Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVELOPMENT OF BUDDHIST WELL-BEING INDICATORS OF THAI YOUTH

The purpose of this research was to develop well-being indicators and to validate the structural validity of the Buddhist well-being indicators of Thai youth. Mixed methods research was designed and divided into 2 phases: phase 1, qualitative researchusingsynthesize documents and collect data from a group of key informants, and was selected by purposive sampling which consisted 23 of Buddhist experts,Buddhist Psychology and Counseling and youths guidance. Phase 2, identifying indicators for creating an assessment.Data were collected from samples, namely 120 youths aged 13-15 years, grade 1-3, of the study. Research tools were interview guidelines and small group meeting and seminar,health assessment form. The statistics used were percentage, mean, secondorder confirmatory factor analysis, and analyze qualitative data through content analysis. The research found that the 4 indicators of well-being created by Bhavana IV which consisted of physical well-being, social well-being, mental well-being, and intellectual well-being. There are four main components and 46 sub-components.The model fit with empirical data (χ = 38.80, df= 27, p-value = 0.066, RMSEA = 0.060).
Broad Language Support for Automatic Translation Insight Extractors of Complex Language Patterns Name : Olzhas Aldabergenov Student ID : s 1928643 Date : 27 / 07 / 2021 Specialisation :

Machine learning algorithms can now handle a wide range of natural language processing (NLP) tasks in order to extract meaningful information. The ability to obtain valuable information, on the other hand, is heavily reliant on the proper representation of language properties. Many techniques have been developed to convert lexical units such as sentences and words into numerical form while retaining their meaning. One of the most recent and practical approaches is language-independent or multilingual representations, which can handle multiple languages with a single technique. We conducted experiments with existing approaches such as LASER and LaBSE in this study and analyzed their performance to determine whether they are truly language-independent. In addition, we investigated the effect of language characteristics on the performance of machine learning models. ZyLAB, an eDiscovery software company, employs machine learning models to detect complex verbal patterns. These classifiers are trained on annotated data sets, one for each language. Language-independent data sets are critical for dealing with multilingual NLP tasks.
The Effects of Layer Thickness on Brittle Boudinage in 3D

We analyse the effects of thickness on brittle boudinage in a metre-scale sample of marble containing a layer of amphibolite recording two phases of ductile pinch-and-swell followed by five generations of brittle boudinage. The amphibolite geometry was reconstructed in 3D, employing a method we call ‘outcrop-scale tomography’. Our data suggests that strain localisation depends on the ration of grain size and layer thickness of amphibolite. In very thin layers (few grains across), strain is diffuse throughout the entire layer, leading to macroscopically homogeneous stretching. Strain localisation increases when layer thickness is more than 10 grains, first through narrow tensile necks and shear zones (<10-20x average grain size), then through extension fractures, and finally shear fractures emerge. The disappearance of shear fractures in thinner layers can be explained by a geometry-related compressive stress decrease in the pinches and expected shear band width exceeding layer thickness. This results in localized shear evolving only in thicker layers. Successive reactivation between fracture generations, geometrical complexity, in the form of splays and branches, and the thickness-dependence of localised strain govern fracture distribution in the layer. We infer a second, temporal trend that records the progressive embrittlement of the rocks as they cool during exhumation, evidenced by a switch from shear to extensional fracturing. In the final stages, the marble is brittle enough to allow fracture propagation from the amphibolite across the material interface and the formation of throughgoing brittle faults.
EFFECTS OF EXPECTATIONS AND SERVICE QUALITY ON CUSTOMER SATISFACTION AT PT. BANK MANDIRI KENDARI BRANCH

This study aims to determine and analyze the effects of expectations and service quality on customer satisfaction at PT. Bank MandiriKendari Branch. The objects of this study are expectations and quality of service to customer satisfaction conducted at PT. Bank MandiriKendari Branch. The populations used are all customers who make transactions at PT. Bank MandiriKendari Branch. Because the population is unknown or infinitive so the population size is not included, in this study there are 30 respondents as the samples. The analytical tool used is multiple linear regression using SPSS software. Based on the results of research conducted, it can be concluded that (1) expectations have a positive and significant effect on customer satisfaction at PT. Bank MandiriKendari Branch; and (2) service quality has a positive and significant effect on customer satisfaction at PT. Bank MandiriKendari Branch.
Using bicoloured text to improve reading abilities and experience

In hopes of finding a new way to improve reading abilities or experiences, we researched, from a gestalt-design point of view, the effects of bicoloured text; texts in which some words are coloured grey instead of black, so to separate them and make for easier interpretation. Our findings show indications that marking function words, sans auxiliary verbs, grey in Dutch could lead to a better understanding of texts.
J an 2 00 0 PHYSICAL CONDITIONS , GRAIN TEMPERATURES , AND ENHANCED VERY SMALL GRAINS IN THE BARNARD LOOP

We derive the radio spectral index of the Barnard Loop (BL) from large-scale radio surveys at four frequencies and find it to be a thermal source. We use the radio data together with Hα data to determine the electron temperature in BL, the λ Ori HII region, and a high-latitude filament; all of these regions are somewhat cooler than typical HII regions. We perform least squares fits of the DIRBE diffuse IR intensities to the 21-cm line and radio continuum intensities. After the resolution of a “geometrical conundrum”, this allows us to derive the electron density ne; we find ne ≈ 2.0 cm −3 and pressure P k ≈ 24000 cm −3 K. Grains within BL are warmer than in HI regions. Trapped Lα accounts for the extra heating that is required. This is a general effect that needs to be accounted for in all analyses that examine IR emission from H+ regions. Very small grains that emit 60 μm radiation are enhanced in BL relative to HI by a factor of 2-3, while PAH’s that emit 12 μm are probably deficient by a factor ∼ 2. Subject headings: ISM: dust — HII regions — ISM: individual (Barnard Loop, λ Ori)
DeepEvent-VO: Fusing Intensity Images and Event Streams for End-to-End Visual Odometry

— With the advent of Deep Learning (DL) algorithms, there has been an increased shift in using these techniques for Visual Odometry. Numerous supervised, self-supervised and unsupervised methods have been proposed. With the recent surge in the use of event-based cameras that capture high-speed intensity changes as log representations, it is possible to encode information that is missed by regular cameras between time steps. Towards this end, we propose DeepEvent-VO, a fusion-based supervised RCNN model that learns geometric features between monocular intensity sequences across time and fuses features learned from the event-frames encoded between these time steps, before passing to LSTMs to learn poses at each time step. To our knowledge, this is the ﬁrst attempt in fusing features from both intensity and event sequences. We explain the architecture in more detail and show different experiments that we have performed along with ablation studies using different learning strategies. From our learnings, we give directions for the future to help improve the results of this project.
Hyper-parameter optimization of convolutional neural networks for classifying COVID-19 X-ray images

For more than a year the COVID-19 epidemic is threatening people all over the world. Numerous researchers are looking for all possible insights into the new corona virus SARS-CoV-2. One of the possibilities is an in-depth analysis of X-ray images from COVID-19 patients, commonly conducted by a radiologist, which are due to high demand facing with overload. With the latest achievements in the field of deep learning, the approaches using transfer learning proved to be successful when tackling such problem. However, when utilizing deep learning methods, we are commonly facing the problem of hyper-parameter settings. In this research, we adapted and generalized transfer learning based classification method for detecting COVID-19 from X-ray images and employed different optimization algorithms for solving the task of hyper-parameter settings. Utilizing different optimization algorithms our method was evaluated on a dataset of 1446 X-ray images, with the overall accuracy of 84.44%, outperforming both conventional CNN method as well as the compared baseline transfer learning method. Besides quantitative analysis, we also conducted a qualitative in-depth analysis using the local interpretable modelagnostic explanations method and gain some in-depth view of COVID-19 characteristics and the predictive model perception.
Assessing sediment yield and streamflow with SWAT model in a small sub-basin of the Cantareira System

Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km), Southeast Brazil. The SUFI-2 algorithm from SWATCUP was applied for calibration, testing, uncertainty, and sensitivity analysis. The model was calibrated and initially tested using discharge and sediment load data, which were measured at the catchment outlet. Additionally, we used soil loss measurements from erosion plots within the catchment as independent data for model evaluation. Average monthly streamflow simulations obtained satisfactory results, with Nash-Sutcliffe coefficient (NSE) values of 0.75 and 0.51 for the calibration and testing periods, respectively. Sediment load simulations also displayed satisfactory results for calibration (NSE = 0.65) and testing (NSE = 0.52). However, the comparison with independent plot data revealed that SWAT severely overestimated hillslope erosion rates and compensated it with high sediment channel deposition. Moreover, the model was not sensitive to the parameters used for calculating hillslope sediment yields. Therefore, it should be used with caution for evaluating the interactions between land use, soil erosion, and sediment delivery. We found that the commonly used outlet-based approach for model calibration and testing can lead to internal misrepresentations, and models can reproduce the right answer for the wrong reasons.
Computer assisted brainstorming Which ideation tool is best and what makes it best?

—The purpose of this study is to determine which ideation software, referred to as tools during the paper, is the best one available and why this is the case. After analyzing 83 different tools, a selection of four tools is made. A fifth tool is developed specifically for this research, with the goal of testing the effectiveness of leveraging a dynamic type of word database, as the four selected tools all rely on static types of word databases. An experiment is then performed with 31 participants, using a specifically designed method for this study and a method based on four well-known characteristics that describe the quality of ideas created through brainstorming methods. The results of the experiment show that a static type of database, focused on specific category, in combination with random selection offer the best ideas.
MOLECULAR DETECTION of PANTON VALENTINE LEUKOCIDIN (PVL) GENES in METHICILLIN-RESISTANCE STAPHYLOCOCCUS AUREUS (MRSA) ISOLATED FROM BURNS INFECTION

Background: Methicillin resistance Staphylococcus aureus (MRSA) infection related to community has been enhanced through the world. One of the significant cytotoxins elaborated by a few strains of S. aureus is the Panton Valentine leukocidin (PVL), coded by two genes, lukSPV and lukF-PV which is element of toxin that created pores in the membranes of cells, the role of PVL in the disease process severity of Staphylococcus aureus till known is debated. Amis: This study was planned to detect the Panton Valentine leucocidin (PVL)genes and MRSA isolated from Burn wound infection in community and hospital acquired settings. Material and methods: Cross-sectional study involved 200 patients who presented with burn wounds from third-degree or fourth-degree. The bacteria from all samples were undergone to Cefoxitin disc diffusion testing utilization a 30 μg cefoxitin disc for methicillin resistant determination. Following DNA extraction, conventional polymerase chain reaction (PCR) was used to detect PVL genes. Results: Twenty-eight samples were positive culture for Staph. aureus, of which 19 (67.8%) patients presented with CAI, and 9 (32.1%) belong Hospital acquired infection A total of 23 (82.1%) showed methicillin resistance. Out of 28 isolates, 7(25%) isolates were harboring PVL gene, all of which were Methicillin resistance community acquired infection. None of S. aureus isolates from hospital acquired infection had PVL. Conclusion: This study indicated high prevalence of PVL among community acquired infection MRSA isolates, and the lack of this gene in Hospital acquired infection.
K OOPMAN Q-LEARNING : O FFLINE R EINFORCEMENT L EARNING VIA S YMMETRIES OF D YNAMICS

Offline reinforcement learning leverages large datasets to train policies without interactions with the environment. The learned policies may then be deployed in real-world settings where interactions are costly or dangerous. Current algorithms over-fit to the training dataset and as a consequence perform poorly when deployed to out-of-distribution generalizations of the environment. We aim to address these limitations by learning a Koopman latent representation which allows us to infer symmetries of the system’s underlying dynamic. The latter is then utilized to extend the otherwise static offline dataset during training; this constitutes a novel data augmentation framework which reflects the system’s dynamic and is thus to be interpreted as an exploration of the environments phase space. To obtain the symmetries we employ Koopman theory in which nonlinear dynamics are represented in terms of a linear operator acting on the space of measurement functions of the system and thus symmetries of the dynamics may be inferred directly. We provide novel theoretical results on the existence and nature of symmetries relevant for control systems such as reinforcement learning settings. Moreover, we empirically evaluate our method on several benchmark offline reinforcement learning tasks and datasets including D4RL, Metaworld and Robosuite and find that by using ourframework we consistently improve the state-of-the-art for Q-learning methods.
AN EFFICIENT EARLY DIAGNOSIS FOR DIABETIC RETINOPATHY USING QUICK CONVOLUTIONAL DIAGNOSIS

Diabetic retinopathy (DR) is derived from diabetes is a serious eye disease and in the most common cause of blindness. DR is an eye disease that is a result of chronic disease of diabetes. A microaneurysm is a small red spot in the retina that raises from the fragile part of the blood vessel, hard exudates, and abnormal growth of blood vessels are the diabetic retinopathy. Predicting the presence of microaneurysmsas an early indication of fundus imaging and diabetic retinopathy has been a major challenge for decades. DR is suffering from a person's chronic high blood sugar levels, microvascular problems, and irreversible vision loss leads.To solve this problem, the rapid advancement of Deep Learning (DL) makes it an effective technique for providing interesting solutions to analytical problems in medicine.The proposed system DL algorithms are Retinal Hidden Linear Selection (RHLS) algorithm and Quick Convolutional Diagnosis (QCD)algorithm. Retinal Hidden Linear Selection (RHLS) algorithm used to pre-process to remove noise from the fundus image, and excessive-performance. Then, a quick convolution diagnosis (QCD) algorithm is a classification to determine whether the fundus image is subjected to a normal or influence. The results obtained are the proposed method, from a very effective and color fundus image shows a successful diabetic retinopathy diagnosis.
Temporal Link Prediction on the WikiLinkGraphs Dataset

The task of predicting network evolution over time is one that has garnered much research interest, especially as new graph datasets become available in a diverse range of ﬁelds. In this paper, we explore methods for temporal link prediction on the new WikiLinkGraphs dataset, which contains snapshots of the Wikipedia hyperlink structure from 2001 to 2018. Broadly, we aim to develop link prediction techniques that can be applied to enhance the structure of the Wikipedia graph, especially for languages that do not beneﬁt from the same number of contributors as the English version. In other words, we want to predict missing hyperlinks that would in the future be added between Wikipedia pages given continued contributions from Wikipedia editors. To do so, we ﬁrst explore baseline methods that utilize the structural properties of the static WikiLinkGraphs network. Then, as our novel contribution, we frame the challenge as a supervised learning task and apply a number of different Graph Neural Network approaches to the link prediction task. In addition, we apply temporal representation network learning to learn node features that leverage the temporal and evolutionary nature of the dataset we have available to further augment the performance of GNN-based methods. Our experiments show an increase in F1 scores for each of our three GNNs when using temporal node embeddings as inputs over blank inputs. In the end, we conclude that GNN-based methods with time-dynamic input node features provide a promising way forward in accomplishing accurate temporal link prediction for the Wikipedia link graph.
Thermal Analysis & Rheology CONTROLLED STRESS RHEOMETER: HOW DOES IT WORK?

Rheology, the study of the deformation and flow of materials, is a valuable technique for characterizing liquids and soft solids. There are two common approaches for making rheological measurements. Those are controlled rate and controlled stress. However, the latter (controlled stress) approach provides a more direct correlation with real-world processes including sedimentation, extrusion, pumping, leveling due to surface tension, and sagging due to gravity. Hence, it has become the preferred approach for most laboratories.
FACTORS INFLUENCING DIETARY BEHAVIOR OF PREGNANT WOMEN IN A PRIVATE HOSPITAL IN BANGKOK

The purpose of this predictive research was to study the level of dietary behavior of pregnant women, and to study the predictive factors of food consumption behavior of pregnant women. The sample consisted of 272 pregnant women who came for antenatal care at the antenatal care department in Navaminthra 1 Hospital, Bangkok. The tool used for collecting data was 7 questionnaires. The content validity of all tools was verified by 5 experts. The validity of the assessment tools 2-6 was checked using Cronbach's alpha coefficient, and the validity values were 0.83, 0.81, 0.93, 0.84, and 0.90 respectively. The seventh questionnaire was tested using the Kuder-Richardson method (KR-20)ม and the reliability was 0.87. The data were analyzed using frequency distribution, percentage, mean and standard deviation. Predictive factors were analyzed using multiple regression statistics. The results showed that the dietary behavior of pregnant women had a high mean score (M = 70.01, S.D. = 7.13). The predictive co-factor of food consumption behavior could be determined by ordering the variables that could be predicted highest to lowest in the following order: perceived dietary benefits, nutritional social support, self-efficacy in food consumption, beliefs about food intake, and dietary knowledge of pregnant women. The co-factor was 77 percent (R2 = .77, p < .01). The results of this research could be used as a baseline to develop a model for promoting appropriate dietary behavior among pregnant women.
Bonding with BlockBot: a field exploration of the effect of common locus on human-robot bonding

The increased presence of robots in human-inhabited environments provide a new frontier in the research of human-robot interaction. Currently, we lack a comprehensive understanding of how humans make sense of robots in their daily lives. We observe a growing body of examples in which humans show behavior that is indicative of a strong social engagement towards robots that do not possess any life-like realism in their appearance or behavior. We propose an extended framework of social robotics, that next to appearance and behavior, includes the concept of a common locus as a relevant factor for passing a social threshold. The common locus is understood as the impression of sharing a life time, space and experiences between a human and a robot. This paper theorizes that a common locus can facilitate an experience of a robot as a social agent, regardless if this robot displays low life-like appearance qualities and low life-like behavioral cues. We present the BlockBots minimal cube-shaped robotic artifacts that are deployed in an unsupervised, open-ended and in-the-field experiment. Participants host the BlockBot in their domestic setting before passing it on. Qualitative data messages, photos and videos about the BlockBot is generated through Whatsapp communication. The data shows that the BlockBot successfully establishes a common locus with participants and that participants refer to the BlockBot as a social agent, which suggests that it passed a social threshold. Participants attribute identity, state of mind and agency to the BlockBot and maintain a common locus by keeping it in their proximity between locations and taking it on trips and activities with them. The results of this study suggest that the presence of robots in human-inhabited environments will impact how humans perceive them. However, results are only indicative and we also discuss alternative interpretations of the data and future studies.