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Computational reproducibility refers to obtaining consistent results when rerunning an experiment. Jupyter Notebook, a web-based computational notebook application, facilitates running, publishing, and sharing computational experiments…
Debugging parallel and distributed programs is a difficult activitiy due to the multiplicity of sequential bugs, the existence of malign effects like race conditions and deadlocks, and the huge amounts of data that have to be processed.…
Replicability and reproducibility (R&R) are critical for the long-term prosperity of a scientific discipline. In GIScience, researchers have discussed R&R related to different research topics and problems, such as local spatial statistics,…
Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources…
Performance regressions have a tremendous impact on the quality of software. One way to catch regressions before they reach production is executing performance tests before deployment, e.g., using microbenchmarks, which measure performance…
Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time…
Robotic systems react to different environmental stimuli, potentially resulting in the dynamic reconfiguration of the software controlling such systems. One effect of such dynamism is the reconfiguration of the software architecture…
Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development,…
Iterative compilation is a widely adopted technique to optimize programs for different constraints such as performance, code size and power consumption in rapidly evolving hardware and software environments. However, in case of statically…
Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications. Its results end up in a paper's "background" section or in standalone articles, which include meta-analyses and systematic…
In recent times, quantum reservoir computing has emerged as a potential resource for time series prediction. Hence, there is a need for a flexible framework to test quantum circuits as nonlinear dynamical systems. We have developed a…
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites of games that have common interfaces but little overlap in…
Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b)…
Autonomous agents are increasingly expected to support scientific research, and recent benchmarks report progress in code repair and autonomous experimentation. However, these evaluations typically assume a pre-configured execution…
You have an environment, a model, and a reinforcement learning library that are designed to work together but don't. PufferLib makes them play nice. The library provides one-line environment wrappers that eliminate common compatibility…
In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learning jobs are frequently applied in streaming conditions,…
Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS…
The adoption of heterogeneous computing systems based on diverse architectures to achieve exascale computing power has worsened the performance portability problem of scientific applications that were designed to run on these platforms. To…
Monitoring software systems at runtime is key for understanding workloads, debugging, and self-adaptation. It typically involves collecting and storing observable software data, which can be analyzed online or offline. Despite the…
The information retrieval (IR) community has a strong tradition of making the computational artifacts and resources available for future reuse, allowing the validation of experimental results. Besides the actual test collections, the…