1,196 research outputs found
Learning representations for effective and explainable software bug detection and fixing
Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a \u27bridge,\u27 known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.
This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning code representations using four distinct methods?context-based, testing results-based, tree-based, and graph-based?thus improving bug detection and fixing approaches, as well as providing developers insight into the foundational reasoning. The experimental results demonstrate that using learning code representations can significantly enhance explainable bug detection and fixing, showcasing the practicability and meaningfulness of the approaches formulated in this dissertation toward improving software quality and reliability
A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data
The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines.
One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials.
In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications.
Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
Cognitive Load Reduction in Commanding Heterogeneous Robotic Teams
With the proliferation of multi-robot systems, the interfaces required to operate them have become increasingly complex compared to those used for single robot systems. This can present challenges for operators who need to extract relevant information in order to make informed decisions about how to operate the robots. To address this issue, this thesis explores a variety of strategies aimed at improving the intuitiveness and usability of such systems. These strategies encompass a range of approaches, from designing user interfaces to integrating physical input devices, knowledge representations, and other modalities to assist operators. In this context, the thesis proposes a decision support system that provides operators with additional information in an intuitive way, focusing specifically on handling a set of distinct commands for a heterogeneous robotic team. A key constraint during the development of this system was the lack of historical data available to train the modules on. As a result, the proposed system was tested in a few-shot environment and was specifically designed for this circumstance. The support system comprises two modules: one that probabilistically classifies the next command using a data mining approach called sequence prediction, which is used to reorder the available commands in the interface; and a second that creates higher-level commands by mining frequent sequences from the historical dataset. These command sequences are presented to the operator, who can add them as additional executable commands. To evaluate the advantages and disadvantages of this novel approach, a user study was conducted, which showed that both modules increased the efficiency and usability of the system, while also identifying opportunities for further improvement
Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy
This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
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