Towards an automated classification of spreadsheets

Many spreadsheets in the wild do not have documentation nor categorization associated with them. This makes difficult to apply spreadsheet research that targets specific spreadsheet domains such as financial or database.We introduce with this paper a methodology to automatically classify spreadsheets into different domains. We exploit existing data mining classification algorithms using spreadsheet-specific features. The algorithms were trained and validated with cross-validation using the EUSES corpus, with an up to 89% accuracy. The best algorithm was applied to the larger Enron corpus in order to get some insight from it and to demonstrate the usefulness of this work

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy

Objective: The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. Methods: We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. Results: We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. Conclusions: Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. Significance: Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

An automated high-content screening image analysis pipeline for the identification of selective autophagic inducers in human cancer cell lines.

Automated image processing is a critical and often rate-limiting step in high-content screening (HCS) workflows. The authors describe an open-source imaging-statistical framework with emphasis on segmentation to identify novel selective pharmacological inducers of autophagy. They screened a human alveolar cancer cell line and evaluated images by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogram-derived segmentation. The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation

Enhancement drugs: are there limits to what we should enhance and why?

Substances, such as alcohol, opiates and cannabis, have been used by humans for millennia. Today, a much wider range of substances are used for a range of purposes, including the enhancement of performance during university studies, sexual experiences, sports, exercise, at celebrations, socializing and the experience of art and music. Substance use is also associated with a range of harmful effects to the individual and society as a whole. Prohibitions, regulation, prevention and treatment have all been used to protect against this harm. In this commentary, it is argued that public health interventions should target relevant harms and not to evaluate which aspects of human endeavors and experiences should be enhanced and which should not. It is argued that interventions should directly target the harmful effects, using the best available evidence. Two examples are given of substances that may be altered to prevent serious harm - one for alcohol and one for cannabis. In the case of alcohol, the addition of dissolved oxygen could reduce both the risk of accidents and the risk of liver damage associated with alcohol consumption. In the case of cannabis, there is strong indication that the reduction of content Δ-tetrahydrocannabinol and the increase of cannabidiol could reduce the risk of psychoses and the addiction associated with its use. The aim of this article is to show that responsible regulation should not necessarily be restricted to preventing the use and/or (in the case of alcohol) a reduction in the amounts and frequency of its use, but should also aim to include a range of other strategies that could reduce the burden of illness associated with illicit substance use

Identify error-sensitive patterns by decision tree

© Springer International Publishing Switzerland 2015. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles’ heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially “weakest” nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development. This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed node-level. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration

Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system

Late HIV Diagnosis and Determinants of Progression to AIDS or Death after HIV Diagnosis among Injection Drug Users, 33 US States, 1996–2004

BACKGROUND: The timeliness of HIV diagnosis and the initiation of antiretroviral treatment are major determinants of survival for HIV-infected people. Injection drug users (IDUs) are less likely than persons in other transmission categories to seek early HIV counseling, testing, and treatment. Our objective was to estimate the proportion of IDUs with a late HIV diagnosis (AIDS diagnosis within 12 months of HIV diagnosis) and determine the factors associated with disease progression after HIV diagnosis. METHODOLOGY/PRINCIPAL FINDINGS: Using data from 33 states with confidential name-based HIV reporting, we determined the proportion of IDUs aged >or=13 years who received a late HIV diagnosis during 1996-2004. We used standardized Kaplan-Meier survival methods to determine differences in time of progression from HIV to AIDS and death, by race/ethnicity, sex, age group, CD4(+) T-cell count, metropolitan residence, and diagnosis year. We compared the survival of IDUs with the survival of persons in other transmission categories. During 1996-2004, 42.2% (11,635) of 27,572 IDUs were diagnosed late. For IDUs, the risk for progression from HIV to AIDS 3 years after HIV diagnosis was greater for nonwhites, males and older persons. Three-year survival after HIV diagnosis was lower for IDU males (87.3%, 95% confidence interval (CI), 87.1-87.4) compared with males exposed through male-to-male sexual contact (91.6%, 95% CI, 91.6-91.7) and males exposed through high-risk heterosexual contact (HRHC) (91.9%, 95% CI, 91.8-91.9). Survival was also lower for IDU females (89.5%, 95% CI, 89.4-89.6) compared to HRHC females (93.3%, 95% CI, 93.3-93.4). CONCLUSIONS/SIGNIFICANCE: A substantial proportion of IDUs living with HIV received their HIV diagnosis late. To improve survival of IDUs, HIV prevention efforts must ensure early access to HIV testing and care, as well as encourage adherence to antiretroviral treatment to slow disease progression

The renin‐angiotensin‐aldosterone system and its suppression

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/1/jvim15454-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/2/jvim15454-sup-0002-figures.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/3/jvim15454-sup-0005-TableS3.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/4/jvim15454-sup-0004-TableS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/5/jvim15454-sup-0007-TableS5.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/6/jvim15454_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/7/jvim15454.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/8/jvim15454-sup-0006-TableS4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148403/9/jvim15454-sup-0003-TableS1.pd