nils-herrmann/clean_scirepeval huggingface dataset:
Osteopathic manual therapy versus conventional conservative therapy in the treatment of temporomandibular disorders: a randomized controlled trial.

Temporomandibular disorders (TMD) is a term reflecting chronic, painful, craniofacial conditions usually of unclear etiology with impaired jaw function. The effect of osteopathic manual therapy (OMT) in patients with TMD is largely unknown, and its use in such patients is controversial. Nevertheless, empiric evidence suggests that OMT might be effective in alleviating symptoms. A randomized controlled clinical trial of efficacy was performed to test this hypothesis.



We performed a randomized, controlled trial that involved adult patients who had TMD. Patients were randomly divided into two groups: an OMT group (25 patients, 12 males and 13 females, age 40.6+/-11.03) and a conventional conservative therapy (CCT) group (25 patients, 10 males and 15 females, age 38.4+/-15.33). At the first visit (T0), at the end of treatment (after six months, T1) and two months after the end of treatment (T2), all patients were subjected to clinical evaluation. Assessments were performed by subjective pain intensity (visual analogue pain scale, VAS), clinical evaluation (Temporomandibular index) and measurements of the range of maximal mouth opening and lateral movement of the head around its axis.



Patients in both groups improved during the six months. The OMT group required significantly less medication (non-steroidal medication and muscle relaxants) (P<0.001).



The two therapeutic modalities had similar clinical results in patients with TMD, even if the use of medication was greater in CCT group. Our findings suggest that OMT is a valid option for the treatment of TMD.
Remembered parenting styles and adjustment in middle and late adulthood.

Authoritative parenting is the parenting style often associated with positive outcomes for children and adolescents. This study considers whether remembered parenting styles in childhood predict multiple dimensions of functioning in adulthood.



We used the 1995 National Survey of Midlife Development in the United States data set (N = 2,232) to assess the association between parenting behaviors remembered from childhood-classified as authoritative, authoritarian, indulgent, and uninvolved-and psychological well-being, depressive symptoms, and substance abuse, in a subsample of mid- and later-life adults. Differences in outcomes by sex, race, and childhood socioeconomic status were also examined across parenting styles.



Adults who remembered authoritative compared with authoritarian and uninvolved parents reported greater psychological well-being and fewer depressive symptoms, and those with uninvolved parents noted greater substance abuse. No outcome differences were found between remembered authoritative and indulgent parenting styles. A few sex and race interactions were identified: Authoritative parenting (compared with uninvolved) was more strongly associated with men's psychological well-being than women's, and authoritative parenting (compared with authoritarian) predicted reduced depressive symptoms for Whites more than non-Whites.



There is some support that remembered parenting styles continue to be related to functioning across the lifespan. There is also evidence of resiliency, flexibility, and malleability in human development.
Learning to Synthesize a 4D RGBD Light Field from a Single Image

We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point.
Anomaly detection using baseline and K-means clustering

Anomaly detection refers to methods that provide warnings of unusual behaviors which may compromise the security and performance of communication networks. In this paper it is proposed a novel model for network anomaly detection combining baseline, K-means clustering and particle swarm optimization (PSO). The baseline consists of network traffic normal behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set, while K-means is a supervised learning clustering algorithm used to recognize patterns or features in data sets. In order to escape from local optima problem, the K-means is associated to PSO, which is a meta-heuristic whose main characteristics include low computational complexity and small number of input parameters dependence. The proposed anomaly detection approach classifies data clusters from baseline and real traffic using the K-means combined with PSO. Anomalous behaviors can be identified by comparing the distance between real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.
Prediction Reweighting for Domain Adaptation

There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective of prediction reweighting, from which a novel approach is derived. Different from the major domain adaptation methods, our idea is to reweight predictions of the training classifier on testing data according to their signed distance to the domain separator, which is a classifier that distinguishes training data (from source domain) and testing data (from target domain). We then propagate the labels of target instances with larger weights to ones with smaller weights by introducing a manifold regularization method. It can be proved that our reweighting scheme effectively brings the source and target domains closer to each other in an appropriate sense, such that classification in target domain becomes easier. The proposed method can be implemented efficiently by a simple two-stage algorithm, and the target classifier has a closed-form solution. The effectiveness of our approach is verified by the experiments on artificial datasets and two standard benchmarks, a visual object recognition task and a cross-domain sentiment analysis of text. Experimental results demonstrate that our method is competitive with the state-of-the-art domain adaptation algorithms.
Independent examination of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV): what does the WAIS-IV measure?

Published empirical evidence for the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) does not address some essential questions pertaining to the applied practice of intellectual assessment. In this study, the structure and cross-age invariance of the latest WAIS-IV revision were examined to (a) elucidate the nature of the constructs measured and (b) determine whether the same constructs are measured across ages. Results suggest that a Cattell-Horn-Carroll (CHC)-inspired structure provides a better description of test performance than the published scoring structure does. Broad CHC abilities measured by the WAIS-IV include crystallized ability (Gc), fluid reasoning (Gf), visual processing (Gv), short-term memory (Gsm), and processing speed (Gs), although some of these abilities are measured more comprehensively than are others. Additionally, the WAIS-IV provides a measure of quantitative reasoning (QR). Results also suggest a lack of cross-age invariance resulting from age-related differences in factor loadings. Formulas for calculating CHC indexes and suggestions for interpretation are provided.
Chapter 4 A fuzzy description logic for the semantic web

In this paper we present a fuzzy version of SHOIN (D), the corresponding Description Logic of the ontology description language OWL DL. We show that the representation and reasoning capabilities of fuzzy SHOIN (D) go clearly beyond classical SHOIN (D). Interesting features are: (i) concept constructors are based on t-norm, t-conorm, negation and implication; (ii) concrete domains are fuzzy sets; (iii) fuzzy modifiers are allowed; and (iv) entailment and subsumption relationships may hold to some degree in the unit interval [0, 1].
Band structure, phonon scattering, and the performance limit of single-walled carbon nanotube transistors.

Semiconducting single-walled carbon nanotubes are studied in the diffusive transport regime. The peak mobility is found to scale with the square of the nanotube diameter and inversely with temperature. The maximum conductance, corrected for the contacts, is linear in the diameter and inverse temperature. These results are in good agreement with theoretical predictions for acoustic phonon scattering in combination with the unusual band structure of nanotubes. These measurements set the upper bound for the performance of nanotube transistors operating in the diffusive regime.
Relative expression of genes of menthol biosynthesis pathway in peppermint (Mentha piperita L.) after chitosan, gibberellic acid and methyl jasmonate treatments

Menthol as an important component of monoterpenes essential oil in peppermint (Mentha piperita L.) is widely applied for medical and industrial uses. In this study, the effect of exogenous applications of chitosan (200 mg/L), gibberellic acid (50 mg/L) and methyl jasmonate (300 μM) was investigated in the main genes of menthol biosynthesis pathways within a 72 h time period using qRT-PCR. Transcript levels of most genes were either unaffected or down-regulated following chitosan treatment relative to control plants. Decreasing of geranyl diphosphate synthase (GDS) and limonene synthase (LS) genes transcript in chitosan treatment could possibly be effective in reducing of limonene level. On the other hand, it seems that an increase in menthone-menthol reductase (MMR) transcription level at 72 h under these treatments had a positive role in increasing the amount of menthol in this plant. Since exogenous application of gibberellic acid (GA3) down-regulated transcript levels of several genes involved in menthol biosynthesis, there is this expectance that GA3 treatment might not have a prominent role in enhancing menthol yield via transcription regulation. Transcript level of the majority genes after methyl jasmonate treatment gradually increased and reached the highest level at 72 h, therefore, it is possible that methyl jasmonate improves medicinal properties of M. piperita.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoderdecoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences.
3D Conceptual Design Using Deep Learning

This article proposes a data-driven methodology to achieve a fast design support, in order to generate or develop novel designs covering multiple object categories. This methodology implements two state-of-the-art Variational Autoencoder dealing with 3D model data. Our methodology constructs a self-defined loss function. The loss function, containing the outputs of certain layers in the autoencoder, obtains combination of different latent features from different 3D model categories. Additionally, this article provide detail explanation to utilize the Princeton ModelNet40 database, a comprehensive clean collection of 3D CAD models for objects. After convert the original 3D mesh file to voxel and point cloud data type, we enable to feed our autoencoder with data of the same size of dimension. The novelty of this work is to leverage the power of deep learning methods as an efficient latent feature extractor to explore unknown designing areas. Through this project, we expect the output can show a clear and smooth interpretation of model from different categories to develop a fast design support to generate novel shapes. This final report will explore 1) the theoretical ideas, 2) the progresses to implement Variantional Autoencoder to attain implicit features from input shapes, 3) the results of output shapes during training in selected domains of both 3D voxel data and 3D point cloud data, and 4) our conclusion and future work to achieve the more outstanding goal.
Neuropathies : Essential oils show promising results in the fight against symptoms

Chronic pain is a widespread problem affecting more than one and a half billion people worldwide. Of those, 116 million people suffering from chronic pain reside in the United States of America and four million of those with chronic pain suffer from neuropathic pain. Neuropathic pain is a very complex and hard to manage pain requiring several approaches to medication and treatment. In this paper, the use of essential oils of Mentha x piperita (Peppermint), Pelargonium x asperum (Geranium Rose), Piper nigrum (Black Pepper) and Rosmarinus officinalis ct. cineole (Rosemary ct. cineole) to increase circulation and decrease pain in patients with peripheral neuropathy of the lower extremities is discussed through two case studies, chemical analysis, current research and future considerations.    Essential oils show promising results in the fight against symptoms. Chronic pain, or pain lasting longer than six months, affects approximately 1.5 billion people worldwide with 116 million of those people residing in the United States of America (American Academy of Pain Medicine [AAPM], 2011). Pain is divided into two categories: nociceptive pain, which includes visceral and somatic pain, and neuropathic pain. In hospice and palliative care settings, bone and cancer pain are also frequently used categories. Of those 116 million people suffering from chronic pain, approximately four million people in the United States of America are currently suffering from neuropathic pain (Dickson, Head, Gitlow, & Osbahr, 2010, p. 1637).  Two case studies will also be reviewed along with analysis of essential oils used for symptom management and possible future considerations for applied aromatherapy research.
A systematic review of approaches to assessing cybersecurity awareness

The purpose of this paper is to survey, explore and inform researchers about the previous methodologies applied, target audience and coverage of previous assessment of cybersecurity awareness by capturing, summarizing, synthesizing and critically comment on it. It is also conducted to identify the gaps in the cybersecurity awareness assessment research which warrants the future work.  The authors used a systematic literature review technique to search the relevant online databases by using pre-defined keywords. The authors limited the search to retrieve only English language academic articles published from 2005 to 2014. Relevant information was extracted from the retrieved articles, and the ensuing discussion centres on providing the answers to the research questions.  From the online searches, 23 studies that matched the search criteria were retrieved, and the information extracted from each study includes the authors, publication year, assessment method used, target audiences, coverage of assessment and assessment goals.  The review of the retrieved articles indicates that no previous research was conducted in the assessment of the cybersecurity awareness using a programme evaluation technique. It was also found that few studies focused on youngsters and on the issue of safeguarding personal information.
Rehabilitation of executive functioning: an experimental-clinical validation of goal management training.

Two studies assessed the effects of a training procedure (Goal Management Training, GMT), derived from Duncan's theory of goal neglect, on disorganized behavior following TBI. In Study 1, patients with traumatic brain injury (TBI) were randomly assigned to brief trials of GMT or motor skills training. GMT, but not motor skills training, was associated with significant gains on everyday paper-and-pencil tasks designed to mimic tasks that are problematic for patients with goal neglect. In Study 2, GMT was applied in a postencephalitic patient seeking to improve her meal-preparation abilities. Both naturalistic observation and self-report measures revealed improved meal preparation performance following GMT. These studies provide both experimental and clinical support for the efficacy of GMT toward the treatment of executive functioning deficits that compromise independence in patients with brain damage.
TensorFlow-Serving: Flexible, High-Performance ML Serving

We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to serving. At the same time, the core code paths around model lookup and inference have been carefully optimized to avoid performance pitfalls observed in naive implementations. Google uses it in many production deployments, including a multi-tenant model hosting service called TFS2.
Mining Relation Extraction Based on Pattern Learning Approach

Semantically, objects in unstructured document are related each other to perform a certain entity relation. This certain entity relation such: drug-drug interaction through their compounds, buyer-seller relationship through the goods or services, etc. Motivated by that kind of interaction, this study proposes a method to extract those objects and their interactions. It is presented a general framework of objectinteraction mining of large corpora. The framework is started with the initial step in extracting a single object in the unstructured document. In this study, the initial step is a pattern learning method that is applied to drug-label documents to extract drug-names. We utilize an existing external knowledge to identify a certain regular expressions surrounding the targeted object and the probabilities of that regular expression, to perform the pattern learning process. The performance of this pattern learning approach is promising to apply in this relation extraction area. As presented in the results of this study, the best f-score performance of this method is 0.78 f-score. With adjusting of some parameters and or improving the method, the performance can be potentially improved
A comprehensive review of recent advances on deep vision systems

Real-time video objects detection, tracking, and recognition are challenging issues due to the real-time processing requirements of the machine learning algorithms. In recent years, video processing is performed by deep learning (DL) based techniques that achieve higher accuracy but require higher computations cost. This paper presents a recent survey of the state-of-the-art DL platforms and architectures used for deep vision systems. It highlights the contributions and challenges from over numerous research studies. In particular, this paper first describes the architecture of various DL models such as AutoEncoders, deep Boltzmann machines, convolution neural networks, recurrent neural networks and deep residual learning. Next, deep real-time video objects detection, tracking and recognition studies are highlighted to illustrate the key trends in terms of cost of computation, number of layers and the accuracy of results. Finally, the paper discusses the challenges of applying DL for real-time video processing and draw some directions for the future of DL algorithms.
Deep Learning: A Critical Appraisal

Although deep learning has historical roots going back decades, neither the term “deep learning” nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton’s now classic 2012 (Krizhevsky, Sutskever, & Hinton, 2012)deep net model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.   ! Departments of Psychology and Neural Science, New York University, gary.marcus at nyu.edu. I thank Christina 1 Chen, François Chollet, Ernie Davis, Zack Lipton, Stefano Pacifico, Suchi Saria, and Athena Vouloumanos for sharp-eyed comments, all generously supplied on short notice during the holidays at the close of 2017.
Internet of Smart Things - IoST: Using Blockchain and CLIPS to Make Things Autonomous

Current networking integrates common "Things" to the Web, creating the Internet of Things (IoT). The considerable number of heterogeneous Things that can be part of an IoT network demands an efficient management of resources. With the advent of Fog computing, some IoT management tasks can be distributed toward the edge of the constrained networks, closer to physical devices. Blockchain protocols hosted on Fog networks can handle IoT management tasks such as communication, storage, and authentication. This research goes beyond the current definition of Things and presents the Internet of "Smart Things." Smart Things are provisioned with Artificial Intelligence (AI) features based on CLIPS programming language to become self-inferenceable and self-monitorable. This work uses the permission-based blockchain protocol Multichain to communicate many Smart Things by reading and writing blocks of information. This paper evaluates Smart Things deployed on Edison Arduino boards. Also, this work evaluates Multichain hosted on a Fog network.