nils-herrmann/clean_scirepeval huggingface dataset:
Initial management of Parkinson's disease.

Parkinson's disease is one of the most common neurodegenerative disorders seen in the United States and United Kingdom. The disease is characterised by two processes-cellular degeneration and the resulting biochemical deficiency of dopamine. Although these processes are inter-related, they are approached separately in the clinical setting. Currently, no proven neuroprotective or disease modifying treatment is available for Parkinson's disease. Several agents can be used to treat the motor symptoms associated with dopamine deficiency, and it is important to choose wisely when starting treatment. Drugs can have mild, moderate, or high potency, and the patient's goals, comorbidities, and the short and long term implications of choosing a specific agent should be taken into account when selecting the appropriate agent. Non-motor symptoms, such as depression, fatigue, and disorders of sleep and wakefulness, also need to be evaluated and treated. Research is under way to deliver dopaminergic therapy more effectively, but studies aimed at slowing or stopping disease progression have not shown promise.
A survey on network attacks and Intrusion detection systems

Network Security is playing a vital role in all types of networks. Nowadays the network is implemented in all places like offices, schools, banks etc. and almost all the individuals are taking part in social network media. Even though many types of network security systems are in use, the vulnerable activities are taking place now and then. This paper presents a survey about various types of network attacks mainly web attacks, and different Intrusion Detection Systems(IDS) which are in use. This may pave a path to design a new type of IDS which may protect the network system from various types of network attacks.
Probabilistic Models for Ad Viewability Prediction on the Web

Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers’ webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users’ screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems.
Cutis verticis gyrata primitif essentiel, une affection cutanée rare: cas clinique et revue de la littérature

Le cutis verticis gyrata (CVG), du cuir chevelu, est une maladie rare et évolutive de la peau du scalp. Elle est caractérisée par une hypertrophie et une hyperlaxité cutanée formant des plis semblables aux gyri du cortex cérébral. Nous présentons le cas d'une patiente de 21 ans atteinte de CVG primitif essentiel, ayant débuté à l'âge de 8 ans, au niveau du scalp et était d'aggravation progressive. La malade présentait une déformation du scalp avec de nombreux plis longitudinaux et transversaux. Sa demande était motivée par une gêne sociale et esthétique. Une résection chirurgicale de l'excédent cutané dans un plan transversal et longitudinal était réalisée. Le traitement de cette maladie est chirurgical, par l'excision des plis cutanés et remise en tension du scalp. Le nombre et la localisation des incisions doivent préserver la vascularisation des lambeaux de scalp et tenir compte du caractère évolutif de cette pathologie. Pan African Medical Journal. 2014;  The Pan African Medical Journal ISSN 1937-8688.  19378688 (www.panafrican-med-journal.com) Published in partnership with the African Field Epidemiology Network (AFENET). (www.afenet.net)
Test of Attention : Normative data for the Latin American Spanish speaking adult population

To generate normative data on the Brief Test of Attention (BTA) across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.  The sample consisted of 3,977 healthy adults who were recruited from Mexico, Argentina, Peru, Paraguay, Honduras, Chile, Cuba, Puerto Rico, Guatemala, El Salvador, and Bolivia. Each subject was administered the BTA as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.  The final multiple linear regression models explained between 11–41% of the variance in BTA scores. Although men had higher scores on the BTA in Honduras, there were no other significant gender differences, and this one effect size was small. As a result, gender-adjusted norms were not generated.  This is the first normative multicenter study conducted in Latin America to create norms for the BTA; this study will have an impact on the future practice of neuropsychology throughout Latin America.
ReasoNet: Learning to Stop Reading in Machine Comprehension

Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.
Performing Efficient NURBS Modeling Operations on the GPU

We present algorithms for evaluating and performing modeling operatyons on NURBS surfaces using the programmable fragment processor on the Graphics Processing Unit (GPU). We extend our GPU-based NURBS evaluator that evaluates NURBS surfaces to compute exact normals for either standard or rational B-spline surfaces for use in rendering and geometric modeling. We build on these calculations in our new GPU algorithms to perform standard modeling operations such as inverse evaluations, ray intersections, and surface-surface intersections on the GPU. Our modeling algorithms run in real time, enabling the user to sketch on the actual surface to create new features. In addition, the designer can edit the surface by interactively trimming it without the need for re-tessellation. We also present a GPU-accelerated algorithm to perform surface-surface intersection operations with NURBS surfaces that can output intersection curves in the model space as well as in the parametric spaces of both the intersecting surfaces at interactive rates.
Peritoneal repair and post-surgical adhesion formation.

It was shown in 1919 that peritoneal healing differs from that of skin. When a defect is made in the parietal peritoneum the entire surface becomes epithelialized simultaneously and not gradually from the borders as in epidermalization of skin wounds. While multiplication and migration of mesothelial cells from the margin of the wound may play a small part in the regenerative process, it cannot play a major role, since new mesothelium develops in the centre of a large wound at the same time as it develops in the centre of a smaller one. Development of intraperitoneal adhesions is a dynamic process whereby surgically traumatized tissues in apposition bind through fibrin bridges which become organized by wound repair cells, often supporting a rich vascular supply as well as neuronal elements.
A new binary support vector system for increasing detection rate of credit card fraud

Recently, a new personalized model has been developed to prevent credit card fraud. This model is promising; however, there remains some problems. Existing approaches cannot identify well credit card frauds from few data with skewed distributions. This paper proposes to address the problem using a binary support vector system (BSVS). The proposed BSVS is based on the support vectors in the support vector machines (SVM) and the genetic algorithm (GA) is employed to select support vectors. To obtain a high true negative rate, self-organizing mapping (SOM) is first employed to estimate the distribution model of the input data. Then BSVS is used to best train the data according to the input data distribution to obtain a high detection rate. Experimental results show that the proposed BSVS is effective especially for predicting a high true negative rate.
Artificial bandwidth extension without side information for ITU-t g.729.1

This paper discusses a potential extension of the ITU-T G.729.1 speech and audio codec. The G.729.1 coder is hierarchically organized, i.e., the obtained quality increases with the amount of bits that is received for each frame. In particular, the bit rates of 8 and 12 kbit/s offer narrowband (50 Hz – 4 kHz) speech transmission. With a received bit rate of at least 14 kbit/s, the output bandwidth is increased to the wideband frequency range (50 Hz – 7 kHz). Here, we investigate efficient methods to provide the full wideband frequency bandwidth already for the lower bit rates of 8 and 12 kbit/s while maintaining interoperability with the standard implementation of G.729.1. These techniques are not necessarily limited to G.729.1 and thus may serve in other applications as well.
Action Language verifier: an infinite-state model checker for reactive software specifications

Action Language is a specification language for reactive sof tware systems. In this paper, we present the syntax and the semantics of the Action Language and we also present an in finite-state symbolic model checker called Action Language Verifier (ALV) that verifies (or falsifies) CTL properti es of Action Language specifications. ALV is built on top of the Composite Symbolic Library, which is a symbolic manip ulator that combines multiple symbolic representations. ALV is a polymorphic model checker that can use different com binations of the symbolic representations implemented in the Composite Symbolic Library. We describe the heuristi cs implemented in ALV for computing fixpoints using the composite symbolic representation. Since Action Langu ge specifications allow declaration of unbounded integer variables and parameterized integer constants, verificati on of Action Language specifications is undecidable. ALV uses several heuristics to conservatively approximate the fixpoint computations. ALV also implements an automated abstraction technique that enables parameterized verifica tion of a concurrent system with an arbitrary number of identical processes.
Extracellular matrix signatures of human mammary carcinoma identify novel metastasis promoters

The extracellular matrix (ECM) is a major component of tumors and a significant contributor to cancer progression. In this study, we use proteomics to investigate the ECM of human mammary carcinoma xenografts and show that primary tumors of differing metastatic potential differ in ECM composition. Both tumor cells and stromal cells contribute to the tumor matrix and tumors of differing metastatic ability differ in both tumor- and stroma-derived ECM components. We define ECM signatures of poorly and highly metastatic mammary carcinomas and these signatures reveal up-regulation of signaling pathways including TGFβ and VEGF. We further demonstrate that several proteins characteristic of highly metastatic tumors (LTBP3, SNED1, EGLN1, and S100A2) play causal roles in metastasis, albeit at different steps. Finally we show that high expression of LTBP3 and SNED1 correlates with poor outcome for ER(-)/PR(-)breast cancer patients. This study thus identifies novel biomarkers that may serve as prognostic and diagnostic tools.
Named data networking for IoT: An architectural perspective

The Named Data Networking (NDN) project is emerging as one of the most promising information-centric future Internet architectures. Besides NDN recognized potential as a content retrieval solution in wired and wireless domains, its innovative concepts, such as named content, name-based routing and in-network caching, particularly suit the requirements of Internet of Things (IoT) interconnecting billions of heterogeneous objects. IoT highly differs from today's Internet due to resource-constrained devices, massive volumes of small exchanged data, and traffic type diversity. The study in this paper addresses the design of a high-level NDN architecture, whose main components are overhauled to specifically meet the IoT challenges.
Automatically Correcting Typing Errors for People with Motor Impairments

People with motor impairments often have difficulty entering text accurately when typing on a keyboard. Thes e users also may have trouble correcting errors. We introdu ce TrueKeys, a system that automatically corrects typing errors as they occur. TrueKeys utilizes a word freque ncy list and a model of the user’s keyboard layout to identi fy typing errors and choose appropriate corrections. We evalu ated TrueKeys with 9 motor-impaired and 9 able-bodied us ers who completed phrase-typing trials with correction enabled and disabled. Results show that using TrueKeys sign ifica tly reduced the number of uncorrected errors for bot h motor-impaired (2.09% vs. 3.44% errors) and able-bodied users (1.03% vs. 1.83% errors).
DeepWrinkles: Accurate and Realistic Clothing Modeling

We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physicsbased simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically suffer from lack of geometric details. Here, we propose an original framework consisting of two modules that work jointly to represent global shape deformation as well as surface details with high fidelity. Global shape deformations are recovered from a subspace model learned from 3D data of clothed people in motion, while high frequency details are added to normal maps created using a conditional Generative Adversarial Network whose architecture is designed to enforce realism and temporal consistency. This leads to unprecedented high-quality rendering of clothing deformation sequences, where fine wrinkles from (real) high resolution observations can be recovered. In addition, as the model is learned independently from body shape and pose, the framework is suitable for applications that require retargeting (e.g., body animation). Our experiments show original high quality results with a flexible model. We claim an entirely data-driven approach to realistic cloth wrinkle generation is possible.
Working with Multilabel Datasets in R: The mldr Package

Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The goal is to provide the exploratory tools needed to analyze multilabel datasets, as well as the transformation and manipulation functions that will make possible to apply binary and multiclass classification models to this data or the development of new multilabel classifiers. Thanks to its integrated user interface, the exploratory functions will be available even to non-specialized R users.
The FGF23/KLOTHO Regulatory Network and Its Roles in Human Disorders.

The functions of Klotho (KL) are multifaceted and include the regulation of aging and mineral metabolism. It was originally identified as the gene responsible for premature aging-like symptoms in mice and was subsequently shown to function as a coreceptor in the fibroblast growth factor (FGF) 23 signaling pathway. The discovery of KL as a partner for FGF23 led to significant advances in understanding of the molecular mechanisms underlying phosphate and vitamin D metabolism, and simultaneously clarified the pathogenic roles of the FGF23 signaling pathway in human diseases. These novel insights led to the development of new strategies to combat disorders associated with the dysregulated metabolism of phosphate and vitamin D, and clinical trials on the blockade of FGF23 signaling in X-linked hypophosphatemic rickets are ongoing. Molecular and functional insights on KL and FGF23 have been discussed in this review and were extended to how dysregulation of the FGF23/KL axis causes human disorders associated with abnormal mineral metabolism.
Time Series Prediction and Neural Networks

Neural Network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are introduced, and the number of false neighbours heuristic is described, as a means of finding the correct embedding dimension, and thence window size. The method is applied to three time series and the resulting generalisation performance of the trained feed-forward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architecture.
Mapping the contribution of single muscles to facial movements in the rhesus macaque

The rhesus macaque (Macaca mulatta) is the most utilized primate model in the biomedical and psychological sciences. Expressive behavior is of interest to scientists studying these animals, both as a direct variable (modeling neuropsychiatric disease, where expressivity is a primary deficit), as an indirect measure of health and welfare, and also in order to understand the evolution of communication. Here, intramuscular electrical stimulation of facial muscles was conducted in the rhesus macaque in order to document the relative contribution of each muscle to the range of facial movements and to compare the expressive function of homologous muscles in humans, chimpanzees and macaques. Despite published accounts that monkeys possess less differentiated and less complex facial musculature, the majority of muscles previously identified in humans and chimpanzees were stimulated successfully in the rhesus macaque and caused similar appearance changes. These observations suggest that the facial muscular apparatus of the monkey has extensive homology to the human face. The muscles of the human face, therefore, do not represent a significant evolutionary departure from those of a monkey species. Thus, facial expressions can be compared between humans and rhesus macaques at the level of the facial musculature, facilitating the systematic investigation of comparative facial communication.
Emotion recognition in human computer interaction using multiple classifier systems

Initial work on automatic emotion recognition concentrates mainly on audio-based emotion classification. Speech is the most important channel for the communication between humans and it may expected that emotional states are trans-fered though content, prosody or paralinguistic cues. Besides the audio modality, with the rapidly developing computer hardware and video-processing devices researches start exploring the video modality. Visual-based emotion recognition works focus mainly on the extraction and recognition of emotional information from the facial expressions. There are also attempts to classify emotional states from body or head gestures and to combine different visual modalities, for instance facial expressions and body gesture captured by two separate cameras . Emotion recognition from psycho-physiological measurements, such as skin conductance, respiration, electro-cardiogram (ECG), electromyography (EMG), electroencephalography (EEG) is another attempt. In contrast to speech, gestures or facial expressions these biopotentials are the result of the autonomic nervous system and cannot be imitated . Research activities in facial expression and speech based emotion recognition [6] are usually performed independently from each other. But in almost all practical applications people speak and exhibit facial expressions at the same time, and consequently both modalities should be used in order to perform robust affect recognition. Therefore, multimodal, and in particularly audiovisual emotion recognition has been emerging in recent times [11], for example multiple classifier systems have been widely investigated for the classification of human emotions [1, 9, 12, 14]. Combining classifiers is a promising approach to improve the overall classifier performance [13, 8]. In multiple classifier systems (MCS) it is assumed that the raw data X originates from an underlying source, but each classifier receives different subsets of (X) of the same raw input data X. Feature vector F j (X) are used as the input to the j−th classifier computing an estimate y j of the class membership of F j (X). This output y j might be a crisp class label or a vector of class memberships, e.g. estimates of posteriori probabilities. Based on the multiple classifier outputs y 1 ,. .. , y N the combiner produces the final decision y. Combiners used in this study are fixed transformations of the multiple classifier outputs y 1 ,. .. , y N. Examples of such combining rules are Voting, (weighted) Averaging, and Multiplying, just to mention the most popular types. 2 Friedhelm Schwenker In addition to a priori fixed combination rules the combiner can be a …
Improving Formula Analysis with Line and Mathematics Identification

The explosive growth of the internet and electronic publishing has led to a huge number of scientific documents being available to users, however, they are usually inaccessible to those with visual impairments and often only partially compatible with software and modern hardware such as tablets and e-readers. In this paper we revisit Maxtract, a tool for analysing and converting documents into accessible formats, and combine it with two advanced segmentation techniques, statistical line identification and machine learning formula identification. We show how these advanced techniques improve the quality of both Maxtract's underlying document analysis and its output. We re-run and compare experimental results over a number of datasets, presenting a qualitative review of the improved output and drawing conclusions.