Soft continuum robots enable new application areas in contrast to standard rigid robots, such as interaction with a varying environment. Due to their compliant continuous structure, they are inherently safe and adaptive to environmental conditions. In this paper, the interaction with the environment is performed at the tool-center-point of a soft continuum manipulator and is realized by a hybrid force-position control. For this, a force estimation model is derived to substitute the force sensor at the tool-center-point. The force estimation is probabilistic and relies on normal distributions considering model parameters and deviations from model identification of the soft continuum robot. It also provides a qualitative measure for the contact estimation. This paper first presents the probabilistic force estimation model and then shows the hybrid force-position control using the presented model. From the results, it is concluded that force sensing is replaceable for the environment interaction.

Online toxicity is ubiquitous across the internet and its negative impact on the people and that online communities that it effects has been well documented. However, toxicity manifests differently on various platforms and toxicity in open source communities, while frequently discussed, is not well understood. We take a first stride at understanding the characteristics of open source toxicity to better inform future work on designing effective intervention and detection methods. To this end, we curate a sample of 100 toxic GitHub issue discussions combining multiple search and sampling strategies. We then qualitatively analyze the sample to gain an understanding of the characteristics of open-source toxicity. We find that the pervasive forms of toxicity in open source differ from those observed on other platforms like Reddit or Wikipedia. In our sample, some of the most prevalent forms of toxicity are entitled, demanding, and arrogant comments from project users as well as insults arising from technical disagreements. In addition, not all toxicity was written by people external to the projects; project members were also common authors of toxicity. We also discuss the implications of our findings. Among others we hope that our findings will be useful for future detection work.

This paper presents the results of an interview study with twelve TikTok users to explore user awareness, perception, and experiences with the app’s algorithm in the context of privacy. The social media entertainment app TikTok collects user data to cater individualized video feeds based on users’ engagement with presented content which is regulated in a complex and overly long privacy policy. Our results demonstrate that participants generally have very little knowledge of the actual privacy regulations which is argued for with the benefit of receiving free entertaining content. However, participants experienced privacy-related downsides when algorithmically catered video content increasingly adapted to their biography, interests, or location and they in turn realized the detail of personal data that TikTok had access to. This illustrates the tradeoff users have to make between allowing TikTok to access their personal data and having favorable video consumption experiences on the app.

Unlike in commercial software development, open source software (OSS) projects do not generally have managers with direct control over how developers spend their time, yet for projects with large, diverse sets of contributors, the need exists to focus and steer development in a particular direction in a coordinated way. This is especially important for "infrastructure" projects, such as critical libraries and programming languages that many other people depend on. Some projects have taken the approach of borrowing planning tools that originated in commercial development, despite the fact that these techniques were designed for very different contexts, e.g. strong top-down control and profit motives. Little research has been done to understand how these practices are adapted to a new context. In this paper, we examine the Rust project's use of roadmaps: how has an important OSS infrastructure project adapted an inherently top-down tool to the freewheeling world of OSS? We find that because Rust's roadmaps are built in part by summarizing what motivated developers most prefer to work on, they are in some ways more a description of the motivated labor available than they are a directive that the community move in a particular direction. They allow the community to avoid wasting time on unpopular proposals by revealing that there will be little help in building them, and encouraging work on popular features by making visible the amount of consensus in those features. Roadmaps generate a collective focus without limiting the full scope of what developers work on: roadmap issues consume proportionally more effort than other issues, but constitute a minority of the work done (i.e issues and pull requests made) by both central and peripheral participants. They also create transparency among and beyond the community into what central contributors' plans are, and allow more rational decision-making by providing a way for evidence about community needs to be linked to decision-making.

Social media, especially Twitter, has always been a part of the professional lives of software developers, with prior work reporting on a diversity of usage scenarios, including sharing information, staying current, and promoting one's work. However, previous studies of Twitter use by software developers typically lack information about activities of the study subjects (and their outcomes) on other platforms. To enable such future research, in this paper we propose a computational approach to cross-link users across Twitter and GitHub, revealing (at least) 70,427 users active on both. As a preliminary analysis of this dataset, we report on a case study of 786 tweets by open-source developers about GitHub work, combining automatic characterization of tweet authors in terms of their relationship to the GitHub items linked in their tweets with qualitative analysis of the tweet contents. We find that different developer roles tend to have different tweeting behaviors, with repository owners being perhaps the most distinctive group compared to other project contributors and followers. We also note a sizeable group of people who follow others on GitHub and tweet about these people's work, but do not otherwise contribute to those open-source projects. Our results and public dataset open up multiple future research directions.