1,720,962 research outputs found
Establishing formal behavioral guarantees for trained neural networks : towards reliable machine learning systems
Full text linkMachine Learning (ML)-based systems, particularly those deploying deep neural networks (DNNs), are widely adopted into real-world applications due to their ability to be trained without being explicitly programmed and high output accuracy. However, despite their high classification accuracy and optimal decision-making in testing scenarios, they are often found to be vulnerable under unseen (but realistic) inputs. This points to the lack of generalization of these data-driven models under unseen input scenarios, hence highlighting the need for behavioral guarantees to ensure their reliable classification and decision-making in the real world. Research efforts constantly provide empirical evidence for the lack of reliable DNN behavior (under seed inputs) for various ML applications. Orthogonally, formal efforts attempt to provide concise formal guarantees for behavioral properties/specifications like robustness and safety to hold for the DNN models. However, due to the scalability challenges associated with formal methods, not only are these efforts often restricted to providing qualitative (binary) guarantees but they also focus only on limited DNN behaviors and verification techniques.To address the aforementioned limitations, this research provides model checking and scalable sampling-based formal frameworks for DNN analysis, focusing on a diverse range of DNN behavioral specifications. These include DNN noise tolerance, input node sensitivity (to noise), node robustness bias, robustness under constrained noise, robustness bias against tail classes and safety under bounded inputs. Realistic noise modeling is proposed for practical DNN analysis, while restraining from the use of unrealistic assumptions during analysis. These lead to formal guarantees that may potentially be leveraged to identify reliable ML systems. The research additionally leverages our DNN analysis to improve training for robust DNNs. The resulting frameworks designed and developed during the research are all accompanied by case studies based on DNNs trained on real-world datasets, hence supporting the efficacy of the research efforts and provide behavioral guarantees essential to ensure more reliable ML systems in practice
Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs
No full textOwing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driven machine learning models is generally as good as the data available to them for training. Hence, training datasets with long-tail distribution pose a challenge for DNNs, since the DNNs trained on them may provide a varying degree of classification performance across different output classes. While the overall bias of such networks is already highlighted in existing works, this work identifies the node bias that leads to a varying sensitivity of the nodes for different output classes. To the best of our knowledge, this is the first work highlighting this unique challenge in DNNs, discussing its probable causes, and providing open challenges for this new research direction. We support our reasoning using an empirical case study of the networks trained on a real-world dataset
Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs
Full text linkOwing to their remarkable learning (and relearning) capabilities, deep neural
networks (DNNs) find use in numerous real-world applications. However, the
learning of these data-driven machine learning models is generally as good as
the data available to them for training. Hence, training datasets with
long-tail distribution pose a challenge for DNNs, since the DNNs trained on
them may provide a varying degree of classification performance across
different output classes. While the overall bias of such networks is already
highlighted in existing works, this work identifies the node bias that leads to
a varying sensitivity of the nodes for different output classes. To the best of
our knowledge, this is the first work highlighting this unique challenge in
DNNs, discussing its probable causes, and providing open challenges for this
new research direction. We support our reasoning using an empirical case study
of the networks trained on a real-world dataset.Comment: To appear at the 16th IEEE International Conference on Software
Testing, Verification and Validation (ICST 2023), Dublin, Irelan
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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