Miracle-dz/UniFAR-FTUs huggingface dataset:
Applied Logistic Regression, Second Edition

"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--
Kininogen-1 as a protein biomarker for schizophrenia through mass spectrometry and genetic association analyses

Background Schizophrenia (SCZ) is a complex and severe mental illness. There is a lack of effective biomarkers for SCZ diagnosis. The aim of this study was to explore the possibility of using serum peptides for the diagnosis of SCZ as well as analyze the association of variants in genes coding for these peptides and SCZ. Methods After bead-based fractionation, the matrix-assisted laser desorption ionization/time-of-flight mass spectrometry technique was used to identify peptides that showed different expressions between 166 SCZ patients and 201 healthy controls. Differentially expressed peptides were verified in a second set of samples (81 SCZ patients and 103 healthy controls). The association of SCZ and three tagSNPs selected in genes coding for differentially expressed peptides was performed in 1,126 SCZ patients and 1,168 controls. Results The expression level of peptides with m/z 1,945.07 was significant lower in SCZ patients than in healthy controls (P < 0.000001). The peptide with m/z 1,945.07 was confirmed to be a fragment of Kininogen-1. In the verification tests, Kininogen-1 had a sensitivity of 95.1% and a specificity of 97.1% in SCZ prediction. Among the three tagSNPs (rs13037490, rs2983639, rs2983640) selected in the Cystatin 9 gene (CST9) which encodes peptides including Kininogen-1, tagSNP rs2983640 had its genotype distributions significantly different between SCZ patients and controls under different genetic models (P < 0.05). Haplotypes CG (rs2983639–rs2983640) and TCG (rs13037490–rs2983639–rs2983640) were significantly associated with SCZ (CG: OR = 1.21, 95% CI [1.02–1.44], P = 0.032; TCG: OR = 24.85, 95% CI [5.98–103.17], P < 0.0001). Conclusions The present study demonstrated that SCZ patients had decreased expression of Kininogen-1 and genetic variants in Kininogen-1 coding gene CST9 were significantly associated with SCZ. The findings from both protein and genetic association studies suggest that Kininogen-1 could be a biomarker of SCZ.
A Multimedia Modular Approach to Lifelog Moment Retrieval

Lifelogging offers a mean for people to record their day to day life. However, without proper tools for retrieving specific moments, the large volume of collected data is rendered less useful. We present in this paper our contribution to the ImageCLEF Lifelog evaluation campaign. We describe a modular approach that covers both textual analysis and semantic enrichment of queries and targeted concept augmentation of visual data. The proposed retrieval system relies on an inverted index of visual concepts and metadata and accommodates different versions of clustering and filtering modules. We tested over 10 fully automatic combinations of modules and a human guided approach. We observed that with even minimal human intervention, we can obtain a significant increase of the F1 score, ranking fourth in the competition leader board.
LTL-INAOE's Participation at eRisk 2019: Detecting Anorexia in Social Media through Shared Personal Information

Detecting mental-health risk behaviours at primary stages is crucial to bring help to people and to avoid undesired consequences. In this paper, we describe our participation at the eRisk 2019 shared task. The proposed approach mainly relies on analysing the sentences that include personal information, i.e., fragments of texts reﬂecting user’s interests, concerns, beliefs, personality traits and psychological state. The obtained results are very competitive, validating the important role of personal information for early detection of traces related to anorexia
A Weighted Rule-Based Model for File Forgery Detection: UA.PT Bioinformatics at ImageCLEF 2019

With today’s digital technology, disparate kinds of data can be easily manipulated. The forgery commonly hides information, by altering files’ extensions, files’ signatures, or by using steganography. Consequently, digital forensic examiners are faced with new problems in the detection of these forged files. The lack of automatised approaches to discover these infractions encourages researchers to explore new computational solutions that can help its identification. This paper describes the methodologies used in the ImageCLEFsecurity 2019 challenge, which were mainly rule-based models. The rules and all of their underlying mechanisms created for each task are described. For the third task, was used a random forest algorithm due to the poor performance of these rules.
MiR-486-5p Serves as a Good Biomarker in Nonsmall Cell Lung Cancer and Suppresses Cell Growth With the Involvement of a Target PIK3R1

MicroRNAs are a class of noncoding RNAs that can be involved in the regulation of gene expression in cancers, including lung cancer. Our previous research has shown that miR-486-5p is one of the most downregulated microRNAs in tissue and serum samples of lung cancer as a good diagnostic biomarker. The objective of this study is to investigate the roles of miR-486-5p in the progression of lung cancer. In this study, miR-486-5p was further validated to be significantly downregulated in additional nonsmall cell lung cancer (NSCLC) tissue, serum, and cell samples by quantitative reverse transcription-polymerase chain reaction (RT-PCR), and the expression level of miR-486-5p was significantly associated with clinical phenotype of NSCLC. The PIK3R1 gene was confirmed to be a direct target of miR-486-5p by dual-luciferase reporter assay, and the expression level of miR-486-5p was inversely correlated with that of PIK3R1 in tumor tissues (r = −0.774, p < 0.01). Overexpressed miR-486-5p effectively inhibited cell proliferation and invasion and successfully induced apoptosis in vitro. PIK3R1 was involved in the suppression of miR-486-5p on cell growth. It can be concluded that miR-486-5p may act as a tumor suppressor contributing to the progression of NSCLC, and miR-486-5p would be a diagnostic and prognostic biomarker and a potential therapeutic target for lung cancer.
Analysis of Security Technologies in Integrated Space-air-ground Networks

In integrated space-ground networks,a higher level of cross-ground / space secure communication and transport is required due to the continuous extension of national interests in the fields of ocean and space and exact needs of handling national contingency events and deeply exploring of space scientific tasks. The security threats and challenges are disclosed thoroughly,and a series of key security technologies to be addressed in the future are proposed,which willguide the direction of research on space-airground network security.
Can Models of Author Intention Support Quality Assessment of Content?

Academics seek to find, understand and critically review the work of other researchers through published scientific articles. In recent years, the volume of available information has significantly increased, partly due to technological advancements and partly due to pressures on academics to ‘publish or perish’. This amount of papers presents a challenge not only for the peer-review process but also for readers, particularly inexperienced readers, to find publications of high quality. Whilst one might rely on citation or journal rankings to help guide this decision, this approach may not be completely reliable due to biased peer-review processes and the fact that the citation count of an article does not per se indicate its quality. Here, we analyse how expected author intentions in a Related Work section can be used to indicate its quality. We show that author intentions can predict the quality with reasonable accuracy and propose that similar approaches could be used in other sections to provide an overall picture of quality. This approach could be useful in supporting peer-review processes and for a reader in prioritising articles to read.
Comparison of Models Used to Predict Flight Delays at Jomo Kenyatta International Airport

Delays in flights have negative socio-economics effects on passengers, airlines and airports, resulting to huge economic loses. Therefore, their prediction is crucial during the decision-making process for all players of aviation industry for proper management. The development of accurate prediction models for flight delays depend on the complexity of air transport system and airport infrastructure, hence may be country specific. However, there exists no prediction models tailored to Kenyan aviation industry. Hence there is need to develop prediction models amenable to Kenya aviation conditions. The objective of this study was to compare the prediction power of the developed models. Secondary data from Jomo Kenya International Airport (JKIA) was used in this study. The data collected included the day of the flight (Monday to Sunday), the month (January to December), the airline, the flight class (domestic or international), season (summer or winter), capacity of the aircraft, flight ID (tail number) and whether the flight had flown at night or during the day. The analysis of the data was done using R- software. Three models, Logistic model, Support Vector Machine model and Random Forest model, were fitted. The strength and utility of the models was determined using bias-variance learning curves. The study revealed that the models predicted delays with different accuracies. The Random Forest model had a prediction accuracy of 68.99% while the Support Vector Machine model (SVM) had an accuracy of 68.62% and the Logistic Regression model had an accuracy of 66.18%. The Random Forest model outperformed the SVM and Logistic Regression with accuracies of 0.37% and 2.71% respectively. The SVM and Random Forest do not assume probability distribution of the response under investigation, probably indicating why they performed better than the logistic regression. The study recommends application of Random Forest model to predict flight delays at JKIA.
Who is Hot and Who is Not? Profiling Celebs on Twitter

We describe the system developed for the Celebrity profiling shared task of PAN 2019, capable of determining the gender, birthyear, occupation and fame of celebrities given their tweets. Our approach is based on a Logistic regression classifier and simple n-gram features. The best performance is achieved on the task of gender prediction, while predicting fame and occupation are slightly harder for the system. The worst performance is unsurprisingly achieved on the task of predicting birthyear, the hardest classification problem with seventy unbalanced classes. The proposed system was 3rd in the global ranking of PAN 2019 Celebrity profiling shared task.
Fast Two-dimensional Otsu's Thresholding Method Based on Integral Image

The traditional two-dimensional Otsu's threshold method uses gray level-average gray level two-dimensional histogram,but there are obvious deficiencies in the regional division of the traditional histogram.In this paper,an improved fast two-dimensional Otsu's threshold method with integral image based on gray level-gradient two-dimensional histogram was proposed.Using integral image to reduce the computational complexity in searching the best threshold value of two-dimensional histogram,thereby reducing the calculation amount.The results show that the method has a good segmentation effect which greatly and significantly increases the computing speed.Therefore,it is a fast and effective image segmentation method with a good real-time quality.
TOBB-ETU at CLEF 2019: Prioritizing Claims Based on Check-Worthiness

In recent years, we witnessed an incredible amount of mis-information spread over the Internet. However, it is extremely time consuming to analyze the veracity of every claim made on the Internet. Thus, we urgently need automated systems that can prioritize claims based on their check-worthiness, helping fact-checkers to focus on important claims. In this paper, we present our hybrid approach which combines rule-based and supervised methods for CLEF-2019 Check That! Lab’s Check-Worthiness task. Our primary model ranked 9 th based on MAP, and 6 th based on R-P, P@5, and P@20 metrics in the oﬃcial evaluation of primary submissions.
AlignFlow: Learning from multiple domains via normalizing flows

The goal of unpaired cross-domain translation is to learn useful mappings between two domains, given only unpaired sets of datapoints from these domains. While this formulation is highly underconstrained, recent work has shown that it is possible to learn mappings useful for downstream tasks by encouraging approximate cycle consistency in the mappings between the two domains (Zhu et al., 2017a). In this work, we propose AlignFlow, a framework for unpaired cross-domain translation that ensures exact cycle consistency in the learned mappings. Our framework uses a normalizing flow model to specify a single invertible mapping between the two domains. In contrast to prior works in cycle-consistent translations, we can learn AlignFlow via adversarial training, maximum likelihood estimation, or a hybrid of the two methods. Theoretically, we derive consistency results for AlignFlow which guarantee recovery of desirable mappings under suitable assumptions. Empirically, AlignFlow demonstrates significant improvements over relevant baselines on imageto-image translation and unsupervised domain adaptation tasks on benchmark datasets.
CheckThat! 2019 UAICS

Investigative journalists or detectives lose a lot of time to prove a certain claim, searching for the source or evidence to support this assertion often by hand. In this context, in order to address this problem, the 2019 CLEF CheckThat! comes with two tasks: (1) Check-Worthiness and (2) Evidence & Factuality. Our group participated to the first task whose aim is to evaluate the check worthiness of a political claim in a debate. The method to achieve the goal of the task was to represent each claim by a feature vector and feed it to a machine learning classification algorithms in order to classify if the claim is check-worthy or not. We submitter 3 runs, one primary and two contrastive, the primary being a Naive Bayes, the first contrastive Linear Regression and the second one SVM. The best result we achieved using the official measure MAP was with the Naive Bayes, the second best was the SVM and the third was the Linear Regression. This paper presents the details of our approaches.
UPV-UMA at CheckThat! Lab: Verifying Arabic Claims using a Cross Lingual Approach

In this paper we present our team participation at CheckThat! - 2019 lab - Task 2 on Arabic claim veriﬁcation. We propose a cross-lingual approach to detect the factuality of claims using three main steps, evidence retrieval, evidence ranking, and textual entailment. Our approach achieves the best performance in subtask-D, with a value of 0.62 as F1.
Modelling the Behavior Classification of Social News Aggregations Users

This paper deals with actual fuzzy logic approach for modelling the behavior classification of social news aggregations users. The peculiarities of the structure of informational content of communities on the basis of social news aggregations are explored. A formal model of social news aggregation model has been developed, which includes user of the social news aggregation on the basis of fuzzy measures of its characteristics. The method of behavioral classification of users and methods for structuring sections and discussions of social news aggregations are developed. The methods for determining the main characteristics of the users of the social news aggregation: activeness, creativeness, attractiveness, reactiveness, loyalty, is developed. Method for defining characteristics and classification of social news aggregations users is presented.
A Baseline Approach for Early Detection of Signs of Anorexia and Self-harm in Reddit Posts

This paper describes the systems developed by the BiTeM team for the CLEF eRisk Task 1 and 2, 2019. The goal was to predict the risk of anorexia and self-harm from user-generated content on Reddit. Several approaches based on supervised learning were used to estimate the risk of anorexia and self-harm. The systems were able to achieve low to moderate results.
The Design and Implementation Ocelot’s Dynamic Binary Translator from PTX to Multi-Core x86

Ocelot is a dynamic compilation framework designed to map the explicitly parallel PTX execution model used by NVIDIA CUDA applications onto diverse many-core architectures. Ocelot includes a dynamic binary translator from PTX to many-core processors that leverages the LLVM code generator to target x86. The binary translator is able to execute CUDA applications without recompilation and Ocelot can in fact dynamically switch between execution on an NVIDIA GPU and a many-core CPU. It has been validated against over 100 applications taken from the CUDA SDK [1], the UIUC Parboil benchmarks [2], the Virginia Rodinia benchmarks [3], the GPUVSIPL signal and image processing library [4], and several domain specific applications. This paper presents a detailed description of the implementation of our binary translator highlighting design decisions and trade-offs, and showcasing their effect on application performance. We explore several code transformations that are applicable only when translating explicitly parallel applications and suggest additional optimization passes that may be useful to this class of applications. We expect this study to inform the design of compilation tools for explicitly parallel programming models (such as OpenCL) as well as future CPU and GPU architectures.
Overview of ImageCLEFtuberculosis 2019 - Automatic CT-based Report Generation and Tuberculosis Severity Assessment

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language-independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well as the retrieval of images. Since 2017, when the tuberculosis task started in ImageCLEF, the number of participants has kept growing. In 2019, 13 groups from 11 countries participated in at least one of the two subtasks proposed: (1) SVR subtask: the assessment of a tuberculosis severity score and (2) CTR subtask: the automatic generation of a CT report based on six relevant CT findings. In this second edition of the SVR subtask the results support the assessment of a severity score based on the CT scan with up to 0.79 area under the curve (AUC) and 74% accuracy, so very good results. In addition, in the first edition of the CTR subtask, impressive results were obtained with 0.80 average AUC and 0.69 minimum AUC for the six CT findings proposed.
Multi-resolution approach to computing locally injective maps on meshes

Computing injective mappings with low distortions on meshes is an important problem for its wide range of practical applications in computer graphics, geometric modeling and physical simulations. Such tasks as surface parametrization or shape deformation are often reduced to minimizing non-convex and non-linear geometric energies defined over triangulated domains. These energies are commonly expressed in a finite element manner as a weighted sum of distortion densities D over simplixes S:[MATHS HERE]where (2) enforces f to preserve orientation of each simplex, and (A, b) is a linear system of the given positional constraints. The orientation constraints are particularly important in parametrization problems, since they avoid undesirable foldover artifacts in the texture, while positional constraints are widely used in shape deformation applications, such as point-to-point deformations, deformations with fixed anchors, and more.
VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019

This paper presents an overview of the Medical Visual Question Answering task (VQA-Med) at ImageCLEF 2019. Participating systems were tasked with answering medical questions based on the visual content of radiology images. In this second edition of VQA-Med, we focused on four categories of clinical questions: Modality, Plane, Organ System, and Abnormality. These categories are designed with diﬀerent degrees of diﬃculty leveraging both classiﬁcation and text generation approaches. We also ensured that all questions can be answered from the image content without requiring additional medical knowledge or domain-speciﬁc inference. We created a new dataset of 4,200 radiology images and 15,292 question-answer pairs following these guidelines. The challenge was well received with 17 participating teams who applied a wide range of approaches such as transfer learning, multi-task learning, and ensemble methods. The best team achieved a BLEU score of 64.4% and an accuracy of 62.4%. In future editions, we will consider designing more goal-oriented datasets and tackling new aspects such as contextual information and domain-speciﬁc inference.
CiteTracked: A Longitudinal Dataset of Peer Reviews and Citations

Scientiﬁc dissemination is of central importance for the sci-entiﬁc process. This paper presents CiteTracked , a dataset of peer reviews and citation statistics covering scientiﬁc papers from the machine learning community and spanning six years. We describe and analyze the data collection of over 3,000 published papers, their peer review texts and citation counts, and depict possible usage directions. The dataset aims at fertilizing novel interdisciplinary work between ﬁelds such as scien-tometrics, information retrieval, computational linguistics and natural language processing to study the scientiﬁc publishing process.
Rapid 3D building modeling by sketching

This paper proposes an intuitive tool for users to create 3D architectural models through 2D sketch input. A user only needs to draw the outline of a frontal or oblique view of a building. Our system recognizes the parts drawn in a sketch and estimates their types. The estimated information is then used to compose the corresponding 3D model. Besides, our system provides additional assistant tools for rapid editing. The modeling process can be iterative and incremental. To accomplish a building complex, a user can gradually create their models from one view to another. Our experiment shows that the proposed interface with sketch analysis tools eases the process of 3D building modeling.