nils-herrmann/clean_scirepeval huggingface dataset:
Feature Selection for Clustering

Clustering is an important data mining task Data mining often concerns large and high dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high dimensionality or both Di erent features a ect clusters di erently some are important for clusters while others may hinder the clustering task An e cient way of handling it is by selecting a subset of important features It helps in nding clusters e ciently understanding the data better and reducing data size for e cient storage collection and process ing The task of nding original important features for unsupervised data is largely untouched Traditional feature selection algorithms work only for supervised data where class information is available For unsuper vised data without class information often principal components PCs are used but PCs still require all features and they may be di cult to understand Our approach rst features are ranked according to their importance on clustering and then a subset of important features are selected For large data we use a scalable method using sampling Em pirical evaluation shows the e ectiveness and scalability of our approach for benchmark and synthetic data sets
Reliability and validity of the visual analogue scale for disability in patients with chronic musculoskeletal pain.

To determine the reliability and concurrent validity of a visual analogue scale (VAS) for disability as a single-item instrument measuring disability in chronic pain patients was the objective of the study. For the reliability study a test-retest design and for the validity study a cross-sectional design was used. A general rehabilitation centre and a university rehabilitation centre was the setting for the study. The study population consisted of patients over 18 years of age, suffering from chronic musculoskeletal pain; 52 patients in the reliability study, 344 patients in the validity study. Main outcome measures were as follows.  Spearman's correlation coefficients (rho values) of the test and retest data of the VAS for disability; validity study: rho values of the VAS disability scores with the scores on four domains of the Short-Form Health Survey (SF-36) and VAS pain scores, and with Roland-Morris Disability Questionnaire scores in chronic low back pain patients. Results were as follows: in the reliability study rho values varied from 0.60 to 0.77; and in the validity study rho values of VAS disability scores with SF-36 domain scores varied from 0.16 to 0.51, with Roland-Morris Disability Questionnaire scores from 0.38 to 0.43 and with VAS pain scores from 0.76 to 0.84. The conclusion of the study was that the reliability of the VAS for disability is moderate to good. Because of a weak correlation with other disability instruments and a strong correlation with the VAS for pain, however, its validity is questionable.
A Secure Electronic Payment Protocol Design and Implementation

Electronic payment is the very important step of the electronic business system, and its security must be ensured. SSL/TLS and SET are two widely discussed means of securing online credit card payments. Because of implementation issues, SET has not really been adopted by e-commerce participants, whereas, despite the fact that it does not address all security issues, SSL/TLS is commonly used for Internet e-commerce security. The three-domain (3D) security schemes, including 3-D Secure and 3D SET have recently been proposed as ways of improving ecommerce transaction security. Based on our research about SSL, SET, 3D security schemes and the requirements of electronic payment, we designed a secure and efficient E-Payment protocol. The new protocol offers an extra layer of protection for cardholders and merchants. Customers are asked to enter an additional password after checkout completion to verify they are truly the cardholder, the authentication is done directly between the cardholder and card issuer using the issuer security certificate and without involving the third party (Visa, MasterCard).
Detection of Plagiarism in Arabic Documents

Many language-sensitive tools for detecting plagiarism in natural language documents have been developed, particularly for English. Languageindependent tools exist as well, but are considered restrictive as they usually do not take into account specific language features. Detecting plagiarism in Arabic documents is particularly a challenging task because of the complex linguistic structure of Arabic. In this paper, we present a plagiarism detection tool for comparison of Arabic documents to identify potential similarities. The tool is based on a new comparison algorithm that uses heuristics to compare suspect documents at different hierarch ical levels to avoid unnecessary comparisons. We evaluate its performance in terms of precision and recall on a large data set of Arabic documents, and show its capability in identifying direct and sophisticated copying, such as sentence reordering and synonym substitution. We also demonstrate its advantages over other plagiarism detection tools, including Turnitin, the well-known language-independent tool.
Natural tongue physique identification using hybrid deep learning methods

Traditional Chinese Medicine (TCM) illustrates that the physique determines the susceptibility of human to certain diseases and treatment programs for illness. Tongue diagnosis is an important way to identify the physique, but now it is performed by the doctor’s professional experience and the design of a questionnaire. Consequently, accurate physique identification cannot be obtained easily. In this paper, we propose a new method to identify the physique through wild tongue images using hybrid deep learning methods. It begins with constructing a large number of tongue images that are taken in natural conditions, instead of in a controlled environment. Based on the resulting database, a new method of tongue coating detection is put forward that applies a rapid deep learning method to complete the initial tongue coating detection, and then utilizes another deep learning method, a calibration neural network, to further improve the accuracy of tongue detection. Finally, an effective deep learning method is applied to identify the tongue physique. Experiments validate the proposed method, illustrating that physique identification can be performed well using hybrid deep learning methods.
Risk factors for respiratory failure following operative stabilization of thoracic and lumbar spine fractures.

Respiratory failure is a serious complication that can adversely affect the hospital course and survival of multiply injured patients. Some studies have suggested that delayed surgical stabilization of spine fractures may increase the incidence of respiratory complications. However, the authors of these studies analyzed small sets of patients and did not assess the independent effects of multiple risk factors.



A retrospective cohort study was conducted at a regional level-I trauma center to identify risk factors for respiratory failure in patients with surgically treated thoracic and lumbar spine fractures. Demographic, diagnostic, and procedural variables were identified. The incidence of respiratory failure was determined in an adult respiratory distress syndrome registry maintained concurrently at the same institution. Univariate and multivariate analyses were used to determine independent risk factors for respiratory failure. An algorithm was formulated to predict respiratory failure.



Respiratory failure developed in 140 of the 1032 patients in the study cohort. Patients with respiratory failure were older; had a higher mean Injury Severity Score (ISS) and Charlson Comorbidity Index Score; had greater incidences of pneumothorax, pulmonary contusion, and thoracic level injury; had a lower mean Glasgow Coma Score (GCS); were more likely to have had a posterior surgical approach; and had a longer mean time from admission to surgical stabilization than the patients without respiratory failure (p < 0.05). Multivariate analysis identified five independent risk factors for respiratory failure: an age of more than thirty-five years, an ISS of > 25 points, a GCS of < or = 12 points, blunt chest injury, and surgical stabilization performed more than two days after admission. An algorithm was created to determine, on the basis of the number of preoperative predictors present, the relative risk of respiratory failure when surgery was delayed for more than two days.



Independent risk factors for respiratory failure were identified in an analysis of a large cohort of patients who had undergone operative stabilization of thoracic and lumbar spine fractures. Early operative stabilization of these fractures, the only risk factor that can be controlled by the physician, may decrease the risk of respiratory failure in multiply injured patients.
TB infection prevention and control experiences of South African nurses - a phenomenological study

The tuberculosis (TB) epidemic in South Africa is characterised by one of the highest levels of TB/HIV co-infection and growing multidrug-resistant TB worldwide. Hospitals play a central role in the management of TB. We investigated nurses' experiences of factors influencing TB infection prevention and control (IPC) practices to identify risks associated with potential nosocomial transmission.



The qualitative study employed a phenomenological approach, using semi-structured interviews with a quota sample of 20 nurses in a large tertiary academic hospital in Cape Town, South Africa. The data was subjected to thematic analysis.



Nurses expressed concerns about the possible risk of TB transmission to both patients and staff. Factors influencing TB-IPC, and increasing the potential risk of nosocomial transmission, emerged in interconnected overarching themes. Influences related to the healthcare system included suboptimal IPC provision such as the lack of isolation facilities and personal protective equipment, and the lack of a TB-IPC policy. Further influences included inadequate TB training for staff and patients, communication barriers owing to cultural and linguistic differences between staff and patients, the excessive workload of nurses, and a sense of duty of care. Influences related to wider contextual conditions included TB concerns and stigma, and the role of traditional healers. Influences related to patient behaviour included late uptake of hospital care owing to poverty and the use of traditional medicine, and poor adherence to IPC measures by patients, family members and carers.



Several interconnected influences related to the healthcare system, wider contextual conditions and patient behavior could increase the potential risk of nosocomial TB transmission at hospital level. There is an urgent need for the implementation and evaluation of a comprehensive contextually appropriate TB IPC policy with the setting and auditing of standards for IPC provision and practice, adequate TB training for both staff and patients, and the establishment of a cross-cultural communication strategy, including rapid access to interpreters.
Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach

We consider the problem of learning Boltzmann machine classifiers from relational data. Our goal is to extend the deep belief framework of RBMs to statistical relational models. This allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL). Specifically, we use lifted random walks to generate features for predicates that are then used to construct the observed features in the RBM in a manner similar to Markov Logic Networks. We show empirically that this method of constructing an RBM is comparable or better than the state-of-theart probabilistic relational learning algorithms on four relational domains.
Crohn's disease

Crohn's disease is a relapsing systemic inflammatory disease, mainly affecting the gastrointestinal tract with extraintestinal manifestations and associated immune disorders. Genome wide association studies identified susceptibility loci that--triggered by environmental factors--result in a disturbed innate (ie, disturbed intestinal barrier, Paneth cell dysfunction, endoplasmic reticulum stress, defective unfolded protein response and autophagy, impaired recognition of microbes by pattern recognition receptors, such as nucleotide binding domain and Toll like receptors on dendritic cells and macrophages) and adaptive (ie, imbalance of effector and regulatory T cells and cytokines, migration and retention of leukocytes) immune response towards a diminished diversity of commensal microbiota. We discuss the epidemiology, immunobiology, amd natural history of Crohn's disease; describe new treatment goals and risk stratification of patients; and provide an evidence based rational approach to diagnosis (ie, work-up algorithm, new imaging methods [ie, enhanced endoscopy, ultrasound, MRI and CT] and biomarkers), management, evolving therapeutic targets (ie, integrins, chemokine receptors, cell-based and stem-cell-based therapies), prevention, and surveillance.
Designing of ORBAC Model For Secure Domain Environments

In the secure domain computing environments, it is important to keep resources and information integrity from unauthorized access. Therefore, there is a strong demand on the access control for shared resources. In the past few years, Role-based Access Control (RBAC) has been introduced and offered a powerful means of specifying access control decisions. In this paper, an Object Oriented RBAC model (ORBAC) that efficiently represents the real world is proposed. It not only supports a single secure domain authorization for an enterprise, but also supports multiple secure domains access control for E-business carried out by multiple enterprises over the Internet.
A Geometry-Sensitive Approach for Photographic Style Classification

Photographs are characterized by different compositional attributes like the Rule of Thirds, depth of field, vanishing-lines etc. The presence or absence of one or more of these attributes contributes to the overall artistic value of an image. In this work, we analyze the ability of deep learning based methods to learn such photographic style attributes. We observe that although a standard CNN learns the texture and appearance based features reasonably well, its understanding of global and geometric features is limited by two factors. First, the data-augmentation strategies (cropping, warping, etc.) distort the composition of a photograph and affect the performance. Secondly, the CNN features, in principle, are translationinvariant and appearance-dependent. But some geometric properties important for aesthetics, e.g. the Rule of Thirds (RoT), are position-dependent and appearance-invariant. Therefore, we propose a novel input representation which is geometry-sensitive, position-cognizant and appearance-invariant. We further introduce a two-column CNN architecture that performs better than the state-of-the-art (SoA) in photographic style classification. From our results, we observe that the proposed network learns both the geometric and appearance-based attributes better than the SoA.
Design of 2.4 GHz Helix antenna for increasing wifi signal strength using Mmanagal and wirelesmon application

Helix antenna consists of a single conductor or multi-conductor open helix-shaped. Helix antenna has a three-dimensional shape. The shape of the helix antenna resembles a spring and the diameter and the distance between the windings of a certain size. This study aimed to design a signal amplifier wifi on 2.4 GHz. Materials used in the form of the pipe, copper wire, various connectors and wireless adapters and various other components. Mmmanagal describing simulation result on helix antenna. Further tested with wirelesmon software to test the wifi signal strength. The results are based Mmanagal, radiation patterns emitted achieve Ganin: 4.5 dBi horizontal polarization, F / B: −0,41dB; rear azimuth 1200 elevation 600, 2400 MHz, R27.9 and jX impedance −430.9, Elev: 64.40 real GND: 0.50 m height, and wifi signal strength increased from 47% to 55%.
Beyond Apollonian Circle Packings : Expander Graphs , Number Theory and Geometry 1

In the last decade tremendous effort has been put in the study of the Apollonian circle packings. Given the great variety of mathematics it exhibits, this topic has attracted experts from different fields: number theory, homogeneous dynamics, expander graphs, group theory, to name a few. The principle investigator (PI) contributed to this program in his PhD studies. The scenery along the way formed the horizon of the PI at his early mathematical career. After his PhD studies, the PI has successfully applied tools and ideas from Apollonian circle packings to the studies of topics from various fields, and will continue this endeavor in his proposed research. The proposed problems are roughly divided into three categories: number theory, expander graphs, geometry. Each of which will be discussed in depth in later sections. Since Apollonian circle packing provides main inspirations for this proposal, let’s briefly review how it comes up and what has been done. We start with four mutually circles, with one circle bounding the other three. We can repeatedly inscribe more and more circles into curvilinear triangular gaps as illustrated in Figure 1, and we call the resultant set an Apollonian circle packing, which consists of infinitely many circles.
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study

We prove that idealised discriminative Bayesian neural networks, capturing perfect epistemic uncertainty, cannot have adversarial examples: Techniques for crafting adversarial examples will necessarily fail to generate perturbed images which fool the classifier. This suggests why MC dropout-based techniques have been observed to be fairly effective against adversarial examples. We support our claims mathematically and empirically. We experiment with HMC on synthetic data derived from MNIST for which we know the ground truth image density, showing that near-perfect epistemic uncertainty correlates to density under image manifold, and that adversarial images lie off the manifold. Using our new-found insights we suggest a new attack for MC dropout-based models by looking for imperfections in uncertainty estimation, and also suggest a mitigation. Lastly, we demonstrate our mitigation on a cats-vs-dogs image classification task with a VGG13 variant.
Software Defect Prediction Models for Quality Improvement : A Literature Study

In spite of meticulous planning, well documentation and proper process control during software development, occurrences of certain defects are inevitable. These software defects may lead to degradation of the quality which might be the underlying cause of failure. In today‟s cutting edge competition it‟s necessary to make conscious efforts to control and minimize defects in software engineering. However, these efforts cost money, time and resources. This paper identifies causative factors which in turn suggest the remedies to improve software quality and productivity. The paper also showcases on how the various defect prediction models are implemented resulting in reduced magnitude of defects.
GNeuron : Parallel Neural Networks with GPU

Artificial Neural networks have been part of an attempt to emulate the learning curve of the human nervous system. However the vital difference of, nervous system being highly parallel and computer processor units remaining largely sequential persists. Here an attempt is made to bridge that gap with the help of Graphics Processing Units (GPUs) which are designed to be highly parallel. In particular Back propagation networks are considered which use supervised learning. Back-propagation algorithms, with no data dependencies are embarrassingly parallel and hence only a totally parallel system can exploit it fully. However, it has also been observed that GPUs underperform when either significant overhead in calculations is incurred or algorithm is not sufficiently parallel.
Exploring Chinese users' acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory

0747-5632/$ see front matter 2008  A doi:10.1016/j.chb.2008.06.002 * Corresponding author.  E-mail addresses: luyb@mail.hust.edu.cn (Y. Lu), zh binwang@utpa.edu (B. Wang). 1 Tel.: +1 956 3813336. Instant messaging (IM) is a popular Internet application around the world. In China, the competition in the IM market is very intense and there are over 10 IM products available. We examine the intrinsic and extrinsic motivations that affect Chinese users’ acceptance of IM based on the theory of planned behavior (TPB), the technology acceptance model (TAM), and the flow theory. Results demonstrate that users’ perceived usefulness and perceived enjoyment significantly influence their attitude towards using IM, which in turn impacts their behavioral intention. Furthermore, perceived usefulness, users’ concentration, and two components of the theory of planned behavior (TPB): subjective norm and perceived behavioral control, also have significant impact on the behavioral intention. Users’ intention determines their actual usage behavior.
Robotic test bench for CubeSat ground testing: Concept and satellite dynamic parameter identification

This paper introduces a novel concept of an air bearing test bench for CubeSat ground testing together with the corresponding dynamic parameter identification method. Contrary to existing air bearing test benches, the proposed concept allows three degree-of-freedom unlimited rotations and minimizes the influence of the test bench on the tested CubeSat. These advantages are made possible by the use of a robotic wrist which rotates air bearings in order to make them follow the CubeSat motion. Another keystone of the test bench is an accurate balancing of the tested CubeSat. Indeed, disturbing factors acting on the satellite shall be minimized, the most significant one being the gravity torque. An efficient balancing requires the CubeSat center of mass position to be accurately known. Usual techniques of dynamic parameter identification cannot be directly applied because of the frictionless suspension of the CubeSat in the test bench and, accordingly, due to the lack of external actuation. In this paper, a new identification method is proposed. This method does not require any external actuation and is based on the sampling of free oscillating motions of the CubeSat mounted on the test bench.
A Walk-Based Semantically Enriched Tree Kernel Over Distributed Word Representations

In this paper, we propose a walk-based graph kernel that generalizes the notion of treekernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed representations. Using vector representations, such an approach captures both distributional semantic similarities among words as well as the structural relations between them (encoded as the structure of the parse tree). We show an efficient formulation to compute this kernel using simple matrix operations. We present our results on three diverse NLP tasks, showing state-of-the-art results.
Qos architectural patterns for self-architecting software systems

This paper discusses the automated application of architectural patterns for tuning the quality of service of service-oriented software. The paper first presents an overview of prior work in self-architecting, SASSY, and a motivating example in the emergency response domain. After summarizing the heuristic used for self-architecting, the paper discusses a number of architectural patterns and the corresponding quantitative models for two concrete aspects of quality of service: availability and response time. A case study illustrates the role of patterns in the application of the self-architecting heuristic to the motivating example.