nils-herrmann/clean_scirepeval huggingface dataset:
A comparison of the grip force distribution in natural hands and in prosthetic hands.

The aim of this study is to analyse the grip force distribution for different prosthetic hand designs and the human hand fulfilling a functional task.



A cylindrical object is held with a power grasp and the contact forces are measured at 20 defined positions. The distributions of contact forces in standard electric prostheses, in a experimental prosthesis with an adaptive grasp, and in human hands as a reference are analysed and compared. Additionally, the joint torques are calculated and compared.



Contact forces of up to 24.7 N are applied by the middle and distal phalanges of the index finger, middle finger, and thumb of standard prosthetic hands, whereas forces of up to 3.8 N are measured for human hands. The maximum contact forces measured in a prosthetic hand with an adaptive grasp are 4.7 N. The joint torques of human hands and the adaptive prosthesis are comparable.



The analysis of grip force distribution is proposed as an additional parameter to rate the performance of different prosthetic hand designs.
Methods for Evaluating Emotions Evoked by Food Experiences: A Literature Review

Besides sensory characteristics of food, food-evoked emotion is a crucial factor in predicting consumer's food preference and therefore in developing new products. Many measures have been developed to assess food-evoked emotions. The aim of this literature review is (i) to give an exhaustive overview of measures used in current research and (ii) to categorize these methods along measurement level (physiological, behavioral, and cognitive) and emotional processing level (unconscious sensory, perceptual/early cognitive, and conscious/decision making) level. This 3 × 3 categorization may help researchers to compile a set of complementary measures ("toolbox") for their studies. We included 101 peer-reviewed articles that evaluate consumer's emotions and were published between 1997 and 2016, providing us with 59 different measures. More than 60% of these measures are based on self-reported, subjective ratings and questionnaires (cognitive measurement level) and assess the conscious/decision-making level of emotional processing. This multitude of measures and their overrepresentation in a single category hinders the comparison of results across studies and building a complete multi-faceted picture of food-evoked emotions. We recommend (1) to use widely applied, validated measures only, (2) to refrain from using (highly correlated) measures from the same category but use measures from different categories instead, preferably covering all three emotional processing levels, and (3) to acquire and share simultaneously collected physiological, behavioral, and cognitive datasets to improve the predictive power of food choice and other models.
Integrating Human-Computer Interaction Development into SDLC: A Methodology

Incorporating a human computer interaction (HCI) perspective into the systems development life cycle (SDLC) is critical to information systems (IS) success and in turn to the success of businesses. However, modern SDLC models are based more on organizational needs than human needs. The human interaction aspect of an information system is considered far too little (only the screen interface) and far too late in the IS development process (only at the design stage). Thus there is often a gap between satisfying organizational needs and supporting and enriching human users as they use the system for their tasks. This problem can be fixed by carefully integrating HCI development into the SDLC process to achieve a truly human-centered IS development approach. This tutorial presents a methodology for such human-centered IS development where human requirements for the whole system are emphasized. An example of applying such methodology is used to illustrate the important concepts and techniques.
Multi-modality Sensor Data Classification with Selective Attention

Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific approaches and is thus ineffective in complex situations where multi-modality sensor data are collected. Moreover, the wearable sensor data are less informative than the conventional data such as texts or images. In this paper, to improve the adaptability of such classification methods across different application domains, we turn this classification task into a game and apply a deep reinforcement learning scheme to deal with complex situations dynamically. Additionally, we introduce a selective attention mechanism into the reinforcement learning scheme to focus on the crucial dimensions of the data. This mechanism helps to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier. We carry out several experiments on three wearable sensor datasets and demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines.
Integrating SDLC and ITSM to 'Servitize' Systems Development

IT Service Management (ITSM) is generating much interest in industry as the quality and reliability of IT services are increasingly recognized as a critical factor for business success. Academic researchers have been slower to respond to industry demand for IT service management research and coursework. This paper argues that academia has an important role to play in integrating ITSM concepts and skills into traditional information systems coursework, specifically system development. The core systems analysis and design course is used to illustrate how IT service management concepts and models can be introduced into existing coursework to support the focus on IT services required by industry today, eventually reducing the growing percentage of IT budgets currently attributed to the operation and maintenance stages of ‘nonservitized’ systems development projects.
Etiological heterogeneity in autism spectrum disorders: More than 100 genetic and genomic disorders and still counting

There is increasing evidence that autism spectrum disorders (ASDs) can arise from rare highly penetrant mutations and genomic imbalances. The rare nature of these variants, and the often differing orbits of clinical and research geneticists, can make it difficult to fully appreciate the extent to which we have made progress in understanding the genetic etiology of autism. In fact, there is a persistent view in the autism research community that there are only a modest number of autism loci known. We carried out an exhaustive review of the clinical genetics and research genetics literature in an attempt to collate all genes and recurrent genomic imbalances that have been implicated in the etiology of ASD. We provide data on 103 disease genes and 44 genomic loci reported in subjects with ASD or autistic behavior. These genes and loci have all been causally implicated in intellectual disability, indicating that these two neurodevelopmental disorders share common genetic bases. A genetic overlap between ASD and epilepsy is also apparent in many cases. Taken together, these findings clearly show that autism is not a single clinical entity but a behavioral manifestation of tens or perhaps hundreds of genetic and genomic disorders. Increased recognition of the etiological heterogeneity of ASD will greatly expand the number of target genes for neurobiological investigations and thereby provide additional avenues for the development of pathway-based pharmacotherapy. Finally, the data provide strong support for high-resolution DNA microarrays as well as whole-exome and whole-genome sequencing as critical approaches for identifying the genetic causes of ASDs.
THE COBE DIFFUSE INFRARED BACKGROUND EXPERIMENT SEARCH FOR THE COSMIC INFRARED BACKGROUND . I . LIMITS AND DETECTIONS

The Di†use Infrared Background Experiment (DIRBE) on the Cosmic Background Explorer (COBE) spacecraft was designed primarily to conduct a systematic search for an isotropic cosmic infrared background (CIB) in 10 photometric bands from 1.25 to 240 km. The results of that search are presented here. Conservative limits on the CIB are obtained from the minimum observed brightness in all-sky maps at each wavelength, with the faintest limits in the DIRBE spectral range being at 3.5 km (lIl \ 64 nW m~2 sr~1, 95% conÐdence level) and at 240 km nW m~2 sr~1, 95% conÐdence level). The (lIl\ 28 bright foregrounds from interplanetary dust scattering and emission, stars, and interstellar dust emission are the principal impediments to the DIRBE measurements of the CIB. These foregrounds have been modeled and removed from the sky maps. Assessment of the random and systematic uncertainties in the residuals and tests for isotropy show that only the 140 and 240 km data provide candidate detections of the CIB. The residuals and their uncertainties provide CIB upper limits more restrictive than the dark sky limits at wavelengths from 1.25 to 100 km. No plausible solar system or Galactic source of the observed 140 and 240 km residuals can be identiÐed, leading to the conclusion that the CIB has been detected at levels of and 14^ 3 nW m~2 sr~1 at 140 and 240 km, respectively. The intelIl\ 25 ^ 7 grated energy from 140 to 240 km, 10.3 nW m~2 sr~1, is about twice the integrated optical light from the galaxies in the Hubble Deep Field, suggesting that star formation might have been heavily enshrouded by dust at high redshift. The detections and upper limits reported here provide new constraints on models of the history of energy-releasing processes and dust production since the decoupling of the cosmic microwave background from matter.
Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis

This paper combines traditional fundamentals, such as earnings and cash flows, with measures tailored for growth firms, such as earnings stability, growth stability and intensity of R&D, capital expenditure and advertising, to create an index – GSCORE. A long–short strategy based on GSCORE earns significant excess returns, though most of the returns come from the short side. Results are robust in partitions of size, analyst following and liquidity and persist after controlling for momentum, book-tomarket, accruals and size. High GSCORE firms have greater market reaction and analyst forecast surprises with respect to future earnings announcements. Further, the results are inconsistent with a riskbased explanation as returns are positive in most years, and firms with lower risk earn higher returns. Finally, a contextual approach towards fundamental analysis works best, with traditional analysis appropriate for high BM stocks and growth oriented fundamental analysis appropriate for low BM stocks.
When context hinders! Learn-test compatibility in face recognition.

Some theories of holistic face processing propose that parts in faces (eyes, nose, mouth, etc.) are not explicitly represented. So far, the empirical evidence has shown that whole-to-part superiority is found when wholes are learned. We substantiated this using photographic faces. More importantly, we investigated whether learning parts also reveals holistic effects. This has not been attempted before. Four experiments showed that after learning facial parts, recognition of these parts was disrupted when the part was shown in the full face. This distraction effect was strongest when perceivers were not directed to focus on a particular facial feature. Thus, it is very difficult to ignore irrelevant parts in faces. In fact, this might be the essence of holistic face processing.
VisDA: The Visual Domain Adaptation Challenge

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains. Unsupervised domain adaptation aims to solve the real-world problem of domain shift, where machine learning models trained on one domain must be transferred and adapted to a novel visual domain without additional supervision. The VisDA2017 challenge is focused on the simulation-to-reality shift and has two associated tasks: image classification and image segmentation. The goal in both tracks is to first train a model on simulated, synthetic data in the source domain and then adapt it to perform well on real image data in the unlabeled test domain. Our dataset is the largest one to date for cross-domain object classification, with over 280K images across 12 categories in the combined training, validation and testing domains. The image segmentation dataset is also large-scale with over 30K images across 18 categories in the three domains. We compare VisDA to existing cross-domain adaptation datasets and provide a baseline performance analysis, as well as results of the challenge.
Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.

tRNAScan-SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan-SE is unacceptable for large sequences. Here, we used a machine learning method to try to improve the tRNAScan-SE results. A new predictor, tRNA-Predict, was designed. We obtained real and pseudo-tRNA sequences as training data sets using tRNAScan-SE and constructed three different tRNA feature sets. We then set up an ensemble classifier, LibMutil, to predict tRNAs from the training data. The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan-SE. Our in silico experiments revealed a prediction accuracy rate of 95.1 % for tRNA-Predict using 10-fold cross-validation. tRNA-Predict was developed to distinguish functional tRNAs from pseudo-tRNAs rather than to predict tRNAs from a genome-wide scan. However, tRNA-Predict can work with the output of tRNAscan-SE, which is a genome-wide scanning method, to improve the tRNAscan-SE annotation results. The tRNA-Predict web server is accessible at http://datamining.xmu.edu.cn/∼gjs/tRNA-Predict.
INTERACTIONAL FEEDBACK AND INSTRUCTIONAL COUNTERBALANCE

This comparative analysis of teacher-student interaction in two different instructional settings at the elementary-school level (18.3 hr in French immersion and 14.8 hr Japanese immersion) investigates the immediate effects of explicit correction, recasts, and prompts on learner uptake and repair. The results clearly show a predominant provision of recasts over prompts and explicit correction, regardless of instructional setting, but distinctively varied student uptake and repair patterns in relation to feedback type, with the largest proportion of repair resulting from prompts in French immersion and from recasts in Japanese immersion. Based on these findings and supported by an analysis of each instructional setting’s overall communicative orientation, we introduce the counterbalance hypothesis, which states that instructional activities and interactional feedback that act as a counterbalance to a classroom’s predominant communicative orientation are likely to prove more effective than instructional activities and interactional feedback that are congruent with its predominant communicative orientation.
Retinal vessel classification: Sorting arteries and veins

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in   and . The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in . The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.
Association of haptoglobin levels with age, parasite density, and haptoglobin genotype in a malaria-endemic area of Gabon.

Haptoglobin (Hp) levels were investigated in relation to host genotype in a malaria-endemic area in Gabon. A cross-sectional study of 1-12-year-old children was conducted in the rainy season, a period of high malaria transmission, to examine this relationship. Variables that influenced Hp levels were Hp genotype, location, and age interacting with parasite density. At low parasite densities, there was a negative correlation between Hp levels and age. At higher densities, there was a positive correlation with age. This suggests that in the presence of greater parasite-induced hemolysis, older children are capable of increased production of Hp. Sickle cell trait and ABO blood group was not associated with Hp levels in this population.
Nonlinear model predictive control hardware implementation with custom-precision floating point operations

Model predictive control (MPC) based techniques have found many applications both in academia and in industry. Its reach, however, may not be compared to classical control techniques due to e.g. the difficulty of solving an optimization problem at each sampling interval with real-time requirements. Most of the efforts to make the application of MPC viable address this problem with more efficient solvers. This paper, in contrast, proposes a new approach for a real-time MPC solution by mapping an approximate off-line solution into an artificial neural network in a FPGA (Field Programmable Gate Array). We implemented a radial basis function artificial neural network on a low cost FPGA using custom precision floating point operations and tested the control on a single-link robotic manipulator. The amount of time used to calculate the control action at each time instant is in around one microsecond. The comparison between the offline and the approximate solution shows the soundness of the idea. We provide an analysis of hardware usage and execution time in order to achieve the best compromise considering the precision for a given application.
A new approach for self adaptive video game for rehabilitation - Experiences in the amblyopia treatment

Self adaptive video games are important for rehabilitation at home. Recent works have explored different techniques with satisfactory results but these have a poor use of game design concepts like Challenge and Conservative Handling of Failure. Dynamic Difficult Adjustment with Help (DDA-Help) approach is presented as a new point of view for self adaptive video games for rehabilitation. Procedural Content Generation (PCG) and automatic helpers are used to a different work on Conservative Handling of Failure and Challenge. An experience with amblyopic children showed the proposal effectiveness, increasing the visual acuity 2-3 level following the Snellen Vision Test and improving the performance curve during the game time.
Steganography and Steganalysis in Digital Multimedia: Hype or Hallelujah?

In this tutorial, we introduce the basic theory behind Stega nography and Steganalysis, and present some recent algorithms and devel opm nts of these fields. We show how the existing techniques used nowadays are relate d to Image Processing and Computer Vision, point out several trendy applicati ons of Steganography and Steganalysis, and list a few great research opportunities j ust waiting to be addressed.
Behavioral economics holds potential to deliver better results for patients, insurers, and employers.

Many programs being implemented by US employers, insurers, and health care providers use incentives to encourage patients to take better care of themselves. We critically review a range of these efforts and show that many programs, although well-meaning, are unlikely to have much impact because they require information, expertise, and self-control that few patients possess. As a result, benefits are likely to accrue disproportionately to patients who already are taking adequate care of their health. We show how these programs could be made more effective through the use of insights from behavioral economics. For example, incentive programs that offer patients small and frequent payments for behavior that would benefit the patients, such as medication adherence, can be more effective than programs with incentives that are far less visible because they are folded into a paycheck or used to reduce a monthly premium. Deploying more-nuanced insights from behavioral economics can lead to policies with the potential to increase patient engagement and deliver dividends for patients and favorable cost-effectiveness ratios for insurers, employers, and other relevant commercial entities.
Clinical Neurology and Epidemiology of the Major Neurodegenerative Diseases.

Neurodegenerative diseases are a common cause of morbidity and cognitive impairment in older adults. Most clinicians who care for the elderly are not trained to diagnose these conditions, perhaps other than typical Alzheimer's disease (AD). Each of these disorders has varied epidemiology, clinical symptomatology, laboratory and neuroimaging features, neuropathology, and management. Thus, it is important that clinicians be able to differentiate and diagnose these conditions accurately. This review summarizes and highlights clinical aspects of several of the most commonly encountered neurodegenerative diseases, including AD, frontotemporal dementia (FTD) and its variants, progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), Parkinson's disease (PD), dementia with Lewy bodies (DLB), multiple system atrophy (MSA), and Huntington's disease (HD). For each condition, we provide a brief overview of the epidemiology, defining clinical symptoms and diagnostic criteria, relevant imaging and laboratory features, genetics, pathology, treatments, and differential diagnosis.
Limited genetic diversity in the PvK12 Kelch protein in Plasmodium vivax isolates from Southeast Asia

Artemisinin resistance in Plasmodium falciparum has emerged as a major threat for malaria control and elimination worldwide. Mutations in the Kelch propeller domain of PfK13 are the only known molecular markers for artemisinin resistance in this parasite. Over 100 non-synonymous mutations have been identified in PfK13 from various malaria endemic regions. This study aimed to investigate the genetic diversity of PvK12, the Plasmodium vivax ortholog of PfK13, in parasite populations from Southeast Asia, where artemisinin resistance in P. falciparum has emerged. The PvK12 sequences in 120 P. vivax isolates collected from Thailand (22), Myanmar (32) and China (66) between 2004 and 2008 were obtained and 353 PvK12 sequences from worldwide populations were retrieved for further analysis. These PvK12 sequences revealed a very low level of genetic diversity (π = 0.00003) with only three single nucleotide polymorphisms (SNPs). Of these three SNPs, only G581R is nonsynonymous. The synonymous mutation S88S is present in 3% (1/32) of the Myanmar samples, while G704G and G581R are present in 1.5% (1/66) and 3% (2/66) of the samples from China, respectively. None of the mutations observed in the P. vivax samples were associated with artemisinin resistance in P. falciparum. Furthermore, analysis of 473 PvK12 sequences from twelve worldwide P. vivax populations confirmed the very limited polymorphism in this gene and detected only five distinct haplotypes. The PvK12 sequences from global P. vivax populations displayed very limited genetic diversity indicating low levels of baseline polymorphisms of PvK12 in these areas.