Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquisition of Vocabulary in Primary Schools Via GoPic with QR Code

Vocabulary acquisition is paramount in English language education yet becomes challenging especially for primary school students in Malaysia. Prior to this, the advancement and integration of information and communication technology (ICT) in 21st-century learning is crucial in teachers? pedagogical skills and students? knowledge resulting in student-centered strategies. However, students are still lacking in vocabulary skills as they are learning through traditional methods. This action research conducted in two cycles using GoPic with QR Code investigates the importance of improving students? language experiences in their acquisition of vocabulary. The first cycle of the intervention was carried out through group approach. The second cycle was carried out using individual approach. The term ? GoPic with QR Code? is generated from the blending concept of using ? BINGO? word game with picture cards to complement the words (concrete nouns) being applied and assessed for the intervention. The respondents comprise of a total of 64 low proficiency primary school students, that is, 58 Year 1 students from Selangor and 6 Year 5 students from Sabah. Pre and post-test, survey questionnaire and field notes were instruments used in the data collection. The findings concluded that by using GoPic with QR Code, students revealed increment in their English language vocabulary. It also reflected positive implications on the teachers, students and school administrators.
Potential use of homogenized whey protein dispersions and process modification for the manufacture of low fat and reduced fat cheddar type cheeses

Interest in cheese with lower fat than regular cheese has grown in recent years due to the changing eating habits of health conscious consumers, but production of such cheeses at low cost, without losing flavour and texture, is a challenge. In this study, modified processes were developed to manufacture low (˜20%) and reduced (˜15%) fat cheddar cheeses. Modifications of cooking time and temperature, along with changes in the amount of starter culture used and the addition of whey proteins, were studied. The results showed that low fat cheese could be manufactured with or without adding homogenized whey proteins, and reduced fat cheese could be manufactured with homogenized whey proteins. This finding reveals that it would be possible to manufacture these cheeses in an existing cheese plant, but that a homogenizer would be required if whey protein is to be added.
Geometry-Guided Adaptation for Road Segmentation

We propose a novel adaptation method for generalizing road segmentation to novel weather, lighting or viewing geometries. The method assumes a source domain consisting of an ensemble of labeled training datasets and an unlabeled target test dataset that deviates substantially from the training ensemble. The training dataset is used to compile a geometry-anchored prior over the road pixel locations and to train a fully-convolutional network road segmentation system. At inference, a probabilistic Houghing method is used to detect line intersections in the test image and thereby estimate the vanishing point of the road, thus anchoring the learned geometric prior. This prior is then used to extract high confidence road and background regions which serve as surrogate ground truth to adapt the network to the target domain. Leave-one-out evaluation across five diverse road segmentation datasets demonstrates substantial improvement in generalization across changes in viewing geometry and weather conditions, yielding results that are on average comparable and in some cases superior to a more complex GAN-based domain adaptation approach. These results demonstrate the potential for classical computer vision methods to guide adaptation of supervised machine learning algorithms, leading to improved generalization across domains.
Effect of Corporate Social Responsibility Disclosure, Good Corporate Governance Implementation, and Profitability on Corporate Value (Asean Country Study 2016-2018)

This study aims to analyze the effect of corporate social responsibility disclosure, good corporate governance, and profitability on corporate value. The sampling technique used was purposive sampling. The study was conducted on Manufacturing Companies in Indonesia and Malaysia that are included in the ASEAN CG Scorecard with a research period of 2016-2018. The estimation of the research model used is multiple regression analysis. The purpose of this study is to determine the effect of corporate social responsibility disclosure, good corporate governance, and profitability on corporate value. The results of this study indicate that Good Corporate Governance Corporate Social Responsibility and profitability affect Company Value. This research targets outcome in the form of International Journals.
Model to Measure the Success of Climate Change Mitigation Performance through the Knowledge Management Systems

Scientific descriptions of climate change are officially recognized at the international level is organized by the Intergovernmental Panel on Climate Change (IPCC). The IPCC compiles Assessment Reports which every five years on scientific, technical and aspects socio-economic, causes, potential impacts and coping strategies climate change. IPCC findings prove that the problem of climate change will affect ecosystems and humans in all parts of continents and oceans world. Climate change can pose great risks to health human, global food security, and economic development. Action to reduce emissions is very important and urgent to do in order to avoid the danger of climate change. Adaptation is very necessary to deal with the risks of climate change. Tiers the adaptation needed depends on the success of the mitigation activities. This paper aims to provide an overview of how mitigation in dealing with climate change can be done optimally through a knowledge management system with reference to the SECI theory (Socialization, Externalization, Combination, and Internalization). The factor analysis method is used to find the success factors of mitigation in the face of climate change, and subsequently through a regression analysis a model can be built involving a number of variables formed in the factor analysis. The results of the study provide a number of new factors that can be represented as variables of public awareness of climate change, mitigation socialization processes, availability of knowledge management systems, and governance systems. The conclusion obtained from this study is that the built model is used to measure climate change mitigation performance and can be simulated for optimal achievement based on the local wisdom of the local community.
Expressing Emotions, Resilience and Subjective Well-Being: An Investigation with Structural Equation Modeling

The present study examined the relationship between expressing emotions, psychological resilience and subjective well-being. The study was carried out with a total of 217 university students, of whom 94 were males and 123 were females, aged between 19 and 25 years. The data of the study were collected using the Emotional Expression Questionnaire, the Psychological Resilience Scale and the Subjective Well-Being Scale, respectively. The relationship between the variables of the study was analyzed via the methods of Pearson Correlation Coefficient and Structural Equation Modeling, and the mediating role of psychological resilience between emotional expression and subjective well-being was tested. The goodness-of-fit indices obtained from the structural equation modeling indicated that the model generated a good fit. According to the results, there was a significant relationship between “expressing emotions” and “psychological resilience” and between “psychological resilience” and “subjective well-being”. It was found that there was no significant relationship between expressing emotions and subjective well-being and that the variable of expressing emotions affected that of subjective well-being by means of the psychological resilience (tool) variable and the model tested was significant.
IJSRD-International Journal for Scientific Research & Development| Vol. 5, Issue 05, 2017 | ISSN (online): 2321-0613

Umbilical cord blood (UCB), a rich source of hematopoietic stem cells and progenitor cells, has been used successfully to treat a variety of genetic and hematologic disorders. The demonstrated success of CBT (Cord Blood Transplantation) and the impressive scientific progress in the last two decades led to the establishment of several cord blood banks world over. Most banks offer storage services of cord blood for one’s own future use (autologous) or solicit cord blood donations from birthing mothers to help develop a pool that can help people in need. The later falls under the public banking category, whereas those that are kept for self or family’s use, in the distant future, is called family or private banking. The most appropriate indication and justification for private banking however is for those families with a history of hematological disorder. We are witnessing increasing arguments and controversies in scientific meetings and the questioning for the scientific basis for family banking, in view of its low probability of use. Service providers are caught between the need to be in business, place facts to the potential customers and the pressure to remain relevant. They are therefore forced to look at innovative ways to address this challenge. The objective at hand is therefore to find ways of helping families in need by a change in approach and demonstrate the good intent. The present review is a critical analysis and discussion on the current family cord blood banking practices, review the trend, and suggest strategies to address current gaps in the public banking programs. The interest of the public at large was given more weightage at all times. An account of challenges one faces in adopting a hybrid model is being discussed in details in this review.
Analysis of the characteristics of diabetic retinopathy inpatient records in 94 grade IIIA hospitals in China over 14 year

Background To investigate the characteristics of epidemiology, clinical treatment and economic cost structure of inpatients with diabetic retinopathy (DR) in Chinese grade IIIA hospitals. Methods Data from the medical record home page (IMRHP) of all inpatients with DR from October 2001 to September 2014 were retrieved from the information management center system of 94 grade IIIA hospitals. Results A total of 65,759 inpatients with DR (71,686 eyes) were enrolled, accounting for 14.00% of all ophthalmology hospitalizations. During the 14-year period, the average length of hospitalization of DR inpatients and the average preoperative time dropped by 49.74% and 48.09%, respectively (p < 0.001). The proportion of vitrectomies increased from 10.53% to 15.62%, and the proportion of intravitreal injections increased from 0% to 4.84%. The average hospitalization cost in 2014 was 2.2 times greater than in 2001, but the increase was lower than that of the per capita annual income in China. Conclusions The number of inpatients, the number of surgeries, and hospitalization costs show a significant upward trend. The statistics of the IMRHP data can provide reasonable suggestions and scientific support for the prevention and treatments of DR.
Participation instruments in persons with spinal cord injury: A narrative review

Background: Spinal cord injury (SCI) has serious impacts on the patient’s function. Therefore, their participation is important as one of the major indicators of the quality of life. This study reviews instruments that evaluate participation among people with spinal cord injury. Methods: Four electronic databases (WebofScience, Scopus, MEDLINE/PubMed, SID) were searched for studies published in the English language between 2000 and 2019 in one or more peer-reviewed journals on the measurement properties (reliability, validity and/or responsiveness) in all populations including adults with SCI. Instruments assessed based on special criteria designed for disability outcome measures. Results: Six instruments were included: Incontinence - Activity Participation Scale, Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P), World Health Organization's disability assessment tool-II (WHODAS-II), ICF Measure of Participation and ACTivities Screener (IMPACT-S), Impact on Participation and Autonomy (IPA) , Participation measure for Post-Acute care (PM-PAC). Evidence related to the reliability and validity was reported for all of the instruments. Only WHODAS-II, USER-P, and IMPACT were compared with each other in recent publications. Responsiveness was not obtained for any of the instruments. Conclusion: As the underlying structure of every instrument is different, the concept of the evaluated participation varies between instruments. The proper instrument for examining participation of the patients with SCI should be selected based on a thorough analysis of the individual's condition and context. Innovative models of disability should be the basis of emerging instruments for evaluation of participation, as well as empirical studies and modern measurement technologies that fill the gap between the perceived participation of the individual and the research's record.
Muscle fibre characteristics of a native pig breed longissimus lumborum muscle

The aim of this study was to investigate the histochemical parameters of muscle fibre in a Spanish autochthonous pig breed. Due to its fatty meat and high prolificacy, this pork is revered among local consumers. However, the aforementioned breed has become endangered. A total of 11 native pigs (Black Canary Pig) were evaluated. Longissimus lumborum muscle samples were collected in order to evaluate contractile and metabolic characteristics of the varying fibre types by histochemical staining techniques (nicotinamide adenine dinucleotide tetrazolium reductase [NADH-TR] and myosin adenosine triphosphatase activity [mATPase]). Cross-sectional area, diameter and percentage of the fibres were calculated. The oxidative fibre percentage was elevated (33.63%) which in turn can improve the colour, tenderness and juiciness of the meat, hence the glycolytic fibre percentage being moderate (66.37%). The size of three fibre types was higher than the values provided by other pig breeds. A higher percentage of IIB fibres is a disadvantage because these fibres cannot store a high percentage of intrafibrilar fat which could be linked to myopathic process. In conclusion, this autochthonous pig breed could potentially produce red meat and high quality, dry-cured pork products thanks to its remarkably high percentage of oxidative fibres, which naturally possess a greater capacity to store intramuscular fat.
Object Detection and Classification for Autonomous Drones

Detecting and classifying objects in a single frame which consists of several objects in a cumbersome task. With the advancement of deep learning techniques, the rate of accuracy has increased significantly. This paper aims to implement the state of the art custom algorithm for detection and classification of objects in a single frame with the goal of attaining high accuracy with a real time performance. The proposed system utilizes SSD architecture coupled with MobileNet to achieve maximum accuracy. The system will be fast enough to detect and recognize multiple objects even at 30 FPS.
Sheep Improvement Limited eight years on

Sheep Improvement Limited (SIL), the performance recording and genetic evaluation service for sheep in New Zealand, was described by Cruickshank et al. (2002) and Newman et al. (2002) eight years ago to this congress. Since that time the SIL system has grown and developed. This paper describes changes that have occurred in SIL and considers what lies ahead for the SIL system in order to maintain industry relevance.
Cross-Domain Imitation Learning with a Dual Structure

In this paper, we consider cross-domain imitation learning (CDIL) in which an agent in a target domain learns a policy to perform well in the target domain by observing expert demonstrations in a source domain without accessing any reward function. In order to overcome the domain difference for imitation learning, we propose a dual-structured learning method. The proposed learning method extracts two feature vectors from each input observation such that one vector contains domain information and the other vector contains policy expertness information, and then enhances feature vectors by synthesizing new feature vectors containing both target-domain and policy expertness information. The proposed CDIL method is tested on several MuJoCo tasks where the domain difference is determined by image angles or colors. Numerical results show that the proposed method shows superior performance in CDIL to other existing algorithms and achieves almost the same performance as imitation learning without domain difference.
A Multilevel Empirical Bayesian Approach to Estimating the Unknown Redshifts of 1366 BATSE Catalog Long-duration Gamma-Ray Bursts

We present a catalog of probabilistic redshift estimates for 1366 individual Long-duration Gamma-ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful selection and modeling of the population distribution of 1366 BATSE LGRBs in the five-dimensional space of redshift and the four intrinsic prompt gamma-ray emission properties: the isotropic 1024 ms peak luminosity ( ), the total isotropic emission ( ), the spectral peak energy ( ), as well as the intrinsic duration ( ), while carefully taking into account the effects of sample incompleteness and the LGRB-detection mechanism of BATSE. Two fundamental plausible assumptions underlie our purely probabilistic approach: (1) LGRBs trace, either exactly or closely, the cosmic star formation rate, with a possibility of the excess rates of LGRBs in the nearby universe, and (2) the joint four-dimensional distribution of the aforementioned prompt gamma-ray emission properties is well described by a multivariate log-normal distribution. Our modeling approach enables us to constrain the redshifts of individual BATSE LGRBs to within 0.36 and 0.96 average uncertainty ranges at 50% and 90% confidence levels, respectively. Our redshift predictions are completely at odds with the previous redshift estimates of BATSE LGRBs that were computed via the proposed phenomenological high-energy relations, specifically, the apparently strong correlation of LGRBs’ peak luminosity with the spectral peak energy, lightcurve variability, and spectral lag. The observed discrepancies between our predictions and the previous works can be explained by the strong influence of detector threshold and sample incompleteness in shaping these phenomenologically proposed high-energy correlations in the literature. Finally, we also discuss the potential effects of an excess cosmic rate of LGRBs at low redshifts and the possibility of a luminosity evolution of LGRBs on our results.
Two variable logic with ultimately periodic counting

We consider the extension of two variable logic with quantifiers that state that the number of elements where a formula holds should belong to a given ultimately periodic set. We show that both satisfiability and finite satisfiability of the logic are decidable. We also show that the spectrum of any sentence is definable in Presburger arithmetic. In the process we present several refinements to the ``biregular graph method''. In this method, decidability issues concerning two-variable logics are reduced to questions about Presburger definability of integer vectors associated with partitioned graphs, where nodes in a partition satisfy certain constraints on their in- and out-degrees.
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

Policy entropy regularization is commonly used for better exploration in deep reinforcement learning (RL). However, policy entropy regularization is sample-inefficient in off-policy learning since it does not take the distribution of previous samples stored in the replay buffer into account. In order to take advantage of the previous sample distribution from the replay buffer for sample-efficient exploration, we propose sample-aware entropy regularization which maximizes the entropy of weighted sum of the policy action distribution and the sample action distribution from the replay buffer. We formulate the problem of sample-aware entropy regularized policy iteration, prove its convergence, and provide a practical algorithm named diversity actor-critic (DAC) which is a generalization of soft actor-critic (SAC). Numerical results show that DAC outperforms SAC and other state-of-the-art RL algorithms.
Three-dimensional general relativistic Poynting-Robertson effect. III. Static and nonspherical quadrupolar massive source

We investigate the three-dimensional motion of a test particle in the gravitational field generated by a non-spherical compact object endowed with a mass quadrupole moment, described by the Erez-Rosen metric, and a radiation field, including the general relativistic Poynting-Robertson effect, coming from a rigidly rotating spherical emitting source located outside of the compact object. We derive the equations of motion for test particles influenced by such radiation field, recovering the two-dimensional description, and the weak-field approximation. This dynamical system admits the existence of a critical hypersurface, region where gravitational and radiation forces balance. Selected test particle orbits for different set of input parameters are displayed. The possible configurations on the critical hypersurfaces can be either latitudinal drift towards the equatorial ring or suspended orbits. We discuss about the existence of multiple hypersurface solutions through a simple method to perform the calculations. We graphically prove also that the critical hypersurfaces are stable configurations within the Lyapunov theory.
Backstepping Control of Muscle Driven Systems with Redundancy Resolution

Due to the several applications on Human-machine interaction (HMI), this area of research has become one of the most popular in recent years. This is the case for instance of advanced training machines, robots for rehabilitation, robotic surgeries and prosthesis. In order to ensure desirable performances, simulations are recommended before real-time experiments. These simulations have not been a problem in HMI on the side of the machine. However, the lack of controllers for human dynamic models suggests the existence of a gap for performing simulations for the human side. This paper offers to fulfill the previous gap by introducing a novel method based on a feedback controller for the dynamics of muscle-driven systems. The approach has been developed for trajectory tracking of systems with redundancy muscle resolution. To illustrate the validation of the method, a shoulder model actuated by a group of eight linkages, eight muscles and three degrees of freedom was used. The controller objective is to move the arm from a static position to another one through muscular activation. The results on this paper show the achievement of the arm movement, musculoskeletal dynamics and muscle activations.