Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulating Religion

The investigation was in response to a number of media reports concerning suspicious or untoward practices being undertaken by various pastors and ‘churches’. These included allegations of congregants being made to eat grass or to drink petrol; stories of people being cajoled into handing over large sums of money in order to be ‘cured’ of a disease; the encouragement of personality cults; and the depositing of church funds in the personal bank accounts of leaders.
Condition assessment of selected reinforced concrete structural elements of the bus station in Kielce

The paper presents the results of the research aimed at assessing the condition of reinforcement and concrete cover in selected elements of the structure of the most recognizable structure in Kielce, i.e. PKS station, located at Czarnowska Street. Currently, demolition works are underway resulting from the planned modernization. The assessment of the corrosion risk of the reinforcement in the construction elements was carried out with the use of a semi-non-destructive electrochemical method. The use of this method made it possible to determine the probability of reinforcement corrosion in the selected areas and to estimate its rate. The protective properties of concrete cover were checked by the carbonation test (test using a 1% phenolphthalein solution) and phase composition analysis (X-ray diffraction analysis). In order to determine the position of the reinforcing bars and to estimate the concrete cover thickness distribution in the areas corresponding to the aforementioned measurements, ferromagnetic detection system was used.
To bridge or buffer? A resource dependence theory of nascent entrepreneurial ecosystems

PurposeVibrant entrepreneurial ecosystems, systems of inter-related forces that promote and sustain regional entrepreneurship, are increasingly viewed as sources of innovation, economic development and community revitalization. Regions with emerging, underdeveloped or depressed economies are attempting to develop their nascent entrepreneurial ecosystems in the hopes of experiencing the positive benefits of entrepreneurial activity. For nascent entrepreneurial ecosystems to grow requires resources. However, how nascent entrepreneurial ecosystems manage their resource dependencies and the tensions that exist between creating and attracting resources are not clear. The purpose of this paper is to propose a theory of nascent entrepreneurial ecosystem resource dependence.Design/methodology/approachThis conceptual paper analyzes entrepreneurial ecosystems as meta-organizations and builds on resource dependence theory to explain how nascent ecosystems respond to environmental dependencies and their resource needs through internal and external strategies.FindingsTwo specific strategies used by nascent entrepreneurial ecosystems to manage resource dependence – bridging and buffer – are explored. It is proposed that there is a positive relationship between the resource dependence of a nascent entrepreneurial ecosystem and its use of bridging and buffering activities. Two ecosystem characteristics that influence the pursuit of bridging and buffering – ecosystem size and the presence of collaborative values – are also identified. In addition, it is theorized that resource dependence strategies influence a key, system-level characteristic of entrepreneurial ecosystems: resilience, the ecosystem’s ability to respond and adapt to internal and external disruptions.Originality/valueThe theory presented generates insights into how nascent entrepreneurial ecosystems create and obtain resources when ecosystems are unmunificent, resource-constrained or underdeveloped. The theorizing addresses which resource dependence strategy – buffering or bridging – has a stronger link to resource dependence (and resilience) and under what conditions these linkages occur. The theoretical model generates insights for research on entrepreneurship in emerging and developed economies and produces practical implications for ecosystem participants, policymakers and economic development organizations.
Valores personales en el baloncesto de formación y su relación con el bienestar y la intención futura de práctica a través de la motivación

Tomando como marco teórico la teoría de los valores humanos (Schwartz, 1992; Schwartz et al., 2012), y la teoría de la autodeterminación (Deci y Ryan, 1985, 2000), el objetivo del estudio consistió en poner a prueba un modelo, en una muestra de 115 jugadores jóvenes de baloncesto (Medad = 13.98, DT = 0.13), en el que se hipotetizó que los diferentes tipos de motivación (motivación autónoma, controlada y no motivación) actuarían como mediadores de la relación entre los valores de los jugadores (auto-trascendencia, conservación, auto-promoción y apertura al cambio) y los indicadores de bienestar (vitalidad subjetiva y afectos positivos), así como de la intención futura de seguir practicando baloncesto. Los resultados confirmaron el efecto indirecto de los valores de autopromoción sobre la percepción de vitalidad de los jugadores, sus afectos positivos y su intención de seguir jugando en el futuro, a través de la motivación autónoma. Sin embargo, los otros tipos de motivación (motivación controlada y no motivación) no mediaron la relación entre el resto de valores y los indicadores de bienestar, ni con la intención futura de práctica deportiva. Estos resultados indican que para favorecer una mayor experiencia de bienestar en el baloncesto habría que evitar poner énfasis en valores centrados en lo individual, en el logro personal y en el dominio sobre los demás (auto-promoción), ya que la vitalidad subjetiva, los afectos positivos y la intención de seguir jugando se van a ver reducidos, a través de una menor motivación autónoma.
Emerging Economy Business Cycles: Interest Rate Shocks vs Trend Shocks

The recent literature studying the source of business cycles in emerging market economies (EMEs) has debated the relative importance of productivity trend shocks versus interest rate shocks coupled with financial frictions. Importantly, the papers where an important role was assigned to interest rate shocks did not force their models to match the historical paths of the world or country interest rate. This could have led to poorly identified interest rate shocks and inaccurate measures of contributions of shocks to EME business cycles. To address this issue, we estimate a small open economy model for Argentina and Mexico using Bayesian methods where world and country interest rate series are included as observables. This estimation strategy brings quantitative accuracy by imposing discipline on the estimated shocks. Although we find evidence in favour of both shocks, including interest rates as observables, shifts explanatory power away from trend shocks towards interest rate shocks.
Structured learning with inexact search: advances in shift-reduce CCG parsing

Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second, I present expected F-measure training and show how to derive a globally normalized RNN model, in which beam search is naturally incorporated and used in conjunction with the objective to learn shift-reduce action sequences optimized for the final evaluation metric. Finally, I describe an LSTM model that is able to construct parser state representations incrementally by following the shift-reduce syntactic derivation process; I show expected F-measure training, which is agnostic to the underlying neural network, can be applied in this setting to obtain globally normalized greedy and beam-search LSTM shift-reduce parsers.
The Investigation of Algorithmic Thinking Skills of 5th and 6th Graders at a Theoretical Dimension

Algoritmik dusunmenin, problem cozme, programlama ve kodlama gibi matematiksel sureclerin merkezinde yer alan temel matematiksel becerilerden biri olmasinin yaninda, literaturde ilgili calismalarin cok sinirli oldugu gorulmektedir. Bu baglamda, bu calisma ortaokul ogrencilerinin algoritmik dusunme becerilerini teorik bir boyutta incelemeyi amaclamaktadir. Bu bir durum calismasidir ve calisma grubu Ordu ilindeki farkli devlet okullarinin 5. ve 6. siniflarinda ogrenim gormekte olan toplam 138 ogrenciden olusmaktadir. Calisma grubunun sirasiyla %54 ve %45'i kiz ve erkek, %50 ve %45'i ise bes ve altinci sinif ogrencisidir. Calisma grubundaki ogrencilerin belirlenmesinde amacli ornekleme yontemlerinden olcut ornekleme, veri toplama araci olarak ise arastirmacilar tarafindan gelistirilmis olan Algoritmik Dusunme Testi kullanilmistir. Calisma sonucunda, ogrencilerin algoritmik dusunme becerilerini %43 oraninda kullanabildikleri gozlenmistir. Ogrencilerin en basarili olduklari sorularin algoritmik sorular kategorisinde, en basarisiz olduklari sorularin ise mantik sorular i kategorisinde yer aldigi gorulmustur. Calisma sonucunda konuyla ilgili olarak yapilabilecek farkli calismalar icin bazi oneriler sunulmustur.
Evaluation of the nutritional status in pre-school and school children, Madeira Island, Portugal

In order to evaluate the nutritional status of Câmara dos Lobos Municipality students, all pre-primary and primary public schools located in the municipality were contacted. The assessment of height and body weight, used to calculate body mass index as a method of diagnosing low weight, overweight, or obesity, was performed in 1405 students aged 3 to 9 years. The cut-off points of the BMI adopted by the IOTF were used to calculate the prevalence of underweight, overweight and obesity. The prevalence of underweight, overweight and obesity was 2.5%, 36.9% and 13.1%, respectively. In children between the ages of 3 and 5 the prevalence of overweight (including obesity) is 6.9-12.8% in boys and 6.99. 8% in girls. Six-year-olds had a higher prevalence of overweight when compared to other children. Students from the schools of the Estreito community in Câmara de Lobos presented the highest prevalence of low weight (3.5%). Students from the community of Curral das Freiras presented a higher prevalence of overweight (42.0%), and the prevalence of obesity was 21.0%. In order to prevent childhood obesity the municipality of Câmara de Lobos might consider these results for appropriate action.
Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area.
Numerical Investigation of Nanofluid Al2O3/Water in Elliptical Tube Equipped with Twisted Tape

1 Department of Mechanical Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1469669191, Iran 2 BioComputing Laboratory, Institute for Bioengineering Application Technology, Department of Computer Science and Engineering, Korea University of Technology and Education (KOREATECH), Cheonan 31253, Republic of Korea 3 College of Information & Communication Engineering, School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
A Method for Forecasting Weather Condition by using Artificial Neural Network Algorithm

This article presents a method to forecast and make decision on weather condition. In most of the cities around the world, people try to decide on leisure activities on their spare time but weather condition would not be suitable for them. By this fact, we suggest a solution to solve this problem with ANN. Therefore, users of our proposed method can organize their daily life in accordance with weather condition. Artificial Neural Network (ANN) is one of the popular research subjects in computer science, thus, this paper aims to familiarize the reader with ANN. In our proposed method, at first, people can organize weather condition, and then the program suggest whether the time is suitable for them or not on chosen hour of day. In ANN, we discuss about neuron that have relation with performance. Mean Square Error (MSE) is the key issue for the performance of our method. At the end, the simulation results show that relation between Neuron and MSE is applicable for daily usage.
Moving obstacles detection based on laser range finder measurements

The objective of this article is to propose data processing from laser range finder that will provide simple, fast, and reliable object recognition including moving objects. The whole method is based on four steps: segmentation, simplification, correspondence between consequent measurements, and object classification. Segmentation uses raw data from laser range finder and it is significant in logical connection of related segments. The most important step is simplification which provides data reduction and acceleration of object classification. The output of simplification is an object represented by significant points. Correspondence between consequent measurements is based on kd-tree nearest neighborhood search. The object is then classified by its spatial changes. These changes are evaluated by position of correspondent significant points. Input of proposed procedure is a probabilistic model of laser range finder. In this article, versatile probabilistic model of Hokuyo URM-30 LX was used. The method was verified by simulations and by tests in real environment. The results show that proposed method is reliable and with small modifications (of parameters), it is usable with any other planar laser range finder.
An Analysis of Contagion Effect on ASEAN Stock Market Using Multivariate Markov Switching DCC GARCH

Contagion effect is a transmission of volatility from shocks arising in one country to other countries. Volatility transmission particularly occurs in emerging countries like the ASEAN. In this study, we investigate the contagion effect in eight stock of the South East Asia stock markets (ASEAN), namely stock exchange of Thailand (SET), Indonesia stock exchange (IDX), Hanoi stock exchange (HNX), Kuala Lumpur Stock Exchange (KSX), Singapore Exchange Limited (STI), The Philippine Stock Exchange, Inc. (PSEi), Cambodia Securities Exchange (CSX) and Lao PDR stock exchange (LSX) (which call ASEAN stock markets). The contagion effect is investigated using correlation analysis, thus, we employ the MS-DCC-GARCH model. The result of this research shows that ASEAN stock markets usually stay in high correlation regime and the degree of volatility is high. This indicates a strong contagion among ASEAN stock markets.
A review of the major drivers of the terrestrial carbon uptake: model-based assessments, consensus, and uncertainties

Terrestrial and oceanic carbon sinks together sequester >50% of the anthropogenic emissions, and the major uncertainty in the global carbon budget is related to the terrestrial carbon cycle. Hence, it is important to understand the major drivers of the land carbon uptake to make informed decisions on climate change mitigation policies. In this paper, we assess the major drivers of the land carbon uptake—CO2 fertilization, nitrogen deposition, climate change, and land use/land cover changes (LULCC)—from existing literature for the historical period and future scenarios, focusing on the results from fifth Coupled Models Intercomparison Project (CMIP5). The existing literature shows that the LULCC fluxes have led to a decline in the terrestrial carbon stocks during the historical period, despite positive contributions from CO2 fertilization and nitrogen deposition. However, several studies find increases in the land carbon sink in recent decades and suggest that CO2 fertilization is the primary driver (up to 85%) of this increase followed by nitrogen deposition (∼10%–20%). For the 21st century, terrestrial carbon stocks are projected to increase in the majority of CMIP5 simulations under the representative concentration pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 scenarios, mainly due to CO2 fertilization. These projections indicate that the effects of nitrogen deposition in future scenarios are small (∼2%–10%), and climate warming would lead to a loss of land carbon. The vast majority of the studies consider the effects of only one or two of the drivers, impairing comprehensive assessments of the relative contributions of the drivers. Further, the broad range in magnitudes and scenario/model dependence of the sensitivity factors pose challenges in unambiguous projections of land carbon uptake. Improved representation of processes such as LULCC, fires, nutrient limitation and permafrost thawing in the models are necessary to constrain the present-day carbon cycle and for more accurate future projections.
CNN-encoded Radical-level Representation for Japanese Processing

Although word embeddings are powerful, weakness on rare words, unknown words and issues of large vocabulary motivated people to explore alternative representations. While the character embeddings have been successful for alphabetical languages, Japanese is difficult to be processed at the character level as well because of the large vocabulary of kanji, written in the Chinese characters. In order to achieve fewer parameters and better generalization on infrequent words and characters, we proposed a model that encodes Japanese texts from the radical-level representation, inspired by the experimental findings in the field of psycholinguistics. The proposed model is comprised of a convolutional local encoder and a recurrent global encoder. For the convolutional encoder, we propose a novel combination of two kinds of convolutional filters of different strides in one layer to extract information from the different levels. We compare the proposed radical-level model with the state-of-the-art word and character embedding-based models in the sentiment classification task. The proposed model outperformed the state-of-the-art models for the randomly sampled texts and the texts that contain unknown characters, with 91% and 12% fewer parameters than the word embedding-based and character embedding-based models, respectively. Especially for the test sets of unknown characters, the results by the proposed model were 4.01% and 2.38% above the word embedding-based and character embedding-based baselines, respectively. The proposed model is powerful with cheaper computational and storage cost, can be used for devices with limited storage and to process texts of rare characters.
Brazil’s policy toward Israel and Palestine in Dilma Rousseff and Michel Temer’s administrations: have there been any shifts?

This paper analyzes Brazil’s relationship with Israel and Palestine during the Rousseff (2011–2016) and Temer (2016–2018) administrations. We consider the political and economic crises in Brazil since 2014, and their consequences in foreign policy. Our question is whether there was room for changes, and we attempt to understand if there were conditions for a shift in Brazilian foreign policy for the Middle East. Therefore, our hypothesis is that in this specific period, besides public declarations, there were no deep changes. We conclude that foreign policy was conditioned by structural objectives, such as economic interests, the relationship with Arab and Jewish communities, and Brazilian international projection.
Keypoint-Less, Heuristic Application of Local 3D Descriptors

Abstract One of the most important topics in the research concerning 3D local descriptors is computational efficiency. The state-of-the-art approach addressing this matter consists in using keypoint detectors that effectively limit the number of points for which the descriptors are computed. However, the choice of keypoints is not trivial and might have negative implications, such as the omission of relevant areas. Instead, focusing on the task of single object detection, we propose a keypoint-less approach to attention focusing in which the full scene is processed in a hierarchical manner: weaker, less rejective and faster classification methods are used as heuristics for increasingly robust descriptors, which allows to use more demanding algorithms at the top level of the hierarchy. We have developed a massively-parallel, open source object recognition framework, which we use to explore the proposed method on demanding, realistic indoor scenes, applying the full power available in modern computers.
A deep learning method for early screening of lung cancer

Lung cancer is the leading cause of cancer-related deaths among men. In this paper, we propose a pulmonary nodule detection method for early screening of lung cancer based on the improved AlexNet model. In order to maintain the same image quality as the existing B/S architecture PACS system, we convert the original CT image into JPEG format image by analyzing the DICOM file firstly. Secondly, in view of the large size and complex background of CT chest images, we design the convolution neural network on basis of AlexNet model and sparse convolution structure. At last we train our models on the software named DIGITS which is provided by NVIDIA. The main contribution of this paper is to apply the convolutional neural network for the early screening of lung cancer and improve the screening accuracy by combining the AlexNet model with the sparse convolution structure. We make a series of experiments on the chest CT images using the proposed method, of which the sensitivity and specificity indicates that the method presented in this paper can effectively improve the accuracy of early screening of lung cancer and it has certain clinical significance at the same time.