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Diverse presentation formats play a pivotal role in effectively conveying code and analytical processes during data analysis. One increasingly popular format is tutorial videos, particularly those based on Jupyter notebooks, which offer an…
Spatial dataflow architectures such as reconfigurable dataflow accelerators (RDA) can provide much higher performance and efficiency than CPUs and GPUs. In particular, vectorized reconfigurable dataflow accelerators (vRDA) in recent…
In Software Engineering, some of the most critical activities are maintenance and evolution. However, to perform both with quality, minimizing impacts and risks, developers need to analyze and identify where the main problems come from…
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment,…
While large language models (LLMs) show promise in code generation, existing benchmarks neglect the flowchart-based code generation. To promote further research on flowchart-based code generation, this work presents Flow2Code, a novel…
R is a language and environment for statistical computing and graphics, which provides a wide variety of statistical tools (modeling, statistical testing, time series analysis, classification problems, machine learning, ...), together with…
The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods typically decouple task descriptions from graph data,…
Writing dataflow analyzers requires both language and domain-specificity. That is to say, each programming language and each program property requires its own analyzer. To enable a streamlined, user-driven approach to dataflow analyzers, we…
Taint analysis using explicit whole-program data-dependence graphs is powerful for vulnerability discovery but faces two major challenges. First, accurately modeling taint propagation through calls to external library procedures requires…
Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static…
Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. In this paper, some…
Applications in cyber-physical systems are increasingly coupled with online instruments to perform long running, continuous data processing. Such "always on" dataflow applications are dynamic, where they need to change the applications…
Vision and language models (VLMs) are expected to analyse complex documents, such as those containing flowcharts, through a question-answering (QA) interface. The ability to recognise and interpret these flowcharts is in high demand, as…
Parallel dataflow systems have become a standard technology for large-scale data analytics. Complex data analysis programs in areas such as machine learning and graph analytics often involve control flow, i.e., iterations and branching.…
With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source…
Understanding how data moves, transforms, and persists, known as data flow, is fundamental to reasoning in procedural tasks. Despite their fluency in natural and programming languages, large language models (LLMs), although increasingly…
Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this…
When faced with new data, we often conduct a cluster analysis to obtain a better understanding of the data's structure and the archetypical samples present in the data. This process often includes visualization of the data, either as a way…
For scientific knowledge to be findable, accessible, interoperable, and reusable, it needs to be machine-readable. Moving forward from post-publication extraction of knowledge, we adopted a pre-publication approach to write research…
The increasing risk of epidemics and a fast-growing world population has contributed to a great investment in phylogenetic analysis, in order to track numerous diseases and conceive effective medication and treatments. Phylogenetic analysis…