nils-herrmann/clean_scirepeval huggingface dataset:
Silymarin/Silybin and Chronic Liver Disease: A Marriage of Many Years

Silymarin is the extract of Silybum marianum, or milk thistle, and its major active compound is silybin, which has a remarkable biological effect. It is used in different liver disorders, particularly chronic liver diseases, cirrhosis and hepatocellular carcinoma, because of its antioxidant, anti-inflammatory and antifibrotic power. Indeed, the anti-oxidant and anti-inflammatory effect of silymarin is oriented towards the reduction of virus-related liver damages through inflammatory cascade softening and immune system modulation. It also has a direct antiviral effect associated with its intravenous administration in hepatitis C virus infection. With respect to alcohol abuse, silymarin is able to increase cellular vitality and to reduce both lipid peroxidation and cellular necrosis. Furthermore, silymarin/silybin use has important biological effects in non-alcoholic fatty liver disease. These substances antagonize the progression of non-alcoholic fatty liver disease, by intervening in various therapeutic targets: oxidative stress, insulin resistance, liver fat accumulation and mitochondrial dysfunction. Silymarin is also used in liver cirrhosis and hepatocellular carcinoma that represent common end stages of different hepatopathies by modulating different molecular patterns. Therefore, the aim of this review is to examine scientific studies concerning the effects derived from silymarin/silybin use in chronic liver diseases, cirrhosis and hepatocellular carcinoma.
DOUBLE INVERTED PENDULUM MODEL OF REACTIVE HUMAN STANCE CONTROL

When maintaining equilibrium in upright stance, humans use sensory feedback control to cope with unforeseen external disturbances such as support surface motion, this despite biological 'complications' such as noisy and inaccurate sensor signals and considerable neural, motor, and processing time delays. The control method they use apparently differs from established methods one normally finds in technical fields. System identification recently led us design a control model that we currently test in our laboratory. The tests include hardware-in-the-loop simulations after the model's embodiment into a robot. The model is called disturbance estimation and compensation (DEC) model. Disturbance estimation is performed by on-line multisensory interactions using joint angle, joint torque, and vestibular cues. For disturbance compensation, the method of direct disturbance rejection is used (" Störgrös-senaufschaltung "). So far, biomechanics of a single inverted pendulum (SIP) were applied. Here we extend the DEC concept to the control of a double inverted pendulum (DIP; moving links: trunk on hip joint and legs on ankle joints). The aim is that the model copes in addition with inter-link torques and still describes human experimental data. As concerns the inter-link torque arising during leg motion in the hip joint (support base of the outer link, the trunk), it is already covered by the DEC concept we so far used for the SIP. The inter-link torque arising from trunk motion in the ankle joint is largely neutralized by the concept's whole-body COM control through the ankle joint (due to the fact that body geometry and thus COM location changes with the inter-link motion). Experimentally, we applied pseudorandom support surface tilt stimuli in the sagittal plane to healthy human subjects who were standing with eyes closed on a motion platform (frequency range, 0.16 – 2.2 Hz). Angular excursions of trunk, leg, and whole-body COM (center of mass) with respect to the space vertical as well as COP (center of pressure) shifts were recorded and analyzed. The human data was compared to corresponding model and robot simulation data. The human findings were well described by the model and robot simulations. This shows that the DIP biomechanics of human reactive stance can be controlled using a purely sensor-based control.
Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language

Human lip-reading is a challenging task. It requires not only knowledge of underlying language but also visual clues to predict spoken words. Experts need certain level of experience and understanding of visual expressions learning to decode spoken words. Now-a-days, with the help of deep learning it is possible to translate lip sequences into meaningful words. The speech recognition in the noisy environments can be increased with the visual information . To demonstrate this, in this project, we have tried to train two different deep-learning models for lip-reading: first one for video sequences using spatiotemporal convolution neural network, Bi-gated recurrent neural network and Connectionist Temporal Classification Loss, and second for audio that inputs the MFCC features to a layer of LSTM cells and output the sequence. We have also collected a small audio-visual dataset to train and test our model. Our target is to integrate our both models to improve the speech recognition in the noisy environment.
Fast and Large-scale Unsupervised Relation Extraction

A common approach to unsupervised relation extraction builds clusters of patterns expressing the same relation. In order to obtain clusters of relational patterns of good quality, we have two major challenges: the semantic representation of relational patterns and the scalability to large data. In this paper, we explore various methods for modeling the meaning of a pattern and for computing the similarity of patterns mined from huge data. In order to achieve this goal, we apply algorithms for approximate frequency counting and efficient dimension reduction to unsupervised relation extraction. The experimental results show that approximate frequency counting and dimension reduction not only speeds up similarity computation but also improves the quality of pattern vectors.
An information retrieval approach to concept location in source code

Concept location identifies parts of a software system that implement a specific concept that originates from the problem or the solution domain. Concept location is a very common software engineering activity that directly supports software maintenance and evolution tasks such as incremental change and reverse engineering. This work addresses the problem of concept location using an advanced information retrieval method, Latent Semantic Indexing (LSI). LSI is used to map concepts expressed in natural language by the programmer to the relevant parts of the source code. Results of a case study on NCSA Mosaic are presented and compared with previously published results of other static methods for concept location.
A Survey of Nonlinear Attitude Estimation Methods

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST and the backwards-smoothing extended Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A twostep approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, Associate Professor, Department of Mechanical & Aerospace Engineering.  Associate Fellow AIAA.  Engineer, Guidance, Navigation and Control Systems Engineering Branch.
Applications of Ontologies and Problem-Solving Methods

Ontologies aim at capturing domain knowledge in a generic way. An ontology, therefore, provides a commonly agreed understanding of a domain, which can be reused and shared across ■ The Workshop on Applications of Ontologies and Problem-Solving Methods (PSMs), held in conjunction with the Thirteenth Biennial European Conference on Artificial Intelligence (ECAI ’98), was held on 24 to 25 August 1998. Twenty-six people participated, and 16 papers were presented. Participants included scientists and practitioners from both the ontology and PSM communities. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration. The reason for the joint session was that in all three workshops, ontologies play a prominent role, and the goal was to bring together researchers working on related issues in different communities. The workshop ended with a discussion about the added value of a combined ontologies-PSM workshop compared to separate workshops.
Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).
Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN), Bayesian Classification and Support vector machine (SVM) are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning

Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequences in security-critical applications. To address this vulnerability, we propose a novel metric called Gradient Similarity that allows us to capture the influence of training data on test inputs. We show that Gradient Similarity behaves differently for normal and adversarial inputs, and enables us to detect a variety of adversarial attacks with a near perfect ROC-AUC of 95-100%. Even white-box adversaries equipped with perfect knowledge of the system cannot bypass our detector easily. On the MNIST dataset, white-box attacks are either detected with a high ROC-AUC of 87-96%, or require very high distortion to bypass our detector.
Kannada speech to text conversion using CMU Sphinx

This paper investigates the complex problem of speech to text conversion of Kannada Language. We propose a novel Kannada Automated Speech to Text conversion System (ASTC). We train and test the Speech Processing System using CMU Sphinx framework. CMU Sphinx is dynamic in nature with support for other languages along with English. We train the Acoustic model for Kannada speech with 1000 general spoken sentences and tested 150 sentences. We build our system utilizing features available in CMU Sphinx, thus showcasing the conceivable flexibility of this framework for Kannada voice to text conversion. In this paper, Kannada sentences with four to ten word length is researched. The speech conversion system permits ordinary people to speak to the computer in order to retrieve information in textual form. The number of alphabets in Kannada are 52. The system investigates extensibility of recognizing all letters and morphological variants of spoken Kannada words.
A Survey on Transfer Learning

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.
Improved Statistical Signal Detection in Pharmacovigilance by Combining Multiple Strength-of-Evidence Aspects in vigiRank

Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment.



Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content.



vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase(®) as of 31 December 2004, at around which time most safety signals in our reference set were emerging.



The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver operating characteristics curve (AUC) over screening based on the Information Component (IC) and raw numbers of reports, respectively (0.775 vs. 0.736 and 0.707, cross-validated).



Accounting for multiple aspects of strength of evidence has clear conceptual and empirical advantages over disproportionality analysis. vigiRank is a first-of-its-kind predictive model to factor in report quality and content in first-pass screening to better meet tomorrow's post-marketing drug safety surveillance needs.
Breast ultrasound image segmentation: a survey

Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation. In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly. We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.
Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades

Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions/registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students’ marks. Students’ key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student’s performance. Finally, a prototype version of software support tool for tutors has been constructed.
DNA sequencing of formalin-fixed crustaceans from archival research collections.

Marine invertebrate collections have historically been maintained in ethanol following fixation in formalin. These collections may represent rare or extinct species or populations, provide detailed time-series samples, or come from presently inaccessible or difficult-to-sample localities. We tested the viability of obtaining DNA sequence data from formalin-fixed, ethanol-preserved (FFEP) deep-sea crustaceans, and found that nucleotide sequences for mitochondrial 16S rRNA and COI genes can be recovered from FFEP collections of varying age, and that these sequences are unmodified compared with those derived from frozen specimens. These results were repeatable among multiple specimens and collections for several species. Our results indicate that in the absence of fresh or frozen tissues, archived FFEP specimens may prove a useful source of material for analysis of gene sequence data by polymerase chain reaction (PCR) and direct sequencing.
Accuracy Assessment of Land Use / Land Cover Classification Using Remote Sensing and GIS

Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.