Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Air Temperature on School Teachers’ Mood and the Perception of Students’ Behavior

This study investigates how temperature, inside and outside the classroom, influence teachers’ mood and mental fatigue as well as the perceived students’ behavior. Two daily random measurements of the temperature inside various classrooms were taken for 7 months. Mood, mental fatigue, and perception of students’ behavior were evaluated for the teachers. Daily external temperature data were obtained from the State Agency of Meteorology. Results showed that indoor temperature, indoor humidity, and the difference between outdoor/indoor temperature significantly explain a worse perception of mood of the teachers and a worse perception of students’ behavior that influences perception of students’ behavior.
PENGARUH LITERASI KEUANGAN, PERENCANAAN KEUANGAN DAN PENDAPATAN TERHADAP KESEJAHTERAAN KEUANGAN GURU PEREMPUAN DI KOTA DENPASAR

Financial literacy is financial knowledge and capability that affects efforts to improve financial well-being. This study aims to obtain empirical evidence that financial literacy, financial planning, and income have an effect on financial well-being. The data used in the research is primary data which was collected by conducting a survey in the form of distributing questionnaires and interviews with female teachers in Denpasar City. The sample is determined by the nonprobability sampling technique, quota sampling type. This study uses data analysis techniques Structural Equation Model (SEM) with Partial Least Square (PLS) software. This study shows that financial literacy, financial planning, and income have a positive and significant effect on the financial welfare of female teachers in Denpasar City. The implications of the theoretical research results add to the reference to empirical research on the concept of financial literacy and welfare theory. This shows that financial literacy which includes the knowledge, skills, and beliefs of individuals in managing their finances will influence financial decisions that will be made to achieve the expected financial well-being. Keywords: Financial Literacy, Planning, Income, Welfare.
SMART: Towards Automated Mapping between Data Specifications

—The ability to perform automated conversions be-tween data conforming to different speciﬁcations is a key ingredient to achieve interoperability among heterogeneous systems— which, in turn, is at the basis of the creation of so-called Systems of Systems. These conversions require the deﬁnition of mappings between concepts of separate data speciﬁcations, which is typically a hard and time-consuming task. In this paper, we present a technique to automatically suggest mappings to users, based on both linguistic and structural similarities between terms. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests carried out using speciﬁcations from the transportation domain.
Analysis of Covid19 Disease using Machine Learning

COVID-19 outbreaks only affect the lives of people, they result in a negative impact on the economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the entire globe by the World Health Organization (WHO). By Apr. 28, 2020, more than 3 million people were infected by this virus and there was no vaccine to prevent. The WHO released certain guidelines for safety, but they were only precautionary measures. The use of information technology with a focus on fields such as data Science and machine learning can help in the fight against this pandemic. It is important to have early warning methods through which one can forecast how much the disease will affect society, on the basis of which the government can take necessary actions without affecting its economy. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain.
Shortcuts to Artificial Intelligence

The current paradigm of Artiﬁcial Intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are apparently small design decisions, that led to a subtle reframing of the ﬁeld’s original goals, and are by now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI. Far from being a series of separate problems, recent cases of unexpected eﬀects of AI are the consequences of those very choices that enabled the ﬁeld to succeed, and this is why it will be diﬃcult to solve them. In this chapter we review three of these choices, investigating their connection to some of today’s challenges in AI, including those relative to bias, value alignment, privacy and explainability. We introduce the notion of “ethical debt” to describe the necessity to undertake expensive rework in the future in order to address ethical problems created by a technical system.
Framing Sustainable Fashion Concepts on Social Media. An Analysis of #slowfashionaustralia Instagram Posts and Post-COVID Visions of the Future

The global fashion industry poses a significant threat to sustainability, occasioning the emergence of sustainable fashion concepts such as slow fashion. However, sustainability as a principle is mostly established in corporate communication and reporting and sustainable fashion is mostly debated from a marketing and consumer perspective. The study at hand fills the existing gap in research on how slow fashion is portrayed on social media, focusing particularly on slow fashion, Instagram, and an Australian context. An explorative content analysis was conducted following the hashtag #slowfashionaustralia; open-coding methods were employed to enable three thematic frames to emerge from the data; namely, slow fashion as Business 2.0: An eco-marketplace, as an authentic experience of self-expression, and as a community value. Further analysis of the identified themes yielded the framing process of slow fashion on Instagram, mainly representing empowerment for women. Methodological limitations are outlined, as well as new research potential in the area of sustainability communication.
FCEP: A Fast Concolic Execution for Reaching Software Patches

—Software updates that bring new features to the users or that ﬁx old errors can easily introduce new errors, which makes it necessary for users to repeatedly consider whether to update the software to the latest version. Therefore, the security testing for updated software is indispensable before its releasing. State-of-the-art increasing number of researchers have been de-voting to develop new techniques that can automatically generate high-coverage test suites and detect software errors introduced by patches. In this paper, we proposed a technique based on concolic execution to ensure the correctness and reliability of a patch. Our method generates test inputs to cover the changed lines of the patch and the relevant function by using a target-based search strategy which combines the selector based on the mapped address and the selector based on the priority. A prototype system called FCEP was implemented and evaluated with 5 C-programs. The experimental results demonstrated that our method reaches the new code introduced by patches quickly and achieves a high coverage.
When Young Children Grieve: Supporting Bereaved Children from the Perspective of Daycare Staff

ABSTRACT This study explores how daycare staff perceive children’s reactions to the illness and loss of a parent, the support needs of bereaved children, and the staff’s role as supporters. Data were generated through conducting focus groups with 23 employees from eight Danish daycare institutions. The study finds that staff focus on assisting children to open up about what has happened, process their grief, and reintegrate back into their respective daycare institutions in a positive manner. Most children displayed grief and sadness following the loss of a parent; reactions seem most pronounced when losses are unexpected. Many staff members go beyond what is widely assumed to be their core role during such times. This led to strong relationships between them and the bereaved children. While many parents often struggle following a loss, the support provided in daycare institutions can be both timely and well-suited to children’s individual needs.
Improving the Performance of Practical Quantum Key Distribution With Advantage Distillation Technology

To improve the maximal transmission distance and the maximal error rate tolerance, we apply the advantage distillation technology to analyze security of the practical decoy-sate quantum key distribution system. Based on the practical experimental parameters, the device-dependent quantum key distribution protocols and the measurement-device-independent quantum key distribution protocols have been respectively analyzed, and our analysis results demonstrate that the advantage distillation technology can significantly improve the performance of different quantum key distribution protocols. In the four-state and six-state device-dependent quantum key distribution protocols, we prove that the maximal transmission distance can be improved from 142 km to 180 km and from 146 km to 187 km respectively. In the four-state and six-state measurement-device-independent quantum key distribution protocols, we prove that the maximal transmission distance can be improved from 195 km to 273 km and from 200 km to 282 km respectively. More interestingly, the advantage distillation technology does not need to change the hardware devices about the quantum step, thus it can be conveniently to be applied in various practical quantum key distribution systems.
Adaptive robust output feedback control for attitude tracking of quadrotor unmanned aerial vehicles

This work proposes a new adaptive robust output feedback control method for attitude reference tracking of a quadrotor unmanned aerial vehicle without using the angular velocity measurements. By using the K‐filters well known in the adaptive control community, the necessity of velocity measurements or estimating is avoided. The attitude system model is transformed into a second‐order model where the angular velocity measurements are not involved. However, the model includes mismatched uncertainties which should be estimated and compensated by the disturbance observers (DOBs). The controller is designed in a backstepping manner, and the dynamic surface technique is adopted to avoid the explosion of the controller complexity. For each Euler angle axis, the prescribed performance control technique is adopted to ensure a prescribed performance, the lumped disturbance is compensated by a DOB, and furthermore an adaptive law is introduced to adaptively update the corresponding uncertain inertia parameter which affects the control performance significantly. The control performance of the overall control system is analyzed rigorously from the viewpoint of input‐to‐state practical stability. In addition, it is shown how the adaptive laws contribute to improving the control performance. And simulation examples are provided to demonstrate the performance of the proposed method.
Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.
Efecto agudo de una sesión de yoga de corta duración en el nivel de estrés en escolares (Acute effect of a short yoga session on the stress level in schoolchildren)

El objetivo del presente estudio fue determinar el efecto agudo de una sesión de yoga en el nivel de estrés cotidiano en escolares. Participaron 21 escolares chilenos de 11,62 ± 0,50 años, los cuales fueron divididos en dos grupos (Grupo Experimental [GE]= 8; Grupo Control [GC]= 13). Se aplicó una sesión de yoga de 25 minutos de duración en el grupo experimental, mientras que el grupo control participó de una clase teórica en conducta sedentaria. El nivel de estrés se midió antes y despúes de la intervención con el Test Inventario de Estrés Cotidiano Infantil (IECI). Se utilizó la prueba de Shapiro Wilk para determinar la normalidad de los datos. La prueba t-Student para muestras relacionadas fue utilizada para comparar los efectos de la sesión previo y posterior a la intervención en el mismo grupo, mientras que la prueba t- Student para muestras independientes se usó para comparar los efectos entre los grupos experimental y control. Además, se usó la d de Cohen para para determinar el tamaño del efecto. El nivel de significancia se estableció en p<0,05. Se observó una disminución del nivel total de estrés cotidiano con un efecto moderado en el grupo experimental posterior a la intervención (Pre=16,25 ± 3,05; Post= 10,75 ± 4,74; p=0,005; TE= 0,57), mientras que en el grupo control no se encontraron efectos significativos post intervención (Pre=13,46 ± 5,57; Post=13,3 ± 5,55; p=0,337; TE=0,01). Se concluye que una sesión de yoga de corta duración puede proporcionar una disminución en los niveles de estrés en escolares. Abstract. The present study aimed to determine the acute effect of a yoga session on the level of stress in schoolchildren. Twenty-one Chilean schoolchildren aged 11.62 ± 0.50 years participated, being divided into two groups (Experimental Group [EG] = 8; Control Group [CG] = 13). A 25-minutes yoga session in the experimental group was applied whereas the control group participated in a sedentary theoretical class. The stress level was measured before and after the intervention using the Children’s Daily Stress Inventory Test (IECI). The Shapiro Wilk test was performed to determine the normality of the data. A t-test for paired samples was used to compare the effects of the session before and after the intervention in each group. Likewise, a t-test for independent samples was performed to compare the effects between the experimental and control group. Then, the effect size was calculated by Cohen’s d. Lastly, the significance level was set at p<0.05. The results showed a decrease in total daily stress level with a moderate effect in the experimental group after the intervention (Pre=16,25 ± 3,05; Post= 10,75 ± 4,74; p=0,005; TE= 0,57), while in the control group no significant effect was found after intervention (Pre=13.46 ± 5.57; Post=13,3 ± 5,55; p=0,337; TE=0,0). It is concluded that a short yoga session can provide a decrease of stress levels in schoolchildren.
An Exploratory Study of the Main Barriers to Lean Construction Implementation in Peru

Lean Construction (LC) has been implemented for 20 years in Peru in different types of projects: buildings, sanitary works, mining, sports infrastructure, and for the development of people, finding significant benefits after its implementation. However, some barriers make complicated Lean Construction from being applied in many projects in Peru. This research aims to identify and classify the obstacles that lead to poor implementation of Lean thinking. First, the study started with a literature review and consultation with six experts with more than ten years of experience in the implementation of Lean Construction in different types of projects, identifying thirty-two barriers to Lean Construction implementation, dividing the barriers into four types: culture barriers, technology barriers, lean philosophy, and other barriers. One hundred and twenty-four engineers from various projects are surveyed, and the main obstacles to Lean Construction implementation are ranked. The findings identified that "lack of government policies," "lack of alliances between academy and organizations," and "high use of time and cost with no return" are the main barriers related to the implementation of Lean. Research is the basis for generating a roadmap and lines of research.
Cats, Kids, and video calls: how working from home affects media self-presentation

ABSTRACT Working from home (WFH) in response to COVID has reduced boundaries between home and work roles. It has also reduced in-person interaction, replacing it with digital communication including Video and text. We use personality theory to compare self-presentation on these media versus in-person communication. We use surveys and interviews to examine media self-presentation before and during COVID, as well as between different groups of students and office workers. Pre-COVID students presented themselves as more Extraverted, more Agreeable, less Open, and less Neurotic on Video. On Texting they are less Open and Neurotic. During WFH, students are more Agreeable and less Neurotic on Video while still less Neurotic on Texting. The office worker WFH sample is more Agreeable and less Neurotic on Video. We discuss practical and theoretical implications of results, and future research directions. Keywords personality, self-presentation, Video, Texting, media theory, affordances, COVID, students, office workers ABOUT THE AUTHOR/S Lee Taber University of California, Santa Cruz ltaber@ucsc.edu I’m a graduate student at UCSC, interested in how people communicate using technology and how those communications affect us. I look at how people present themselves differently on Facebook and Snapchat, and why. I’ve examined self-presentation differences on main Instagram accounts as well as Finstas, or “fake” Instagram accounts. I’m also interested in parasocial relationships with lo-fi hip hop beats and how to make a more readable ToS. More here: http://leetaber.com/ Steve Whittaker University of California, Santa Cruz swhittak@ucsc.edu Technology is transforming our everyday lives, how we think, interact and feel. I work at the intersection of Social and Computer Science. I study how technology is affecting fundamental aspects of our everyday lives, and use insights from Social Science to design new digital tools to support memory and collaboration and to help manage personal information. My main current interest is in designing new technologies to promote well-being. Research Areas: Human-computer interaction, digital well-being, computational social science New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
Potential Therapeutic Value of Mangiferin for Lung Adenocarcinoma (Luad): A Comprehensive Study Based on in Vitro Experiments and Bioinformatics

Background: Lung adenocarcinoma (LUAD), which is the most common lung cancer type in never-smokers and primarily occurs in women, requires effective treatment methods. Mangiferin is a polyphenol widely found in mango trees and has been reported to have chemotherapeutic and preventive potential against various types of cancer. Thus, we investigated the potential therapeutic value of mangiferin for LUAD.Methods: Cell lines A549, H2030 and H1299 were processed with mangiferin to detect and screen for differentially expressed lncRNAs and mRNAs. Close associations between extracted common lncRNAs and mRNAs were identified for lncRNA-mRNA network construction. Based on the network and an online database, target lncRNAs, target genes and meaningful lncRNA-mRNA pairs were identified, and signaling pathway analysis was performed.Results: The top 200 lncRNA-mRNA pairs were used to construct the network. We identified 12 target lncRNAs and 18 target genes. Gene nodes in the network were mostly visualized on the PI3K-Akt signaling pathway (P < 0.01). Furthermore, lncRNA-mRNA pairs that contained the genes ARHGAP29, BRIX1, CD109, CDK1, CTNNAL1, DAB2IP, DDIT4L, GPR162, ICAM5, KCNAB3 and MMP7 were considered to be meaningful lncRNA-mRNA pairs affected by mangiferin in LUAD.Conclusions: Our study provided a comprehensive understanding of the potential therapeutic value of mangiferin for LUAD.
Complement C3 as therapeutic target in diabetic nephropathy by bioinformatics analysis

Background: Various public platforms have contained extensive data for deeper bioinformatics analysis. The pathogenesis of diabetic nephropathy is always a hot topic and the underlying molecular events are not completely clear. Methods: Differential expression analysis and weighted correlation network analysis (WGCNA) were applied to screen meaningful gene in GSE30529. The combined result was used for gene ontology (GO) annotation and KEGG pathway enrichment analysis. STRING was used for protein-protein interaction (PPI) network construction and Cytoscape software was used for hub gene identification. Gene methylation profile GSE121820 and miRNA profile GSE51674 were also acquired to perform multi-omics analysis. Clinical features were obtained form Nephroseq and potential drugs were identified by CMap. Results: 345 genes were obtained form GSE30529 after differential expression analysis and WGCNA. GO analysis mainly included neutrophil activation, regulation of immune effector process, positive regulation of cytokine production and neutrophil mediated immunity. KEGG pathway analysis mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and AGE-RAGE signaling pathway in diabetic complications. 16 down regulated miRNAs were obtained from GSE57674 to construct miRNA-mRNA network. Top 20 genes were discerned from PPI network and there were DNA methylation differences in 15 genes among them. Correlation analysis showed SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may involved in DN. 10 small molecule compounds have been identified as potential therapeutic drugs. Conclusion: We screened 11 target genes and suggested C3 may serve as a therapeutic target in diabetic nephropathy.