning, research, and artistic practice through creation of new Processing/p5 libraries for creative coding and algorithmic art.

cs.SE↗

arXiv2026

Designing L5: A Permacomputing Approach to Creative Coding

Creative coding libraries provide high-level tools that make computational and algorithmic art accessible to artists and learners. Processing/p5 is one such family of libraries, known for its beginner-friendly approach and wide reach across artistic and technical communities. L5 is a new member of this family, implemented in Lua using the LOVE framework. It applies permacomputing principles, a movement addressing sustainability in computing inspired by permaculture, bringing these values to a community of practice not historically centered on them. This paper explores L5's design decisions and tensions between sustainability and usability through five case studies: 1. balancing perceived simplicity versus exposing the seams, 2. designing for lower resource consumption, 3. ensuring long-term stability, 4. constraining functionality, and 5. designing documentation for resource-constrained access. Rather than optimizing for a single metric, sustainable creative tools require navigating competing values transparently.

cs.SE↗

arXiv2019

Toward Best Practices for Explainable B2B Machine Learning

To design tools and data pipelines for explainable B2B machine learning (ML) systems, we need to recognize not only the immediate audience of such tools and data, but also (1) their organizational context and (2) secondary audiences. Our learnings are based on building custom ML-based chatbots for recruitment. We believe that in the B2B context, "explainable" ML means not only a system that can "explain itself" through tools and data pipelines, but also enables its domain-expert users to explain it to other stakeholders.

cs.HC↗

arXiv2019

Evaluation and Improvement of Chatbot Text Classification Data Quality Using Plausible Negative Examples

We describe and validate a metric for estimating multi-class classifier performance based on cross-validation and adapted for improvement of small, unbalanced natural-language datasets used in chatbot design. Our experiences draw upon building recruitment chatbots that mediate communication between job-seekers and recruiters by exposing the ML/NLP dataset to the recruiting team. Evaluation approaches must be understandable to various stakeholders, and useful for improving chatbot performance. The metric, nex-cv, uses negative examples in the evaluation of text classification, and fulfils three requirements. First, it is actionable: it can be used by non-developer staff. Second, it is not overly optimistic compared to human ratings, making it a fast method for comparing classifiers. Third, it allows model-agnostic comparison, making it useful for comparing systems despite implementation differences. We validate the metric based on seven recruitment-domain datasets in English and German over the course of one year.

cs.IR↗

arXiv2019

Transparency in Maintenance of Recruitment Chatbots

We report on experiences with implementing conversational agents in the recruitment domain based on a machine learning (ML) system. Recruitment chatbots mediate communication between job-seekers and recruiters by exposing ML data to recruiter teams. Errors are difficult to understand, communicate, and resolve because they may span and combine UX, ML, and software issues. In an effort to improve organizational and technical transparency, we came to rely on a key contact role. Though effective for design and development, the centralization of this role poses challenges for transparency in sustained maintenance of this kind of ML-based mediating system.

cs.AI↗