Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflection as pedagogy in action research

ABSTRACT In this paper, we attend to the pedagogical role of reflection within action research practices. We discuss educational considerations of the complex process of improving curriculum, while undertaking collaborative research in which reflection within the iterative process of action research became pedagogical. We draw upon our reflections from an action research project on research-based learning in course-based, professional graduate programs. In particular, our purpose was to think about our reflections from diverse roles and viewpoints, from associate dean, academic coordinator, instructor, and co-researchers, to explore the various ways in which our collaborative understandings informed graduate program design. Our narrative reflections allowed us to learn about our individual and collective beliefs about action research, and the ways in which our beliefs and practices shaped graduate students’ experiences learning about and conducting action research. In drawing on our critical reflective processes, we show both the tensions and possibilities of collaboration in action research. We conclude, after reflecting on our collective processes engaged in this paper, that documenting researchers’ experiences can be vital in action research for addressing complex educational challenges in the improvement of curriculum and programs, and in creating the conditions for enhanced student learning experiences.
The Influence of Speaker Pitch on Inferring Semantic Valence

ABSTRACT Research on metaphors has shown that individuals form associations between the verticality, brightness, and distance of stimuli and their valence. Building on the literature on conceptual metaphor theory, the pitch–valence hypothesis predicts an association between the pitch of spoken words and their valence. A study was conducted recording participants’ accuracy and response latencies in identifying positive and negative words that were spoken in high and low pitches to see whether pitch affects the accuracy and speed when choosing words that systematically vary in their semantic valence. The results supported the pitch–valence hypothesis by revealing systematic differences in performance. The observed effects were mainly due to participants’ accuracies when words were presented in a high pitch.
Use of Meteorological Data to Estimate Field Sugar Levels in Concord Grapes

This study explored the use of regression analysis involving meteorological data to estimate field sugar levels in Concord grapes. The appropriate type of regression depended upon the starting date for grape heat unit (GRHU), corn heat unit (CHU), or sunshine hour (SUN) summations. Two successful regressions were developed using first bloom as the starting date. The regression of sugar (Y) on CHU had an R-value of 0.952 and accurately (± 1.0°Brix) estimated 100% of the field sugar samplings during the 1978 vintage. The regression of sugar (Y) on GRHU had an R-value of 0.872 and accurately estimated 90% of the field sugar samplings during the 1978 vintage. A successful regression developed using April 1 as the starting date was the multiple regression of sugar (Y) on (CHU + SUN). This multiple regression had an R-value of 0.924 and accurately estimated 100% of the field samplings during the 1978 vintage. Results suggest that regression equations can be useful in estimating the extent of grape sugar production during vintage.
Electrochemical performance study of polyaniline and polypyrrole based flexible electrodes

Abstract The increasing demand for compact and functional electronics has led to significant rise in research interest in the development of versatile energy storage systems. With this perspective notion, herein, we fabricated electrically conducting polymer-based flexible carbon cloth (CC) electrodes and assessed their electrochemical performance. Briefly, polyaniline nanofibers (Pani-NFs) and polypyrrole nanotubes (PPy-NTs) were synthesized by interfacial polymerization and reactive self-degrade template method, respectively. The physico-chemical properties of the as-synthesized nanostructured conducting polymers were characterized with the help of X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), field emission transmission electron microscope (FETEM) and Raman spectroscopy. The electrochemical performance of the Pani-NFs and PPy-NTs coated flexible CC electrodes was evaluated in 1 M H2SO4 electrolyte with a potential window of −0.2 to 0.8 V versus saturated calomel electrode (SCE). The Pani-NFs and PPy-NTs coated electrodes yielded the maximum areal capacitance of 0.79 and 0.74 F.cm−2, respectively at a constant discharge current density of 10 mA.cm−2, which was determined from the charge-discharge curve during the discharging period. The results demonstrate the potential of the nanostructured conducting polymer-based flexible electrodes for portable and wearable energy storage applications.
Summer Meets Winter: African Nations Participating at the Winter Olympics, 1960–2018

Abstract A record number of eight African countries competed at the 2018 Olympic Winter Games in PyeongChang, South Korea despite Africa being regarded as one of the hottest continents in the world. The continent’s almost complete lack of snow or ice weather conditions is the most obvious hurdle to African winter Olympic hopefuls. Although athletes from this continent compete successfully at the summer Olympics, very few African countries send teams of athletes to compete at the winter Olympics on a consistent basis. By 2014 less than a quarter of the fifty-four countries in Africa had ever competed at the winter Olympics, yet the history of African countries participating at the winter Olympics dates back almost six decades. The first appearance of an African country at the winter Olympics was at the 1960 games in Squaw Valley, USA, when South Africa participated for the first time. Since 1984, at least one African nation has competed at each subsequent winter Olympics. The lack of climate for winter sports, such as bobsleigh, skiing and snowboarding, limits the level of participation in winter sports. However, globalization and the relatively limited access to tertiary institutions in Africa have brought young African athletes in contact with many forms of winter sport while studying or working abroad, predominantly in the northern hemisphere.
Biomimetic fabrication of calcium phosphate/chitosan nanohybrid composite in modified simulated body fluids

In this study, nucleation and growth of bone-like hydroxyapatite (HAp) mineral in modified simulated body fluids (m-SBF) were induced on chitosan (CS) substrates, which were prepared by spin coating of chitosan on Ti substrate. The m-SBF showed a two fold increase in the concentrations of calcium and phosphate ions compared to SBF, and the post- NaOH treatment provided stabilization of the coatings. The calcium phosphate/chitosan composite prepared in m-SBF showed homogeneous distribution of approximately 350 nm-sized spherical clusters composed of octacalcium phosphate (OCP; Ca8H2(PO4)6·5H2O) crystalline structure. Chitosan provided a control over the size of calcium phosphate prepared by immersion in m-SBF, and post-NaOH treatment supported the binding of calcium phosphate compound on the Ti surface. Post-NaOH treatment increased hydrophilicity and crystallinity of carbonate apatite, which increased its potential for bio- medical application.
A comparative analysis of early childhood socioeconomic conditions and educational achievement 15 years later

Scholars agree that the early childhood years are highly consequential for later educational outcomes, but we know little about the impact of early childhood socioeconomic conditions at the societal level. This study contributes to the literature by bringing to comparative sociology the proposition that variations in the social structures into which children are born have lasting impacts on their educational outcomes as they progress through secondary education. Drawing on theories of welfare regimes and social stratification, we analyze nation-level data on socioeconomic conditions from the early 2000s via the Organisation for Economic Cooperation and Development and educational performance data from the 2015 Programme for International Student Assessment (N = 35). In line with our hypotheses, societal inequalities and disadvantages present when children are young are associated with negative achievement outcomes 15 years later and policies that guarantee people basic securities are more consistently associated with positive achievement outcomes than are broad measures of social spending. We conclude by highlighting the value of our approach and suggesting directions for future research on cross-national variations in achievement as a product of socioeconomic conditions during early childhood.
Institutional characteristics of education systems and inequalities—Introduction I

This is the introduction into the first of multiple themed issues of International Journal of Comparative Sociology (IJCS) that are dedicated to the role of education systems as institutional settings on the reproduction of inequalities. While Introduction I presents the research program, outlines a conceptual background and discusses methodological challenges in the study of how education systems shape inequalities, introductions to the successive themed issues will deal with the current state-of-research and finally with research desiderata in terms of an outlook. The contributions will be presented at the end of each introduction. The contributions of this themed issue focus on the role of country characteristics during early childhood and the role of shadow education on educational inequalities.
Amendment to: Comparison of biosynthetic versus synthetic mesh in clean and contaminated ventral hernia repairs

We wish to make an amendment to our recently published paper in order to clarify (and justify) the use of mesh in the clinical situation described. The discussion section on page 545 reads ‘two patients had intraperitoneal BioA (GORE BIO-A, W.L. Gore & Associates, Newark, DE, USA) placement due to the lack of availability of retrorectus or preperitoneal plane – both of which were class IV wounds’. In both of these patients, the presence of a pre-existing permanent colostomy prevented dissection of the retrorectus or preperitoneal plane on the affected side of the ventral hernia; hence, the BioA product was placed as an intra-peritoneal tissue reinforcement. Note that these were true ventral incisional hernias and not parastomal hernias. The wounds were designated CDC (Centers for Disease Control) class IV (dirty) with respect to (or as defined by) the CDC guidelines due to the incidental presence of the colostomy; however, the specific intra-peritoneal field where the mesh was placed was not visibly contaminated or infected at the time of the procedure. Clarification of this point confirms our intended ‘on-label’ use of the mesh product in this circumstance. This amendment aims to eliminate any confusion regarding the use of biosynthetic absorbable mesh as described in our study.
An uncertain future: Predicting events using conditional event evolutionary graph

Event evolutionary graph (EEG) reflects sequential and causal relations between events, which is of great value for event prediction. However, lacking event context in the EEG raises the problems of direction uncertainty and low accuracy when making predictions. In this article, we propose a conditional event evolutionary graph (CEEG) to deal with these problems. CEEG extends EEG with an additional four types of event context, including state, cause, sub‐type, and object. We first extract event context by matching the input with self‐adaptive semantic templates and generalize the context for each event. To identify the evolution direction, we treat it as a binary classification problem and calculate the event transition probability for each direction given the generalized context. Experimental results show that CEEG has a strong ability to generate better event evolutionary paths compared with NAR, EEM, and other non‐context‐based methods.
A new concept based on ensemble strategy and derivative for the quantitative analysis of infrared data

Preprocessing and variable selection are the most widely used strategies to develop accurate predictive models based on infrared spectroscopy. In our study, a new conception that the derivative combined with ensemble strategy based on competitive adaptive reweighted sampling (CARS), stability competitive adaptive reweighted sampling (SCARS), Monte Carlo uninformative variables elimination (MCUVE), and bootstrapping soft shrinkage (BOSS) is put forward. The proposed concept makes the best of the derivative spectra information and successfully combines the strengths of derivative spectra, CARS, SCARS, MCUVE, BOSS, and ensemble submodels. Compared with other methods in this study, this new method can establish good calibration models without increasing the complexity from the perspective of an end user. Also, overfitting issues can be prevented. Derivative1st‐ECARS and Derivative1st‐ESCARS have shown significant improvements in partial least regression calibration based on the experiments of three datasets. The proposed concept shows great potential of the chemometrics approaches applied to infrared data in multivariate calibration.
A Client-Centric Evaluation System to Evaluate Guest's Satisfaction on Airbnb Using Machine Learning and NLP

Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to.
System Dynamic Simulation of Online Customers for Cruise Travel: Based on the Customer Life Cycle Perspective

Cruise tourism is an emerging tourism industry. Under the current online consumer market, expanding the number of online customers is an important consideration for the sound development of cruise tourism, starting from the reality of cruise travel, integrating life cycle theory, demand theory, and network consumption behavior theory, defining the evolution model of customer life cycle, using system dynamics for simulation analysis to discover the dynamic changes in the number of cruise travel online customers in different life periods. The analysis of the simulation results found that at different stages of the life cycle evolution of cruise travel network customers, travel demand is comprehensively affected by various factors such as basic needs, novelty, offline experience, Internet word-of-mouth, and information quality. The number of potential customers first accelerates and then declines. The trend is flat, the number of waiting customers is normally distributed over time, and the number of existing customers and lost customers tends to stabilize after an accelerated increase. The simulation results with word-of-mouth factor as the test function show that the model has good robustness and sensitivity. The number of waiting customers is sensitive to changes in word-of-mouth impact factors, and the number of existing customers has not increased significantly. Finally, summarize the development strategies of cruise tourism from the perspective of life cycle: increase the promotion of cruise tourism network and expand the range of potential customers, highlight the characteristic orientation of cruise tourism differentiation, induce waiting for customers to pay online, improve the quality of cruise tourism experience and maintain existing customer loyalty, and optimize the quality of travel information on the online platform to attract lost customers to turn back.
Big Data Research in the Tourism Industry: Requirements and Challenges

Nowadays, people and things are becoming permanently interconnected. This interaction overloaded the world with an incredible digital data deluge—termed big data—generated from a wide range of data sources. Indeed, big data has invaded the domain of tourism as a source of innovation that serves to better understand tourists' behavior and enhance tourism destination management and marketing. Thus, tourism stakeholders have increasingly leveraging tourism-related big data sources to gather abundant information concerning all tourism industry axes. However, big data has several complexity aspects and brings commensurate challenges that go along with its exploitation. It has specifically changed the way data is acquired and managed, which may influence the nature and the quality of the conducted analyses and the made decisions. Thus, this article investigates the big data acquisition process and thoroughly identifies its challenges and requirements. It also reveals its current state-of-the-art protocols and frameworks.
An IoT-Based Network for Smart Urbanization

Internet of Things (IoT) is considered one of the world’s ruling technologies. Billions of IoT devices connected together through IoT forming smart cities. As the concept grows, it is very challenging to design an infrastructure that is capable of handling large number of devices and process data effectively in a smart city paradigm. This paper proposed a structure for smart cities. It is implemented using a lightweight easy to implement network design and a simpler data format for information exchange that is suitable for developing countries like Pakistan. Using MQTT as network protocol, different sensor nodes were deployed for collecting data from the environment. Environmental factors like temperature, moisture, humidity, and percentage of CO2 and methane gas were recorded and transferred to sink node for information sharing over the IoT cloud using an MQTT broker that can be accessed any time using Mosquitto client. The experiment results provide the performance analysis of the proposed network at different QoS levels for the MQTT protocol for IoT-based smart cities. JSON structure is used to formulate the communication data structure for the proposed system.
OEKG: The Open Event Knowledge Graph

Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available.
A Stochastic Approach for Prediction of Partially Measured Concentrations of Benzo[a]pyrene in the Ambient Air in Korea

Large quantities of air pollutants are released into the atmosphere and hence, must be monitored and routinely assessed for their health implications. This paper proposes a stochastic technique to predict unobserved hazardous air pollutants (HAPs), especially Benzo[a]pyrene (BaP), which can have negative effects on human health. The proposed approach constructs a nearest-neighbor structure by incorporating the linkage between BaP and meteorology and meteorological effects. This approach is adopted in order to predict unobserved BaP concentrations based on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing the cancer risk. The efficient prediction provides useful information relating to the optimal monitoring period and projections of future BaP concentrations for both industrial and residential areas within Korea.
Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L∗a∗b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector’s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.