nils-herrmann/clean_scirepeval huggingface dataset:
Human Identification using Face and Voice Recognition

In this paper we proposed new technique for human identification using fusion of both face and speech which can substantially improve the rate of recognition as compared to the single biometric identification for security system development. The proposed system uses principal component analysis (PCA) as feature extraction techniques which calculate the Eigen vectors and Eigen values. These feature vectors are compared using the similarity measure algorithm like Mahalanobis Distances for the decision making. The Mel-Frequency cestrum coefficients (MFCC) feature extraction techniques are used for speech recognition in our project. Cross correlation coefficients are considered as primary features. The Hidden Markov Model (HMM) is used to calculate the like hoods in the MFCC extracted features to make the decision about the spoken wards.
Tofacitinib or Adalimumab versus Placebo for Psoriatic Arthritis.

Tofacitinib is an oral Janus kinase inhibitor that is under investigation for the treatment of psoriatic arthritis. We evaluated tofacitinib in patients with active psoriatic arthritis who previously had an inadequate response to conventional synthetic disease-modifying antirheumatic drugs (DMARDs).



In this 12-month, double-blind, active-controlled and placebo-controlled, phase 3 trial, we randomly assigned patients in a 2:2:2:1:1 ratio to receive one of the following regimens: tofacitinib at a 5-mg dose taken orally twice daily (107 patients), tofacitinib at a 10-mg dose taken orally twice daily (104), adalimumab at a 40-mg dose administered subcutaneously once every 2 weeks (106), placebo with a blinded switch to the 5-mg tofacitinib dose at 3 months (52), or placebo with a blinded switch to the 10-mg tofacitinib dose at 3 months (53). Placebo groups were pooled for analyses up to month 3. Primary end points were the proportion of patients who had an American College of Rheumatology 20 (ACR20) response (≥20% improvement from baseline in the number of tender and swollen joints and at least three of five other important domains) at month 3 and the change from baseline in the Health Assessment Questionnaire-Disability Index (HAQ-DI) score (scores range from 0 to 3, with higher scores indicating greater disability) at month 3.



ACR20 response rates at month 3 were 50% in the 5-mg tofacitinib group and 61% in the 10-mg tofacitinib group, as compared with 33% in the placebo group (P=0.01 for the comparison of the 5-mg dose with placebo; P<0.001 for the comparison of the 10-mg dose with placebo); the rate was 52% in the adalimumab group. The mean change in the HAQ-DI score was -0.35 in the 5-mg tofacitinib group and -0.40 in the 10-mg tofacitinib group, as compared with -0.18 in the placebo group (P=0.006 for the comparison of the 5-mg dose with placebo; P<0.001 for the comparison of the 10-mg dose with placebo); the score change was -0.38 in the adalimumab group. The rate of adverse events through month 12 was 66% in the 5-mg tofacitinib group, 71% in the 10-mg tofacitinib group, 72% in the adalimumab group, 69% in the placebo group that switched to the 5-mg tofacitinib dose, and 64% in the placebo group that switched to the 10-mg tofacitinib dose. There were four cases of cancer, three serious infections, and four cases of herpes zoster in patients who received tofacitinib during the trial.



The efficacy of tofacitinib was superior to that of placebo at month 3 in patients with psoriatic arthritis who had previously had an inadequate response to conventional synthetic DMARDs. Adverse events were more frequent with tofacitinib than with placebo.
19.2 A self-interference-cancelling receiver for in-band full-duplex wireless with low distortion under cancellation of strong TX leakage

In-band full-duplex (FD) wireless communication, i.e. simultaneous transmission and reception at the same frequency, in the same channel, promises up to 2x spectral efficiency, along with advantages in higher network layers . the main challenge is dealing with strong in-band leakage from the transmitter to the receiver (i.e. self-interference (SI)), as TX powers are typically >100dB stronger than the weakest signal to be received, necessitating TX-RX isolation and SI cancellation. Performing this SI-cancellation solely in the digital domain, if at all possible, would require extremely clean (low-EVM) transmission and a huge dynamic range in the RX and ADC, which is currently not feasible . Cancelling SI entirely in analog is not feasible either, since the SI contains delayed TX components reflected by the environment. Cancelling these requires impractically large amounts of tunable analog delay. Hence, FD-solutions proposed thus far combine SI-rejection at RF, analog BB, digital BB and cross-domain.
A study of modern Linux API usage and compatibility: what to support when you're supporting

This paper presents a study of Linux API usage across all applications and libraries in the Ubuntu Linux 15.04 distribution. We propose metrics for reasoning about the importance of various system APIs, including system calls, pseudo-files, and libc functions. Our metrics are designed for evaluating the relative maturity of a prototype system or compatibility layer, and this paper focuses on compatibility with Linux applications. This study uses a combination of static analysis to understand API usage and survey data to weight the relative importance of applications to end users.
 This paper yields several insights for developers and researchers, which are useful for assessing the complexity and security of Linux APIs. For example, every Ubuntu installation requires 224 system calls, 208 ioctl, fcntl, and prctl codes and hundreds of pseudo files. For each API type, a significant number of APIs are rarely used, if ever. Moreover, several security-relevant API changes, such as replacing access with faccessat, have met with slow adoption. Finally, hundreds of libc interfaces are effectively unused, yielding opportunities to improve security and efficiency by restructuring libc.
Packet and Flow Based Network Intrusion Dataset

With exponential growth in the number of computer applications and the size of networks, the potential damage that can be caused by attacks launched over the internet keeps increasing dramatically. A number of network intrusion detection methods have been developed with their respective strengths and weaknesses. The majority of research in the area of network intrusion detection is still based on the simulated datasets because of non-availability of real datasets. A simulated dataset cannot represent the real network intrusion scenario. It is important to generate real and timely datasets to ensure accurate and consistent evaluation of methods. We propose a new real dataset to ameliorate this crucial shortcoming. We have set up a testbed to launch network traffic of both attack as well as normal nature using attack tools. We capture the network traffic in packet and flow format. The captured traffic is filtered and preprocessed to generate a featured dataset. The dataset is made available for research purpose.
Collaborative Sequence Prediction for Sequential Recommender

With the surge of deep learning, more and more attention has been put on the sequential recommender. It can be casted as sequence prediction problem, where we will predict the next item given the previous items. RNN approaches are able to capture the global sequential features from the data compared with the local features derived in Markov Chain methods. However, both approaches rely on the independence of users' sequences, which are not true in practice. We propose to formulate the sequential recommendation problem as collaborative sequence prediction problem to take the dependency of users' sequences into account. In order to solve the collaborative sequence prediction problem, we define the dynamic neighborhood relationship between users and introduce manifold regularization to RNN on the basis of the multi-facets of collaborative filtering, referred to as MrRNN. Experimental results on benchmark datasets show that our approach outperforms the state-of-the-art baselines.
School Engagement : Potential of the Concept , State of the Evidence

The concept of school engagement has attracted increasing attention as representing a possible antidote to declining academic motivation and achievement. Engagement is presumed to be malleable, responsive to contextualfeatures, and amenable to environmental change. Researchers describe behavioral, emotional, and cognitive engagement and recommend studying engagement as a multifaceted construct. This article reviews definitions, measures, precursors, and outcomes of engagement; discusses limitations in the existing research; and suggests improvements. The authors conclude that, although much has been learned, the potential contribution of the concept of school engagement to research on student experience has yet to be realized. They callfor richer characterizations of how students behave, feel, and think-research that could aid in the development offinely tuned interventions.
Improving LDA topic models for microblogs via tweet pooling and automatic labeling

Twitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic abstracts, they are often less coherent when applied to microblog content like Twitter. In this paper, we investigate methods to improve topics learned from Twitter content without modifying the basic machinery of LDA; we achieve this through various pooling schemes that aggregate tweets in a data preprocessing step for LDA. We empirically establish that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a variety of pooling schemes. An additional contribution of automatic hashtag labeling further improves on the hashtag pooling results for a subset of metrics. Overall, these two novel schemes lead to significantly improved LDA topic models on Twitter content.
Forensic applications of chemical imaging: latent fingerprint detection using visible absorption and luminescence.

Chemical imaging technology is a rapid examination technique that combines molecular spectroscopy and digital imaging, providing information on morphology, composition, structure, and concentration of a material. Among many other applications, chemical imaging offers an array of novel analytical testing methods, which limits sample preparation and provides high-quality imaging data essential in the detection of latent fingerprints. Luminescence chemical imaging and visible absorbance chemical imaging have been successfully applied to ninhydrin, DFO, cyanoacrylate, and luminescent dye-treated latent fingerprints, demonstrating the potential of this technology to aid forensic investigations. In addition, visible absorption chemical imaging has been applied successfully to visualize untreated latent fingerprints.
Functional neural correlates of fluid and crystallized analogizing

The main aim of this study was to characterize neural correlates of analogizing as a cognitive contributor to fluid and crystallized intelligence. In a previous fMRI study which employed fluid analogy letter strings as criteria in a multiple plausibility design (Geake and Hansen, 2005), two frontal ROIs associated with working memory (WM) load (within BA 9 and BA 45/46) were identified as regions in which BOLD increase correlated positively with a crystallized measure of (verbal) IQ. In this fMRI study we used fluid letter, number and polygon strings to further investigate the role of analogizing in fluid (transformation string completion) and non fluid or crystallized (unique symbol counting) cognitive tasks. The multi stimulus type (letter, number, polygon) design of the analogy strings enabled investigation of a secondary research question concerning the generalizability of fluid analogizing at a neural level. A selective psychometric battery, including the Raven's Progressive Matrices (RPM), measured individual cognitive abilities. Neural activations for the effect of task-fluid analogizing (string transformation plausibility) vs. crystallized analogizing (unique symbol counting)-included bilateral frontal and parietal areas associated with WM load and fronto parietal models of general intelligence. Neural activations for stimulus type differences were mainly confined to visually specific posterior regions. ROI covariate analyses of the psychometric measures failed to find consistent co-relationships between fluid analogizing and the RPM and other subtests, except for the WAIS Digit Symbol subtest in a group of bilateral frontal cortical regions associated with the maintenance of WM load. Together, these results support claims for separate developmental trajectories for fluid cognition and general intelligence as assessed by these psychometric subtests.
Face-to-Face Interactions of Postpartum Depressed and Nondepressed Mother-Infant Pairs at 2 Months

Depression's influence on mother-infant interactious at 2 months postpartum was studied in 24 depressed and 22 nondepressed mothex-infant dyads. Depression was diagnosed using the SADS-L and RDC. In S's homes, structured interactions of 3 min duration were videotaped and later coded using behavioral descriptors and a l-s time base. Unstructured interactions were described using rating scales. During structured interactions, depressed mothers were more negative and their babies were less positive than were nondepressed dyads. The reduced positivity of depressed dyads was achieved through contingent resixmfiveness. Ratings from unstructured interactions were consistent with these findings. Results support the hypothesis that depression negatively influences motherinfant behaviol; but indicate that influence may vary with development, chronicity, and presence of other risk factors.
Lumbar lordosis in prone position and prone hip extension test: comparison between subjects with and without low back pain

Prone hip extension (PHE) is a common and widely accepted test used for assessment of the lumbo-pelvic movement pattern. Considerable increased in lumbar lordosis during this test has been considered as impairment of movement patterns in lumbo-pelvic region. The purpose of this study was to investigate the change of lumbar lordosis in PHE test in subjects with and without low back pain (LBP).



A two-way mixed design with repeated measurements was used to investigate the lumbar lordosis changes during PHE in two groups of subjects with and without LBP. An equal number of subjects (N = 30) were allocated to each group. A standard flexible ruler was used to measure the size of lumbar lordosis in prone-relaxed position and PHE test in each group.



The result of two-way mixed-design analysis of variance revealed significant health status by position interaction effect for lumbar lordosis (P < 0.001). The main effect of test position on lumbar lordosis was statistically significant (P < 0.001). The lumbar lordosis was significantly greater in the PHE compared to prone-relaxed position in both subjects with and without LBP. The amount of difference in positions was statistically significant between two groups (P < 0.001) and greater change in lumbar lordosis was found in the healthy group compared to the subjects with LBP.



Greater change in lumbar lordosis during this test may be due to more stiffness in lumbopelvic muscles in the individuals with LBP.
Analyzing the Semantic Relatedness of Paper Abstracts: An Application to the Educational Research Field

Each domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-to-day activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., Word Net. Moreover, the defined mechanisms are enforced on two different sub domains from the corpora generated around the keywords "e-learning" and "computer". Graph visual representations are used to highlight the keywords of each sub domain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework.
Hybrid classification of Bayesian and extreme learning machine for heartbeat classification of arrhythmia detection

A cardiac circumstance affected through irregular electrical action of the heart is called an arrhythmia. A noninvasive method called Electrocardiogram (ECG) is used to diagnosis arrhythmias or irregularities of the heart. The difficulty encountered by doctors in the analysis of heartbeat irregularities id due to the non-stationary of ECG signal, the existence of noise and the abnormality of the heartbeat. The computer-assisted study of ECG signal supports doctors to diagnoses diseases of cardiovascular. The major limitations of all the ECG signal analysis of arrhythmia detection are because to the non-stationary behavior of the ECG signals and unobserved information existent in the ECG signals. In addition, detection based on Extreme learning machine (ELM) has become a common technique in machine learning. However, it easily suffers from overfitting. This paper proposes a hybrid classification technique using Bayesian and Extreme Learning Machine (B-ELM) technique for heartbeat recognition of arrhythmia detection AD. The proposed technique is capable of detecting arrhythmia classes with a maximum accuracy of (98.09%) and less computational time about 2.5s.
Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.
Switched-capacitor regulator with digital feedback

The wireless sensor node makes use of energy harvesters in order to power its modules. However, by nature of these nodes being placed in diverse locations and fluctuating conditions, their energy harvesters obtain unregulated DC voltage. Regulators undertake this problem by appropriately handling the voltage being harvested to power the node. Most regulators today use analog control methods which are inefficient at delivering power, consume a large amount of area, are more vulnerable to noise, and are less dynamic and adaptive. The 65-nm based switched capacitor regulator with digital control feedback presented in this paper addresses these problems by providing tight voltage regulation with an output accuracy of 9.5mV, peak efficiency of 89.02%, and voltage ripple as small as 2.9mV. The digital control used in the system consumes only 8.777μW of power and is able to perform more complex arithmetic to adapt to the regulator's operating conditions.
Comparison of six different methods of cleaning and preparing occlusal fissure surface before placement of pit and fissure sealant: an in vitro study.

The purpose of this in vitro study was to evaluate and compare the microleakage of pit and fissure sealants after using six different preparation techniques: (a) brush, (b) pumice slurry application, (c) bur, (d) air polishing, (e) air abrasion, and (f) longer etching time.



The study was conducted on 60 caries-free first premolars extracted for orthodontic purpose. These teeth were randomly assigned to six groups of 10 teeth each. Teeth were prepared using one of six occlusal surface treatments prior to placement of Clinpro" 3M ESPE light-cured sealant. The teeth were thermocycled for 500 cycles and stored in 0.9% normal saline. Teeth were sealed apically and coated with nail varnish 1 mm from the margin and stained in 1% methylene blue for 24 hours. Each tooth was divided buccolingually parallel to the long axis of the tooth, yielding two sections per tooth for analysis. The surfaces were scored from 0 to 2 for the extent of microleakage.



Results obtained for microleakage were analyzed by using t-tests at sectional level and chi-square test and analysis of variance (ANOVA) at the group level.



The results of round bur group were significantly superior when compared to all other groups. The application of air polishing and air abrasion showed better results than pumice slurry, bristle brush, and longer etching time. Round bur group was the most successful cleaning and preparing technique. Air polishing and air abrasion produced significantly less microleakage than traditional pumice slurry, bristle brush, and longer etching time.
Interpretable Adversarial Perturbation in Input Embedding Space for Text

Following great success in the image processing field, the idea of adversarial training has been applied to tasks in the natural language processing (NLP) field. One promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts. However, this approach abandons such interpretability as generating adversarial texts to significantly improve the performance of NLP tasks. This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space. As a result, we can straightforwardly reconstruct each input with perturbations to an actual text by considering the perturbations to be the replacement of words in the sentence while maintaining or even improving the task performance1.