Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of smallholder farmers’ welfare through participation in on-farm regional projects in East Africa

ABSTRACT This paper uses panel data from 1,160 smallholder farmers, especially participants and non-participants in twenty-three regional projects from five countries in East Africa – Burundi, Kenya, Rwanda, Tanzania and Uganda. In this paper, regional projects mean projects implemented jointly in at least three countries, thereby providing sustainable regional public goods. Propensity score matching analysis is used to determine the difference in net benefits accrued to the on-farm participants compared to non-participants. The propensity scores show that participants have overall higher crop and livestock productivity, enhanced household income, increased soil and water management, and access to biofortified foods compared to non-participants. These findings indicate that regional projects can catalyse the achievement of smallholder farmers’ food and nutrition security, besides enhancing achievements of the African Union Commission's Comprehensive Africa Agriculture Development Programme (CAADP) and Sustainable Development Goals (SDGs).
Evaluating security products: formal model and requirements of a new approach

In a previous paper, we presented a new method to generate evaluation data for the evaluation of security products and services. That approach tackles the issues of producing a workload with a rich semantic at a large scale. Testbed environments are the most appropriate tool for such task but induce a lot of effort and costs to implement. We presented a model to produce semantic data that can be implemented on light virtual networks and thus deployed at a large scale. This paper is an extension of our complete formal model. In this extension, we identify additional requirements for our model and define our ambitions. We translate those ambitions in verifiable properties of our model. Our prototype, although currently limited, provides the basis for an evaluation method that is customizable, reproducible, realistic, accurate and scalable. We generate realistic activity for up to 250 simulated users interacting with a real-world webmail server in an experiment to verify the properties of our mode
Women’s education and work in China : the menstrual cycle and the power of water

This study investigates the joint impact of menstrual cycle and poor access to water on women’s education and labour market outcomes. The research context is chosen to be rural China. Two parallel hypotheses that are tested in this study are as follows: (1) Girls have less probability of school enrolment and shorter schooling duration due to the joint impact of poor access to water and menarche presumably because that poor access to water may raise time/health/psychic costs of school enrolment for girls post-menarche. (2) Women have less probability of participating in work for wages due to the joint impact of poor access to water and menstrual cycle presumably because that poor access to water may generate lower productivity and raise time/health/psychic costs of wage work participation for women pre-menopause. For testing, the researcher uses the data from rural villages in the China Health and Nutrition Survey. This study conducts two sets of empirical tests on each of the above hypotheses using regression models and propensity score matching estimators. It is found that the joint impact of poor access to water and menstrual cycle is indeed largely adverse on women’s education and wage work participation. When the impacts of other confounding factors such as poverty and backward geographical location are controlled for, access to poor water is found to decrease the probability of school enrolment of post-menarche girls by 20 – 25 percentage points, and the probability of wage work participation of women premenopause by about 10 percentage points. This study concludes that a major benefit of policies to improve water supplies may not be the obvious household or industrial benefit, but rather an unseen benefit, the improvement in the position of women
Long Island Sound temperature variability and its associations with the ridge–trough dipole and tropical modes of sea surface temperature variability

Abstract. Possible mechanisms behind the longevity of intense Long Island Sound (LIS) water temperature events are examined using an event-based approach. By decomposing an LIS surface water temperature time series into negative and positive events, it is revealed that the most intense LIS water temperature event in the 1979–2013 period occurred around 2012, coinciding with the 2012 ocean heat wave across the Mid-Atlantic Bight. The LIS events are related to a ridge–trough dipole pattern whose strength and evolution can be determined using a dipole index. The dipole index was shown to be strongly correlated with LIS water temperature anomalies, explaining close to 64 % of cool-season LIS water temperature variability. Consistently, a major dipole pattern event coincided with the intense 2012 LIS warm event. A composite analysis revealed that long-lived intense LIS water temperature events are associated with tropical sea surface temperature (SST) patterns. The onset and mature phases of LIS cold events were shown to coincide with central Pacific El Niño events, whereas the termination of LIS cold events was shown to possibly coincide with canonical El Niño events or El Niño events that are a mixture of eastern and central Pacific El Niño flavors. The mature phase of LIS warm events was shown to be associated with negative SST anomalies across the central equatorial Pacific, though the results were not found to be robust. The dipole pattern was also shown to be related to tropical SST patterns, and fluctuations in central Pacific SST anomalies were shown to evolve coherently with the dipole pattern and the strongly related East Pacific–North Pacific pattern on decadal timescales. The results from this study have important implications for seasonal and decadal prediction of the LIS thermal system.
Bayesian HMM Based x-Vector Clustering for Speaker Diarization

This paper presents a simpliﬁed version of the previously proposed diarization algorithm based on Bayesian Hidden Markov Models, which uses Variational Bayesian inference for very fast and robust clustering of x-vector (neural network based speaker embeddings). The presented results show that this clustering algorithm provides signiﬁcant improvements in diarization performance as compared to the previously used Agglomerative Hierarchical Clustering. The output of this system can be further employed as an initialization for a second stage VB diarization system, using frame-wise MFCC features as input, to obtain optimal results.
How does Caste Affect Entrepreneurship? Birth vs Worth

This paper examines the relative importance of the caste system in explaining the resource misallocation in India and quantifies its impact on aggregate productivity. I document that the historically disadvantaged castes (LC) are less likely to enter entrepreneurship even though they are more productive on average. At the intensive margin, the LC entrepreneurs are less capital-intensive but have higher marginal revenue product of capital relative to high castes. In a quantitative model of entrepreneurship, I find that the LC face higher entry cost and stricter financial constraints and that such asymmetries reduce aggregate TFP by 2.54% and output by 6%.
Geospatial object detection using deep networks

In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to be applied for satellite imagery. In this study, aircraft, building, and ship detection in 4-band remote sensing images by using convolutional neural networks based on popular YOLO network is examined and the accuracy comparison between 4-band and 3-band images are tested. Based on simulation results, it can be concluded that state-of-the-art object detectors can be utilized for geospatial objection detection purposes.
Early Predictors of Early Freshman Year Attrition in Female Hispanic Students

The Texas Hispanic population is projected to grow to 18.8 million, almost tripling its number within the state, in only 30 years. This rapid growth is a concern for Texas higher education because this group has traditionally been under-represented in colleges and universities. Also, according to national, state, and local data, Hispanic students are retained at a lower rate than are other ethnic groups. Because of lower retention rates for Hispanic students and because the majority of Hispanic college students are female, an increasing number of Hispanic women are heads of households. Studying the attrition rates of Hispanic females could provide a better understanding of how the state can improve both the participation and retention rates of this population. This study utilized descriptive statistics and regression analysis to identify the correlations between and among the dependent variable of attrition and independent variables derived from (1) pre-college survey responses measuring college expectations and (2) early-first semester survey responses measuring actual college experience. Institutional data were used to confirm enrollment status at the beginning of the second semester. The sample of the study was all female, full-time, first-time-in college student survey respondents attending a public 4-year institution in Texas. This number included Hispanic females (n = 176), Caucasian females (n = 278), and African American females (n = 209). Although not a focus of the study, Caucasian and African American females were included to enhance the understanding of Hispanic females’ responses. The dependent variable of attrition in college attendance for Hispanic females correlated negatively with each two independent variables: (1) joining one or more campus organizations (r = -.252, p = 0.045) and (2) campus social life providing many opportunities for participation (r = -.272, p = 0.030). The dependent variable correlated positively with one independent variable, satisfaction with academic progress at the end of the freshman year (r = .301, p = 0.016). To have a positive impact on the attrition rates of Hispanic females, educators at Texas institutions of higher education must better understand Hispanic females’ college expectations.
Energy Separation-Based Instantaneous Frequency Estimation for Cochlear Cepstral Feature for Replay Spoof Detection

Replay attack poses significant threat to Automatic Speaker Verification (ASV) system among various spoofing attacks, as it is easily accessible by low cost and high quality recording and playback devices. This paper presents a novel feature set, i.e., Cochlear Filter Cepstral Coefficient Instantaneous Frequency using Energy Separation Algorithm (CFCCIF-ESA) to develop countermeasure against replay spoofing attacks. Experimental results on ASVspoof 2017 Version 2.0 database reveal that the proposed CFCCIF-ESA performs better than the earlier proposed CFCCIF (using analytic signal generation via Hilbert transform) feature set. This is because ESA uses extremely short window to estimate instantaneous frequency being able to adapt during speech transitions across phonemes. Experiments are performed using Gaussian Mixture Model (GMM) as a classifier. Baseline Constant Q Cepstral Coefficient (CQCC) performs slightly better than CFCCIF-ESA on development set (i.e., 12.47 % and 12.98 % Equal Error Rate (EER) for CQCC and CFCCIF-ESA, respectively). However, contrasting results on evaluation set (i.e., 18.81 % and 14.77 % EER for CQCC and CFCCIF-ESA, respectively) indicates that the proposed CFCCIF-ESA gives relatively better performance for unseen attacks in evaluation data. Also, the proposed feature set gives an EER of 11.56 % and 13.26 % on development and evaluation dataset when fused with state-of-the-art Mel Frequency Cepstral Coefficient (MFCC).
A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions

The importance of the healthcare industry, benefiting from the synergies between sciences, adds to the necessity of discovering knowledge, which is achievable with big data analytics tools. The purpose of this article is to examine the challenges and provide solutions for using big data in the healthcare industry. The methods of this article are derived from PRISMA guidelines and its models. A variety of databases and search engines including PubMed, Scopus, Elsevier, IEEE, Springer, Web of Science, Proquest, and Google Scholar were searched according to credible keywords. The results of the present study showed that the problems associated with the use of big data in the healthcare industry could be classified in four groups including "data gathering, storage and integration", "data analysis", "knowledge discovery and information interpretation", and "infrastructure". Although the results point to a high frequency of challenges in the "data gathering, storage and integration" group, the greatest weight of problems, due to their importance, appears to be visible in the "infrastructure" group. Considering the numerous benefits of using big data, it is imperative to identify the challenges and resolve them accurately. It is expected that all the barriers can be removed soon. Big data analytics tools will be able to offer the best possible strategies based on human individual and social conditions in the context of artificial intelligence methods.
Enhancing diversity via affirmative action: A case study of the Nelson Mandela Metropolitan Municipality

Background: This article examines diversity within the context of affirmation action, with specific reference to the Nelson Mandela Metropolitan Municipality. The Employment Equity Act, 1998 (Act 55 of 1998), is a policy instrument through which affirmation action and organisational diversity are promoted in post-apartheid South Africa to increase the economic participation of designated groups.Aim: Against this background, the aim of this article is to investigate the influence of the Employment Equity Act, 1998, on diversity management in the Nelson Mandela Bay Municipality.Setting: The guiding research question of this article is: what is the effective way of implementing affirmative action measures through Employment Equity Act, 1998 (Act 55 of 1998), in particular in promoting diversity in the Nelson Mandela Metropolitan Municipality? Methods: A qualitative approach is employed to explore and describe the complexities at play in this article.Results: The findings of this article suggest that the goal and vision of diversity management should consistently be communicated in clear terms to all affected, especially the employees as they are most affected by it.Conclusion: This article contributes to the scholarship of Public Administration and Management surrounding the ongoing debate in South Africa with regard to transformation, organisation diversity and policy implementation of affirmative action. The central thesis of this article is that adequate policy execution of diversity and employment equity will improve service delivery alongside customer satisfaction.
Ensemble Classification Model for Diabetes Prediction in Data Mining

The prediction analysis is the approach which can predict the future possibilities based on the current information. The diabetes prediction is the approach which is applied to predict the diabetes based on the various attributes. The diabetes dataset has various attributes and based on that attributes diabetes can be predicted. In the previous years approach of SVM is applied for the diabetes prediction. To improve accuracy of diabetes prediction voting based classification is applied in this paper. The proposed model is implemented in python and results are analyzed in terms of accuracy, execution time
UAV Photogrammetry-Based 3D Road Distress Detection

The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.
Development of User-Integrated Semi-Autonomous Lawn Mowing Systems: A Systems Engineering Perspective and Proposed Architecture

This concept paper outlines a conceptual framework for the design and development of user-integrated semi-autonomous lawn mowing systems. This is approached from a systems engineering perspective, considering both hardware and software elements and integration with the user. This is an important and emerging field of study within the several engineering communities, including robotics, agricultural engineering, smart city development, and general systems engineering. In several sections of this paper, a review of current progress on this problem is presented (both in general and related to specific aspects of the system), followed by a discussion of the problem from a systems engineering perspective, a general system architecture developed by the authors, a preliminary set of design requirements, and a discussion of some practical implementation strategies. This work is meant to provide a baseline and motivation for the further development and refinement of these systems within the agricultural engineering and robotics communities and is relevant to both academic and commercial research.
Deep Attention Gated Dilated Temporal Convolutional Networks with Intra-Parallel Convolutional Modules for End-to-End Monaural Speech Separation

Monaural speech separation techniques are far from satisfactory and are a challenging task due to interference from multiple sources. Recently the deep dilated temporal convolutional networks (TCN) has proven to be very effective in sequence modeling. This work explores how to extend TCN to result a new, state-of-the-art monaural speech separation method. First, a new gating mechanism is introduced and added to generate a gated TCN. The gated activation controls the flow of information. Further in order to combine multiple training models to reduce the performance variance and improve the effect of speech separation, we propose to use the principle of ensemble learning in the gated TCN architecture by replacing the convolutional modules corresponding to each dilated factor with multiple identical branches of the convolutional components. For the sake of objectivity, we propose to train the network by directly optimizing in a permutation invariant training (PIT) style of the utterance level signal-to-distortion ratio (SDR). Our experiments with the public WSJ0-2mix data corpus resulted in an 18.2 dB improvement in SDR, indicating that our proposed network can improve the performance of speaker separation tasks.
GETTING TO THE OTHER SIDE: AN EXPLORATION OF THE HEAD AND NECK CANCER TREATMENT EXPERIENCE

OF DISSERTATION GETTING TO THE OTHER SIDE: AN EXPLORATION OF THE HEAD AND NECK CANCER TREATMENT EXPERIENCE Diagnosis of head and neck squamous cell carcinoma (HNSCC) presents a multifarious problem. Late stage diagnosis, uncertainty regarding appropriate clinical treatment, as well as the high potential for disfigurement and functional loss resulting in diminished quality of life, contributes to anxiety, stress, fear, and uncertainty throughout the cancer treatment experience. This qualitative study sought to explore the cancer treatment experience of adults with newly diagnosed HNSCC, including laryngeal, esophageal, and oral cancers. Study participants were recruited from the University of Kentucky Ear Nose and Throat Clinic in Lexington KY. Participants agreed to be interviewed after receipt of their cancer diagnosis and again after completion of their cancer treatment. Socio-emotional Selectivity Theory, and Leventhal’s Self-Regulation Model provided the theoretical foundation for exploring the ongoing emotional, psychological, and physical aspects of the cancer experience while also recognizing the role of age and time perception. Forty-one patients completed two in depth semistructured interviews. Transcripts were coded for key themes. Findings indicated that HNSCC in older patients is often preceded by lifelong alcohol, tobacco, and substance use. Despite frequent interaction with health and substance abuse treatment professionals, very few patients had prior knowledge of HNSCC risk or had been screened for these cancers. Experience with addiction treatment programs and perceptions of time seem to influence cancer treatment experience. The following themes were identified: (1) dynamic time perspectives including taking time, making time, junk time and time out; (2) recovery vs. cure from disease; (3) the role of reconciliation, hope, self-inventory, reflection, and spirituality in navigating the cancer experience; (4) the role of healing vs. cure; and (5) patient's moving forward to a life after cancer. Findings from this investigation suggest that patients with a history of lifelong substance use could benefit from earlier detection and improved awareness and knowledge of HNSCC risk. Findings can be applied to improve access to cancer screening through addiction and cessation programs, reduce lags in diagnosis, improve prognosis and contribute to the development of clinical tools. Additionally, the intersection of advancing chronological age, comorbidity, and perception of time warrants further investigation.
Microwave-assisted direct transformation of lignocellulose into methyl glycopyranoside in ionic liquid

Abstract Recently, conversion of lignocellulose into useful substances has attracted increasing attention. In our previous investigations, microcrystalline cellulose was successfully converted to methyl glucopyranosides (MeGlc) by the combined use of ionic liquid (IL) and microwave irradiation under moderate reaction conditions. In this study, lignocelluloses, including softwood, hardwood, and rice straw, were directly converted to methyl glycopyranosides (MG), including MeGlc, methyl mannopyranosides (MeMan), and methyl xylopyranosides (MeXyl) using acid-catalyzed methanolysis under microwave irradiation in ILs. Lignocellulose ball-milling was quite effective as a crucial process of increasing the yield of MG. Under the optimized reaction conditions, the molar yield of MeGlc reached 40% from softwood, which was a comparable yield from microcrystalline cellulose. MeXyl was also obtained in a 48% yield. These results showed that the combination of the dissolution of ball-milled lignocellulose in IL and the microwave-assisted methanolysis was an effective method of converting lignocellulose into a high-value-added substance.