Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of Common Bean (Phaseolus vulgaris L.) Cultivars to Nitrogen Fertilizer Application at Nedjo, Western Ethiopia

Common bean (Phaseolus vulgaris L) is one of the most important pulse crops grown in Ethiopia. However, low soil fertility and poor soil fertility management constrains the productivity of the crop. Therefore, a field experiment was conducted to investigate the response of the crop to nitrogen (N) fertilizer at the research field of Nedjo ATVET College during the main cropping season of 2011. The treatments comprised four levels of nitrogen (0, 23, 46 and 69 kg N ha) and three bean cultivars (Dimtu, Beshbesh and Local Washkate). The experiment was laid out as a randomized complete block design in a factorial arrangement. The result of the study revealed that the main effects of N had significant (P < 0.05) influence on days to flowering, days to maturity, plant height, and hundred seed weight. Nitrogen and cultivar interaction significantly influenced most of the agronomic traits. Increasing the rates of nitrogen from 0 to 69 kg N ha -1 delayed days to flowering and days to maturity. The highest grain yield (4.58 t ha) was recorded for the Beshbesh cultivar at the rate of 69 kg N ha with the yield advantage of 75% over the local cultivar. Grain yield (r = 0.50) was positively and highly significantly correlated with hundred seed weight. Thus, it could be concluded that applying 69 kg N ha and cultivating the Beshbesh and Dimtu cultivar resulted in the highest grain yield of the crop. If Beshbesh is not available Dimtu should be used.
Exploring Education Factors Affecting the Character Traits of TAEKWONDO Trainees

Purpose: The study aims to clarify the educational myths that explored and explored the educational myths of the leader that affect the personalities of the trainees who practice taekwondo at the taekwondo stadium. Method: The study conducted an open survey of 116 taekwondo instructors' of taekwondo centers located in Seoul and Gyeonggi Province with the aim of exploring the taekwondo instructors's educational belief factors that affect the personality of taekwondo trainees. In this study, the data collected through open questionnaires to the leaders who guide Taekwondo trainees was conducted an inductive content analysis, an analysis process that categorizes raw materials into detailed areas according to the similarity of semantics and categorizes common topics in detailed areas into general areas. Results: An open questionnaire collected a total of 435 cases, and the collected data were obtained through inductive content analysis through expert meetings. As a result, the 435 cases of raw materials were classified into 15 detailed areas of manners, moral ethics, parent education, fighting spirit, self-confidence, attitude, communication, mindset, knowledge, education philosophy, humanity, interpersonal relationships, lifestyle, and community consciousness, and finally became four general categories of personal growth education, self-discipline, taekwondo instructors' quality and guidance on life learning emerged. The proportion of the general areas that were finally categorized was the highest in the education of personality growth(44.83%), followed by the fostering of self-worth(26.21%), the cultivation of taekwondo instructors' quality(19.54%), and the guidance of life learning(9.43%). Conclusion: The educational principles of a leader who influences the personality of Taekwondo trainees have been shown in four general areas: character growth education, self-value fostering, leadership qualities development, and life-learning guidance. [Keywords] Taekwondo, Prsonality, Trainees, Taekwondo Instructors, Educational Beliefs
An analysis of drought evolution characteristics based on standardized precipitation index: a case study in Southwest Guizhou Autonomous Prefecture, China

Drought is a worldwide concerned issue which causes huge losses in agriculture, economic and damages in natural ecosystems. The precise assessment of drought evolution characteristics is essential for agricultural water management and drought resistance, while such work is rarely reported. Thus, eight meteorological stations located within the Southwest Guizhou Autonomous Prefecture (SGAP) were selected, and the Standardized Precipitation Index (SPI) was used to assess the drought evolution characteristics. The results revealed that the drought occurrences number in Pu'an station was the largest (23 droughts), and the average drought duration in Xingren station was the longest (48.75 months). Moreover, the drought characteristics of the eight stations have account for the largest proportion under normal conditions, was more than 60%, the frequency of drought disaster occurring in Xingren is the highest (30.05%), followed by Wangmo (23.73%). The results of this study will provide theoretical guidance for drought resistance and agricultural production in Southwest Guizhou Autonomous Prefecture of China.
Super-early neurological rehabilitation nursing intervention for HICH patients can improve prognosis and promote neurological functional recovery

Super-early neurological rehabilitation nursing intervention for hypertensive intracerebral hemorrhage (HICH) patients can improve prognosis and promote neurological functional recovery. Altogether 175 patients with HICH from June 2017 to December 2018 were selected and grouped according to different nursing interventions. Routine nursing was given to 82 patients as the control group (CG) and super-early neurological rehabilitation nursing was given to 93 patients as the research group (RG). Adverse events that occurred in the nursing process of the CG and the RG were observed. Neurological impairment score (NIHSS score), activity of daily living (ADL) scale and Fugl-Meyer score were used to evaluate the neurological impairment, as well as ADL and motor function of the CG and the RG at 24 h, 1 month, 3 months and 6 months after operation. Mini Mental State Examination (MMSE), Glasgow Outcome Score (GOS) and SF-36 Quality of Life (QOL) Scale were applied to evaluate the cognitive function, prognosis and QOL of patients at 6 months after their operation. Finally, a self-made nursing satisfaction questionnaire was applied to explore the nursing satisfaction. The incidence of adverse events after nursing intervention in the RG was lower than that in the CG. The NIHSS score of the RG patients after nursing intervention was lower than that of the CG. The ADL score, Fugl-Meyer score, MMSE score, GOS score and SF-36 QOL score of patients in the RG after nursing intervention were higher than those in the CG. The satisfaction degree of patients in the RG was higher than that in the CG. Super-early neurological rehabilitation nursing for hypertensive intracerebral hemorrhage patients can improve the motor ability, daily activity ability, and the recovery of neurological function. It can also reduce the occurrence of adverse events, improve the patients’ nursing satisfaction, and improve the patients’ cognitive function, prognosis and quality of life.
The utilization of fruit and vegetable wastes for bioethanol production with the inoculation of indigenous yeasts consortium

Utama, G. L. , Sidabutar, F. E. E. , Felina, H. , Wira, D. W. , &. Balia, R. L. (2019). The utilization of fruit and vegetable wastes for bioethanol production with the inoculation of indigenous yeasts consortium. Bulgarian Journal of Agricultural Science, 25(2), 264–270 The aims of the study were to determine the utilization of fruit and vegetable wastes combination towards bioethanol production with the used of indigenous yeasts as starter. Banana, papaya and napa cabbage as dominates wastes were taken as materials from Central market around Bandung City. Candida krusei and Hanseniaspora guilliermondii were indigenous yeasts that isolated from wastes and determined as ethanol and glucose-tolerance isolates on preliminary research. The best ethanol fermenting activities of the isolates were tested into mixed wastes and resulted the consortium with ratio 1:1 of C. krusei and H. guilliermondii as the best starter with bioethanol production of 5.09% at 48 h fermentation. The isolates were grown in modifi ed nutrient broth/NB (Oxoid Ltd.) with 3% yeast extract/YE (Kraft Foods) and 10 ppm amoxicillin for 48 h at room temperatures (23-28°C). Various fruit and vegetable wastes had been set into treatment combination of banana (W1), papaya (W2), napa cabbage (W3). Water was added for 1.5 times of wastes volumes and then mashed with blender until homogeny. Every treatment was inoculated with 3% of C. krusei and H. guilliermondii (1:1) then incubated for 72 h at room temperatures (23-28°C). The results shown the best bioethanol production gained by W1 at 24 h incubation with the bioethanol contents of 7.38% and the production of bioethanol tend to decrease after 24 h incubation.
Financial innovation and economic growth in Cameroon

Article Type : Full Length Research Article This study assessed the impact of financial innovation on the economic growth prospects of Cameroon. The study adopted domestic credit to private sector (DCP), the ratio of broad money as a percentage of GDP (M2) and mobile banking (MB) penetration as the three proxies for financial innovation. Gross domestic product per capita growth (GDPPCG) is used as an indicator of economic growth. The autoregressive distributed lag (ARDL) model was used to test the degree of relationship that exists between financial innovation and economic growth in Cameroon. Bounds test of co-integration provided high F-values for the innovation proxies. Long run estimations showed positive correlations, however DCP and M2 had negative coefficients in the short run. Financial innovation contributes to economic growth in Cameroon in the long run.
Understanding and Exploring the Network with Stochastic Architectures

There is an emerging trend to train a network with stochastic architectures to enable various architectures to be plugged and played during inference. However, the existing investigation is highly entangled with neural architecture search (NAS), limiting its widespread use across scenarios. In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem. We first uncover the characteristics of NSA in various aspects ranging from training stability, convergence, predictive behaviour, to generalization capacity to unseen architectures. We identify various issues of the vanilla NSA, such as training/test disparity and function mode collapse, and further propose the solutions to these issues with theoretical and empirical insights. We believe that these results could also serve as good heuristics for NAS. Given these understandings, we further apply the NSA with our improvements into diverse scenarios to fully exploit its promise of inference-time architecture stochasticity, including model ensemble, uncertainty estimation and semi-supervised learning. Remarkable performance (e.g., 2.75% error rate and 0.0032 expected calibration error on CIFAR-10) validate the effectiveness of such a model, providing new perspectives of exploring the potential of the network with stochastic architectures, beyond NAS.
Controlling Propagation of Epidemics via Mean-Field Control

The coronavirus disease 2019 (COVID-19) pandemic is changing and impacting lives on a global scale. In this paper, we introduce a mean-field game model in controlling the propagation of epidemics on a spatial domain. The control variable, the spatial velocity, is first introduced for the classical disease models, such as the SIR model. For this proposed model, we provide fast numerical algorithms based on proximal primal-dual methods. Numerical experiments demonstrate that the proposed model illustrates how to separate infected patients in a spatial domain effectively.
Local Finite Element Approximation of Sobolev Differential Forms

We address fundamental aspects in the approximation theory of vector-valued finite element methods, using finite element exterior calculus as a unifying framework. We generalize the Clément interpolant and the Scott-Zhang interpolant to finite element differential forms, and we derive a broken Bramble-Hilbert lemma. Our interpolants require only minimal smoothness assumptions and respect partial boundary conditions. This permits us to state local error estimates in terms of the mesh size. Our theoretical results apply to curl-conforming and divergence-conforming finite element methods over simplicial triangulations.
The Potential of Machine Learning and NLP for Handling Students' Feedback (A Short Survey)

This article provides a review of the literature of students' feedback papers published in recent years employing data mining techniques. In particular, the focus is to highlight those papers which are using either machine learning or deep learning approaches. Student feedback assessment is a hot topic which has attracted a lot of attention in recent times. The importance has increased manyfold due to the recent pandemic outbreak which pushed many colleges and universities to shift teaching from on-campus physical classes to online via eLearning platforms and tools including massive open online courses (MOOCs). Assessing student feedback is even more important now. This short survey paper, therefore, highlights recent trends in the natural language processing domain on the topic of automatic student feedback assessment. It presents techniques commonly utilized in this domain and discusses some future research directions.
Self-supervised Learning of LiDAR Odometry for Robotic Applications

Reliable robot pose estimation is a key building block of many robot autonomy pipelines, with LiDAR localization being an active research domain. In this work, a versatile self-supervised LiDAR odometry estimation method is presented, in order to enable the efficient utilization of all available LiDAR data while maintaining real-time performance. The proposed approach selectively applies geometric losses during training, being cognizant of the amount of information that can be extracted from scan points. In addition, no labeled or ground-truth data is required, hence making the presented approach suitable for pose estimation in applications where accurate ground-truth is difficult to obtain. Furthermore, the presented network architecture is applicable to a wide range of environments and sensor modalities without requiring any network or loss function adjustments. The proposed approach is thoroughly tested for both indoor and outdoor real-world applications through a variety of experiments using legged, tracked and wheeled robots, demonstrating the suitability of learning-based LiDAR odometry for complex robotic applications.
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects

There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Motivated by such objects, we propose a novel problem setting, Exploratory Grasping, for efficiently discovering reliable grasps on an unknown polyhedral object via sequential grasping, releasing, and toppling. We formalize Exploratory Grasping as a Markov Decision Process, study the theoretical complexity of Exploratory Grasping in the context of reinforcement learning and present an efficient bandit-style algorithm, Bandits for Online Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the problem to efficiently discover high performing grasps for each object stable pose. BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn policies for objects in which they exhibit persistent failures. Simulation experiments suggest that BORGES can significantly outperform both general-purpose grasping pipelines and two other online learning algorithms and achieves performance within 5% of the optimal policy within 1000 and 8000 timesteps on average across 46 challenging objects from the Dex-Net adversarial and EGAD! object datasets, respectively. Initial physical experiments suggest that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with just 200 grasp attempts in the real world. See this https URL for supplementary material and videos.
Dense U-net for super-resolution with shuffle pooling layer

Single image super-resolution (SISR) in unconstrained environments is challenging because of various illuminations, occlusion and complex environments. Recent researches have achieved great progress on super-resolution due to the development of deep learning in the field of computer vision. In this letter, a Dense U-net with shuffle pooling method is proposed. First, a modified U-net with dense blocks, called dense U-net, is proposed for SISR. Second, a novel pooling strategy called shuffle pooling is designed, which is applied to the dense U-Net for super-resolution task. Third, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index (SSIM) and Mean Gradient Error (MGE), is proposed to solve the perception loss and high-frequency information loss. The proposed method achieves superior accuracy over previous state-of-the-arts on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online.
Forget-me-block: Exploring digital preservation strategies using Distributed Ledger Technology in the context of personal information management

Received wisdom portrays digital records as guaranteeing perpetuity; as the New York Times wrote a decade ago: "the web means the end of forgetting". The reality however is that digital records suffer similar risks of access loss as the analogue versions they replace. Often this risk is outsourced to specialised third parties. Common use cases include Personal Information Management (PIM): e.g. calendars, diaries, tasks, etc. Frequently these are outsourced at two removes - firstly by the individual to their employer (e.g. using a company system) and then by their employer to an external provider. So enters a new risk: organisational change; by the time the information is required the organisational chain that links user to data may be broken: the employer transitions to a different provider, the employee leaves the company, the IS provider pivots to new offerings. The advent of Distributed Ledger Technology (DLT) could help mitigate these risks; and has led to a re-evaluation of the relationship between data creation and ownership. Although DLT is an imprecise term, it typically involves data storage across organisationally separate entities in a cryptographically secure form; and therefore could present a partial solution to the risk. This project presents the first research that applies DLT to the field of PIM, furthering design science state of the art by a novel implementation of a calendar application on the Ethereum blockchain. It also extends current research in utilising DLT in digital preservation, namely by enacting a continuum approach within a DL that allows for transfer of ownership of digital objects as they transition from individual to collective relevance. Finally it provides guidelines for future use of DLT within digital preservation.
CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction

Inspired by the success of deep learning, recent industrial Click-Through Rate (CTR) prediction models have made the transition from traditional shallow approaches to deep approaches. Deep Neural Networks (DNNs) are known for its ability to learn non-linear interactions from raw feature automatically, however, the non-linear feature interaction is learned in an implicit manner. The non-linear interaction may be hard to capture and explicitly model the \textit{co-action} of raw feature is beneficial for CTR prediction. \textit{Co-action} refers to the collective effects of features toward final prediction. In this paper, we argue that current CTR models do not fully explore the potential of feature co-action. We conduct experiments and show that the effect of feature co-action is underestimated seriously. Motivated by our observation, we propose feature Co-Action Network (CAN) to explore the potential of feature co-action. The proposed model can efficiently and effectively capture the feature co-action, which improves the model performance while reduce the storage and computation consumption. Experiment results on public and industrial datasets show that CAN outperforms state-of-the-art CTR models by a large margin. Up to now, CAN has been deployed in the Alibaba display advertisement system, obtaining averaging 12\% improvement on CTR and 8\% on RPM.
Privacy Preservation in Federated Learning: Insights from the GDPR Perspective

Along with the blooming of AI and Machine Learning-based applications and services, data privacy and security have become a critical challenge. Conventionally, data is collected and aggregated in a data centre on which machine learning models are trained. This centralised approach has induced severe privacy risks to personal data leakage, misuse, and abuse. Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution. This is not only because of the difficulties in transferring and sharing data across data sources but also the challenges on complying with rigorous data protection regulations and complicated administrative procedures such as the EU General Data Protection Regulation (GDPR). In this respect, Federated learning (FL) emerges as a prospective solution that facilitates distributed collaborative learning without disclosing original training data whilst naturally complying with the GDPR. Recent research has demonstrated that retaining data and computation on-device in FL is not sufficient enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system still conceal sensitive information, which can be exploited in some privacy attacks. Therefore, FL systems shall be empowered by efficient privacy-preserving techniques to comply with the GDPR. This article is dedicated to surveying on the state-of-the-art privacy-preserving techniques which can be employed in FL in a systematic fashion, as well as how these techniques mitigate data security and privacy risks. Furthermore, we provide insights into the challenges along with prospective approaches following the GDPR regulatory guidelines that an FL system shall implement to comply with the GDPR.
Giant energy storage effect in nanolayer capacitors charged by the field emission tunneling

We fabricate nanolayer alumina capacitor and apply high electric fields, close to 1 GV m−1, to inject charges in the dielectric. Asymmetric charge distributions have been achieved due to the selectivity of the quantum tunneling process. Namely, the electrons near the Fermi level cannot tunnel intoregions near the cathode, where the total energy would be less than the potential energy. This mechanism exhibits a strong tendency to populate charge traps located near the anode, i.e. the regions where their potential energy is the lowest. Such spatially selective charging of the dielectric allows a permanent bulk charge storage in the dielectric layer, even if the capacitor plates are short-circuited, provided that the temperature is sufficiently low so that the conductivity of the dielectric is negligible. The stored charge can be recovered if the temperature is increased above ~250 K for the dielectric tested, i.e. Al2O3. In our experiments, the total charge stored in the dielectric was up to seven and a half times higher than the charge stored on the capacitor plates. Also, measurements of the breakdown voltage show that the breakdown electric field, i.e. the dielectric strength, is independent of the thickness of the dielectric.