Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Networks for Improving Imbalanced Classification Performance

The advent of deep learning has resulted in extremely powerful classification models which have led to significant progress in many domains of computer vision such as object recognition and image classification. However, most existing classifiers assume the underlying training dataset to be evenly distributed. In real-life datasets, it is often observed that some of the classes have far less quantity of data samples than the others. An example of this could be a dataset for animal detection collected from a wildlife sanctuary where very few animals belonging to ‘ endangered species ’ class were present. Due to the underrepresentation of the minority classes, the data samples belonging to them often pass as noise or outliers, or are ignored, ultimately resulting in their misclassification. More often than not, the correct detection of minority class instances is of the utmost importance. Therefore, we study the detrimental effects of class imbalance on the classification performance of the Hybrid CNN-SVM architecture, and aim to introduce measures to overcome this issue. We artificially introduce class imbalance in two benchmark datasets, FMNIST and CIFAR-10, and observe that the classification Accuracy and F1-Score both drop by an average of 7%, when compared to the performance on the original dataset. To combat this problem, we use data augmentation strategies to re-balance the datasets. We first explore the traditional data augmentation practices of applying geometric and photo-metric transformations on the existing images of minority classes, such as image rotation, scaling, zooming, blurring, whitening, shearing, etc. Then, we propose the use of a modified architecture of Generative Adversarial Networks (GAN), called Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate new data samples. Since their introduction in 2014, GANs have shown immense potential in mimicking data distribution and generating realistic images using low amounts of training data. Training the classifier on the datasets augmented using WGAN-GP, we observe an average increase of 4% in the classification Accuracy and F1-Score for FMNIST dataset, and this is even
Effects of Financial Liberalization on the Productivity Growth of Agriculture Sector in the Presence of Structural Breaks: Evidence from Ghana

The study examined the relationship between financial liberalization and productivity growth of the Agriculture Sector in Ghana using the annual (yearly) data over the period 1970-2013. In the econometric analysis, the credits provided to private sector, investment, trade liberalization and capital account openness are considered as financial liberalization index while sector level value added as a percentage of GDP represented productivity growth. The stationarity of the series and the long run relationship were analyzed using Zivot-Andrews (1992) and Clemente, Montanes and Reyes (1998) test and Gregory Hansen tests in which structural breaks are considered. The findings of the study revealed that, opening up the economy will yield a positive result of sustainable productivity growth at sector levels. It behooves on the government to ensure that any structural reform programs that are initiated is comprehensively and completely implemented and also accompanied by sound macroeconomic policies to maintained a lasting effect, because the effect of such structural reforms in the long run growth path are prone to be thrown out of gear by other external shocks. The favorable influence of financial liberalization on productivity growth of agriculture sector is confirmed in Ghana. Future studies could be focused on whether financial liberalization will yield the expected effect on agriculture and other economic sectors’ productivity growth using primary data from the various sectors of the economy in a survey study.
Counting solutions of equations over two-element algebras

Solving equations is one of the most important problems in computer science. Apart from the problem of existence of solutions of equations we may consider the problem of a number of solutions of equations. Such a problem is much more difficult than the decision one. This paper presents a complete classification of the complexity of the problem of counting solutions of equations over any fixed two-element algebra. It is shown that the complexity of such problems depends only on the clone of term operations of the algebra and for any fixed two-element algebra such a problem is either in FP or #Pcomplete.
Adaptive Sequential Experiments with Unknown Information Arrival Processes

Problem definition: Sequential experiments that are deployed in a broad range of practices are characterized by an exploration-exploitation trade-off that is well understood when in each time period feedback is received only on the action that was selected in that period. However, in many practical settings, additional information may become available between decision epochs. We study the performance that one may achieve when leveraging such auxiliary information and the design of algorithms that effectively do so without prior knowledge of the information arrival process. Methodology/results: Our formulation considers a broad class of distributions that are informative about rewards from actions and allows auxiliary observations from these distributions to arrive according to an arbitrary and a priori unknown process. When it is known how to map auxiliary observations to reward estimates, we characterize the best achievable performance as a function of the information arrival process. In terms of achieving optimal performance, we establish that upper confidence bound and Thompson sampling algorithms possess natural robustness with respect to the information arrival process, which uncovers a novel property of these popular algorithms. When the mappings connecting auxiliary observations and rewards are a priori unknown, we characterize a necessary and sufficient condition under which auxiliary information allows performance improvement and devise an adaptive policy (termed 2UCBs) that guarantees near optimality. We use a data set from a large media site to analyze the value that may be captured by leveraging auxiliary observations in the design of content recommendations. Managerial implications: Our study highlights the importance of utilizing auxiliary information in the design of sequential experiments and characterizes how salient features of the auxiliary information stream impact performance. Our study also emphasizes the risk in processing auxiliary information using nonadaptive approaches that are predicated on correct interpretation of this information, as opposed to deploying flexible, adaptive methods.
The Vermont Connection The Vermont Connection The Space They Take: Evaluating Historically White Fraternities The Space They Take: Evaluating Historically White Fraternities through Critical Race Theory through Critical Race Theory

Fraternities and sororities are not often thought of as the starting points for social justice education, especially not historically White fraternities and sororities. In this paper, I outline the missions and values of a select group of historically White fraternities to better understand the foundation from which they are starting their organization. I give an overview of Critical Race Theory (CRT) that gives context for how critical race theory can work in higher education. I conclude with recommendations for reworking historically White fraternities with a CRT lens; recommendations are written for national organizations and students, and then for professional staff working with fraternities and sororities, especially historically White fraternities. and who part of I provide some historical context for fraternities as they have come to exist and an overview of relevant missions and values documents of a select group of historically White fraternities. Following this analysis I overview Critical Race Theory (CRT) as it is relevant in higher education context, which I then use future recommendations. I conclude this piece with recommendations for student affairs professionals and those within national organizations. This structure aims to show why there is a need to approach historically White fraternities from a critical race theory lens to promote justice within these organizations.
Correlation effect between transmitter and receiver azimuthal directions on the illumination function from a random rough surface

Abstract The resolution of some problems of electromagnetic scattering from random rough surfaces implies the derivation of the illumination function, especially when the geometrical optics approximation is valid. In current models, the shadowing effects occurring for both the transmitter and the receiver are assumed to be independent of their azimuthal directions. This assumption makes it possible to compute separately the shadowing probabilities in each direction. However, if the transmitter and receiver azimuthal directions are close, these probabilities become strongly correlated. In such a configuration, the uncorrelation approximation induces an overestimation of the average illumination function up to as well as a discontinuity. In this paper, assuming surfaces of isotropic Gaussian statistics with independent heights and slopes, this correlation is taken into account. Comparisons of our model with Monte Carlo simulations are made and show a very good agreement, which validates our approach.
Magnitude and Associated Factors of Postoperative Myalgia Among Patients Who Undergo Surgery in Wolaita Sodo Teaching and Referral Hospital, Southern Ethiopia

Introduction: Even though, Suxamethonium is the only short acting and accepted standard for rapid sequence intubation, it is associated with a high risk of complications. Some complications are minor, but others are potentially life threatening. Post-operative myalgia and fasciculation are the two most common and distressing side effects of succinylcholine administration. Objective: To assess the magnitude and associated factors of postoperative myalgia following Suxamethonium administration among patients undergoing surgery in Wolaita Sodo Teaching and Referral Hospital from April 30- May 30, 2019. Methods: Facility based cross sectional survey will be conducted by using convenient sampling technique. Training was given for data collectors and supervisors. Regular supervision and follow up were made. Data was entered in to Epi info version 7 software and transported to SPSS version 20 for analysis. Odd ratio and 95% confidence interval was computed. Bivariate and Multivariate analysis used to identify factors associated with hypotension. Results: The magnitude of postoperative myalgia is high at 12 hour in which 18 (36%) develop mild pain where us the magnitude is lower at 24 hour, 14 (28%). More than half 27 (54%) have no postoperative myalgia at 12 hour. Patients who did not take pretreatment agent before administration of Suxamethonium were 15 times more likely to develop post-operative myalgia at 12 hour when compared to patients who take pretreatment agent before Suxamethonium administration (AOR=15.12, 95%CI: 1.39, 163.53) Conclusion: The magnitude of Suxamethonium induced myalgia is higher at 12 hour than 24 hour post operative and lack of per-treatment agent and repeated dose of Suxamethonium are main risk factors of postoperative myalgia.
Sluice networks: Learning what to share between loosely related tasks

Multi-task learning is partly motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from related tasks by sharing parameters with other networks. However, humans do not consciously decide to transfer knowledge between tasks (and are typically not aware of the transfer). In machine learning, it is hard to estimate if sharing will lead to improvements; especially if tasks are only loosely related. To overcome this, we introduce Sluice Networks, a general framework for multi-task learning where trainable parameters control the amount of sharing -- including which parts of the models to share. Our framework goes beyond and generalizes over previous proposals in enabling hard or soft sharing of all combinations of subspaces, layers, and skip connections. We perform experiments on three task pairs from natural language processing, and across seven different domains, using data from OntoNotes 5.0, and achieve up to 15% average error reductions over common approaches to multi-task learning. We analyze when the architecture is particularly helpful, as well as its ability to fit noise. We show that a) label entropy is predictive of gains in sluice networks, confirming findings for hard parameter sharing, and b) while sluice networks easily fit noise, they are robust across domains in practice.
Towards compositional and generative tensor optimizations

Many numerical algorithms are naturally expressed as operations on tensors (i.e. multi-dimensional arrays). Hence, tensor expressions occur in a wide range of application domains, e.g. quantum chemistry and physics; big data analysis and machine learning; and computational fluid dynamics. Each domain, typically, has developed its own strategies for efficiently generating optimized code, supported by tools such as domain-specific languages, compilers, and libraries. However, strategies and tools are rarely portable between domains, and generic solutions typically act as ''black boxes'' that offer little control over code generation and optimization. As a consequence, there are application domains without adequate support for easily generating optimized code, e.g. computational fluid dynamics. In this paper we propose a generic and easily extensible intermediate language for expressing tensor computations and code transformations in a modular and generative fashion. Beyond being an intermediate language, our solution also offers meta-programming capabilities for experts in code optimization. While applications from the domain of computational fluid dynamics serve to illustrate our proposed solution, we believe that our general approach can help unify research in tensor optimizations and make solutions more portable between domains.
Species suitability for fish farming in irrigated rice fields of drought prone Barind area of Bangladesh

Species Suitability for fish farming in irrigated rice-fish culture system of drought prone barind area were evaluated on the basis of production performance and cost analysis for a period of 4 months. Three most commonly used fish species were tested under three treatments (T1: Nile tilapiaOreochromis niloticus; T2: Thai SarpuntiBarbodes gonionotus and T3: carpioCyprinus carpio) in rice-fish culture system of drought prone Barind area to find out suitable fish species. The fish fingerlings stocking density (4940 ha) of three species and management practices were similar in all plots. The water quality parameters, such as temperature, transparency, pH, dissolved oxygen, ammonia nitrogen and alkalinity were studied. Soil organic matter, available nitrogen and phosphorous were increased significantly (P < 0.05) in all plots after cultivating the fish. Specific growth rate, survival and fish production were significantly (P < 0.05) varied among the treatments. However, significantly (P < 0.05) higher net income and benefit–cost ratio were obtained in treatment T1 with the values of 34296.24 Tk ha and 1.55 higher than the treatment T2 and treatment T3 respectively. Present findings indicate that the hardy fish like tilapia performed better in terms of production and economics than that of others fish species (Thai sarpunti and common carp) in rice-fish system in Barind area. So, it can be recommended that tilapia can be a good option while selecting aquaculture species for rice field in this red soil zone of Bangladesh.
x-only point addition formula and faster torsion basis generation in compressed SIKE

The optimization of the main key compression bottlenecks of the supersingular isogeny key encapsulation mechanism (SIKE) has been a target of research in the last few years. Sig-niﬁcant improvements were introduced in the recent works of Costello et al. [ 6 ] and Zanon et al. [ 17,18 ] . The combination of the techniques in [ 17,18 ] reduced the running time of binary torsion basis generation in decompression by a factor of 29 compared to previous work [ 6 ] . On the other hand, generating such a basis still takes almost a million cycles on an Intel Core i5-6267U. In this paper, we continue the work of [ 18 ] and introduce a technique that drops the complexity of binary torsion basis generation by a factor log p in the number of underlying ﬁeld multiplications. In particular, our experimental results show that a basis can be generated in about 1.3 k cycles, attaining an improvement by a factor more than 600. Although this result eliminates one of the key compression bottlenecks, many other bottlenecks remain. In addition, we give further improvements for the ternary torsion generation with signiﬁcant impact on the related decompression procedure. Moreover, a new trade-off between ciphertext sizes vs decapsulation speed and storage is introduced and achieves a 2 times faster decapsulation.
DOPPLER VELOCIMETRIC INDICES IN DIFFERENTIATING BENIGN AND MALIGNANT OVARIAN TUMORS

Of all the gynecological cancers, ovarian malignancies represent the greatest clinical challenge. The value of tumor marker and ultrasonography to screen for epithelial ovarian cancers has not been clear established by prospective studies. In present study the relative value, predictive value and differentiate between benign and malignant ovarian tumors of B mode ultrasonography alone and in combination with colour differentiating benign from malignant ovarian neoplasms and to correlate the imaging findings with histopathological findings. The present study established a multi – fold increase in the specificity and positive predictive value in establishing the pre-operative diagnosis of ovarian masses, especially in terms of benignity and malignancy, when using B-mode ultrasonography in combination with colour and spectral Doppler as compared to B-mode ultrasound alone.
Evaluating the Factual Correctness for Abstractive Summarization

Summarization aims to distill essential information from the source text and has been widely applied to headline generation, lawsuit abstraction, biomedical and clinical text summarization. There are two main approaches for summarization: extractive summarization and abstractive summarization. While extractive summarization directly copies words and sentences from the source text, abstractive summarization can paraphrase the source text, leading to more flexible and compressed summaries.
A System for the Simplification of Numerical Expressions at Different Levels of Understandability

The purpose of this paper is to motivate and describe a system that simplifies numerical expression in texts, along with an evaluation study in which experts in numeracy and literacy assessed the outputs of this system. We have worked with a collection of newspaper articles with a significant number of numerical expressions. The results are discussed in comparison to conclusions obtained from a prior empirical survey.
Teargas, Water Cannons and Twitter: A case study on detecting protest repression events in Turkey 2013

Since the Arab spring in 2011, protests have been spreading around the world for di ↵ erent reasons, often these protests are faced with violent repression. Studying protest repression requires appropriate datasets. Existing datasets like GDELT focus mainly on events reported in news media. However, news media reports have issues including censorship and coverage bias. Recently, social scientists have started using Machine Learning (ML) to detect political events, but it is costly and time consuming to hand label data for training ML models. This paper proposes using ML and crowdsourcing to detect protest repression events from Twitter. Our case study is the Turkish Gezi Park protest in 2013. Our results show that Twitter is a reliable source reﬂecting events happening on the ground as soon as they happen. Moreover, training conventional ML models on crowdsourced labelled data gave good results with an AUC score of 0.896 to detect protest events and 0.8189 to detect repression events.
Fallacies of Agreement

Discovering gestures that gain consensus is a key goal of gesture elicitation. To this end, HCI research has developed statistical methods to reason about agreement. We review these methods and identify three major problems. First, we show that raw agreement rates disregard agreement that occurs by chance and do not reliably capture how participants distinguish among referents. Second, we explain why current recommendations on how to interpret agreement scores rely on problematic assumptions. Third, we demonstrate that significance tests for comparing agreement rates, either within or between participants, yield large Type I error rates (>40% for α =.05). As alternatives, we present agreement indices that are routinely used in inter-rater reliability studies. We discuss how to apply them to gesture elicitation studies. We also demonstrate how to use common resampling techniques to support statistical inference with interval estimates. We apply these methods to reanalyze and reinterpret the findings of four gesture elicitation studies.
19-23_Yusuf M 2

The study was carried out between the months of July and December 2013 to determine the prevalence of hepatitis B surface antigen among patients presenting with febrile illnesses attending Murtala Muhammad Specialist Hospital, Kano. Two hundred (200) blood samples were obtained from the consented patients with age ranged from 15-64 years, that were processed according to standard operating procedures (SOPs) and screened for the presence of HBsAg using a third generation enzyme linked immunosorbent assay method. A total of 22 (11%) patients were positive for HBsAg. Higher rate of infection was observed among males 72.2% (n=16) than their female counterparts 27.3% (n=6). Infection rate was found to be higher in age group 25-34 years, 45.4 % (n=10) than 15-24 and 35-44 years each with 27.3% (n=6), and none was found to be positive among 45-54 and 55-64 years age groups. Also more infection was observed among the married population 54.5 % (n=12) than single 40.9 % (n=9) and widow 4.5% (n=1). However, high sero-positivity is also occupationally dependent. More infections were seen among business occupational groups 50 % (n=11) than students 31.8 % (n=7) while 18.2 % (n=4) was seen among civil servants. The major route of transmission among the study group is sexual transmission in those with multiple sexual partners and history of blood transfusions. Those with history of vaccination against hepatitis B had no positive result (n=0). The study revealed a considerable high infection among males, married business occupation class and students these where however not statistically significantly different (p<0.005), and also pointed out potency and importance of vaccination for all vaccine preventable diseases.