Miracle-dz/UniFAR-FTUs huggingface dataset:
HbA1c Reduction in Diabetic Older Blacks and Hispanics: A Study on Mobile Physical Activity Tracking

ABSTRACT Introduction/Purpose Diabetes is a leading cause of morbidity and mortality and is more prevalent in Blacks and Hispanics. This study aims to examine if different physical activity (PA) intensities reduced hemoglobin A1c (HbA1c) in Blacks and Hispanics with type 2 diabetes mellitus (DM2). Methods This pilot study used a one-sample pre- and postintervention design, including four in-person visits during a period of 3 months from January 2018 to December 2020 in Black or Hispanic populations with DM2 in the Greater Hartford, Connecticut, area. At each in-person visit HbA1c; specific, measurable, achievable, realistic, and time-bound goals; and daily PA (categorized into high-, moderate-, or low-intensity levels by a Fitbit® Charge 2) were obtained. Change scores for minutes engaged in each PA level and HbA1c values were created between visits. A linear mixed model was used to analyze these relationships over time. Results Among 17 completed participants, mean age was 56 yr, 11 were Black, 6 were Hispanic, 13 were female, and 4 were male. At baseline, mean body mass index was 34.8 kg·m−2 and mean systolic/diastolic blood pressure was 135/81 mm Hg. From 70.2 mmol·mol−1 at baseline, HbA1c decreased to 66.3 after 4 wk, 66.0 after 8 wk, and 63.5 by 12 wk (9.5% reduction, P < 0.05). Average daily PA time increased over 3 months from 15.8 to 20.3 (P = 0.04) to 20.8 (P = 0.02) minutes for moderate-intensity PA (P = 0.05), and from 12.1 to 13.6 (P = 0.08) to 15.2 (P = 0.01) minutes for high-intensity PA. PA increases were significantly associated with an overall 0.58% reduction of HbA1c (P = 0.04). Conclusion Guided by carefully selected, evidenced-based behavioral change strategies, this study found that increases of ~7 min of moderate- or high-intensity daily PA were associated with ~10% decrease in HbA1c in Blacks and Hispanics with DM2. This is consistent with the current consensus statement from the American College of Sports Medicine.
Detection of Typical Sentence Errors in Speech Recognition Output

This paper presents a deep learning based model to detect the completeness and correctness of a sentence. It’s designed specifically for detecting errors in speech recognition systems and takes several typical recognition errors into account, including false sentence boundary, missing words, repeating words and false word recognition. The model can be applied to evaluate the quality of the recognized transcripts, and the optimal model reports over 90.5% accuracy on detecting whether the system completely and correctly recognizes a sentence.
Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil

Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.
Extractive Text Summarization using Meta-heuristic Approach

This paper describes a system for Indian Language Extractive Summarization which has been developed under the shared task ILSUM of FIRE 2022[1][2] Conference by SUMIL22 team. This system works by picking up sentences directly from the text using a ranking function to form the corresponding summary. In our approach, for each sentence, we first calculated various text features such as sentence position, sentence length, sentence similarity, frequent words, and sentence numbers. Then, these text features along with their optimized weights are used for ranking the sentences, and then the summary is generated by selecting top-ranked sentences. For optimizing the weights of the text features, we have used a population based meta-heuristic approach, Genetic Algorithm (GA). We submitted three runs and got an F-score of 0.3843 for ROUGE-1, 0.2584 for ROUGE-2, 0.1997 for ROUGE-3 and 0.2190 for ROUGE-4 in the best run.
A User-Centered Approach to Create Realistic Datasets for AI. Case Study: Creditworthiness in the Banking Sector

Nowadays businesses are evolving, as new digital tools ensure greater efficiency of their information systems. Decision-making and strategic processes can benefit from innovation opportunities such as Machine Learning. The main issue encountered in Artificial Intelligence applications, is that data can be not available or unsuitable for the case of study. This paper proposes the solution for this problem, by generating simulated data for AI. The case of study is creditworthiness in the banking sector; a loan is considered the main source of income for the banking sector, as well as the main source of risk . Consequently, the evaluation of creditworthiness is a key activity both for banks and for customers. To address this need, we propose a solution tailored to lenders to evaluate credit applications and to customers to be aware of behaviors that can reduce their credit score. The approach proposed in this paper aims at realizing realistic datasets for Artificial Intelligence (named IDEA) to meet specific business needs, and to respect users’ requests. An analysis of the current literature and methods for the evolution of conceptual models will be conducted, through pre-existing datasets. The proposed approach draws from and extends such literature. The intended application is to adopt this approach in the banking sector for considering the creditworthiness of customers who have entered into financial relationships. Therefore, the envisaged use case is to forecast the probability of borrowers going bankrupt. The paper defines the approach applied to specific financial datasets for the use case. Moreover, a validation of datasets is done, thanks to the Data Quality Index, before applying IDEA to predict credit solvency.
An improved torsion pendulum based on image processing for single fibers

An improved torsion pendulum technique based on image processing is developed, which allows the measurement of the torsional oscillation of individual fibers. The torsion pendulum is composed of a length of fiber specimen and a ring-shaped mass attached to one of its ends. The oscillation motion of the mass is recorded with a video camera overhead. An image processing method is developed to accurately calculate the twist angle of single fibers in the torsion pendulum. All images are firstly converted into matrices and then the edge detection is applied to calculate the twist angle at corresponding time to obtain the angle-time relation. The oscillation frequency is determined by the fast Fourier transform (FFT) method. By applying this technique, we have performed tests on four types of fibers, namely T300 carbon fiber, copper, silver and tungsten wires. The longitudinal shear modulus measured for each type of these fibers is presented.
Compass: A Canvas for Changing Capabilities

The dynamic business environments pose a challenge that is commonly addressed by performing changes to the capabilities of the organizations. Enterprise Modeling provides the necessary methods and tools to support the organizations that are facing ongoing changes or need to, however, the complexity of the procedures and models are often leading to limited understanding and usability for individuals without a modeling expertise. This paper introduces Compass, a canvas-based approach aimed to support the management of changing capabilities in organizations. The areas of Compass, which address the motivation to change, the decision alternatives, the capability components, the transition, the impact, and the attributes of change, are described and applied on an illustrative case study in the arts and culture domain of a public municipality.
Study on the Coordination of New Urbanization and Water Ecological Civilization and Its Driving Factors: Evidence from the Yangtze River Economic Belt, China

For sustainable development of the world, it is crucial to solve the problems related to water environment pollution, water shortage, and the inefficient utilization of water resources during the process of urbanization in developing countries. At present, scholars mainly focus on the measurement of new urbanization (NU) and the water ecological civilization (WEC) level and the coordination relationship between NU and ecological civilization. However, there have been few studies on the coordination relationship between NU and WEC and its driving factors. We take the Yangtze River Economic Belt (YREB) in China as a case study, construct the indicator system of NU and WEC, analyze the current situation of NU and WEC in the YREB, and study the coordination state of NU and WEC in the YREB from 2011 to 2020 by using a state coordination function. We further examine the factors driving the coordination of NU and WEC by employing a two-way fixed-effects model. The results show the following: (1) The growth rate of NU and WEC in the YREB shows a fluctuating upward trend, where there is significant heterogeneity between the upper reaches, the middle reaches, and the lower reaches of the YREB. (2) The static coordination degree of NU and WEC in the YREB shows a trend of fluctuating upwards and then falling, and the dynamic coordination degree deviated from the coordinated development trajectory from 2018 to 2020. The classification of the static coordination degree of various regions in the YREB gradually becomes obvious with significant spatial aggregation characteristics, and the dynamic coordination degree of various regions has significant heterogeneity. (3) The opening-up degree, foreign direct investment, population growth, and urban–rural income gap are not advantageous to the coordination degree, while the marketization level, industrial structure, and human capital are advantageous to the coordination degree, but the regression coefficients of the latter two are not significant. The regional regression results show that the impacts of driving factors on the coordination degree have obvious heterogeneity. The research results provide a new idea and method that can be used by developing countries similar to the YERB to control water pollution, improve the ecological environment, alleviate water shortages, and improve the level of WEC in the process of NU.
Research on Test Case Generation Method of Airborne Software Based on NLP

Software testing is a key stage in the life cycle of airborne software development. At this stage, airborne software test cases are developed manually, so the preparation of test cases requires a lot of time and labor costs and is prone to human errors. To solve this problem, on the basis of Long Short-Term Memory, this paper proposes an airborne software test case automatic generation algorithm based on Bi-LSTM-CRF named entity recognition model and Part-Of-Speech tagging. First, preprocess the airborne software requirement document, replace the testable variable name and filter out the untestable requirement statements. Then, the airborne software domain corpus is trained through Bi-LSTM-CRF model to obtain named entity recognition model. Finally, the tag sequence is generated from the requirement statement through the named entity identification model, and the test case is generated through the triplet generation algorithm and the coverage criteria processing algorithm. The experiment uses the engine indicator software requirements document to verify the effect. The results show that compared with the traditional Bi-LSTM-CRF model, the training method with Part-Of-Speech tagging is more accurate, and the accuracy of the final test case generation can reach more than 80%.
Coarse and Fine-Grained Conversational Hate Speech and Offensive Content Identification in Code-Mixed Languages using Fine-Tuned Multilingual Embedding

We are seeing an increase in hateful and offensive tweets and comments on social media platforms like Facebook and Twitter, impacting our social lives. Because of this, there is an increasing need to identify online postings that can violate accepted norms. For resource-rich languages like English, the challenge of identifying hateful and offensive posts has been well investigated. However, it remains unexplored for languages with limited resources like Marathi. Code-mixing frequently occurs in the social media sphere. Therefore identification of conversational hate and offensive posts and comments in Code-Mixed languages is also challenging and unexplored. In three different objectives of the HASOC 2022 shared task, we proposed approaches for recognizing offensive language on Twitter in Marathi and two code-mixed languages (i.e., Hinglish and German). Some tasks can be expressed as binary classification (also known as coarse-grained, which entails categorizing hate and offensive tweets as either present or absent). At the same time, others can be expressed as multi-class classification (also known as fine-grained, where we must further categorize hate and offensive tweets as Standalone Hate or Contextual Hate). We concatenate the parent-comment-reply data set to create a dataset with additional context. We use the multilingual bidirectional encoder representations of the transformer (mBERT), which has been pre-trained to acquire the contextual representations of tweets. We have carried out several trials using various pre-processing methods and pre-trained models. Finally, the highest-scoring models were used for our submissions in the competition, which ranked our team (irlab@iitbhu) second out of 14, seventh out of 11, sixth out of 10, fourth out of 7, and fifth out of six for the ICHCL task 1, ICHCL task 2, Marathi subtask 3A, subtask 3B and subtask 3C
Bearing Fault Diagnosis Based on EMD and Smoothed Pseudo Wigner-Ville Spectrum Entropy

A method of fault diagnosis for rolling bearings based on empirical mode decomposition(EMD) and smoothed pseudo Wigner-Ville distribution(SPWVD) spectral entropy is proposed. In this method, the nonlinear and nonstationary characteristics of the signal in the EMD method, which has a great advantage in signal filtering and de-noising, are fully reserved. The SPWVD spectral entropy is used to quantitatively characterize the time-frequency energy distribution of the vibration signals in different states of the bearing. The intelligent model is designed based on the least square support vector machines(LS-SVM). The automatic classification of bearing state and identification of fault type of the bearing are realized. Through the mutual comparison of the SPWVD spectral entropy method and spectral kurtosis method, the effectiveness of the SPWVD spectral entropy is verified. The results show that this method can effectively extract the characteristics of the bearing fault information and improve the rate of bearing fault diagnosis.
On the Cosmical Zero Point Energy Density

The frequency spectrum of the cosmical Zero Point Energy (ZPE) and its total density are so far unknown in their details. In the present complementary investigation, a revised theory forms the basis for studies of this concept in two respects. It first applies to the observable universe considered as an entity, as well as to included subregions such as the galaxies with supermassive black holes. Second, experiments are proposed on the maximum Casimir force arising between two metal plates of different materials and with a vanishing air gap in their spacing. This serves the purpose of making an indirect determination of the ZPE energy density in the laboratory, i.e. at the Earth’s orbit. The ZPE energy density is interpreted as dark matter density and its pressure gradient as dark energy force density.
Cross-correlation image registration approach based on gradient pre-processing in frequencey doamin

Traditional image registration methods in frequency domain have two kinds:one is cross-correlation method;the other is phase-correlation method.The both methods obtain registration information by determining maximum peak in inverse Fourier Transforms domain.Cross-correlation method that compare with phase-correlation method has two disadvantages:one is that sometimes the presence of secondary maxima makes it difficult to select the actual maximum;moreover,the cross-correlation peak may be rather broad,so that the accuracy in determining its position may be poor.This paper proposes an improved cross-correlation method that is based on gradient pre-processing.Two pre-registering images are first pre-processed by gradient information before both images are registered with traditional cross-correlation.This method overcomes two disadvantages of traditional one and obtains precise results that have been proved by experiments.
Numerical analysis on the effect of angle of attack on evaluating radio-frequency blackout in atmospheric reentry

A three-dimensional numerical simulation model that considers the effect of the angle of attack was developed to evaluate plasma flows around reentry vehicles. In this simulation model, thermochemical nonequilibrium of flowfields is considered by using a four-temperature model for high-accuracy simulations. Numerical simulations were performed for the orbital reentry experiment of the Japan Aerospace Exploration Agency, and the results were compared with experimental data to validate the simulation model. A comparison of measured and predicted results showed good agreement. Moreover, to evaluate the effect of the angle of attack, we performed numerical simulations around the Atmospheric Reentry Demonstrator of the European Space Agency by using an axisymmetric model and a three-dimensional model. Although there were no differences in the flowfields in the shock layer between the results of the axisymmetric and the three-dimensional models, the formation of the electron number density, which is an important parameter in evaluating radio-frequency blackout, was greatly changed in the wake region when a non-zero angle of attack was considered. Additionally, the number of altitudes at which radio-frequency blackout was predicted in the numerical simulations declined when using the three-dimensional model for considering the angle of attack.
The Effect of Inflation Rate and Foreign Exchange on Company Value at Bank Mandiri Tbk

The global economic crisis in 2008 still has an impact on the Indonesian capital market. The impact of the crisis was reflected in the correction of stock prices by 40-60 percent from the initial position in 2008. Two of the main impacts of stock price fluctuations in the capital market due to the global economic crisis in 2008 were the inflation rate and volatile foreign exchange rates. The research method used is the ex post facto method. Data collection is done by recording observations, interviews, and literature research. The hypothesis is that there is an influence of the inflation rate and foreign exchange rates on the value of the company PT Bank Mandiri (Persero) Tbk. The analysis is in the form of simple and multiple linear regression analysis, correlation coefficient analysis, coefficient of determination analysis, and hypothesis testing. The result is that there is a moderate relationship with a positive correlation between the inflation rate and foreign exchange rates on the value of the company with a correlation value (R) of 0.595 and linear regression analysis with the equation Y = 319.972 + 71.431X1 + 0.843X2 besides that obtained Kd of 0.354 and distribution Fhitung 15.639 > Ftabel 3.162 means Ho is rejected and Ha has accepted means that overall the inflation rate and foreign exchange rates together affect the value of the company at PT. Bank Mandiri (Persero) Tbk
Moist Stability of a Baroclinic Zonal Flow with Conditionally Unstable Stratification

Abstract The moist stability of a midlatitude zonal flow with a conditionally unstable layer in the presence of an Ekman layer is investigated. The vertical velocity employed in a simplified Kuo's parameterization is sustained by baroclinic wave forcing, diabatic heating and Ekman pumping. A general dispersion relation and eigenfunction are derived analytically for a class of flows with various vertical heating profiles. The moist unstable mode may be regarded as a baroclinic wave modified by the bulk effect of the convective heating, for which the fundamental dependences of the baroclinic growth rate on the Burger number and vertical shear remain qualitatively valid. Waves longer than the Rossby radius of deformation are not appreciably affected, while the shorter waves are significantly destablized by the convective heating. The growth rates and wavelengths of the most unstable modes are nonlinear functions of the averaged specific humidity of the moist layer, and there is an optimum specific humidity t.
A New Method for Tracking Configuration for Dirichlet Process Sampling

The method of fitting a hierarchical model with Dirichlet process mixing is a versatile tool for data analysts. It has been applied to density estimation, classification, clustering, and high dimensional data analysis. Many computing algorithms have been proposed to evaluate this mixture. Different labels in the algorithm that assign data points into clusters may actually yield the same partition configuration. This paper makes this notion rigorous by establishing an equivalence theorem. Thus, we would recommend adding the step of checking for equivalent configurations to the algorithms for evaluating hierarchical Dirichlet process mixing models for improved results, especially when cluster assignments are the major goals of the analysis. DOI: http://dx.doi. org/10.4038/sljastats.v5i4. 7781