nils-herrmann/clean_scirepeval huggingface dataset:
Neurolinguistic programming: a systematic review of the effects on health outcomes.

Neurolinguistic programming (NLP) in health care has captured the interest of doctors, healthcare professionals, and managers.



To evaluate the effects of NLP on health-related outcomes.



Systematic review of experimental studies.



The following data sources were searched: MEDLINE, PsycINFO, ASSIA, AMED, CINAHL, Web of Knowledge, CENTRAL, NLP specialist databases, reference lists, review articles, and NLP professional associations, training providers, and research groups.



Searches revealed 1459 titles from which 10 experimental studies were included. Five studies were randomised controlled trials (RCTs) and five were pre-post studies. Targeted health conditions were anxiety disorders, weight maintenance, morning sickness, substance misuse, and claustrophobia during MRI scanning. NLP interventions were mainly delivered across 4-20 sessions although three were single session. Eighteen outcomes were reported and the RCT sample sizes ranged from 22 to 106. Four RCTs reported no significant between group differences with the fifth finding in favour of the NLP arm (F = 8.114, P<0.001). Three RCTs and five pre-post studies reported within group improvements. Risk of bias across all studies was high or uncertain.



There is little evidence that NLP interventions improve health-related outcomes. This conclusion reflects the limited quantity and quality of NLP research, rather than robust evidence of no effect. There is currently insufficient evidence to support the allocation of NHS resources to NLP activities outside of research purposes.
Text Extraction in Complex Color Document Images for Enhanced Readability

Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture; and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.
Design of single-sided AC magnetic field generating coil for wireless power transfer

In this paper, wireless power tranfer coil with novel winding arrangement is proposed to reduce unwanted magnetic field leakage in electronics charging application. The new winding arrangement generates single-sided magnetic field to suppress magnetic field leakage horizontally and vertically. The shielding effectiveness and wireless power transfer efficiency were compared with conventional coil which simply has windings in single direction by 3D EM solver simulation.
Passive ultrasonic irrigation of the root canal: a review of the literature.

Ultrasonic irrigation of the root canal can be performed with or without simultaneous ultrasonic instrumentation. When canal shaping is not undertaken the term passive ultrasonic irrigation (PUI) can be used to describe the technique. In this paper the relevant literature on PUI is reviewed from a MEDLINE database search. Passive ultrasonic irrigation can be performed with a small file or smooth wire (size 10-20) oscillating freely in the root canal to induce powerful acoustic microstreaming. PUI can be an important supplement for cleaning the root canal system and, compared with traditional syringe irrigation, it removes more organic tissue, planktonic bacteria and dentine debris from the root canal. PUI is more efficient in cleaning canals than ultrasonic irrigation with simultaneous ultrasonic instrumentation. PUI can be effective in curved canals and a smooth wire can be as effective as a cutting K-file. The taper and the diameter of the root canal were found to be important parameters in determining the efficacies of dentine debris removal. Irrigation with sodium hypochlorite is more effective than with water and ultrasonic irrigation is more effective than sonic irrigation in the removal of dentine debris from the root canal. The role of cavitation during PUI remains inconclusive. No detailed information is available on the influence of the irrigation time, the volume of the irrigant, the penetration depth of the instrument and the shape and material properties of the instrument. The influence of irrigation frequency and intensity on the streaming pattern as well as the complicated interaction of acoustic streaming with the adherent biofilm needs to be clarified to reveal the underlying physical mechanisms of PUI.
Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex

IN the hippocampus and neocortex, high-frequency (tetanic) stimu-lation of an afferent pathway leads to long-term potentiation (LTP) of synaptic transmission1–5. In the hippocampus it has recently been shown that long-term depression (LTD) of excitatory transmission can also be induced by certain combinations of synaptic activation6,7. In most hippocampal8 and all neocortical pathways4,9–11 studied so far, the induction of LTP requires the activation of JV-methyl-D-aspartate (NMDA) receptorgated conductances. Here we report that LTD can occur in neurons of slices of the rat visual cortex and that the same tetanic stimulation can induce either LTP or LTD depending on the level of depolarization of the postsynaptic neuron. By applying intracellular current injections or pharmacological disinhibition to modify the depolarizing response of the postsynaptic neuron to tetanic stimulation, we show that the mechanisms of induction of LTD and LTP are both postsynaptic. LTD is obtained if postsynaptic depolarization exceeds a critical level but remains below a threshold related to NMDA receptorgated conductances, whereas LTP is induced if this second threshold is reached.
Design and Analysis of a 3-Arm Spiral Antenna

A novel 3-arm spiral antenna structure is presented in this paper. This antenna similar to traditional two-arm or four-arm spiral antennas exhibits wideband radiation characteristic and circular polarization. Advantages offered by the new design are two fold. Unlike the traditional spiral antennas the three-arm spiral can be fed by an unbalanced transmission line, such as a coaxial line or coplanar waveguide, and therefore an external balun is not needed at the feed point. Also by proper choice of arms' dimensions the antenna can be directly matched to any practical transmission line characteristic impedance and therefore external matching networks are not required. This is accomplished by feeding the antenna at the outer radius by a coplanar waveguide (CPW) transmission line and tapering it towards the center. The antenna can also be fed from the center using a coaxial or CPW line perpendicular to the plane of the spiral antenna. A full-wave numerical simulation tool is used to optimize the geometry of the proposed 3-arm spiral to achieve a compact size, wide bandwidth operation, and low axial ratio. The antenna is also designed over a ground plane to achieve a unidirectional radiation and center loading is examined that improves the axial ratio. Simulated results like return loss, radiation pattern, gain, and axial ratio are compared with those obtained from measurements and good agreements are shown. Because of its unique feed structure and compact size, application of the proposed 3-arm spiral antenna for wideband array applications is demonstrated
Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e.g., the work of Abdulhai , on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai and Cools , as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.
Active Learning Literature Survey

The most time consuming and expensive task in machine learning is the gathering of labeled data to train the model or to estimate its parameters. In the real-world scenario, the availability of labeled data is scarce and we have limited resources to label the abundantly available unlabeled data. Hence it makes sense to pick only the most informative instances from the unlabeled data and request an expert to provide the label for that instance. Active learning algorithms aim at minimizing the amount of labeled data required to achieve the goal of the machine learning task in hand by strategically selecting the data instance to be labeled by the expert. A lot of research has been conducted in this area over the past two decades leading to great improvements in performance of several existing machine learning algorithms and has also been applied to diverse fields like text classification, information retrieval, computer vision and bioinformatics, to name a few. This survey aims at providing an insight into the research in this area and categorizes the diverse algorithms proposed based on main characteristics. We also provides a desk where different active learning algorithms can be compared by evaluation on benchmark datasets.
Thermal Analysis of the Permanent-Magnet Spherical Motor

There are many kinds of permanent-magnet (PM) motors, which are widely used in modern industry, but the problem of temperature rise is likely to affect the operation performance and even the life of the motor. The semiclosed structure of the PM spherical motor (PMSM) tends to increase the temperature when it works, so the thermal analysis of the PMSM is considered necessary. Based on double Fourier series decomposition, this paper establishes a three-dimensional (3-D) analytical model of PM eddy current loss in the PMSM, which considers the effect of time and space harmonics. Meanwhile, the heat sources such as hysteresis loss and copper loss are discussed. With those heat sources, a 3-D equivalent thermal network model is established. The thermal resistances are calculated. By applying the thermal network model, the influence of different speed and load on temperature rise is analyzed, and a steady-state thermal analysis of the motor is performed by using finite element analysis. The structure of stator is improved and good heat dissipation effect is achieved. This paper provides the theoretical basis for the design of ventilation and cooling system of the PMSM.
Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding

There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and learn to transfer manipulation strategy across different objects by embedding point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects and appliances with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot with our model can even prepare a cup of a latte with appliances it has never seen before.
Types of radical hysterectomies


The treatment for cervical cancer is a complex, multidisciplinary issue, which applies according to the stage of the disease. The surgical elective treatment of cervical cancer is represented by the radical abdominal hysterectomy. In time, many surgeons perfected this surgical technique; the ones who stood up for this idea were Thoma Ionescu and Ernst Wertheim. There are many varieties of radical hysterectomies performed by using the abdominal method and some of them through vaginal and mixed way. Each method employed has advantages and disadvantages. At present, there are three classifications of radical hysterectomies which are used for the simplification of the surgical protocols: Piver-Rutledge-Smith classification which is the oldest, GCG-EORTC classification and Querlow and Morrow classification. The last is the most evolved and recent classification; its techniques can be adapted for conservative operations and for different types of surgical approaches: abdominal, vaginal, laparoscopic or robotic.
Optimization strategies for task specific path-following capabilities of a binary actuated snake-like robot using follow-the-leader control

Snake-like robots allow to perform examination tasks of difficultly accessible areas. In this context a hyper-redundant shaft concept based on unique binary, electromagnetic tilting actuators was proposed earlier. Because of the electromagnetic actuation principle, the system provides good path following capabilities in combination with high resistance against manipulation forces. An efficient and intuitive path planning algorithm for those snake-like, serial chain robots is a follow-the-leader approach, as it is less complex and promises high accuracy at the same time. However, classical follow-the-leader approaches are designed for continuously actuated joints and their applicability to binary actuated systems is limited. Therefore, an adaption to binary actuation with optimized switching sequences to achieve an appropriate path following performance in spatial settings despite of the restricted motion is presented in this paper. Optimization strategies are outlined and different cost functions for different use cases, including the adherence to constrains, are evaluated.
Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty

We suggest a general approach to quantification of different forms of uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function. One of the networks performs predictions and the other simultaneously quantifies the uncertainty of predictions by estimating the locally averaged loss of the first one. Unlike in many classical uncertainty quantification methods, the targets are not assumed to be sampled from a probability distribution of an a priori given form. We analyze how the hyperparameters affect the learning process and, additionally, show that our method even allows for better predictions compared to standard neural networks without uncertainty counterparts. Finally, we show that particular cases of our approach include maximization of loglikelihood, assuming Gaussian or Laplace noise.
Green house monitoring and controlling using Android mobile application

The existing system has the ability to yet lack the ability to control indoor humidity. Green House Monitoring and Controlling is a complete system designed to monitor and control the humidity inside a green house. This software uses an Android mobile phone, connected using Wifi to a central server which connects via serial communication to a microcontroller and humidity sensor. The result shows that the condition specified in sensor's datasheet and system in reality is appropriate. The achieved test result concludes that the system is working properly.
LEARning Next gEneration Rankers (LEARNER 2017)

The aim of LEARNER@ICTIR2017 is to investigate new solutions for LtR. In details, we identify some research areas related to LtR which are of actual interest and which have not been fully explored yet. We solicit the submission of position papers on novel LtR algorithms, on evaluation of LtR algorithms, on dataset creation and curation, and on domain specific applications of LtR. LEARNER@ICTIR2017 will be a gathering of academic people interested in IR, ML and related application areas. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to LtR focused on foundational and conceptual aspects, which need to be properly framed and modeled.
Examining the factors that influence early adopters' smartphone adoption: The case of college students

The influence of early adopters on potential adopters’ decisions of whether or not to adopt a product is known to be critical. In this paper, we examine the factors that influence the adoption behavior of smartphone early adopters by looking at smartphone adoption behavior of college students, because a large portion of the early adopters of smartphones are college students. Our focus is on the effect of normative peer influence on a college student’s smartphone adoption. We also examine the influence of other factors such as selfinnovativeness, self-efficacy, the decision maker’s attitudes towards a product, financial burden of using the product, familial influence, and other demographic factors (e.g., age and gender). College students’ adoption behavior is studied using logit and probit choice models developed based on random utility theory. The discrete choice models are empirically estimated using survey data. We find important influence of friends, financial burden, and other family members on the smartphone adoption of college students who adopted smartphones earlier than other students.
A Longitudinal Assessment of the Persistence of Twitter Datasets

With social media datasets being increasingly shared by researchers, it also presents the caveat that those datasets are not always completely replicable. Having to adhere to requirements of platforms like Twitter, researchers cannot release the raw data and instead have to release a list of unique identifiers, which others can then use to recollect the data from the platform themselves. This leads to the problem that subsets of the data may no longer be available, as content can be deleted or user accounts deactivated. To quantify the impact of content deletion in the replicability of datasets in a long term, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include over 147 million tweets. Having the original datasets collected between 2012 and 2016, and recollecting them later by using the tweet IDs, we look at four different factors that quantify the extent to which recollected datasets resemble original ones: completeness, representativity, similarity and changingness. Even though the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the whole dataset that was originally collected. The representativity of the metadata, however, keeps decreasing over time, both because the dataset shrinks and because certain metadata, such as the users’ number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research.
The FEELING of WHAT HAPPENS : BODY AND EMOTION IN THE MAKING OF CONSCIOUSNESS

Antonio Damasio is a Portuguese-born American behavioral neurologist and neuroscientist. For a person without any knowledge of neuroscience, biology, or psychology, The Feeling of What Happens would presumably be a fascinating read. How often does one typically contemplate their own contemplation? The concepts presented by Damasio are abstract and uncommon, and undoubtedly are a new phenomenon for the average reader.
Information security: The moving target

Information security has evolved from addressing minor and harmless security breaches to managing those with a huge impact on organisations’ economic growth. This paper investigates the evolution of information security; where it came from, where it is today and the direction in which it is moving. It is argued that information security is not about looking at the past in anger of an attack once faced; neither is it about looking at the present in fear of being attacked; nor about looking at the future with uncertainty about what might befall us. The message is that organisations and individuals must be alert at all times. Research conducted for this paper explored literature on past security issues to set the scene. This is followed by the assessment and analysis of information security publications in conjunction with surveys conducted in industry. Results obtained are compared and analysed, enabling the development of a comprehensive view regarding the current status of the information security landscape. Furthermore, this paper also highlights critical information security issues that are being overlooked or not being addressed by research efforts currently undertaken. New research efforts are required that minimise the gap between regulatory issues and technical implementations. a 2008 Elsevier Ltd. All rights reserved.