Large language models (LLMs) like GPT-4 can convert natural-language descriptions of a task into computer code, making them a promising interface for end-user programming. We undertake a systematic analysis of how people with and without programming experience describe information-processing tasks (IPTs) in natural language, focusing on the characteristics of successful communication. Across two online between-subjects studies, we paired crowdworkers either with one another or with an LLM, asking senders (always humans) to communicate IPTs in natural language to their receiver (either a human or LLM). Both senders and receivers tried to answer test cases, the latter based on their sender’s description. While participants with programming experience tended to communicate IPTs more successfully than non-programmers, this advantage was not overwhelming. Furthermore, a user interface that solicited example test cases from senders often, but not always, improved IPT communication. Allowing receivers to request clarification, though, was less successful at improving communication.

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon’s working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.

Function Secret Sharing (FSS; Eurocrypt 2015) allows a dealer to share a function f with two or more evaluators. Given secret shares of a function f, the evaluators can locally compute secret shares of f (x) for any input x, without learning information about f in the process.In this paper, we initiate the study of access control for FSS. Given the shares of f, the evaluators can ensure that the dealer is authorized to share the provided function. For a function family $\mathcal{F}$ and an access control list defined over the family, the evaluators receiving the shares of $f \in \mathcal{F}$ can efficiently check that the dealer knows the access key for f.This model enables new applications of FSS, such as: (1) anonymous authentication in a multi-party setting, (2) access control in private databases, and (3) authentication and spam prevention in anonymous communication systems.Our definitions and constructions abstract and improve the concrete efficiency of several recent systems that implement ad-hoc mechanisms for access control over FSS. The main building block behind our efficiency improvement is a discrete-logarithm zero-knowledge proof-of-knowledge over secret-shared elements, which may be of independent interest.We evaluate our constructions and show a 50–70× reduction in computational overhead compared to existing access control techniques used in anonymous communication. In other applications, such as private databases, the processing cost of introducing access control is only 1.5–3×, when amortized over databases with 500,000 or more items.

Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art.