nils-herrmann/clean_scirepeval huggingface dataset:
Application of text mining in the biomedical domain.

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.
Implementation of Total Quality Management in Colleges of Physical Education in Jordan

The general purpose of this study was to investigate the perceptions of physical education faculty members regarding the application of TQM principles to colleges of physical education in Jordan. In addition, the study examined the extent to which these faculty members differ in their perceptions and the extent to which differences in perceptions were influenced by rank, educational level and years of experience. The sample consisted of 72 faculty members from four colleges of physical education in Jordan. They completed a 45-item questionnaire covering five dimensions of TQM namely: (1) commitment of top management, (2) employee involvement and teamwork, (3) training and education for quality, (4) strategic planning, and (5) focus on customer satisfaction. The findings of this study indicated that TQM principles were poorly implemented in Jordanian colleges of physical education. In addition, the findings revealed that academic rank, years of experience, and education level did not significantly affect the faculty members' perceptions of TQM implementation.
Efficient Locally Weighted Polynomial Regression Predictions

Locally weighted polynomial regression (LWPR) is a popular instance-based algorithm for learning continuous non-linear mappings. For more than two or three inputs and for more than a few thousand datapoints the computational expense of predictions is daunting. We discuss drawbacks with previous approaches to dealing with this problem, and present a new algorithm based on a multiresolution search of a quicklyconstructible augmented kd-tree. Without needing to rebuild the tree, we can make fast predictions with arbitrary local weighting functions, arbitrary kernel widths and arbitrary queries. The paper begins with a new, faster, algorithm for exact LWPR predictions. Next we introduce an approximation that achieves up to a two-ordersof-magnitude speedup with negligible accuracy losses. Increasing a certain approximation parameter achieves greater speedups still, but with a correspondingly larger accuracy degradation. This is nevertheless useful during operations such as the early stages of model selection and locating optima of a tted surface. We also show how the approximations can permit real-time query-speci c optimization of the kernel width. We conclude with a brief discussion of potential extensions for tractable instance-based learning on datasets that are too large to t in a computer's main memory. 1 Locally Weighted Polynomial Regression Locally weighted polynomial regression (LWPR) is a form of instance-based (a.k.a memory-based) algorithm for learning continuous non-linear mappings from real-valued input vectors to real-valued output vectors. It is particularly appropriate for learning complex highly non-linear functions of up to about 30 inputs from noisy data. Popularized in the statistics literature in the past decades (Cleveland and Delvin, 1988; Grosse, 1989; Atkeson et al., 1997a) it is enjoying increasing use in applications such as learning robot dynamics (Moore, 1992; Schaal and Atkeson, 1994) and learning process models. Both classical and Bayesian linear regression analysis tools can be extended to work in the locally weighted framework (Hastie and Tibshirani, 1990), providing con dence intervals on predictions, on gradient estimates and on noise estimates|all important when a learned mapping is to be used by a controller (Atkeson et al., 1997b; Schneider, 1997). Let us review LWPR. We begin with linear regression on one input and one output. Global linear regression (left of Figure 1) nds the line that minimizes the sum squared residuals. If this is represented as
Cloud computing adoption decision modelling for SMEs: a conjoint analysis

Cloud computing is an emerging technology that promises competitive advantages, cost savings, enhanced business processes and services, and various other benefits to enterprises. Despite the rapid technological advancement, the adoption of cloud computing is still growing slowly among small and mediumsized enterprises (SMEs). This paper presents a model to support the decisionmaking process, using a multi-criteria decision method PAPRIKA for the socio-technical aspects influencing SMEs cloud adoption decision. Due to the multifaceted nature of the cloud computing adoption process, the evaluation of various cloud services and deployment models have become a major challenge. This paper presents a systematic approach to evaluating cloud computing services and deployment models. Subsequently, we have conducted conjoint analysis activities with five SMEs decision makers as part of the distribution process of this decision modelling based on predetermined criteria. With the help of the proposed model, cloud services and deployment models can be ranked and selected.
Generating Reference Texts for Short Answer Scoring Using Graph-based Summarization

Automated scoring of short answers often involves matching a students response against one or more sample reference texts. Each reference text provided contains very specific instances of correct responses and may not cover the variety of possibly correct responses. Finding or hand-creating additional references can be very time consuming and expensive. In order to overcome this problem we propose a technique to generate alternative reference texts by summarizing the content of top-scoring student responses. We use a graph-based cohesion technique that extracts the most representative answers from among the top-scorers. We also use a state-of-the-art extractive summarization tool called MEAD. The extracted set of responses may be used as alternative reference texts to score student responses. We evaluate this approach on short answer data from Semeval 2013’s Joint Student Response Analysis task.
Intelligent Predictive Maintenance ( IPdM ) system – Industry 4 . 0 scenario

Industry 4.0 is a collective term for technologies and concepts of value chain organization. Based on the technological concepts of Radio Identification (RFID), Cyber-Physical Systems (CPS), the Internet of Things (IoT), Internet of Services (IoS), and Data Mining (DM), it facilitates the vision of the Smart Factory. Within the modular structured Smart Factories of Industry 4.0, CPS monitor physical objects and processes to create a virtual copy of the physical world and make decentralized controls and decisions. Over the Internet of Things, Cyber-Physical Systems communicate and cooperate with each other and humans in real time. Via the Data Mining and Internet of Services, both internal and cross-organizational services are offered and utilized by participants of the value chain. Although Industry 4.0 is currently a top priority for many industries, research institutes, and universities, a generally accepted definition of the term does not exist. This paper provides a definition of Industry 4.0 and identifies some main principles for implementation. In order to clearly understand what Industry 4.0 looks like, we will show an Intelligent Predictive Maintenance (IPdM) system for reaching Zero-Defect Manufacturing (ZDM).
InTouch Tactile Tales: Haptic Feedback and Long-Distance Storytelling

The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.
Predicting the Speed, Scale, and Range of Information Diffusion in Twitter

We present results of network analyses of information diffusion on Twitter, via users’ ongoing social interactions as denoted by “@username” mentions. Incorporating survival analysis, we constructed a novel model to capture the three major properties of information diffusion: speed, scale, and range. On the whole, we find that some properties of the tweets themselves predict greater information propagation but that properties of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors. Implications for end users and system designers are discussed.
High gain two-stage amplifier with positive capacitive feedback compensation

A novel topology for a high gain two-stage amplifier is proposed. The proposed circuit is designed in a way that the non-dominant pole is at output of the first stage. A positive capacitive feedback (PCF) around the second stage introduces a left half plane (LHP) zero which cancels the phase shift introduced by the non-dominant pole, considerably. The dominant pole is at the output node which means that increasing the load capacitance has minimal effect on stability. Moreover, a simple and effective method is proposed to enhance slew rate. Simulation shows that slew rate is improved by a factor of 2.44 using the proposed method. The proposed amplifier is designed in a 0.18um CMOS process. It consumes 0.86mW power from a 1.8V power supply and occupies 3038.5μm of chip area. The DC gain is 82.7dB and gain bandwidth (GBW) is 88.9 MHz when driving a 5pF capacitive load. Also low frequency CMRR and PSRR are 127dB and 83.2dB, respectively. They are 24.8dB and 24.2dB at GBW frequency, which are relatively high and are other important properties of the proposed amplifier. Moreover, Simulations show convenient performance of the circuit in process corners and also presence of mismatch.
Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding

Electroencephalography (EEG) signals are known to manifest differential patterns when individuals visually concentrate on different objects (e.g., a car). In this work, we present an end-to-end digital fabrication system , Brain2Object, to print the 3D object that an individual is observing by solely decoding visually-evoked EEG brain signal streams. We propose a unified training framework which combines multi-class Common Spatial Pattern and deep Convolutional Neural Networks to support the backend computation. Specially, a Dynamical Graph Representation of EEG signals is learned for accurately capturing the structured spatial correlations of EEG channels in an adaptive manner. A user friendly interface is developed as the system front end. Brain2Object presents a streamlined end-to-end workflow which can serve as a template for deeper integration of BCI technologies to assist with our routine activities. The proposed system is evaluated extensively using offline experiments and through an online demonstrator. For the former, we use a rich widely used public dataset and a limited but locally collected dataset. The experiment results show that our approach consistently outperforms a wide range of baseline and state-of-the-art approaches. The proof-of-concept corroborates the practicality of our approach and illustrates the ease with which such a system could be deployed.
Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning

We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Most current algorithms compare spatial/temporal variables with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori, and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, they do not often scale in response to increase in system size since direct distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality-sensitive hashing. Theoretical analysis and evaluations show that significant locations are accurately detected with a loose parameter setting even under high noise levels.
HyperUAS - Imaging Spectroscopy from a Multirotor Unmanned Aircraft System

One of the key advantages of a low-flying unmanned aircraft system (UAS) is its ability to acquire digital images at an ultrahigh spatial resolution of a few centimeters. Remote sensing of quantitative biochemical and biophysical characteristics of small-sized spatially fragmented vegetation canopies requires, however, not only high spatial, but also high spectral (i.e., hyperspectral) resolution. In this paper, we describe the design, development, airborne operations, calibration, processing, and interpretation of image data collected with a new hyperspectral unmanned aircraft system (HyperUAS). HyperUAS is a remotely controlled multirotor prototype carrying onboard a lightweight pushbroom spectroradiometer coupled with a dual frequency GPS and an inertial movement unit. The prototype was built to remotely acquire imaging spectroscopy data of 324 spectral bands (162 bands in a spectrally binned mode) with bandwidths between 4 and 5 nm at an ultrahigh spatial resolution of 2–5 cm. Three field airborne experiments, conducted over agricultural crops and over natural ecosystems of Antarctic mosses, proved operability of the system in standard field conditions, but also in a remote and harsh, low-temperature environment of East Antarctica. Experimental results demonstrate that HyperUAS is capable of delivering georeferenced maps of quantitative biochemical and biophysical variables of vegetation and of actual vegetation health state at an unprecedented spatial resolution of 5 cm.
Reconfigurable Skyrmion Logic Gates.

Magnetic skyrmion, a nanosized spin texture with topological property, has become an area of significant interest due to the scientific insight that it can provide and also its potential impact on applications such as ultra-low-energy and ultra-high-density logic gates. In the quest for the reconfiguration of single logic device and the implementation of the complete logic functions, a novel reconfigurable skyrmion logic (RSL) is proposed and verified by micromagnetic simulations. Logic functions including AND, OR, NOT, NAND, NOR, XOR, and XNOR are implemented in the ferromagnetic (FM) nanotrack by virtue of various effects including spin orbit torque, skyrmion Hall effect, skyrmion-edge repulsions, and skyrmion-skyrmion collision. Different logic functions can be selected in an RSL by applying voltage to specific region(s) of the device, changing the local anisotropy energy of FM film. Material properties and geometrical scaling studies suggest RSL gates fit for energy-efficient computing as well as provide the guidelines for the design and optimization of this new logic family.
Dual origins of the Japanese: common ground for hunter-gatherer and farmer Y chromosomes

AbstractHistoric Japanese culture evolved from at least two distinct migrations that originated on the Asian continent. Hunter-gatherers arrived before land bridges were submerged after the last glacial maximum (>12,000 years ago) and gave rise to the Jomon culture, and the Yayoi migration brought wet rice agriculture from Korea beginning ∼2,300 years ago. A set of 81 Y chromosome single nucleotide polymorphisms (SNPs) was used to trace the origins of Paleolithic and Neolithic components of the Japanese paternal gene pool, and to determine the relative contribution of Jomon and Yayoi Y chromosome lineages to modern Japanese. Our global sample consisted of >2,500 males from 39 Asian populations, including six populations sampled from across the Japanese archipelago. Japanese populations were characterized by the presence of two major (D and O) and two minor (C and N) clades of Y chromosomes, each with several sub-lineages. Haplogroup D chromosomes were present at 34.7% and were distributed in a U-shaped pattern with the highest frequency in the northern Ainu and southern Ryukyuans. In contrast, haplogroup O lineages (51.8%) were distributed in an inverted U-shaped pattern with a maximum frequency on Kyushu. Coalescent analyses of Y chromosome short tandem repeat diversity indicated that haplogroups D and C began their expansions in Japan ∼20,000 and ∼12,000 years ago, respectively, while haplogroup O-47z began its expansion only ∼4,000 years ago. We infer that these patterns result from separate and distinct genetic contributions from both the Jomon and the Yayoi cultures to modern Japanese, with varying levels of admixture between these two populations across the archipelago. The results also support the hypothesis of a Central Asian origin of Jomonese ancestors, and a Southeast Asian origin of the ancestors of the Yayoi, contra previous models based on morphological and genetic evidence.
Language is not just for talking: redundant labels facilitate learning of novel categories.

In addition to having communicative functions, verbal labels may play a role in shaping concepts. Two experiments assessed whether the presence of labels affected category formation. Subjects learned to categorize "aliens" as those to be approached or those to be avoided. After accuracy feedback on each response was provided, a nonsense label was either presented or not. Providing nonsense category labels facilitated category learning even though the labels were redundant and all subjects had equivalent experience with supervised categorization of the stimuli. A follow-up study investigated differences between learning verbal and nonverbal associations and showed that learning a nonverbal association did not facilitate categorization. The findings show that labels make category distinctions more concrete and bear directly on the language-and-thought debate.
A method to infer emotions from facial Action Units

We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.
Characterization of Polymeric Microneedle Arrays for Transdermal Drug Delivery

Microfabrication of dissolvable, swellable, and biodegradable polymeric microneedle arrays (MNs) were extensively investigated based in a nano sensitive fabrication style known as micromilling that is then combined with conventional micromolding technique. The aim of this study was to describe the polymer selection, and optimize formulation compounding parameters for various polymeric MNs. Inverse replication of micromilled master MNs reproduced with polydimethylsiloxane (PDMS), where solid out of plane polymeric MNs were subsequently assembled, and physicochemically characterized. Dissolvable, swellable, and biodegradable MNs were constructed to depth of less than 1 mm with an aspect ratio of 3.6, and 1/2 mm of both inter needle tip and base spacing. Micromolding step also enabled to replicate the MNs very precisely and accurate. Polymeric microneedles (MN) precision was ranging from ± 0.18 to ± 1.82% for microneedle height, ± 0.45 to ± 1.42% for base diameter, and ± 0.22 to ± 0.95% for interbase spacing. Although dissolvable sodium alginate MN showed less physical robustness than biodegradable polylactic-co-glycolic acid MN, their thermogravimetric analysis is of promise for constructing these polymeric types of matrix devices.
Feature and model space speaker adaptation with full covariance Gaussians

Full covariance models can give better results for speech recognition than diagonal models, yet they introduce complications for standard speaker adaptation techniques such as MLLR and fMLLR. Here we introduce efficient update methods to train adaptation matrices for the full covariance case. We also experiment with a simplified technique in which we pretend that the full covariance Gaussians are diagonal and obtain adaptation matrices under that assumption. We show that this approximate method works almost as well as the exact method.
A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants

This paper presents the first meta-analysis of studies that computed correlations between the h index and variants of the h index (such as the g index; in total 37 different variants) that have been proposed and discussed in the literature. A high correlation between the h index and its variants would indicate that the h index variants hardly provide added information to the h index. This meta-analysis included 135 correlation coefficients from 32 studies. The studies were based on a total sample size of N = 9005; on average, each study had a sample size of n = 257. The results of a three-level cross-classified mixed-effects metaanalysis show a high correlation between the h index and its variants: Depending on the model, the mean correlation coefficient varies between .8 and .9. This means that there is redundancy between most of the h index variants and the h index. There is a statistically significant study-to-study variation of the correlation coefficients in the information they yield. The lowest correlation coefficients with the h index are found for the h index variants MII and m index. Hence, these h index variants make a non-redundant contribution to the h index.