Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting Turnover Tax Collection Performance: A Case of West Shoa Selected Woredas

Factors affecting turnover tax collection performance. A case of West Shoa Zone selected wore das. In 2017/18 then targeted revenue was 9041224 birr with the actual revenue being 7888536 birr (equivalent to 87.25% or a difference of 1152688) was existence of turnover tax collection gap. This study was using mixed research approach. Sampling technique used by Systematic random and purposive sampling. Sample sizes of 373 respondents were selected and distributed questionnaires and interview. Data analyze by SPSS software 20 and factor analysis. Findings revealed that; employee qualification and manpower, taxpayer registrations, technology and information system, management commitment level and tax knowledge affects revenue performance of turnover tax positively.
Design, Simulation, Fabrication and Evaluation of a Textile PIFA for Wearable IoT Application

The ever increasing use of body-worn systems in the Internet of Things application needs better antenna subsystem designs compatible with its requirements. Several challenges limiting the performance of a body-worn system, from materials, and environmental conditions to the e ects of on body application and its hazards are discussed. As a test case, a exible textile Planar Inverted-F antenna is presented and discussed. The choice of this topology is due to its simplicity in design and fabrication, relatively broad bandwidth and the presence of a rear ground plane, which minimizes the impacts of the human body on the antenna performance. It is designed on a felt substrate, whereas Aaronia-shield conductive textile is utilized as its conductive parts (radiator, shorting wall and ground plane). The antenna performance is studied in two cases, rst in free space and then in bent conditions in the close proximity to the human body. The in uence of the relative humidity on the textile antenna performance is also investigated numerically. Simulated and measured results indicated good agreements. Finally, the proposed antenna is integrated with a transceiver module and evaluated on the body in practice. Its wireless link quality is assessed in an indoor laboratory.
Swine health: history, challenges and prospects

In swine production systems, one of the critical points that must be strictly attended to is the health of the pigs. Health is a structural component of animal welfare and reflects an optimal state of the animals, which has a direct impact on a higher productive performance and better development conditions. Infectious diseases are one of the greatest threats to the health of pigs and can cause losses of up to 100 % of production; therefore, it requires constant attention and continuous monitoring by the veterinarian and producers, in perfect coordination with the official health authorities. Currently, the implementation of best practices in the production chain is of interest to both producers and consumers. The control of infectious diseases requires collaboration between the various actors in the environment Rev Mex Cienc Pecu 2021;12(Supl 3):149-185 151 and must be considered a public good, since their negative repercussions can range from the local to the global level. This review will address the main infectious diseases that endanger swine health, their impact, the main contributions made by the National Institute for Research in Forestry, Agriculture and Livestock (INIFAP) in its 35 years of life, mainly at the National Center for Disciplinary Research in Animal Health and Safety (CENID-SAI), formerly known as the emblematic CENID-Microbiología or Palo Alto.
GENEROSITY IN ESSAY GRADING

Fifteen teachers graded 12 themes. The 15 teachers were randomly assigned, five each, to one of three types of rating category procedures. The three procedures were: Conventional (two positive, one neutral, two negative categories); Generosity (three positive, one neutral, one negative category); and Number (5, 4, 3, 2, 1). As expected, the conventional five categories produced a higher score than did the unbalanced five categ or ie s recommended by Guilford for offsetting the generosity error.
Do School and School District Influence Students’ Performance on Civics End-of-Course Assessment? A Multilevel Analysis

This study examined the influence of school and school district variables on schools’ average results for the civics end-of-course (EOC) assessment. A two-level hierarchical analysis was conducted using the percentage of students from low-SES per school, school size, and school type as Level 1 predictors and the average district poverty ratio and non-Hispanic White population as Level 2 moderators. The results showed that a higher percentage of poor students, district poverty level, and the district non-Hispanic White population had a negative impact on the average number of students who score proficient on civics EOC assessments. Similarly, school size and school type were significant predictors of the average school civics EOC proficiency rate.
SURVEY ON SALIENT OBJECT DETECTION

: Detecting and segmenting Salient and co-salient object in an image and video has attracted a lot of interest in computer vision. Now a day's many applications have emerged and models have been proposed. We aim to supply a comprehensive analysis of recent add salient object detection and provides this field, completely different areas like generic scene segmentation, object proposal generation, strikingness for fixation prediction and co-saliency detection via looking deep and wide. Covering (20) publications, our survey (1) root, key concepts and task (2) core techniques and modeling trends, and (3) datasets that are used in the salient object detection and segmentation. We also suggest future research work.
INDONESIAN JOURNAL OF ENGINEERING RESEARCH

Differential quadrature phase-shift keying (DQPSK) modulation methods and variations are commonly used in wireless communication, including high-speed optical fiber, Bluetooth, and satellite communication. DQPSK cannot be isolated from possible interference with its present application. The interference cancellation process has been observed and analyzed using a device model built in this study. The core components of the supporting block for this device model have raised cosine filters for pulse shaping and adaptive filters. The presence of these core components has resulted in a dramatic improvement in device efficiency, according to robust Simulink data. The best bit error rate (BER) of 5.7e-03 provides evidence of this.
Ethical Leadership and Its Relationship with Organizational Performance: Organizational Trust as a Mediator

This article examines the influence of ethical leadership (based on leaders and followers reports) and their impact on organizational performance. It also examines organizational trust and its role as a mediator in ethical leadership and its performance using partial least square (PLS-SEM) analysis. The findings show that ethical leadership (leaders and followers reports) can predict 17.6% of organizational trust while ethical leadership and organizational trust can predict organizational performance by as much as 20.4%. In addition, organizational trust is seen to act as a full mediation between ethical leadership and organizational performance (leaders’ reports) and as a partial mediation in the case of ethical leadership based on followers' reports. This research can contribute to the development of a leadership profile especially for the civil service in Malaysia.
On the complexity of computing the algebraic closure of a finitely generated matrix semigroup

Invariants are an important methodology in the area of program verification. Given a linear program, finding the invariants that hold throughout the entire execution of the program can help us prove various temporal safety properties of the program. An algebraic invariant, in particular, assigns to each program location a set of polynomial equations over the program variables that hold in the given location. The strongest algebraic invariant is determined by giving, for each program location, the set of all equations holding at that location. It has been shown that the problem of computing the strongest algebraic invariant of a program can be reduced to that of computing the Zariski closure of a finitely generated semigroup of rational matrices. The algorithm to do so, in turn, generalises (and uses as a subroutine) an algorithm to compute the Zariski closure of a finitely generated group of invertible matrices. Both of the aforementioned algorithms are important technical contributions in computational algebraic geometry, yet the complexity of neither algorithm has been analysed. The motivation behind this internship was to study the two algorithms and attempt to analyse their complexity starting with the group closure algorithm first. In this report, we first present the mathematical background behind the algorithm to compute the Zariski closure of a finitely generated group of invertible matrices. We give intuition on the complexity of the algorithm and provide algebraic group theoretic facts we can use to reason about the complexity. In particular, we give a proof of a bound on the dimension of a quotient group of two algebraic groups. We propose an adjusted version of the algorithm, which in combination with the aforementioned group theoretic bound reduced the problem to the problem of finding an element of infinite order in a matrix group. We review several approaches to solving the latter problem and propose an appropriate one for our reasoning. Finally, we list all the components of the complexity bound we have found and discuss what is missing in order to establish one.
How Well Does Body Mass Index (BMI) Predict Undiagnosed Hypertension and Diabetes in Indonesian Adults Community Population?

BACKGROUND: Previous studies have reported that Body Mass Index (BMI) cut-off was related to non-communicable diseases. This study aimed to give the latest evidence related to the accuracy of BMI cut-off towards undiagnosed hypertension and diabetes in the Indonesian population. METHODS: This was A cross-sectional study that involved data of the 2018 national population-based health survey, with the samples were 15,516 male and female populations aged between 19 years old and above. This study only included those claimed to have never been diagnosed as suffering from diabetes and hypertension by health workers. Receiver operating characteristic (ROC) analysis was conducted to assess the optimal BMI cut-off. The logistic regression was performed to assess the association of BMI on undiagnosed hypertension and diabetes controlled by several variables. RESULTS: The average BMI sample was 24 kg/m2 (SD = 4.6 kg/m2. The proportion of undiagnosed hypertension was 36.9%, and 12.3% for the proportion of undiagnosed diabetes. According to the ROC, the result shows BMI was more sensitive to hypertension conditions compared to diabetes. BMI cut-off points at 23.9 kg/m2 (AUC=0.59;Se=64.3%;Sp=53.4%) was the optimum value to predict hypertension and 24.9 kg/m2 (AUC=0.55;Se=53.1%;Sp=56.4%) was the optimum for diabetes. CONCLUSIONS: Based on the optimal AUC cut-off points for BMI which is around 0.5, BMI needs to be reconsidered as an anthropometric index in predicting undiagnosed hypertension and diabetes. And an assessment can be made using other anthropometric indices, such as waist circumference to predict undiagnosed hypertension and diabetes.
Discussion Flows: An Interactive Visualization for Analyzing Engagement in Multi-Party Meetings

Engagement in multi-party meetings is a key indicator of outcome. Poor attendee involvement can hinder progress and hurt team cohesion. Thus, there is a strong motivation for organizations to better understand what happens in meetings and improve upon their experience. However, analyzing multi-party meetings is a challenging task, as one needs to consider both verbal exchanges and meeting dynamics among speakers. There is currently a lack of support on these unique tasks. In this paper, we present a new visual approach to help analyze multi-party meetings in industry settings: Discussion Flows, a multi-level interactive visualization tool. Its glyph-based overview allows effortless comparison of overall interactions among different meetings, whereas the individual meeting view uses ﬂow diagrams to convey the relative participation of different speakers throughout the meeting agenda in different levels of details. We demonstrate our approach with meeting recordings from an open source dialogue corpora and use them as the benchmark dataset.
Differentiable Simulation of Soft Multi-body Systems

We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynamics and derive a generalized dry friction model for soft continuum using a new matrix splitting strategy. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes. The experiments demonstrate that our designs make soft body simulation more stable and realistic compared to other frameworks. Our method accelerates the solution of system identification problems by more than an order of magnitude, and enables efficient gradient-based learning of motion control with soft robots.
Inverse-Weighted Survival Games

Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other criteria, such as binary classification losses at a chosen set of time horizons, e.g. Brier score (BS) and Bernoulli log likelihood (BLL). Models trained with maximum likelihood may have poor BS or BLL since maximum likelihood does not directly optimize these criteria. Directly optimizing criteria like BS requires inverse-weighting by the censoring distribution. However, estimating the censoring model under these metrics requires inverse-weighting by the failure distribution. The objective for each model requires the other, but neither are known. To resolve this dilemma, we introduce Inverse-Weighted Survival Games. In these games, objectives for each model are built from re-weighted estimates featuring the other model, where the latter is held fixed during training. When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point. This means models in the game do not leave the correct distributions once reached. We construct one case where this stationary point is unique. We show that these games optimize BS on simulations and then apply these principles on real world cancer and critically-ill patient data.
Retrieving Comparative Arguments using Ensemble Methods and BERT

In this paper, we present a submission to the Touche lab's Task 2 on Argument Retrieval for Comparative Questions. Our team Katana supplies several approaches based on decision tree ensembles algorithms to rank comparative documents in accordance with their relevance and argumentative support. We use PyTerrier library to apply ensembles models to a ranking problem, considering statistical text features and features based on comparative structures. We also employ large contextualized language modelling techniques, such as BERT, to solve the proposed ranking task. To merge this technique with ranking modelling, we leverage neural ranking library OpenNIR. Our systems substantially outperforming the proposed baseline and scored first in relevance and second in quality according to the official metrics of the competition (for measure NDCG@5 score). Presented models could help to improve the performance of processing comparative queries in information retrieval and dialogue systems.
Constraint Synthesis for Parametric CAD

Parametric CAD, in conjunction with 3D-printing, is democratizing design and production pipelines. End-users can easily change parameters of publicly available designs, and 3D-print the customized objects. In research and industry, parametric designs are being used to ﬁnd optimal, or unique ﬁnal objects. Unfortunately, for most designs, many combinations of parameter values are invalid. Restricting the parameter space of designs to only the valid conﬁgurations is a difﬁcult problem. Most publicly available designs do not contain this information. Using ideas from program analysis, we synthesize constraints on parameters of parametric designs. Some constraints are synthesized statically, by exploiting implicit assumptions of the design process. Several others are inferred by evaluating the design on many different samples
Effect of harvest time on seed germination and seedlings growth of Sour orange and Mexican lime under in vitro conditions

The aim of this research was to determine the best time to harvest the fruits for seed production which would ultimately lead to the production of citrus rootstocks of optimum quality. The sour orange and Mexican lime fruits were harvested on 7 and 5 occasions, respectively. The very first fruits were harvested 80 days after flowering and subsequent harvests were gathered every 30 days. An in vitro experiment was carried out in a completely random­ ized design, with four replications and 20 seeds in each replication. Based on fruit growth curve the time of fruit harvest affected seed germination (percent­ age and rate) and seedling growth (stem and root length, fresh and dry weight of stems, roots and leaves). The results showed that the best time to harvest the fruits of sour orange and Mexican lime was 230 and 170 days after flower­ ing, respectively, which led to maximum seed germination (Mexican lime 100% and sour orange 85%) and seedling growth. The highest root, stem and leaf fresh and dry weight was also obtained at 230 and 170 days after flowering in sour orange and Mexican lime respectively.
Evaluation of updating strategies for conformal predictive systems in the presence of extreme events

Six different strategies for updating split conformal predictive systems in an online (streaming) setting are evaluated. The updating strategies vary in the extent and frequency of retraining as well as in how training data is split into proper training and calibration sets. An empirical evaluation is presented, considering passenger booking data from a ferry company, which stretches over a number of years. The passenger volumes have changed drastically during 2020 due to COVID-19 and part of the evaluation is focusing on which updating strategies work best under such circumstances. Some strategies are observed to outperform others with respect to continuous ranked probability score and validity, highlighting the potential value of choosing a proper strategy.
Short-Term Inflation Forecast Combination Analysis for Uzbekistan

In this paper, we produce the short-term inflation forecast for Uzbekistan, using univariate and multivariate econometric models. In particular, we use Auto Regressive Integrated Moving Average (ARIMA) model, Bayesian Vector Auto regression Model (BVAR) and Vector Error Correction model (VECM) to project CPI inflation and its decomposed subcomponents. The results of the forecast combination analysis are in line with the outcomes of the other research done in this field. The relative performance of combined forecasts based on the RMSE weighting scheme are on average 33% better for 6-month ahead. Despite some individual models demonstrate better performance in certain time horizons, the overall results reveal that forecast combination method permits to reduce the forecast error in comparison with the aforementioned models taken separately.
Sentiment Analysis On Movie Reviews Using NAÏVE BAYES Classifier

—Sentiment Analysis or Opinion mining on movie reviews. Sentiment is a thought, view, or attitude, especially one based mainly on emotion instead of reason For this we need to classify each and every review, this can be done through classifiers such as NAÏVE BAYES, SVM, K-NN, we preferred NAÏVE BAYES classifier, since it is a probabilistic approach we depend upon each word’s probability to be positive and negative, through this we will classify the review to be one of those. After classifying each review the sentiment is drawn based on the count of the classes. Consider 100 reviews, in those reviews there will be positive as well as negative reviews of that particular movie, the review is purely based on the public opinions being expressed in the portal. After classifying the reviews we go for the max count method to predict the outcome. This algorithm is proposed in order to avoid the fake reviewing system followed now a days to promote the movie by the producers and crew of the movie. This gives the straight opinion of the public.
Setting Mastery Test Cut-Scares-an Evaluative Model Approach

Introduction and Review of Literature The purpose of this study was to investigate procedures for setting cut-scores for mastery tests which would maximize the reliability of mastery classification decision and minimize two types of misclassification errors. An approach for a new model to set cut-scores for mastery tests, especially when a conflict exists between cutscores for mastery tests, especially when a conflict exists between cut-scores setters, is proposed. A review of literature regarding the establishment of cut-scores indicates that some doubt exists concernIng any realistic establishment of such scores.
SecureFL: Privacy Preserving Federated Learning with SGX and TrustZone

Federated learning allows a large group of edge workers to collaboratively train a shared model without revealing their local data. It has become a powerful tool for deep learning in heterogeneous environments. User privacy is preserved by keeping the training data local to each device. However, federated learning still requires workers to share their weights, which can leak private information during collaboration. This paper introduces SecureFL, a practical framework that provides end-to-end security of federated learning. SecureFL integrates widely available Trusted Execution Environments (TEE) to protect against privacy leaks. SecureFL also uses carefully designed partitioning and aggregation techniques to ensure TEE efficiency on both the cloud and edge workers. SecureFL is both practical and efficient in securing the end-to-end process of federated learning, providing reasonable overhead given the privacy benefits. The paper provides thorough security analysis and performance evaluation of SecureFL, which show that the overhead is reasonable considering the substantial privacy benefits that it provides.