Miracle-dz/UniFAR-FTUs huggingface dataset:
Context-Awar e Ad Contents Scheduling over DOOH Networ ks based on Factor ization Machine

DOOH(Digital Out Of Home) advertising targets for reaching consumers through outdoor digital display medias. Traditionally, scheduling of advertisement contents over DOOH medias is usually done by operator’s strategy, but an efficient ad scheduling strategy is not easy to find under various advertising contexts. In this paper, we present a context-aware factorization machine-based recommendation model for the scheduling under various advertising contexts, and provide analysis for understanding of the contexts’ effects on advertising based on the recommendation model. Through simulation results on the dataset adapted from a real dataset of RecSys challenge 2015, it is shown that the proposed model and analysis based on the model will be effective for better scheduling of ad contents under advertising contexts over DOOH networks.
Brunt of Online Product Reviews over Customers Assessment on Online Platform Instagram

The purpose of the study is to understand the attitude of consumers towards user-generated content (UGC) in relation to brand or expert feedback when it comes to online product reviews given by other users. This research explored the role of various contexts, perceived usefulness, perceived risk, perceived credibility, the intention of review valance, review source, and purchase. This study has also attempted to comprehend influence over customer’s attitudes towards UGC and explored its contributions to the perceived intention to buy. Every antecedent has a distinct impact on the buying mindset and intention. The goal of this analysis is to investigate how these contexts impact the attitude of customers towards UGC individually.
Genetic Variation of Traits Affecting Meal Quality in eded Doubled Haploid Population of Winter Oilseed Rape

The by-product of oil production from oilseed rape (Brassica napus L.) is protein rich rapeseed meal. It is of great interest to improve the quality of rapeseed meal for poultry feed by reducing the level of anti-nutritional factors, mainly fibre and glucosinolates. The aim of the study was to assess genetic variation of traits affecting rapeseed meal quality in seeds from the M305 (blacktion of winter oilseed rape doubled haploid (DH) plants. The influence of weather conditions on these traits was tested under two-year field growing conditions in Poland. Significant effect of genotypes and the year of experiment was found for all of the studied traits, apart from neutral detergent fibre (NDF). The significant phenotypic variation in all of these traits makes future selection to improve quality of rapeseed meal possible. It was also found that all of the traits, apart from neutral detergent fibre, are regulated in a complex genetic manner involving additive and epistatic gene action. NDF is regulated by the additive gene action indicating that this trait might be easier for selection. Low heritability found for seed colour, fibre and glucobrassicin indicates great environmental effect on these traits. Large phenotypic variation for protein, oil, acid detergent fibre, neutral detergent fibre and glucosinolates found in this study will allow future genetic mapping analysis to identify key genes regulating these traits. The application of such genetic markers could enhance breeding programs aiming to improve oilseed rape which could be successfully introduced as poultry feed.
Quaternary semiconductor Cu2FeSnS4 nanoparticles as an alternative to Pt catalysts

We demonstrate an N719 dye sensitized solar cell based on Cu2FeSnS4 (CFTS) as a counter electrode. The elements for the material are all earth abundant and environmentally benign. The power conversion efficiency of a DSSC using CFTS was comparable to that of a DSSC using Pt under A.M. 1.5G (100 mW cm−2).
AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach

Artificial intelligence has emerged as a technology that is profoundly reshaping society and enabling rapid improvements in science, engineering, and mathematics, as well as information technology itself. This has generated increased demand for fostering an AI-literate populace as well as a growing recognition of the importance of promoting K-12 students’ awareness and interest in AI. Although efforts are be-ginning to incorporate AI learning within K-12 education, there is little research exploring how to introduce students to AI and how to support teachers to integrate AI learning experiences in their classrooms. This is especially true at the elementary school level. A particularly promising approach for providing effective and engaging AI learning experiences for elementary students is game-based learning. In this paper, we explore how to introduce AI-infused collaborative inquiry learning into upper elementary school (student ages 8 to 11) using game-based learning. To ground the work in the realities of elementary school classrooms, we present insights from interviews with elementary school teachers to under-stand how best to support them in integrating AI into their classrooms. We then present the design of PrimaryAI, a game-based learning environment that supports rich problem-based learning activities within upper elementary classrooms centered on AI applied toward solving life-science problems. Finally, we discuss some of the challenges we face in bringing AI-infused collaborative inquiry learning to upper elementary students.
Nonprofit Partners’ Perceptions of Organizational and Community Impact Based on a Long-Term Academic Service-Learning Partnership

This research summarizes organizational and community impacts reported by nonprofit community partners participating in an academic service-learning program with communication capstone students at Eastern Michigan University. Community partners discussed internal and external communication-focused capstone projects, perceived short-term and long-term impacts, as well as organizational versus community impact. Analysis of the data revealed that internally focused projects delivered more long-term impact at the organizational level, while several of the externally focused projects delivered short-term impact at both the organizational and community level. A small number of projects delivered little to no impact. In addition, three specific long-term organizational impacts were discovered: new organizational learning, enhanced personal and professional development, and increased student engagement. While student learning outcomes related to service-learning have been well documented, this research adds to the growing body of literature on the organizational and community outcomes associated with academic service-learning courses. This research also illustrates the need for communication-focused projects within the nonprofit sector. Advanced undergraduate students in communication and other allied disciplines are ready and able to fill such needs in meaningful ways through these partnerships.
The Influence of Job Motivation on Job Satisfaction: A Case Study of Polytechnic Academic Staff

Job motivation plays a vital role in determining job satisfaction levels. In the Malaysian Polytechnic context, limited studies have investigated the influence of job motivation on job satisfaction among academic staff. Therefore, this study aims is to examine the influence of job motivation on job satisfaction among polytechnic academic staff. This study employs quantitative research and single-cross sectional study. The data is gathered using the survey method. The result of multiple regression analysis shows that job motivation significantly and positively influences job satisfaction. More specifically, intrinsic and extrinsic motivation show a significant and positive influence on job satisfaction. It implies that improving job motivation in terms of intrinsic and extrinsic motivation is critically important to increase job satisfaction among polytechnic academic staff. Therefore, this study will contribute to polytechnic management as an indicator in the decision-making process to achieve the desired goals in the future.
Fast attribute selection based on the rough set boundary region

The problem of clustering exists in numerous fields such as bioinformatics, data mining, and the recognition of patterns. The function of techniques is to suitably select the best attribute from numerous contending attribute(s). RST-based approaches for definite data has gained significant attention, but cannot select clustering attributes for optimum performance. In this paper, the focus is on the processes that exhibit a similar degree of results to an identical attribute value. First, the MIA algorithm was identified as the supplement to the MSA algorithm, which experiences set approximation. Second, the proposition that MIA accomplishes lesser computational complexity through the indiscernibility relation measurement was highlighted. This observation is ascribed to the relationship between various attributes, which is markedly similar to those induced by others. Based on the fact that the size of the attribute domain is relatively small, the selection of such an attribute under such circumstances is problematic. Failure to choose the most suitable clustering attribute is challenging and the set is defined rather than computing the relative mean where it can only be implemented with a distinctive category of the information system, as illustrated with an example. Lastly, a substitute method for selecting a clustering attribute-based RST using Mean Dependency degree attribute(s) (MMD) was proposed. This involved selecting the maximum value of a mean attribute(s) as a clustering attribute through a considerable targeting procedure for the rapid selection of an attribute to settle the instability in selecting clustering attributes. Thus, the comparative performance of the selected clustering attributes-based RST techniques MSA and MIA was conducted.
Identification of genotype and phenotype of antimicrobial resistance of Escherichia coli isolates from pigs in southern Vietnam

Escherichia coli is a primary reservoir of antimicrobial resistance, known chiefly for the container of AMRencoding genes (ARGs), and poses potential risks to human and animal health. This study investigated AMR phenotypes and ARGs in 90 E. coli isolates from different pig groups in 10 farms in southern Vietnam. The minimum inhibitory concentration (MIC) of 19 common antimicrobial agents was determined, and polymerase chain reaction (PCR) was used to investigate seven ARGs (blaTEM, aadA1, strA, dfrA12, sul3, cmlA and tetA). Cohen’s kappa statistic (κ) was applied to assess the concordance between phenotypic and genotypic profiles. A total of 81.1% of E. coli isolates were multi-drug resistant (MDR). The amphenicol class accounted for the highest resistance (100% isolates), followed by the tetracycline class (97.8%), the quinolones and penicillin classes (85.6% each), sulfonamides (67.8%) and aminoglycosides (63.3%). A greater proportion of isolates from weaner pigs showed resistance to multi-antibiotics (43.0%), followed by growers (39.5%) and finishers (36.3%), although the difference was not significant (P>0.05). The prevalence of ARGs was greatly variable and was highest for aadA1 (98.9%), cmlA (98.9%), blaTEM (97.8%), dfrA12 (97.8%), tetA (97.8%), sul3 (97.8%) and strA (83.3%). No significant correlation between ARGs and phenotypic resistance was identified. The results indicate a great diversity of genotypic and phenotypic AMR profiles in pig E. coli isolates. The lack of correlation might be a reflection of additional genes encoding the observed genotypic profiles or the presence of non-plasmid mediated resistance in many cases.
Characteristics and patient-reported outcomes associated to dropout in severely affected oncological patients

BackgroundPatient-reported outcome measures (PROMs) are commonly used and are surrogates for clinical outcomes in cancer research. In the research setting of very severe diseases such as cancer, it is difficult to avoid the problem of incomplete questionnaires from drop-out or missing data due to patients who deceased during observation period. We aimed to explore patient characteristics and patient-reported outcomes associated with the time-to-dropout. MethodsIn the Oncological Social Care Project (OSCAR) study the condition of participants was assessed four times within 12 months (t0: baseline, t1: 3 months, t2: 6 months, and t3: 12 months) by validated PROMs. We performed competing-risks regression based on Fine and Gray’s proportional sub-distribution hazards model for exploring factors associated with time-to-dropout. Death was considered as competing risk. ResultsThree hundred sixty-two participants were analyzed in the study. 193 (53.3%) completed follow-up at 12 months, 67 (18.5%) patients dropped out, and 102 patients (28.2%) died during the study period. Poor subjective social support was related to higher risk for drop-out (SHR=2.10; 95%CI: 1.01 – 4.35). Lower values in health-related quality of life were related to drop-out and death. The subscales global health status/QoL, role functioning, physical functioning, and fatigue symptom in the EORTC QLQ-C30 were key characteristics associated with early drop-out.ConclusionSeverely affected cancer patients with poor social support and poor quality of life seem more likely to drop out of studies compared to patients with higher levels of social support and quality of life. This should be considered when planning studies assessing cancer patients. Methods to monitor drop-outs timely and handle missing outcomes might be used. Results of such studies have to be interpreted with caution in light of the particular drop-out mechanisms.
Properties of TiN protective coatings on steel

The properties of fast cutting steel SW7M, stainless steel 1H18N9T, and tool steel NC11 coated with a TiN protective layer were studied using 57 Fe Conversion Electron Mossbauer Spectroscopy. The adhesion of the coatings is determined by the thickness of Fe-N and Fe-Ti interface. The model of the abrasion of the protected tools, based on fast diffusion of Fe ions to the surface though the TiN layer and further oxidation is proposed.
Experimental validation of a rigorous absorber model for CO2 postcombustion capture

A rigorous rate-based model for acid gas absorption was developed and validated against mass-transfer data obtained from a 3-month campaign in a laboratory pilot-plant absorber in which the experimental gas–liquid material balance was within an average of 6%. The mass-transfer model is based on the penetration theory where the liquid film is discretized using an adaptive grid. The model was validated against all data and the deviation between simulated and averaged gas and liquid side experimental mass-transfer rates yielded a total variability of 6.26%, while the total average deviation was 6.16%. Simpler enhancement factor mass-transfer models were also tested, but showed slight over-prediction of mass-transfer rates. A sensitivity analysis shows that the accuracy of the equilibrium model is the single most important source of deviation between experiments and model, in particular at high loadings. Experimental data for the absorber in the integrated pilot plant are included. © 2007 American Institute of Chemical Engineers AIChE J, 2007
A Comparative Study of Different Classifiers for Myers-Brigg Personality Prediction Model

—Personality can be understood as specific features of an individual which determines its preferences over things. Personality has been shown to be relevant to many types of interactions such as in predicting recommendations e.g. movie preferences, social interactions, music preferences, criminal activities and correlation between personality and job performance. Predicting personality from social media become one of the most trending things among researchers and various commercial organizations as to help individual improve their experience over computerized user interfaces and help others to study the various personality preferences. Thus, many algorithms have been performed to predict personality from social media. In this paper, we compared the performance of several classifiers such as Naïve Bayes, Random Forest etc. in predicting Kaggle Users personality. Based on the user profile and comments, the user data of Kagglers were extracted from Kaggle Repository, analyzed, and then classified in the automatic personality prediction. The user data is extracted and mapped on the Mayer’s – Brigg personality Model. All sixteen co-ordinates of the MB-Model was considered in this study. A 5-fold cross validation was used to evaluate the classifiers. Several parameters that were observed in the performance of the classifiers are classification accuracy, F-measure, Logarithm-Log function. Experimental evaluation demonstrated that Logistic Regression algorithm is the best classifier in terms of the accuracy and F-measure.
RC-02202_R1 Obesity and Asthma: Impact on severity, asthma control and response to therapy

Asthma is more prevalent in obese compared with normal weight subjects. Our aim has been to review current knowledge of the impact of obesity on asthma severity, asthma control, and response to therapy. Several studies have shown that overweight and obesity is associated with more severe asthma and impaired quality of life compared with normal weight individuals. Furthermore, obesity is associated with poorer asthma control, as assessed by asthma control questionnaires, limitations in daily activities, breathlessness and wheezing, use of rescue medication, unscheduled doctor visits, emergency department visits, and hospitalizations for acute asthma. Studies of the impact of a high body mass index (BMI) on response to asthma therapy have, however, revealed conflicting results. Most studies show that overweight and obesity is associated with less favorable response to asthma therapy with regard to symptoms, level of FEV1, fraction of exhaled nitric oxide, and airway responsiveness. Some studies suggest that asthma in the obese patient might be more responsive to leukotriene modifiers, orchestrated by leptin and/or adiponectin derived from adipose tissue, than to inhaled corticosteroids, possibly reflecting differences in the underlying airway inflammation in obese vs. non-obese asthmatics. In conclusion, overweight and obesity is associated with poorer asthma control and, very importantly, overall poorer response to asthma therapy compared with normal weight individuals. RESPIRATORY CARE Paper in Press. Published on December 11, 2012 as DOI: 10.4187/respcare.02202 Epub ahead of print papers have been peer-reviewed and accepted for publication but are posted before being copy edited and proofread, and as a result, may differ substantially when published in final version in the online and print editions of RESPIRATORY CARE. Copyright (C) 2012 Daedalus Enterprises
Design And Modeling Of Optical Structure Capacity Of Ten Megawatts Power Plants

This manages the parts design and simulates a PV life with MATLAB and Simulink programming. The power plant is made of photovoltaic panels connected in series and parallel, a DC-DC support transformer and a three-phase inverter interferes with a three-phase low voltage array of 0.4 kV and a medium voltage grid of 20 kV by phase methods Up to Transformers. The DCDC support transformer uses an MPPT controller and the inverter uses a d-q concession control strategy with the current PI controller. Some instances are recreated where the dynamic nature of an ordered photoelectric frame is intriguing. They treat solar radiation and temperature changes.
Github Reviewer Recommendation using Graph

—Pull-Requests play a central role in Github as a premier social coding platform and facilitate the technical communication between project members and community contributors. For large-scale and popular projects, the number of Pull-Requests are substantially high and can overwhelm the core team maintenance schedule, which can lead to late responses and appropriate Pull-Requests being ignored. For the project, we construct the Expertise-Authority Recommendation (EARec) Network with additional modiﬁcations on reviewer-reviewer edge weights and Pull-Requests’ content similarity measure. We perform recommendations using this network via Random Walk with Restart [2] and compare the performance with baselines from the literature including Activeness and Expertise models. We then explore Supervised Random Walks to perform link prediction on a bipartite graph between Pull-Requests and Reviewers as a second method of recommendation. We ﬁnd that both methods show reasonable improvement though training and hyper parameter tuning, but are limited due to the amount of computation needed. Index Terms
The Macroeconomics of Populism A Quantitative Analysis of the Economic Consequences of Populism in Europe

This paper analyses the economic consequences of political populism and its effect on future returns of macroeconomic key variables such as foreign exchange rates, equity indices, inflation, and total reserves including gold. In particular, we construct a right-wing authoritarian populism indicator to identify populist governments during the period from 2000 to 2015 and link historical information on elections to financial data, which allows us to observe the phenomena of populism in an interdisciplinary context. We then design an empirical analysis to investigate a potential correlation between the indicator and subsequent returns of macro-variables. While the precision of the constructed indicator cannot be evaluated we find some evidence for a negative relationship between the election of populist parties and returns of foreign exchange rates, predominantly in low GDP/capita countries. We confirm this negative correlation for returns of reserves, however cannot prove its statistical relevancy. The effects of the remaining variables remain unclear and do not point towards any systematic interaction with the populism indicator.
Investigating the Effect of Locus of Control, Sex and Age on Perceptions of Petty Crime

The present research investigated locus of control, sex and age to identify which influenced crime perceptions and to what extent. A 2 (internal versus external locus of control) x 2 (male versus female) x 3 (young, middle and older adult) between groups’ design was used. Sixty participants completed Rotter’s (1966) Locus of Control Questionnaire and a perceptions of crime questionnaire. A 3x2x2 MANOVA was conducted. Only one main effect of sex was present, with males reporting themselves to be less likely to report suspicious behaviour than females. No other main effects were present across the remaining eight crime perception questions. Only one significant interaction existed, which suggested that older males and younger females were in favour of increased punishment for petty crimes. When investigating petty crime perceptions, locus of control is a less important consideration than sex and age differences.
Supplementary Material: Neural Predictor for Neural Architecture Search

Figure 1 includes ablation study of different architectures for the Neural Predictor on NASBench-101. We compared Graph Convolutional Networks (GCN) and Multi-layer Perceptrons (MLP) in the figure. To generate inputs for a MLP, we simply concatenate the one-hot codes of node operations with the upper triangle of the adjacency matrix. From the figure, we can see such a simple MLP can outperform state-of-the-art Regularized Evolution; more importantly, the GCN that we selected achieves the best. We also tried Convolutional Neural Networks (CNN) but completely failed with a performance near to random search. During our development, we also proposed a data augmentation to improve the performance of MLP and CNN. In this augmentation, we randomly permute the order of nodes to generate new inputs online. However, we needed to perform the permutation during validation; otherwise, the validation data distribution is different from training data distribution. More importantly, GCN encodes the inductive bias that the prediction should be permutation invariant. Therefore, GCN is our final decision.