nils-herrmann/clean_scirepeval huggingface dataset:
Robust nonsingular fast terminal sliding mode control for Sit to Stand task using a mobile lower limb exoskeleton
Year: 2020

In this paper, a Nonsingular Fast Terminal Sliding Mode Control (RNFTSMC) is proposed to regulate the position of a mobile lower limb exoskeleton powered by linear actuators with four degrees of freedom. The developed lower limb exoskeleton has the purpose to assist the sit to stand and stand to sit tasks for people with spinal cord injury (SCI) or elderly people. Currently most of the developed lower limb exoskeletons are dealing with the presence of uncertainties (unmodeled dynamics) and external disturbances (mass changes, opposite forces) caused by the high complexity of the human limbs dynamics. The RNFTSMC has the purpose of dealing with these uncertainties, ensuring a rapid convergence in finite time to the desired position, and reducing the chattering effect produced by the nature of the controller actuators. The human intention movements have been detected using an sEMG signal classifier performed by the Myo Armband device and processed to activate the preprogrammed routines in the RNFTSMC. The desired paths for the knee and hip exoskeleton joints were obtained by recording data from a volunteer who rises from a chair and sat on it, using a motion capture system. To evaluate the effectiveness of the proposed control scheme, real time experiments under three different scenarios have been carried out.
Functional soil organic matter fractions in response to long term fertilization in upland and paddy systems in South China
Year: 2018

Soil organic matter (SOM) and its fractions play key roles in optimizing crop yield and improving soil quality. However, how functional SOM fractions responded to long term fertilization and their relative importance for C sequestration were less addressed. In this study, we determined the effects of long term fertilization on six functional SOM fractions (unprotected, physically protected, physico biochemically protected, physico chemically protected, chemically protected, and biochemically protected) based on two long term fertilization experiments carried out in South China. The unprotected coarse particulate organic matter (cPOM) the biochemically and chemically protected silt sized fractions (NH dSilt and H dSilt) were the primary C storage fractions under long term fertilization, accounting for 23.6 46.2% 15.7 19.4% and 14.4 17.4% of the total soil organic carbon (SOC) content in upland soil and 19.5 29.3% 9.9 15.5% and 14.2 17.2% of the total SOC content in paddy soil, respectively. Compared with the control treatment (CK) in upland soil, the application of manure combined with mineral NPK (NPKM) resulted in an increase in the SOC content in the cPOM, pure physically protected fraction (iPOM) the physico chemically protected (H mSilt) and the chemically protected (H dSilt) fraction by 233% 166% 124% and 58% respectively. Besides, the SOC increase in upland soil expressed as SOC content per unit of total SOC for iPOM, H mSilt, cPOM and H dSilt were the highest and as large as 283% 248% 194% and 105% respectively. In paddy soil, the highest increase per unit of total SOC was H dSilt (190% followed by H dClay (156% and H mSilt (155% These results suggested that the upland soil could stabilize more C through the pure physical, whereas the chemical protection mechanism played a more important role in paddy soil. Chemical protection mechanism within the microaggregates played important roles in sequestrating C in both upland and paddy soils. Overall, the different responses of functional SOM fractions to long term fertilization indicate different mechanisms for SOM cycling in terms of C sequestration under upland and paddy systems.
Detection and Grading of Breast Cancer via Spatial Features in Histopathological Images
Venue: 2019 Medical Technologies Congress (TIPTEKNO)
Year: 2019

In this study, the detection and grading of breast cancer, which is a very common type of cancer, are studied. In feature extraction phase, feature matrices are created by using local binary pattern histograms and Gabor filters on a dataset with 4812 train data. Afterwards, the classification process are carried out using k nearest neighbor, decision trees and several ensemble classification methods such as bagging, adaboost, random forests. The accuracies of the classifications are measured with 491 test image which are selected from dataset. At the end of the study, success of feature extraction methods and classifiers are compared and the results were presented in tables.
A cost benefit analysis of terrorist attacks
Year: 2018

A cost benefit analysis of terrorist attacks is developed and placed within a systematic theoretical structure. For the target or object of the attack, we consider the lost value of human lives, lost economic value, and lost influence value, counted as benefits for the terrorist. The corresponding losses for the terrorist are counted as costs. The terrorist attacks if benefits outweigh costs. Bounded rationality is enabled where the three kinds of benefits and costs can be weighted differently. We account for two ex ante probabilities of successful planning and attack, and enable the terrorist to assign different weights to its multiple stakeholders. We introduce multiple time periods, time discounting, attitudes towards risk, and subcategories for the benefits and costs. The cost benefit analysis is illustrated with the 11 September 2001 attack, and 53 incidents in the Global Terrorism Database yielding both positive and negative expected utilities. The paper is intended as a tool for scientists and policy makers, as a way of thinking about costs and benefits of terrorist attacks.
Repetitive Process Based Design of PD Type Iterative Learning Control Laws
Venue: 2018 26th Mediterranean Conference on Control and Automation (MED)
Year: 2018

New results on the design of iterative learning control laws are developed. The analysis is in the repetitive process setting. The iterative learning law combines a PD type learning function and state feedback, where only a relatively small number of parameters need to be tuned. The analysis is extended to allow different finite frequency range performance specifications, where this facility is relevant to many applications. The design computations required are linear matrix inequality based. The new design is illustrated by a simulation based study on robotic manipulator behavior, where the model used has been constructed from experimental data.
A Manifold Learning Approach for Personalizing HRTFs from Anthropometric Features
Venue: IEEE/ACM Transactions on Audio, Speech, and Language Processing
Year: 2016

We present a new anthropometry based method to personalize head related transfer functions (HRTFs) using manifold learning in both azimuth and elevation angles with a single nonlinear regression model. The core element of our approach is a domain specific nonlinear dimensionality reduction technique, denominated Isomap, over the intraconic component of HRTFs resulting from a spectral decomposition. HRTF intraconic components encode the most important cues for HRTF individualization, leaving out subject independent cues. First, we modify the graph construction procedure of Isomap to integrate relevant prior knowledge of spatial audio into a single manifold for all subjects by exploiting the existing correlations among HRTFs across individuals, directions, and ears. Then, with the aim of preserving the multifactor nature of HRTFs (i.e. subject, direction and frequency) we train a single artificial neural network to predict low dimensional HRTFs from anthropometric features. Finally, we reconstruct the HRTF from its estimated low dimensional version using a neighborhood based reconstruction approach. Our findings show that introducing prior knowledge in Isomap's manifold is a powerful way to capture the underlying factors of spatial hearing. Our experiments show, with p values less than 0.05, that our approach outperforms using, either a PCA linear reduction, or the full HTRF, in its intermediate stages.
Weiterentwicklung des Enzym Immuntests (EIA) fur Progesteron und Anwendung auf Blut Milch und Speichelproben vom Rind
Year: 2010

Zusammenfassung Der Enzym Immuntest fur Progesteron wurde sowohl empfindlicher, als auch praktikabler gestaltet. Der modifizierte EIA zeigte bei der Anwendung auf Rinderserum und Milchfettproben eine gute Ubereinstimmung mit Progesteronwerten, die mit Hilfe des RIA ermittelt wurden. Die Bestimmung von Progesteron im Speichel vom Rind dagegen ist nicht moglich.  Improvement and application of an enzyme immunoassay (EIA) for progesterone The enzyme immunoassay for progesterone was improved to become more sensitive and more practical. With milkfat samples and in the first application to bovine serum the modified EIA has a good correlation with values determined by RIA. The determination of progesterone in bovine saliva, however, is not possible. Resume Nouveau developpement et utilisation de l'Enzyme Immuno Essai (EIA) pour la progesterone.  Essai pour la progesterone est devenu plus sensible et plus praticable. EIA modifie a montre une bonne correlation avec les valeurs de progesterone obtenue avec RIA en utilisant des echantillons de graisse du lait et pour la premiere fois du serum bovin. La determination de progesterone dans la salive du bovin n'est par contre pas possible. Resumen Desarrollo ulterior de la prueba enzimo inmune (EIA) para la progesterona y su aplicacion en muestras de sangre, leche y saliva de vacunos La prueba enzimo inmune para identificar la progesterona se torno mas sensitiva y mas practicable. Mostraba la EIA modificada, al ser empleada con suero bovino y muestras de grasa de leche, una correlacion buena con los valores obtenidos con RIA. Sin embargo, no resulta posible la determinacion de la progesterona en la saliva vacuna.
Social Network Analysis for Program Implementation
Venue: PloS one
Year: 2015

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues  and our experience in developing multi sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.
Analysis of Conceptual Understanding of Botany and Metacognitive Skill in Pre Service Biology Teacher in Jakarta, Indonesia
Venue: Journal for the Education of Gifted Young Scientists
Year: 2019

The achievement of learning objective of Biology, especially Phanerogamic Botany requires several important aspects, namely conceptual understanding and metacognitive skill. Analysis of conceptual understanding of Botany and metacognitive skill is needed to know to what extent are both aspects of achieving the learning objectives. The research aims to know and analyze the conceptual understanding and metacognitive skill of college students to achieve course objectives. The research is conducted in July 2018 at the Universitas Negeri Jakarta in Phanerogamic Botany course Eudicot sub course material with 77 students as sample. The research includes in quantitative research with descriptive approach. Results obtained are, among others, the mean of conceptual understanding of Botany and metacognitive skill is 63.07 and 50.15, respectively, which is low based on the achievement score. The analysis result indicates that conceptual understanding of Botany is considered as low due to the difficulty faced by students in finding an example for each clade and in understanding the scientific terms that are the base of the learning. Students, however, are able to apply the concept into problems faced and students' metacognitive skill in student planning aspect is unable to arrange goals to be achieved, to prioritize the learning.
Evolving Deep CNN LSTMs for Inventory Time Series Prediction
Venue: 2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non stationary characteristics of inventory data, the models employ Long Short Term Memory (LSTM) to capture long temporal dependencies and Convolutional Neural Network (CNN) to learn the local trend features. However, designing optimal CNN LSTM network architecture and tuning parameters can be challenging and would require consistent human supervision. To automate optimal architecture searching of CNN LSTM, we implement three meta heuristics: a Particle Swarm Optimisation (PSO) and two Differential Evolution (DE) variants. Computational experiments on real world inventory forecasting problems are conducted to evaluate the performance of the applied meta heuristics in terms of evolved network architectures for obtaining prediction accuracy. Moreover, the evolved CNN LSTM models are also compared to Seasonal Auto regressive Integrated Moving Average (SARIMA) models for inventory forecasting problems. The experimental results indicate that the evolved CNN LSTM models are capable of dealing with complex nonlinear inventory forecasting problem.
A Momentum incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems
Venue: 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)
Year: 2020

Latent factor (LF) model is an effective method for extracting useful knowledge from high dimensional and sparse (HiDS) data generated by various industrial applications. Parallelized stochastic gradient descent (SGD) is widely used in building a parallelized LF model for handling large scale HiDS data, but parallelized SGD suffers from slow convergence and considerable time cost. To address this issue, this study incorporates the principle of momentum into parallelized SGD, where momentum decay coefficient and learning rate are adjusted dynamically, and proposes a momentum incorporated fast parallelized SGD (MFSGD) method to discover latent patterns from large scale HiDS data. The experiments on two datasets show that the proposed MFSGD method outperforms state of the art parallel SGD methods in terms of computational efficiency.
Delito e impunidad en la Guatemala de posguerra: El cojo bueno y Noche de piedras, de Rodrigo Rey Rosa
Year: 2012

Este trabajo se centra en las novelas El cojo bueno (1996) y Noche de piedras (2002) de Rodrigo Rey Rosa (Guatemala, 1958) uno de los narradores mas destacados de la escena literaria guatemalteca y latinoamericana, y de los de mayor reconocimiento a nivel internacional. Aqui propongo un examen de las representaciones del delito, de la investigacion criminal y de la maquinaria de la impunidad en Guatemala plasmadas en los textos, los cuales se inscriben dentro de la tradicion del genero negro latinoamericano: ambos plantean una investigacion extra oficial de la verdad que deja imprecisos los contornos del delito cometido asi como la identidad de los culpables, que confirma la impunidad frente a la violencia, y expresa la imposibilidad de restaurar simbolicamente el orden y la justicia. Argumentare que en El cojo bueno, ubicado en el contexto de la Firma de los Acuerdos de Paz, despunta la posibilidad de detener la venganza personal sistematica y reemplazarla por el accionar ecuanime de la justicia, si bi.
Mindfulness and Psychological Health Outcomes: a Latent Profile Analysis Among Military Personnel and College Students
Venue: Mindfulness
Year: 2018

Previous research on trait mindfulness facets using person centered analyses (e.g. latent profile analysis [LPA] has identified four distinct mindfulness profiles among college students: a high mindfulness group (high on all facets of the Five Facet Mindfulness Questionnaire [FFMQ] a judgmentally observing group (highest on observing, but low on non judging of inner experience and acting with awareness) a non judgmentally aware group (high on non judging of inner experience and acting with awareness, but very low on observing) and a low mindfulness group (low on all facets of the FFMQ) In the present study, we used LPA to identify distinct mindfulness profiles in a community based sample of US military personnel (majority veterans; n 407) and non military college students (n 310) and compare these profiles on symptoms of psychological health outcomes (e.g. suicidality, PTSD, anxiety, rumination) and percentage of participants exceeding clinically significant cutoffs for depressive symptoms, substance use, and alcohol use. In the subsample of college students, we replicated previous research and found four distinct mindfulness profiles; however, in the military subsample, we found three distinct mindfulness profiles (a combined low mindfulness/judgmentally observing class) In both subsamples, we found that the most adaptive profile was the "high mindfulness" profile (i.e. demonstrated the lowest scores on all psychological symptoms and the lowest probability of exceeding clinical cutoffs) Based on these findings, we purport that the comprehensive examination of an individual's mindfulness profile could help clinicians tailor interventions/treatments that capitalize on an individual's specific strengths and work to address their specific deficits.
Concerns over use of glyphosate based herbicides and risks associated with exposures: a consensus statement
Venue: Environmental Health
Year: 2016

The broad spectrum herbicide glyphosate (common trade name "Roundup" was first sold to farmers in 1974. Since the late 1970s, the volume of glyphosate based herbicides (GBHs) applied has increased approximately 100 fold. Further increases in the volume applied are likely due to more and higher rates of application in response to the widespread emergence of glyphosate resistant weeds and new, pre harvest, dessicant use patterns. GBHs were developed to replace or reduce reliance on herbicides causing well documented problems associated with drift and crop damage, slipping efficacy, and human health risks. Initial industry toxicity testing suggested that GBHs posed relatively low risks to non target species, including mammals, leading regulatory authorities worldwide to set high acceptable exposure limits. To accommodate changes in GBH use patterns associated with genetically engineered, herbicide tolerant crops, regulators have dramatically increased tolerance levels in maize, oilseed (soybeans and canola) and alfalfa crops and related livestock feeds. Animal and epidemiology studies published in the last decade, however, point to the need for a fresh look at glyphosate toxicity. Furthermore, the World Health Organization's International Agency for Research on Cancer recently concluded that glyphosate is "probably carcinogenic to humans." In response to changing GBH use patterns and advances in scientific understanding of their potential hazards, we have produced a Statement of Concern drawing on emerging science relevant to the safety of GBHs. Our Statement of Concern considers current published literature describing GBH uses, mechanisms of action, toxicity in laboratory animals, and epidemiological studies. It also examines the derivation of current human safety standards. We conclude that: (1) GBHs are the most heavily applied herbicide in the world and usage continues to rise; (2) Worldwide, GBHs often contaminate drinking water sources, precipitation, and air, especially in agricultural regions; (3) The half life of glyphosate in water and soil is longer than previously recognized; (4) Glyphosate and its metabolites are widely present in the global soybean supply; (5) Human exposures to GBHs are rising; (6) Glyphosate is now authoritatively classified as a probable human carcinogen; (7) Regulatory estimates of tolerable daily intakes for glyphosate in the United States and European Union are based on outdated science. We offer a series of recommendations related to the need for new investments in epidemiological studies, biomonitoring, and toxicology studies that draw on the principles of endocrinology to determine whether the effects of GBHs are due to endocrine disrupting activities. We suggest that common commercial formulations of GBHs should be prioritized for inclusion in government led toxicology testing programs such as the U.S. National Toxicology Program, as well as for biomonitoring as conducted by the U.S. Centers for Disease Control and Prevention.
A deep learning based COVID 19 automatic diagnostic framework using chest X ray images
Venue: Biocybernetics and Biomedical Engineering
Year: 2021

The lethal novel coronavirus disease 2019 (COVID 19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID 19 as a quick alternative diagnostic option to control its spread. A robust deep learning based system is proposed to detect the COVID 19 using chest X ray images. Infected patient's chest X ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID 19 vs. non COVID) COVID 19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 2.71% was achieved for multi class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID 19 vs. non COVID) The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real time.
Current Perspectives on Teaching World Englishes and English as a Lingua Franca
Year: 2006

The purpose of this article is to explore recent research into World Englishes (henceforth WEs) and English as a Lingua Franca (ELF) focusing on its implications for TESOL, and the extent to which it is being taken into account by English language teachers, linguists, and second language acquisition researchers. After a brief introduction comparing the current situation with that of 15 years ago, I look more closely at definitions of WEs and ELF. Then follows an overview of relevant developments in WEs and ELF research during the past 15 years, along with a more detailed discussion of some key research projects and any controversies they have aroused. I then address the implications of WEs/ELF research for TESOL vis a vis English language standards and standard English, and the longstanding native versus nonnative teacher debate. Finally, I assess the consensus on WEs and ELF that is emerging both among researchers and between researchers and language teaching professionals. The article concludes by raising a number of questions that remain to be investigated in future research.