Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Approach for Assessing Uncommonness of Ideas

A posteriori novelty metrics are often used in design research, in order to extract important information about creativity. However, different assessment approaches can be found in the literature, each of them with related pros and cons. In particular, weighted uncommonness, overall uncommonness and uncommonness across groups are the three main families of a-posteriori novelty metrics identified in this paper. Each of the considered literature metrics can provide specific types of information about the uncommonness of ideas, but in certain experimental circumstances, it could be difficult to rapidly identify the best-suited approach. This paper proposes an integrated procedure where the advantages offered by the three families of metrics can be applied concurrently. A generic case study is used for a first application of the proposal, and the obtained results show that a more comprehensive set of information about a-posteriori novelty can be extracted. In particular, novelty data from the three families of metrics are extracted in a single assessment process.
Multi-Goal Path Planning in Mobile Robotic Tasks

This habilitation thesis presents advancements in multi-goal path planning for mobile robotic problems arising from inspection, surveillance, exploration or data collecting missions. The main discussed approach is based on self-organizing map (an unsupervised neural network) technique in which the path being found is represented by a sequence of neurons’ weights. These weights are adapted to the goals requested to be visited by an unsupervised learning procedure during which the neural network evolves in the problem domain. The simplicity of the adaptation rules provides a great flexibility to address various problems of inspection planning in 2D environments, where the goals can be represented by points or areas of interest. In addition, the same technique is extended to planning in 3D environments with considering kinematic constraints. This approach allows to combine the task planning with a lower level motion planning in a unified way. Moreover, a problem of path planning considering localization uncertainty of a mobile robot during autonomous navigation is addressed by the same principle. The adaptation procedure utilizes a mathematical model of evolution of the localization uncertainty in a map-and-replay navigation. Based on the found principle of the uncertainty decreasing, the self-organizing neural network finds a path to visit the given set of goals that increases the precision of the goal visits and thus it increases reliability and robustness of the autonomous navigation. Finally, the multi-goal path planning problem is considered in exploration missions, where it is employed to select the next goals for single or a group of mobile robots operating in an unknown environment. Although, the found plan is followed only for a moment due to re-planning when new information about the environment being explored is collected, the proposed method improves performance of exploration missions in comparison with standard approaches that just consider only an immediate reward by consideration of the visitation of a single goal.
Massive Open Online Course Related Learning Style and Technology Usage Patterns of Thai Tourism Professionals

The e-learning under the concept of massive open online courses (MOOCs) was adopted as an online learning system platform called “Thailand ACCSTP online learning system” with the aim of promoting the Mutual Recognition Arrangement (MRA) of qualifications for ASEAN Tourism Professionals as knowledge workers in Thailand. However, since the system has been implemented, the completion rate is very low compared to other MOOCs. For a better completion rate and learning outcome in MOOCs, the learning style and technology usage of learners in Thailand using the ACCSTP online learning system needs to be investigated. The purpose of this research is to explore the learning styles of knowledge workers working as tourism professionals in five stars’ hotels in Chiang Mai, Thailand. The total number of participants is 144, covering six divisions as follows: housekeeping, front office, food and beverages services, food production for the hotel division, travel agencies and tour operators of the travel division. Our research method was based on questionnaires consisted of two parts. The first part refers to the technology usage consisting 12 items with the aim of identifying the usage of technology devices, such as smartphones, tablets and PCs. The second part is the Index of Learning Styles (ILS) developed by Felder and Silverman (1988) that consist of 44 items. The learning style is divided into 4 dimensions. Each of the dimensions contains 11 items. Data were analyzed descriptively using the SPSS 20.0 for Windows. Results from the analysis showed that the smartphone is playing a dominant role which influences the technology usage of tourism professionals. The following learning styles could be identified as typical for the different learning dimensions: visual style for the input dimension; active style for the procession dimension; sequential style for the comprehension dimension and sensing style for the perception dimension. Based on the findings, the researchers suggest that the course designers and instructors of MOOCs should align their teaching approaches, strategies, methods and techniques with the dominant learning styles of each learning dimension.
Adherence to dietary recommendation and associated factors among diabetic patients in Ethiopian teaching hospitals

Introduction Dietary management is considered to be one of the cornerstones of diabetes care. Improvement of dietary practice alone can reduce glycosylated hemoglobin (HbA1c) by an absolute 1 to 2% with the greatest impact at the initial stages of diabetes. Methods Data from Hospital based cross sectional study were used to assess the level of dietary adherence and its determinants among diabetic patients. The morisky 8 item medication adherence scale was used to develop 10 item tool for evaluation of dietary adherence. Multiple logistic regression was conducted to identify factors which affect dietary adherence and variables with P vale < 0.05 were considered statistically significant. Results More than half of 303 participants (55.7%) were found to be non-adherent to the recommended dietary approach. Gathering with family and friends and eating out were the major reasons for not being compliant with the recommended regimen. Attending diabetic nutrition education (AOR=2.8 95% C 1.97, 5.61) and having the disease for more than 10 years (AOR 2.9 95% CI 1.32, 5.84) were statistically significant with adherence to dietary recommendation. Conclusion Non-adherence to recommended dietary practice was observed in more than fifty percent of patients; it is therefore a major public health problem. Attending diabetic nutrition education and length of diabetes greater than 10 years were the factors associated with adherence to dietary recommendation. This findings indicate that it is important to design strategies to help patients understand their dietary regimens and improve their adherence.
Predicting race results usingartificial neural networks

In this thesis Artificial Neural Networks are used to predict Formula One finish results. The last four races of the season 16/17 are predicted based on the first seventeen races. The first part of this thesis is dedicated to the theory behind Artificial Neural Networks. The aim is to give readers insights in the world and terminology of artificial neural networks. All the important terminology is discussed and explained, using some simple examples. Furthermore some key aspect are discussed in more depth. We look at what the in uences of multiple layers and multiple neurons are. We make clear why a deep neural network is used. The next key as- pect we discuss is; 'training an artificial neural network'. During training the free parameters are optimized. This optimization is done by solving a minimization problem. Therefore, training an artificial neural network can be done by using state-of-the-art optimization methods. Other key aspect are the different activation functions and cost functions which are used in artificial neural networks. Lastly, we explain how possible errors during training can be avoided by using so called regularization methods. The second part is dedicated to the experimental research. First we give and explain public available data which we use as prediction features. Afterwards we show how to initialize an arti- ficial neural network, where we show how multiple layers, activation functions, etc. in uence the predicted results. We finish the experimental research by using the initialized network to predict the outcome of the last four Formula One races of the season 16/17. We compare three different data-sets, the first is the actual data of the season 16/17, the other two are data-sets where we added data in a certain way. Next to this, we compare the predicted results of the artificial neural network with two simple prediction methods and with multiclass logistic regression. The conclu- sion of this comparison is that using artificial neural networks has benefits as it predicts better outcome results. Keywords: Artificial Neural Networks, multi-layer network, activation function, cost function, regularization, minimization, optimization
L'ENSEIGNEMENT DE LA STATISTIQUE: EN PREMIER LIEU, L'APPRENANT

Depuis plus de trente ans, l’enseignement de la statistique œuvre pour une « pédagogie de la proximité ». Or, si cette dernière semble amener une meilleure réussite, elle ne garantit guère la compréhension des notions statistiques. À partir de l’analyse d’une expérience menée par Gattuso et Mary (2003, 2005), et d’une observation menée par l’auteur, est mis en avant le phénomène d’« isolation cognitive ». Ce dernier souligne l’importance de la conception que se font les apprenants de la statistique. Les travaux de Reid et Petocz (2002) corroborent cela et permettent d’entrevoir la nécessité d’une perturbation exogène à l’apprentissage pour que ce dernier se réalise pleinement. Il s’en dégage pour la recherche sur l’enseignement de la statistique que les méthodes qualitatives y ont toute leur place, y compris comme opportunité d’en renouveler l’objet. First published November 2010 at Statistics Education Research Journal: Archives
Correlation Between Dysminorrhea Incidence and Student’s Grade Point

Adolescence is a period of transitions children toward adulthood. Teenagers experience many changes to either physical or psychic; physical changes are characterized by the maturity of the reproductive organs. The timeline of these, starting with the onset of menstruation, teen not uncommon experience painful menstruation (dysmenorrhea). Dysmenorrhea often interferes with daily activities including learning activities. Learning activities that disturbed the learning achievements may cause a decline in students. The purpose of this research is to know the correlation of the incidence of dysmenorrhea with the student achievement index. The population in this research is a biology study program of Sunan Ampel Islamic University Surabaya. The sampling techniques used in this research is quota sampling. The number of samples used is 60 respondents. Statistical tests used were chi-square with 0.05 significance level. From the results of the analysis of the data by using the chi-square test results obtained by the significance of 0.722 (0.722 > 0.05), so that it can be concluded that there is no correlation between the incidence of dysmenorrhea and grade point of a student.
USING FOUR SQUARE WRITING METHOD (FSWM) IN WRITING CLASS

This research was aimed: (1) to find out whether the use of Four Square Writing Method can improve the writing skill of eighth grade students of SMP N 7 Surakarta in the academic year of 2015/2016. (2) to know how far the use of Four Square Writing Method improves the writing skill of eighth grade students of SMP N 7 Surakarta in academic year of 2015/2016. The research was a Classroom Action Research. It was conducted from April until May 2016. The subject of the research was VIII-G students of SMP N 7 Surakarta in the academic year of 2015/2016. In collecting data, the researcher used tests and non-tests. In tests, the students wrote recount text. Meanwhile, non-tests comprised observation, interview, documentation, and field notes. To analyze data, the researcher used qualitative and quantitative data analysis. Consisting of data reduction, display, conclusion drawing and verification. To analyze the quantitative data, the researcher used mean score and t-test formula. The results of the research show that Four Square Writing Method can improve students’ writing skill of the students of VIII-G of SMP N 7 Surakarta. The improvements of the students’ writing skill were on grammar, vocabulary, content, organization, and mechanics aspect. Besides that, the improvement of students’ writing skill can be seen from the students’ writing achievement. The results of mean scores are pre-test (48,3), post-test 1 (70,9), and post-test 2 (76,4). Then, the t-test value of cycle one is 17,12 and the t-test value of cycle two is 6,11. Based on the finding, the researcher concludes that Four Square Writing Method is an appropriate method to improve students’ writing skill. Keywords: Writing Skill, Four Square Writing Method, Classroom Action Research.
Four decades of research on quality: summarising, Trendspotting and looking ahead

The purpose of this paper is to identify and depict the key areas around which research on quality has orbited during the past 37 years. Additionally, this paper aims to explore longitudinal patterns and trends in the identified key areas. Thereby, this study aims to present new perspectives on the foundational elements and evolutionary patterns of research on quality as well as future directions. The paper applies data- and text modelling methodology to a chronological dataset covering 37 years and consisting of scientific journals specialising in research on quality; it also includes scientific journals with a broader spectrum of operations management (OM) research. The study identifies seven central topics around which research on quality has centred during this time period: Service Quality & Customer Satisfaction; Process design & Control; ISO Certification & Standards; TQM - Implementation, Performance & Culture; QM - Practices & Performance; Reliability, Costs, Failure & Problems and Excellence - BEMs, Quality Awards & Excellence in Higher Education. The results also show that the total number of entries has risen constantly since 1980; however, there was a period of decline between 2000 and 2012, indicating that after almost four decades, research on quality is still vibrant and relevant.
Factors Affecting the Adoption of Knowledge Management Technologies: A Discriminative Approach

The increasing importance of knowledge management has prompted many researchers to examine facets of this important topic. However, the nature of the variables influencing the adoption process is not well understood. This study surveyed IT executives to determine the relationship among organizational, environmental, and technological variables and the adoption of knowledge management technologies. The results suggest that organizations most receptive to knowledge management technologies view information technology as strategic to their organization, have experience with user-centric technologies, and a commitment to relatively high IT investment in the past.
Zum Refraktionseinfluss auf terrestrische geodätische Messungen im Kontext der Messtechnik und der Instrumentenentwicklung

Regarding terrestrial geodetic methods refraction effects often exceed the accuracy achieved by precision surveying instruments. This is especially crucial with respect to direction and height transfer. In this contribution, this issue is illustrated by deriving typical magnitudes of interference for conventional observation set ups. For elimination or reduction of refraction effects, institutes succeeded recently in two fields: Refraction-free directions were observed using a functional model based on dispersometry which was developed with respect to a future implementation in an ordinary theodolite. In contradiction to the elimination of refraction effects, a technologically definitely less demanding method based on the OTTM (Optical Turbulent Transfer Model) reduces refraction effects more or less dramatically. This method is explained phenomenologically and first encouraging results are presented which were generated with similar technologies as already integrated in modern geodetic instruments. The possibility of implementation of this OTTM-based method in ordinary instruments is discussed.
Cost and performance modeling of the MU-Decoder

In this thesis we study the implementation details of the MU-Decoders, a recently proposed hardware module that has been theoretically shown to be superior to other methods for generating subsets of large sets. Our study confirms this advantage. Specifically, we compare the performance of implementations of the LUT-Decoder (the most common configurable decoder) to the MU-Decoder. We show that for while the LUT-Decoder is slightly better than the MU-Decoder for arbitrary (and artifical) inputs, for a large class of inputs called totally ordered subsets, that have practical significance, the MU-Decoder is vastly superior in area than the LUT-Decoder. In terms of delay and power too, the MU-Decoder performs better than the LUT-Decoder. This work is based on a series of time-optimized and area-optimized implementations of key building blocks of the MU-Decoder that help construct models for both the LUT-Decoder and the MU-Decoder. These models serve to predict the delay, area and power of these decoders at sizes that may not be practical to implement in an academic setting. As part of the work, multiplexers, one-hot decoders, multicast hard-wiring, and memory modules are implemented and modeled. These are all commonly used elements of digital systems. Therefore this work may also be of independent interest, beyond MU-Decoders.
Scalable inference for stochastic block models

Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of “big data,” traditional inference algorithms for such a model are increasingly limited due to their high time complexity and poor scalability. In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges. We also propose a parallel algorithm based on message passing. Our algorithm can overlap communication and computation, providing speedup without compromising accuracy as the number of processors grows. For example, to process a real-world graph with about 1.3 million nodes and 10 million edges, our algorithm requires about 6 seconds on 64 cores of a contemporary commodity Linux cluster. Experiments demonstrate that the algorithm can produce high quality results on both benchmark and real-world graphs. An example of finding more meaningful communities is illustrated consequently in comparison with a popular modularity maximization algorithm.
To what extent does children’s spelling improve as a result of learning words with the look, say, cover, write, check, fix strategy compared with phonological spelling strategies?

Abstract The ubiquitous weekly spelling test assumes that words are best learned by memorisation and testing but is this the best way? This study compared two well-known approaches to spelling instruction, the rule based and visual memory approaches. A group of 55 seven-year-olds in two Year 3 classrooms was taught spelling in small groups for three lessons a week, 20-min per lesson, over ten weeks. In the first intervention, students learned statistically likely spelling strategies for vowel sounds, syllable breaking strategies, and the doubling rule. In the second intervention, students used a look, say, cover, write, check, fix strategy, listed words in alphabetical order, and wrote them in sentences. The control group completed non-spelling activities. Results showed that although both intervention groups learned to spell taught words better than the control group, the rule-based approach had greater transfer to spelling of new words for both proficient and less proficient spellers.
How a Himalayan Master and Teacher Promotes Healthy Behavior among His Followers in the Vedic Tradition

The current article describes how a Himalayan Master and Satguru, Professor and Dr. Sri Svami Purna Maharaj, uses the Vedic/Ayurvedic and other related Himalayan methodology and tradition to address the prevention of disease and promote healthy behavior among the spiritual community of students and seekers through a system developed under his guidance and supervision called the Purna Health Management System (PHMS). The article also explains some of the Vedic/Ayurvedic traditions the PHMS is based on and how they are supported by Western literature and research. Finally, it reports findings of a pilot study investigating outcomes related to self-reported physical health, mental/emotional health, and stress for those who follow the PHMS teachings and framework. The pilot study involved a cross-sectional, mixed-methods anonymous online survey of people who were practicing the PHMS teachings and implementing the system. Out of the 100 potential participants, 41 chose to complete the survey, a 41% response rate. Non-parametric Spearman’s ρ Correlation Coefficients were used to test hypotheses for relationships between frequency of implementation of the four key factors of the PHMS and the self-reported outcomes of physical health, mental/emotional health, overall stress level, and ability to manage stress. A two-tailed p < . 05 was considered statistically significant. Participants of the pilot study who reported frequently implementing the PHMS also reported positive health outcomes; for example, those who reported implementing the life balance (stress management) aspect of the PHMS more frequently also reported increased physical health, increased mental/emotional health, increased ability to manage stress, and decreased overall stress. Although further research is needed, the findings of this pilot study suggest that the PHMS approach may be useful to both faith communities and health professionals wanting to promote health and prevent disease in those whom they serve.