Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Analysis on Stunting Events Based on Community Led Total Sanitation and Poor families in Cianjur District

Stunting is a condition of failure to thrive in children under five (infants under five years old), a result of chronic malnutrition so that the child experiences a size that is too short of her age in general (National Team for the Acceleration of Poverty Reduction (2017). In the first 1000 days, from conception to age 2 years a window of critical opportunity, where timely intervention can have a measurable impact on the prevention of stunting in children. The stunting problem illustrates the existence of chronic nutritional problems, influenced by the condition of the mother, the condition of the fetus, and infancy including illnesses suffered during that period. By using secondary data which is then processed using geographic information system(GIS) software, the percentage of stunting, communitybased total sanitation stop chapter can be seen so that it can be known priority areas in Cianjur district.
Active Learning for Chinese Word Segmentation in Medical Text

Electronic health records (EHRs) stored in hospital information systems completely reflect the patients' diagnosis and treatment processes, which are essential to clinical data mining. Chinese word segmentation (CWS) is a fundamental and important task for Chinese natural language processing. Currently, most state-of-the-art CWS methods greatly depend on large-scale manually-annotated data, which is a very time-consuming and expensive work, specially for the annotation in medical field. In this paper, we present an active learning method for CWS in medical text. To effectively utilize complete segmentation history, a new scoring model in sampling strategy is proposed, which combines information entropy with neural network. Besides, to capture interactions between adjacent characters, K-means clustering features are additionally added in word segmenter. We experimentally evaluate our proposed CWS method in medical text, experimental results based on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine show that our proposed method outperforms other reference methods, which can effectively save the cost of manual annotation.
How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

ABSTRACT The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies.
National pride, sporting success and event hosting: an analysis of intangible effects related to major athletic tournaments

ABSTRACT Contemporary research into the impact of major sports events shows that the widely used (and popular) claim of economic benefits associated with hosting them is misleading or – at best – overrated. In this paper, we aim to measure whether other potential intangible effects can be found, specifically that of national pride. We expand on existing research by including more international sports events and nations while also including a medal index into our regression models to test the effect of athletic achievement. Our results suggest that international sporting success is not a significant driver of national pride. Hosting mega sports events is positively correlated with pride, although this is not significant in our estimations. Implications for nations are that they should become much more strategic in order to harvest potential intangible effects. (JEL: D60; I31)
Ensemble Performance of Biometric Authentication Systems Based on Secret Key Generation

We study the ensemble performance of biometric authentication systems, based on secret key generation, which work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity as one of the subscribers, he/she has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared with the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected. Referring to an ensemble of systems based on Slepian–Wolf binning, we provide a detailed analysis of the false–reject (FR) and false–accept (FA) probabilities, for a wide class of stochastic decoders. We also derive converse bounds. The converse bound of the FA probability matches the direct theorem, whereas the one for the FR probability is tight for some ranges of rates. Finally, we outline derivations of the secrecy leakage (for the typical code in the ensemble) and the privacy leakage.
Redesigning Entrepreneurship Curriculum in Nigerian Universities For Knowledge Creation And Labor Market Competition

Two problems gnawing at the economic foundation of Nigeria, these are unemployment and unemployability. Based on the extant literature, the solution lies in the creation, proliferation and perpetuation of the entrepreneurship to become vital in the economic development of the nation. This is with the assumption that entrepreneurship skills can be acquired through education. Universities in Nigeria are expected to graduate students who are well equipped with entrepreneurial vision for pre-start ventures and job creators. Nigerian Universities have introduced and embedded entrepreneurship in the curricula, however, minimal positive results abound due to poor contents of the programs, pedagogy and learning styles. The teaching of entrepreneurship in Nigerian un iversities emerged as “all comers” venture with no regard to expertise nor streamlining how entrepreneurship should be taught, what should be taught, who should teach, and the type of entrepreneurs to be produced. The objective of the study therefore was to find an answer to the key research questions: how can Nigerian Universities achieve the anticipated objective of building the entrepreneurship economy for development? The study identified two approaches for attaining the objective, namely, redesign of entrepreneurship curriculum in Nigerian Universities to foster entrepreneurship development, create knowledge based economy, and second, streamline qualifications and skills of lecturers that teach entrepreneurship, determine how it should be taught and what should be taught. In essence, Nigerian Universities must become catalyst for entrepreneurship development conditioning the emergence of dynamic entrepreneurs. Some of the problems in restructuring the entrepreneur curriculum will be highlighted and possible solutions proffered. The research developed a model incorporating the elements and participants involved in redesigning entrepreneurship curriculum. Further research efforts to fine-tune the model and to institutionalize it through policy articulation was also identified.
Seasonal Flux Measurements over a Colorado Pine Forest Demonstrate a Persistent Source of Organic Acids

Forests can be both sources and sinks of volatile organic compounds to the atmosphere. The role that forests play in controlling organic acid concentrations remains poorly understood with multiple model-measurement comparisons reporting missing sources of formic acid. We conducted seasonal measurements of concentrations and eddy covariance fluxes of oxidized volatile organic compounds over a ponderosa pine forest in Colorado in 2016. Diel concentration profiles show mid-day maxima, consistent with previous studies. We observed persistent but variable upward fluxes of formic, propionic, methacrylic, and butyric acids from the pine forest during all seasons. Formic acid concentrations and fluxes were ∼10 times higher than the other organic acids with daytime exchange velocities on the order of 4–6 cm s–1. The other organic acids had similar exchange velocities as formic acid in the warmer seasons and much smaller exchange velocities in the colder seasons. The upward fluxes for all organic acids increased ex.
An Integrated Land Cover Mapping Method Suitable for Low-Accuracy Areas in Global Land Cover Maps

In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data generated. In this paper, low-accuracy areas in China (extracted from the MODIS global LCC maps) were taken as examples, identified as the regions having lower accuracy than the average OA of China. An integrated land cover mapping method targeting low-accuracy regions was developed and tested in eight representative low-accuracy regions of China. The method optimized procedures of image choosing and sample selection based on an existent visually-interpreted regional LCC dataset with high accuracies. Five algorithms and 16 groups of classification features were compared to achieve the highest OA. The support vector machine (SVM) achieved the highest mean OA (81.5%) when only spectral bands were classified. Aspect tended to attenuate OA as a classification feature. The optimal classification features for different regions largely depends on the topographic feature of vegetation. The mean OA for eight low-accuracy regions was 84.4% by the proposed method in this study, which exceeded the mean OA of most precedent global land cover datasets. The new method can be applied worldwide to improve land cover mapping of low-accuracy areas in global land cover maps.
Novice social studies teachers’ implementation of differentiation: A longitudinal multicase study

ABSTRACT This longitudinal qualitative multicase study of social studies teachers’ implementation of differentiation examined two educators’ experiences as teacher candidates and beginning teachers, drawing upon theoretical perspectives of novice teacher appropriation of pedagogies across learning-to-teach settings. The study’s first phase explored how participants conceptualized differentiation and anticipated implementing it in future practice as they learned about it in depth and with fidelity in a teacher preparation course about this pedagogical approach. The second phase examined how, as first and second-year teachers, participants’ conceptions of differentiation evolved as they gained experience, participants implemented differentiation, and contextual factors shaped their conceptions and implementation. Extensive data collection across three years supported rich exploration of the evolution of participant experiences. Findings indicated that when their early career social studies instruction involved fact-focused curricula rather than authentic disciplinary inquiry, participants did not see meaningful differences among students or a corresponding need for differentiation.
Research on Machine Learning and Its Algorithm

With the popularity of the Internet and the rapid development of information technology, the data show explosive growth, how to quickly found valuable information and take advantage of it in numerous and complicated data, the emergence of machine learning algorithms and technology make it possible. This paper briefly describes the basic concepts and development of machine learning, and then elaborates the classic algorithm of machine learning in details and finally forecasts the prospects of the machine learning.
A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

Abstract The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-sec NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.
Evaluating the Green Energy Potential of the DOT type wind turbine for Mining Operations

Applying wind turbines to create renewable energy is used by many companies around the world. However, in the mining industry, only two projects use wind energy. In the mining industry, energy consumption can be up to twenty-five megawatts per tonne of produced material. The main reason is the use of fossil fuel for generators in off-grid situations. This paper evaluates the benefits of wind power for mining operations and the benefits of the Delft Offshore Turbine (DOT) type wind turbine compared to other turbines. Using the DOT type wind turbine, wind energy can be used for three different applications, namely: 1) generation of electricity, 2) direct hydraulic power and 3) direct dewatering. Different areas in the world are then analysed on the aspects: 1) soil strength, 2) elevation or relief of the terrain and 3) the wind velocities. A case study is performed for the most suitable area in the world: Australia. Next the best-chosen location is divided into smaller regions, where the same evaluation is applied. A case study was conducted on a favourable area, calculating various cost and CO2 reduction scenarios based on simulated wind speeds. The DOT type turbine can provide electricity, which costs 23 to 44 US$/MWh. Compared to a 1-MW diesel generator, in the best-case scenario, a wind farm generating 25 MW per hour, can save $4200/hour. Overall, implementing the DOT at the right location, can 1) remove polluting diesel generators from mining operations, 2) save energy costs up to $4200/hour and 3) reduce CO2 emissions up to 770 kilograms per MW per hour.
Review of Data mining (Knowledge discovery) in the Future

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user (e.g., association rule mining). Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery. In Future different Scope of data mining are. 1. Developing a unifying theory of data mining. 2. Scaling up for high dimensional data and high speed data streams. 3. Data mining in a network setting. 4. Data mining for biological and environmental problems. 5. Security, privacy and data integrity. 6. Dealing with non-static, unbalanced and cost-sensitive data
Correcting for norm misperception of anti-bullying attitudes

Research has documented the presence of norm misperception in the context of school bullying, as children and adolescents typically underestimate the degree to which their peers disapprove of bullying behavior. Despite commonly held attitudes in opposition to bullies and in support of helping victims, widespread misperception of the norm makes students vulnerable to acting in a manner that reinforces bullying, as they aim to align themselves with perceived peer beliefs. This study investigated whether personalized normative feedback, a social norms intervention that juxtaposes individuals’ own perceptions of peer norms against their peers’ true normative values, could operate as a mechanism by which to reduce norm misperception of peer attitudes toward bullying. Whereas this type of intervention has shown promising effects in a variety of contexts, no study to date has examined its utility in the specific context of bullying. Baseline participants included 188 seventh grade students, 175 of whom were randomized into four study groups for follow-up data collection. Individuals in the experimental condition received personalized normative feedback on attitudes toward bullying. Control conditions were the following: general normative feedback on anti-bullying attitudes, the absence of normative feedback, and personalized normative feedback on a construct separate from bullying (i.e., antidrug use attitudes). Findings indicated that personalized normative feedback on bullying attitudes led to significant change in perceived peer attitudes in the direction of the group norm, with an effect size in the small-to-medium range. No intervention effects emerged on personal attitude change. Implications highlight strategies for improving the strength of similar interventions in future research as well as the positive clinical outcomes that could result from reduced norm misperception and increased engagement in prosocial bystander behavior.
Study of the Deep Offshore Regions Off the Southwestern Continental Margin of India and its Conjugate Regions to Understand their Pre-Breakup Juxtapositions and Subsequent Plate Tectonic Evolution

Published models for the plate tectonic evolution of the Western Indian Ocean suggest that the Southern Mascarene Basin opened by oceanic crustal accretion between the continental margins of southwestern India and southeastern Madagascar. However, with the cessation of the Mascarene Basin spreading centre followed by a ridge jump resulting in the opening of the Carlsberg Ridge, almost all the traces of India-Madagascar divergence were carved away from the Indian Plate and attached to the African Plate. According to some recent studies, the Chain-Kairali Escarpment, a prominent feature on the southwestern continental margin of India, is possibly the only trace of India-Madagascar divergence that remained on the Indian Plate. But the exact conjugate correspondence of this feature on the Madagascar side is uncertain. Published plate tectonic reconstructions imply that the Vishnu Fracture Zone on the Indian side and the Mauritius Fracture Zone on the Madagascar side are aligned at chron C22ny (∼49.04Ma). Based on the near collinearity of gravity anomaly trends, the Chain-Kairali Escarpment appears to be the landward extension of the Vishnu FZ. However, at chron C34ny (∼83.0Ma), the Chain-Kairali Escarpment was in close proximity to the incipient Mahanoro Fracture Zone. In this study we investigate this incompatibility, using an up-to-date compilation of the tectonic elements from the conjugate regions of India and Madagascar and the latest available rotation parameters that describe India-Madagascar separation through a direct India-Seychelles-Madagascar plate circuit. Our revised plate reconstruction model suggests that the Chain-Kairali Escarpment was formed due to the strike-slip motion between the southeast coast of Madagascar and the then southwest coast of India during the initial stages of India-Madagascar breakup. The migration of the Chain-Kairali Escarpment from the proximity of the Mahanoro FZ and aligning with the Vishnu FZ was the result of several successive events. The first among those events was asymmetric crustal accretion in the Mascarene Basin due to ridge propagation, between chrons C34ny (83.0 Ma) to C33ny (∼73.62Ma). The Chain-Kairali Escarpment and associated crustal weak zones offshore India appear to have facilitated subsequent initiation of the Mauritius FZ and its conjugate Vishnu FZ during a plate reorganization at about chron C33ny (∼73.62Ma). The cessation of spreading in the Mascarene Basin and development of full extent of the Carlsberg Ridge, shortly after chron C27ny (60.92Ma), resulted in the initiation of a long transform fault, coinciding with the Vishnu FZ, which connected the Carlsberg Ridge with the spreading centre of the Madagascar Basin. Therefore, the Vishnu FZ and the Chain-Kairali Escarpment are two independent features created during different episodes of evolution of the Western Indian Ocean and the Chain-Kairali Escarpment is not a landward extension of the Vishnu FZ.
Freihandel der EG und der EFTA mit Ost-Mitteleuropa

Die "Europavertrage" der EG mit Ost-Mitteleuropa sind ein wichtiger Schritt zur Einbeziehung dieser Region in den europaischen Freihandel. Sowohl der Zeitvorsprung der EG gegenuber der EFTA als auch verschiedene "technische" Probleme (vor allem im Bereich der Ursprungsregelung) konnten die Marktstellung Osterreichs in Ost-Mitteleuropa beeintrachtigen.