nils-herrmann/clean_scirepeval huggingface dataset:
Misrepresentation of Financial Statements: An Accounting Fraud Case from Turkey
Year: 2012

The objective of this paper is to show to the public in general and auditors in particular that in the absence of control there is always a risk of fraud. Fraud can be in various forms. Larceny may be the most obvious case of fraud, but fraud may be done in many other ways too. Balance sheet fraud or financial statements fraud is a broader issue, it is far fletched than a few hundred dollars of a larceny case. In financial statement fraud the deep down effect may be millions or billions of dollars. The paper has been designed based on a fraud theory. The author has observed the implications of a possible fraud in a real audit case. The fraud theory has been tested through on financial analysis and audit tests. The theory then been revised and the existence of a financial statement fraud has been proven. The paper explores that banks and group companies controlled by unreliable owners can lead to misuse of public's funds in accordance with the directives of the owner. Public's money can be transferred to other group companies in an illegal manner in excessive amounts and never returned to the bank by means of applying different accounting fraud techniques. Auditors, who may audit group companies that include a bank or banks with deposit receiving and lending rights should pay attention to the transactions between the group's bank and the other group companies. The lending may be excessive in amount and/or never paid back and the financial statements would be misrepresented covering various fraud schemes. The case that the paper deals with reflects the author's own audit experiences. The names of the companies have been changed but not the essence of the events. From this perspective it sheds light onto the path of an auditor who happens to be in a similar situation.
Root Cause Analysis of Compressor Failure by Machine Learning
Venue: 2019 Petroleum and Chemical Industry Conference Europe (PCIC EUROPE)
Year: 2019

The machine learning revolution is starting to be implemented in machinery maintenance and has become inevitable in highly industrialized and integrated plants. These measures save time, money and effort through new and dynamic condition monitoring strategies. Moreover, knowledge can be extracted from these models about the potential root causes of machinery breakdowns. This results in key information to prevent similar situations in the future. Finding patterns in these breakdowns and root causes through advanced data analytics is not commonplace however. This case study then develops a strategy to implement predictive modeling and to perform root cause analysis on a compressor unit running at one of the largest refineries in Europe. Findings are presented to field experts and are deemed to add to their intuition due to the presence of unanticipated triggers. We show that root causes can be identified by constructing an intelligent data pipeline based on a multitude of readily available sensor data.
"You Don't Understand. It's Not About Virginity" Sexual Markets, Identity Construction, and Violent Masculinity on an Incel Forum Board
Year: 2020

The "manosphere" is an online collection of antifeminists and men's rights activists. Incels, short for involuntary celibates, interact in this space and have been labeled as extreme misogynists, white supremacists, and domestic terrorists. I engage with popular sociological theories of masculinity (including hegemonic, hybrid, and inclusive masculinities) to analyze dominant discourses on the website incels.is. The data for this project are comments from 100 threads randomly sampled from 4,532 total threads posted in 2018. Using a grounded theory approach, I first explore how incels think about sexuality as an economy, devising a scheme that places everyone in a sexual hierarchy based on their "sexual market value." Next, I explore questions of identity, attempting to make sense of who is able to call themselves an incel and who is not. Finally, I draw together ideas of entitlement and victimhood to identify ways that incels perform violence online in an attempt to reassert masculinity. This study ultimately provides a case study to understand ways in which marginalized men still work to reproduce gendered and sexual hierarchies. Disclaimer Incels are often described in the media as extreme misogynists, white supremacists, and domestic terrorists. Their language is often vulgar, graphic, and violent. Acknowledgements First, thank you to my thesis advisor, Dr. Jamie Small, who supported me throughout my entire research process. Your encouragement to continue mulling, generating, and revisiting was invaluable. Next, Dr. Anya Galli Robertson, who was instrumental through my project design, analysis, and writing phases. I don't believe I could have finished this project without your constant, unwavering encouragement. To my thesis cohort members, Ruthey Schultz and Mary McLoughlin, I will forever remember our table in the library, where we worked for hours on our own projects, offered ideas to one another, and reviewed each other's work. Thank you for journeying with me through everything. Thank you to all of the faculty in the Department of Sociology, Anthropology, and Social Work for creating an environment that fosters inquiry and a love of learning. Thank you to all of my friends at UD for listening to my constant talking about this project, and for continuing to love me through it all. And, thank you to my family; your support is endless, and endlessly appreciated.
Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images
Venue: Remote. Sens.
Year: 2018

Building footprint information is vital for 3D building modeling. Traditionally, in remote sensing, building footprints are extracted and delineated from aerial imagery and/or LiDAR point cloud. Taking a different approach, this paper is dedicated to the optimization of OpenStreetMap (OSM) building footprints exploiting the contour information, which is derived from deep learning based semantic segmentation of oblique images acquired by the Unmanned Aerial Vehicle (UAV) First, a simplified 3D building model of Level of Detail 1 (LoD 1) is initialized using the footprint information from OSM and the elevation information from Digital Surface Model (DSM) In parallel, a deep neural network for pixel wise semantic image segmentation is trained in order to extract the building boundaries as contour evidence. Subsequently, an optimization integrating the contour evidence from multi view images as a constraint results in a refined 3D building model with optimized footprints and height. Our method is leveraged to optimize OSM building footprints for four datasets with different building types, demonstrating robust performance for both individual buildings and multiple buildings regardless of image resolution. Finally, we compare our result with reference data from German Authority Topographic Cartographic Information System (ATKIS) Quantitative and qualitative evaluations reveal that the original OSM building footprints have large offset, but can be significantly improved from meter level to decimeter level after optimization.
Effects of Voluntary Heart Rate Control on User Engagement in Virtual Reality
Venue: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
Year: 2019

The usage of biofeedback in Virtual Reality (VR) is becoming more and more important in providing fully immersive experiences. With the rapid evolution of physiological monitoring technologies it is important to study how different modalities of biofeedback can alter user experience. While previous studies use biofeedback as an additional interaction mechanic. We created a protocol to assess heart rate control competency and used the results of said protocol to immerse our participants in a VR experience where the biofeedback mechanics are mandatory to complete a game. We observed consistent results between our competency scale and the participants' mastery of the biofeedback game mechanic in the VR experience. We also found that the biofeedback mechanic has a significant impact on engagement.
Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
Venue: Journal of healthcare engineering
Year: 2020

Patients in the intensive care unit require fast and efficient handling, including in diagnosis service. The objectives of this study are to produce a computer aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build applications that can determine the location of brain tumors from CT scan images; and to get the results of the analysis of the system design. The combination of the zoning algorithm with Learning Vector Quantization can increase the speed of computing and can classify normal and abnormal brains with an average accuracy of 85%
Spectral decomposition of discrepancy kernels on the Euclidean ball, the special orthogonal group, and the Grassmannian manifold
Venue: ArXiv
Year: 2019

To numerically approximate Borel probability measures by finite atomic measures, we study the spectral decomposition of discrepancy kernels when restricted to compact subsets of \mathbb{R}^d$ For restrictions to the Euclidean ball in odd dimensions, to the rotation group $SO(3) and to the Grassmannian manifold \mathcal{G}_{2,4} we compute the kernels' Fourier coefficients and determine their asymptotics. The $L_2$ discrepancy is then expressed in the Fourier domain that enables efficient numerical minimization based on the nonequispaced fast Fourier transform. For $SO(3) the nonequispaced fast Fourier transform is publicly available, and, for \mathcal{G}_{2,4} the transform is derived here. We also provide numerical experiments for $SO(3) and \mathcal{G}_{2,4}
machine learning based framework to identify type 2 diabetes hrough electronic health records
Year: 2016

To discover diverse genotype phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome wide association study (GWAS) and phenome wide association study (PheWAS) more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g. via Electronic Health Records (EHR) However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate.  We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely used machine learning models within our framework, including k NearestNeighbors, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects) randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014.  We apply top performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state of the art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances ~0.98 in average AUC) which are much higher than the state of the art algorithm (0.71 in AUC)  Expert algorithm based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls.  Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.  T2DM is a leading cause of morbidity and mortality and contributes to increased risks of heart disease by 2 to 4 times  A significant number of research investigations have been devoted to it, notably by means of genome wide association
Light Rail Investment in Seattle: Gentrification Pressures and Trends in Neighborhood Ethnoracial Composition
Year: 2020

Research often finds a positive relationship between public transportation investment and gentrification in nearby neighborhoods. This dynamic is particularly important in urban contexts that plan for transit oriented development and creating future "walkability." In this study, I demonstrate a link between transit investment and changing neighborhood racial and ethnic composition, using a case study of the recent light rail project in Seattle, Washington. Descriptive analyses and difference in difference models suggest that affected neighborhoods in Seattle experienced rising shares of non Hispanic Whites following the start of light rail construction, while neighborhoods at the suburban periphery of the line saw substantial growth in racial and ethnic diversity. These findings highlight the role of transit infrastructure in restructuring demographic trajectories of nearby neighborhoods and contribute evidence about shifting patterns of residential segregation in the area around the transit line.
The Disk Substructures at High Angular Resolution Project (DSHARP) II. Characteristics of Annular Substructures
Year: 2018

The Disk Substructures at High Angular Resolution Project used ALMA to map the 1.25 millimeter continuum of protoplanetary disks at a spatial resolution of ~5 au. We present a systematic analysis of annular substructures in the 18 single disk systems targeted in this survey. No dominant architecture emerges from this sample; instead, remarkably diverse morphologies are observed. Annular substructures can occur at virtually any radius where millimeter continuum emission is detected and range in widths from a few au to tens of au. Intensity ratios between gaps and adjacent rings range from near unity to just a few percent. In a minority of cases, annular substructures co exist with other types of substructures, including spiral arms (3/18) and crescent like azimuthal asymmetries (2/18) No clear trend is observed between the positions of the substructures and stellar host properties. In particular, the absence of an obvious association with stellar host luminosity (and hence the disk thermal structure) suggests that substructures do not occur preferentially near major molecular snowlines. Annular substructures like those observed in DSHARP have long been hypothesized to be due to planet disk interactions. A few disks exhibit characteristics particularly suggestive of this scenario, including substructures in possible mean motion resonance and "double gap" features reminiscent of hydrodynamical simulations of multiple gaps opened by a planet in a low viscosity disk.
Efficacy of extended adjuvant therapy with aromatase inhibitors in early breast cancer among common clinicopathologically defined subgroups: A systematic review and meta analysis.
Venue: Cancer treatment reviews
Year: 2017

Randomized controlled trials (RCTs) have shown improvements in breast cancer outcomes from extending treatment with aromatase inhibitors (AIs) beyond the initial 5years after diagnosis. Consistency of this effect in common clinicopathologically defined subgroups was not been reported systematically.  We identified RCTs comparing extended AIs to placebo or no treatment using a systematic search of MEDLINE and a review of abstracts from key conferences between 2013 and 2016. Hazard ratios (HRs) and 95% confidence intervals (CI) for disease free survival (DFS) were included in a meta analysis using generic inverse variance and random effects modelling. Pre specified subgroups included: age <60+ 5years versus =60+ 5 years) tumor size >2cm versus =2cm) nodal status (positive versus negative) hormone receptor status (double versus single receptor expression) and receipt of adjuvant chemotherapy (yes versus no) RESULTS Seven trials comprising 16,349 patients were analyzed. Overall, the effect of extended AIs was similar in all subgroups. Non significantly greater effect sizes were seen in patients with larger tumors (HR for DFS 0.77 versus 0.88, p for difference=0.44) nodal involvement (HR=0.72 versus 0.83, p for difference=0.22) double hormone receptor expression (HR=0.68 versus 1.01, p for difference=0.31) and receipt of adjuvant chemotherapy (HR=0.71 versus 0.80, p for difference=0.51)  Extended treatment with AIs is associated with similar relative improvements in DFS in all subgroups analyzed. The combination of greater effect size and higher absolute risk of recurrence in node positive and larger tumors will likely translate to higher absolute benefits from extended AIs in these groups.
Acute Encephalitis Syndrome in Gorakhpur, Uttar Pradesh, 2016: Clinical and Laboratory Findings
Venue: The Pediatric infectious disease journal
Year: 2018

Seasonal outbreaks of acute encephalitis syndrome (AES) with high fatality have been occurring in Gorakhpur, Uttar Pradesh, India, for several years. We conducted investigations during the 2016 outbreak to identify the etiology.  We included 407 hospitalized AES patients with cerebrospinal fluid pleocytosis >5 cells/mm3) in our study. These patients were clinically examined; their blood and cerebrospinal fluid samples were collected and investigated for scrub typhus (ST) Japanese encephalitis virus (JEV) dengue virus and spotted fever group of Rickettsia by serology and/or polymerase chain reaction.  Of the 407 AES patients, 266 (65.4% 42 (10.3% and 29 (7.1% were diagnosed to have ST, JEV and dengue infection, respectively. Four patients were diagnosed to have spotted fever group of Rickettsia infection. A significantly higher proportion of ST patients with AES had hepatomegaly, splenomegaly and facial edema. The common hematologic and biochemical abnormalities among ST positive patients include thrombocytopenia, raised liver enzymes and bilirubin levels. The case fatality ratio was significantly higher among ST negative AES patients (36.2% vs. 15.2% P 0.05)  ST accounted for approximately two third of the AES case patients. Efforts are required to identify the etiology of AES case patients who are negative for ST, JEV and dengue fever.
Song2Quartet: A System for Generating String Quartet Cover Songs from Polyphonic Audio of Popular Music
Venue: ISMIR
Year: 2015

We present Song2Quartet, a system for generating string quartet versions of popular songs by combining probabilistic models estimated from a corpus of symbolic classical music with the target audio file of any song. Song2Quartet allows users to add novelty to listening experience of their favorite songs and gain familiarity with string quartets. Previous work in automatic arrangement of music only used symbolic scores to achieve a particular musical style; our challenge is to also consider audio features of the target popular song. In addition to typical audio music content analysis such as beat and chord estimation, we also use timefrequency spectral analysis in order to better reflect partial phrases of the song in its cover version. Song2Quartet produces a probabilistic network of possible musical notes at every sixteenth note for each accompanying instrument of the quartet by combining beats, chords, and spectrogram from the target song with Markov chains estimated from our corpora of quartet music. As a result, the musical score of the cover version can be generated by finding the optimal paths through these networks. We show that the generated results follow the conventions of classical string quartet music while retaining some partial phrases and chord voicings from the target audio.
Did It Really Happen? How the Public Interprets Journalistic Disclaimers
Year: 2020

Immediately following dramatic events, news reporting must be both fast and accurate. In an attempt to reconcile the inherent conflict between these two ambitions, journalists often use disclaimers, for example, "unconfirmed" or "reports of." These disclaimers allow for the rapid publication of less than reliable content. The results from our survey experiment suggest that strong disclaimers, as intended, do lower the perceived reliability of stories among news consumers. Furthermore, the results indicate that the context influences the effectiveness of disclaimers. It appears that in a crisis environment, such as after a large terrorist attack, individuals are less sensitive to disclaimers.
Generalized discriminant analysis model and its extension for facial expression recognition
Venue: 2014 12th International Conference on Signal Processing (ICSP)
Year: 2014

Over the past few decades, numerous linear discriminant analysis based extensions are proposed for dimensionality reduction. However, most of them are developed intuitively according to specific motivations by employing various techniques. Therefore, it will be instructive to provide a unified discriminant analysis model for exploring the commonalities and differences. In this paper, we propose a generalized discriminant analysis model (GDA model) by comprehensively considering the key components in designing a discriminator in which various LDA based methods can be unified into this framework. Thus we can get better understanding of the inherent relationship among these algorithms by interpreting them from a unified perspective.
Differential urinary proteomics analysis of myocardial infarction using iTRAQ quantification
Venue: Molecular medicine reports
Year: 2019

Myocardial infarction (MI) is a disease characterized by high morbidity and mortality rates. MI biomarkers are frequently used in clinical diagnosis; however, their specificity and sensitivity remain unsatisfactory. Urinary proteome is an easy, efficient and noninvasive source to examine biomarkers associated with various diseases. The present study, to the best of the authors' knowledge, is the first to examine the urinary proteome using the isobaric tags for relative and absolute quantitation (iTRAQ) technology to identify potential diagnostic biomarkers of MI. The urinary proteome was analyzed within 12 h following the first symptoms of early onset MI and at day 7 following percutaneous coronary intervention via iTRAQ labeling and two dimensional liquid chromatography tandem mass spectrometry. Candidate biomarkers were validated by multiple reaction monitoring (MRM) analysis. A total of 233 urinary proteins were differentially expressed. Gene enrichment analysis identified that the urinary proteome in patients with MI was associated with atherosclerosis, abnormal coagulation and abnormal cell metabolism. In total, 12 differentially expressed urinary proteins were validated by MRM analysis, five of which were associated with MI for the first time in the present study. Binary logistic regression analysis suggested that the combination of five urinary proteins (antithrombin III, complement C3, a 1 acid glycoprotein 1, serotransferrin and cathepsin Z) may be used to diagnose MI with 94% sensitivity and 93% specificity. In addition, the protein expression levels of three proteins were significantly restored to normal levels following surgical treatment. The verified candidate biomarkers may be used for the diagnosis of MI, and for monitoring the disease status and the effects of treatments for MI. The present results may facilitate future clinical applications of the urinary proteome to diagnose MI.