Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Use Efficiency in Maize Production by Small-Scale Farmers in Two Districts of Mwanza Region, Tanzania

This study investigates the resource use efficiency in the production of maize by small-scale farmers in Mwanza region. Specifically, the study seeks to determine what the resources that farmers use in maize production and also seeks to determine the challenges that farmers face in maize production in the study area. This study was conducted in two districts of Mwanza region. In each of the district, the study sampled three wards and randomly selected twelve small-scale farmers from each of the three wards selected. In total 72 small-scale farmers were selected from whom the data was collected. A Cobb-Douglas production function was used to fit and analyse the data for its good fit. The R² was 0.77, indicating that 77 percent of the variation in dependent variable was explained by the independent variables of the model. The use of inputs in the production of maize was noted to be increasing returns to scale. The results indicated that variables land (X 1 ), labour (X 2 ), age (X 6 ), and experience (X 8 ) were all positive and significant at the 1% while soil preparation (X 4 ) variable was negative but significant at the 5%. The major constraints to maize production included lack of fertilizer (manure), finance (capital) and access to land. The study concluded that more exposure of small-scale farmers to frequent extension services is important to assist them to produce efficiently.
Multiple Stock Time Series Jointly Forecasting with Multi-Task Learning

Due to the strong connections among stocks, the information valuable for forecasting is not only included in individual stocks, but also included in the stocks related to them. These inter-correlations can provide invaluable information to be further leveraged to improve the overall forecasting performances. However, most previous works focus on the forecasting task of one single stock, which easily ignore the valuable information in others. Therefore, in this paper, we propose a jointly forecasting approach to process the time series of multiple related stocks simultaneously, using multi-task learning framework. In particular, this framework processes multiple forecasting tasks of different stocks simultaneously by sharing the information extracted based on latent inter-correlations. Meanwhile, each stock has their private encoding networks to keep their own information. Moreover, to dynamically balance private and shared information, we propose an attention based method, called Shared-private Attention, to optimally combine the shared and private information of stocks, which is inspired by the idea of Capital Asset Pricing Model (CAPM). Experimental results on the datasets of both stock and other domains demonstrate the proposed method can outperform other methods in forecasting performance.
Testing Artiﬁcial Intelligence System Towards Safety and Robustness: State of the Art

—With the increasing development of machine learning, conventional embedded systems cannot meet the requirement of current academic researches and industrial applications. Artiﬁcial Intelligence System (AIS) based on machine learning has been widely used in various safety-critical systems, such as machine vision, autonomous vehicles, collision avoidance system. Different from conventional embedded systems, AIS generates and updates control strategies through learning algorithms which make the control behaviors nondeterministic and bring about the test oracle problem in AIS testing procedure. There have been various testing approaches for AIS to guarantee the safety and robustness. However, few researches explain how to conduct AIS testing with a complete workﬂow systematically. This paper provides a comprehensive survey of existing testing techniques to detect the erroneous behaviors of AIS, and sums up the involved key steps and testing components in terms of test coverage criterion, test data generation, testing approach and common dataset. This literature review aims at organizing a standardized workﬂow and leading to a practicable insight and research trend towards AIS testing.
COVIDSeer : Filling missing pieces in the CORD-19 dataset

We develop an enhanced version of CORD-19 dataset released by the Allen Institute for AI. Our tools in our SeerSuite project are used to exploit information in original articles but not directly provided in the CORD-19 datasets. We add 728 new abstracts, 70,102 ﬁgures and 31,446 tables with captions that are not provided in the current data release. We also built a vertical search engine COVIDSeer based on the new dataset we created. COVIDSeer is a relatively simple architecture with features like keyword ﬁltering, and similar paper recommendation. The goal was to provide a sys-tem and dataset that can help scientists better navigate through the literature around COVID-19. The enriched dataset serves as a supplement to the existing dataset. The search engine, which offers keyphrase-enhanced search, will hopefully help biomedical and life science researchers, medical students, and the general public to explore coronavirus-related literature more effectively. The entire data set and sys-tem will be made open source.
Role Adjustment in Southeast Asian Refugee Families

Little is known about the relationship between family history and family identity. One way to initially explore whether or how families incorporate their history into the development of their identity would be to talk to members of families who have had a discrete event in their history. Refugee families are such families. Seventeen members from ten refugee families who fled Cambodia or Vietnam and resettled in the United States between 1975 and 1990, were interviewed about their perceptions of how their experience affected ·their family identity. Transcripts were qualitatively analyzed. The findings presented here are limited to descriptions of adjustment that occurred in family roles.
Generation Analysis of Blockchain Technology: Bitcoin and Ethereum

: In this paper, the importance of blockchain technology have been discussed and the generations of blockchain (Bitcoin and Ethereum) have been compared provided different aspects. The blockchain is a technology which allows direct transaction without involving third party. Also, it offers many facilities like high translucency, high safety and security, improved trace-ability, greater proficient and transactions’ speed, and reduced costs. Moreover, the cryptocurrencies provide advance security level. The basic purpose of this study is to highlight different aspects of Blockchain, Bitcoin and Ethereum and to show which cryptocurrency is better approach. The research contributes to show the impact of this technology in different fields and a comparison of bitcoin and ethereum is presented to analyze and furnish a decision regarding the best among them
cpSSR: a New Tool to Analyze Chloroplast Genome of Citrus Somatic Hybrids

Chloroplast simple sequence repeat (cpSSR) markers in Citrus were developed and successfully used to analyze chloroplast genome inheritance of Citrus somatic hybrids. Twenty-two previously reported cpSSR primer pairs from pine (Pinus thunbergii Part), rice (Oryza sativa L.) and tobacco (Nicotiana tabacum L.) were tested in Citrus, nine of which could amplify intensive PCR products by agarose gel electrophoresis. Chloroplast genome inheritance of Citrus somatic hybrids from nine fusions was then analyzed, and five of the nine pre-screened primer pairs showed polymorphisms by polyacrylamide gel electrophoresis. The results revealed the random inheritance nature of chloroplast genome in all analyzed Citrus somatic hybrids, which was in agreement with previous reports based on RFLP or CAPS analyses. It was also shown that cpSSR is a more efficient tool in chloroplast genome analyses of somatic hybrids in higher plants, compared with the conventional RFLP or CAPS analyses.
ATTENTION-BASED WAVENET-CTC FOR TIBETAN MULTI-DIALECT MULTITASK SPEECH RECOGNITION

—To date, Tibetan has very limited resources for conventional automatic speech recognition. It lacks of enough data, sub-word units, lexicons, and word inventories for some dialects. In this paper, we present an end-to-end model, attention-based WaveNet-CTC, to perform simultaneous Tibetan speech content recognition and dialect identification. This model avoids processing the pronunciation dictionary and word segmentation for new dialects while allowing for training of multi-dialect speech content recognition and dialect identification in a single model. We introduced the attention mechanism into WaveNet to capture the context information and relations of discontinuous speech frames among different dialects and tasks. And the dialect information is used in the output for multitask learning. The experimental results show that attention-based WaveNet-CTC has better performance compared with WaveNet-CTC and task-specific models for the multi-dialect multitask speech recognition.
International Journal of Social Sciences and Management Review

In the situation of the COVID-19 pandemic which lasted for approximately 1.5 years, it has slightly changed the activity or order in life in society, one of which is in the world of shopping. This change in activity can be seen through the increasing use of e-commerce in shopping to meet daily needs. Therefore, companies must compete with each other to attract consumers' hearts so that the services provided can continue to be used. Efforts to use ecommerce by the community have triggered an increase in people's buying interest online, not least what happened at Tokopedia. This increase in people's buying interest can also be seen by the increase in public visits to the company's website in the e-commerce industry and the increase in sales which is also directly proportional to the increase in purchases. The purpose of this study was to determine the effect of the quality of information available on the Tokopedia platform, social psychological distance, and consumer trust on the purchase intention of Tokopedia consumers. In addition, this study also aims to examine the effect of the moderating role of the sense of power that Tokopedia consumers have in providing information to foster a sense of trust among other consumers. The phenomena that occurred in this study were analyzed using quantitative methods. Researchers do not have the authority to intervene in the data with individual units of analysis and the time the study was carried out in a cross-section. The population chosen to be respondents in this study is the people of the city of Nusa Tenggara Barat. In order to obtain data, the author will distribute a questionnaire in the form of a google form to 450 respondents. The data was obtained in the form of an ordinal scale (Likert), processed, and analyzed using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique through the SmartPLS 3 software. The results of the study show that the information quality variable has a positive effect on social psychological distance, information quality (IQ) has a positive effect on trust, social psychological distance has a positive effect on trust, social psychological distance has a positive effect on mediating the relationship between information quality and trust, trust has a positive effect on the purchase. intention and sense of power have a positive effect on moderating the relationship between information quality and trust. International Journal of Social Sciences and Management Review Volume: 05, Issue: 04 “July August 2022” ISSN 2582-0176 www.ijssmr. org Copyright © IJSSMR 2022, All right reserved Page 106 The author hopes that through this research Tokopedia can pay attention to the quality of the information in its application to increase user confidence in using Tokopedia. In addition, Tokopedia can choose content creators who are able to be close to the community so that more people use the application.
The effectiveness of a chart audit and feedback intervention to improve cardiology provider delivery of heart failure self-care education

Heart failure is a challenging disease process that presents a high risk of mortality, frequent hospitalizations, and an overwhelming economic burden to the health system. Patient self-care education has been shown to decrease unplanned hospitalizations, emergency room visits, hospitalized days, costs, and mortality rates. However, only 61% of adults with HF receive self-care education within a 12-month period. Under the framework of the Iowa Model for Evidence-Based Practice, the purpose of this project was to implement an electronic health record audit and written and verbal provider feedback intervention with the aim of improving provider delivery of heart failure self-care education to adults with heart failure with reduced ejection fraction. The objectives of this project were to: 1) Improve cardiology provider performance of heart failure self-care education delivery to adult patients with heart failure and 2) Cardiology providers will report satisfaction with the chart audit and performance feedback process. Data analysis revealed an improvement of provider delivery of heart failure selfcare education in all heart failure self-care education elements. This project provided the opportunity to design, develop, and evaluate an evidence-based initiative aimed at improving health care quality. Furthermore, this project has provided a foundation for future clinical initiatives aimed at improving provider delivery of heart failure self-care education. iii IMPROVE HF SELF-CARE EDUCATION DELIVERY
Data-Driven Space-Filling Curves

Abstract-We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.
Corrective feedback, emphatic speech synthesis, visual-speech exaggeration, pronunciation learning

To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT). The speech exaggeration is realized by an emphatic speech generation neural network based on Tacotron, while the visual exaggeration is accomplished by ADC Viseme Blending, namely increasing Amplitude of movement, extending the phone's Duration and enhancing the color Contrast. User studies show that exaggerated feedback outperforms non-exaggerated version on helping learners with pronunciation identification and pronunciation improvement.
Fairness Constraints in Semi-supervised Learning

Fairness in machine learning has received considerable attention. However, most studies on fair learning focus on either supervised learning or unsupervised learning. Very few consider semi-supervised settings. Yet, in reality, most machine learning tasks rely on large datasets that contain both labeled and unlabeled data. One of key issues with fair learning is the balance between fairness and accuracy. Previous studies arguing that increasing the size of the training set can have a better trade-off. We believe that increasing the training set with unlabeled data may achieve the similar result. Hence, we develop a framework for fair semi-supervised learning, which is formulated as an optimization problem. This includes classifier loss to optimize accuracy, label propagation loss to optimize unlabled data prediction, and fairness constraints over labeled and unlabeled data to optimize the fairness level. The framework is conducted in logistic regression and support vector machines under the fairness metrics of disparate impact and disparate mistreatment. We theoretically analyze the source of discrimination in semi-supervised learning via bias, variance and noise decomposition. Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
Efficient strain-induced light emission in lonsdaleite germanium

Lonsdaleite germanium has a direct band gap, but it is not an efficient light emitter due to the vanishing oscillator strength of electronic transitions at the fundamental gap. Transitions involving the second lowest conduction band are instead at least three orders of magnitude stronger. The inversion of the two lowest conduction bands would therefore make hexagonal germanium ideal for optoelectronic applications. In this work, we investigate the possibility to achieve this band inversion by applying strain. To this end we perform ab initio calculations of the electronic band structure and optical properties of strained hexagonal germanium, using density functional theory with the modified Becke-Johnson exchange-correlation functional and including spin-orbit interaction. We consider hydrostatic pressure, uniaxial strain along the hexagonal c axis, as well as biaxial strain in planes perpendicular to and containing the hexagonal c axis to simulate the effect of a substrate. We find that the conduction-band inversion, and therefore the transition from a pseudo-direct to a direct band gap, is attainable for moderate tensile uniaxial strain parallel to the lonsdaleite c axis.
Optical Study of PKS B1322-110, the Intra-hour Variable Radio Source

Observations with the Australia Telescope Compact Array revealed intrahour variations in the radio source PKS B1322-110. As part of an optical follow-up, we obtained Gemini Hα and Hα continuum (HαC) images of the PKS B1322-110 field. A robust 19σ detection of PKS B1322-110 in the Hα−HαC image prompted us to obtain the first optical spectrum of PKS B1322-110. With the Gemini spectrum we determine that PKS B1322-110 is a flat-spectrum radio quasar at a redshift of z = 3.007 ± 0.002. The apparent flux detected in the Hα filter is likely to originate from He ii emission redshifted precisely on the Galactic Hα narrowband filter. We set upper limits on the emission measure of the Galactic plasma, for various possible cloud geometries.
Monitoring L\'evy-Process Crossovers

The crossover among two or more types of diffusive processes represents a vibrant theme in nonequilibrium statistical physics. In this work we propose two models to generate crossovers among different Levy processes: in the first model we change gradually the order of the derivative in the Laplacian term of the diffusion equation, whereas in the second one we consider a combination of fractional-derivative diffusive terms characterized by coefficients that change in time. The proposals are illustrated by considering semi-analytical (i.e., analytical together with numerical) procedures to follow the time-dependent solutions. We find changes between two different regimes and it is shown that, far from the crossover regime, both models yield qualitatively similar results, although these changes may occur in different forms for the two models. The models introduced herein are expected to be useful for describing crossovers among distinct diffusive regimes that occur frequently in complex systems.
Answering Multi-Dimensional Range Queries under Local Differential Privacy

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlations among attributes, avoiding the curse of dimensionality, and dealing with the large domains of attributes. None of the existing approaches satisfactorily deals with all three challenges. Overcoming these three challenges, we first propose an approach called Two-Dimensional Grids (TDG). Its main idea is to carefully use binning to partition the two-dimensional (2-D) domains of all attribute pairs into 2-D grids that can answer all 2-D range queries and then estimate the answer of a higher dimensional range query from the answers of the associated 2-D range queries. However, in order to reduce errors due to noises, coarse granularities are needed for each attribute in 2-D grids, losing fine-grained distribution information for individual attributes. To correct this deficiency, we further propose Hybrid-Dimensional Grids (HDG), which also introduces 1-D grids to capture finer-grained information on distribution of each individual attribute and combines information from 1-D and 2-D grids to answer range queries. To make HDG consistently effective, we provide a guideline for properly choosing granularities of grids based on an analysis of how different sources of errors are impacted by these choices. Extensive experiments conducted on real and synthetic datasets show that HDG can give a significant improvement over the existing approaches.
A Hybrid Framework for Topology Identification of Distribution Grid With Renewables Integration

Topology identification (TI) is a key task for state estimation (SE) in distribution grids, especially the one with high-penetration renewables. The uncertainties, initiated by the time-series behavior of renewables, will <italic>almost certainly</italic> lead to bad TI results <italic>without a proper treatment</italic>. These uncertainties are <italic>analytically intractable</italic> under conventional framework—they are usually <italic>jointly spatial-temporal dependent</italic>, and hence cannot be simply treated as white noise. For this purpose, a hybrid framework is suggested in this paper to handle these uncertainties in a <italic>systematic and theoretical</italic> way; in particular, big data analytics are studied <italic>to harness the jointly spatial-temporal statistical properties</italic> of those uncertainties. With some prior knowledge, a model bank is built first to store the <italic>countable</italic> typical models of network configurations; therefore, the difference between the SE outputs of each bank model and our observation <italic>is capable of being defined as a matrix variate</italic>—the so-called <italic>random matrix</italic>. In order to gain insight into the random matrix, a <italic>well-designed metric space</italic> is needed. Auto-regression (AR) model, factor analysis (FA), and random matrix theory (RMT) are tied together for the metric space design, followed by <italic>jointly temporal-spatial analysis</italic> of those matrices which is conducted <italic>in a high-dimensional (vector) space</italic>. Under the proposed framework, some <italic>big data analytics</italic> and <italic>theoretical results</italic> are obtained to improve the TI performance. Our framework is validated using IEEE standard distribution network with some field data in practice.