Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF PREGNANCY INTENSION RISK FACTORS IN INDONESIA WITH DEMOGRAPHIC HEALTH SURVEY (DHS) IN 2017

The pregnancy intention can be divided into two, namely the desired pregnancy and unwanted pregnancy/Kehamilan Tidak Diinginkan (KTD). Unwanted pregnancy can cause negative impacts on the mother, such as not carrying out antenatal care (ANC) and abortion. Besides, babies born from KTD are more at risk of experiencing low birth weight and not getting breast milk/Air Susu Ibu (ASI) from their mothers. Therefore, the risk factors of pregnancy intention need to be known to prevent the incidence of KTD in Indonesia. This study analyzes the risk factors for pregnancy intention in Indonesia, such as maternal age, parity, sex of the baby born, mother's work status, education level, type of area of residence, family economy, and history of contraceptive use or family planning/Keluarga Berencana (KB). The research design was cross-sectional with analysis using logistic regression statistical tests. This study used secondary data from the 2017 Demographic Health Survey (DHS) with 14,778 subjects. The results of this analysis are the risk factors for pregnancy intention in Indonesia, namely the age of the mother (p-value = 0,000 and OR = 1.403), parity (p-value = 0,000 and OR = 2,860), the type of area of residence (p-value = 0,000 and OR = 1,518) and history of use of family planning (p-value = 0.000 and OR = 0.711). The risk of adverse events is greater in mothers aged <20 years and> 35 years, parity more than 3, residing in urban areas, and having a history of using family planning. There is no significant relationship between the sex of the baby, the mother's employment status, education level, and family economy on pregnancy intention.
CUSTOMER SATISFACTION ON RETAIL

The main purpose of this research is to study about consumer satisfaction. The study looks into the numeric brand preference and it’s various attributes like quality and policies to analyse satisfaction of consumers, to enhance its resources in the best way to respond to consumers’ needs and wants and also to enhance the products distribution. It's to analyse the problems consumers face and to rectify them in the best possible ways so as to improve customer retention and attract new consumers to increase sales of the products. The focus of the study is based on the consumers satisfaction in the city of Port Blair, using a sample size of 120 consumers was taken into consideration. The study to find satisfaction among consumers provides results that are relevant, reliable, and valid and have predictive financial capability. Customer satisfaction research should be done with greatest care. Measuring customer satisfaction must be a continuously, consistent, timely, accurate and reliable process. This is where a new customer satisfaction approach becomes a powerful strategic business development tool for organisation.
CONTEMPORARY APPROACHES IN INSULIN ADMINISTRATION FOR THE TREATMENT OF DIABETES

Diabetes mellitus is a metabolic disorder, caused by dysfunction of insulin secretion or insulin function or both, leading to persistent hyperglycemia and other vascular complications as the disease progresses. Several factors contribute to the progression of the disease, such as family history or genetics, environmental factors, and various genetic disorders. A sedentary lifestyle, unhealthy diet, physical inactivity, and obesity are the major responsible factors. Treatment of diabetes is started with the use of drugs such as metformin, Sulfonylureas, thiazolidinediones, SGLT-2 inhibitors, DPP-4 antagonists, etc. Moreover, cellbased treatments such as the pancreas or pancreatic cell transplantations are being explored broadly for the treatment of type-1 diabetes mellitus. Whereas, insulin is the major player in the treatment of diabetes mellitus. For several decades, insulin has been administered as a subcutaneous injection, several times a day. However, the subcutaneous route has several limitations, such as pain at the injection site, infections due to improper administration/self-administration, hypertrophy due to deposition of insulin at the site of injection, patient discomfort; fail to achieve blood glucose control in several patients. This led to the exploration of alternative routes for insulin delivery, soon after its discovery. Various non-invasive and novel routes have been studied, including oral route, nasal route, pulmonary, transdermal route, and buccal, ocular, intrauterine, and rectal routes. These routes have proven successful up to some extent and several complications are also associated with these novel delivery systems. Toxicity associated with long-term use, safety and efficacy studies, feasible formulation of these delivery systems for efficient human use, still need to be explored deeply. In this review, we will study diabetes mellitus, alternative insulin delivery routes, and various advantages and disadvantages associated with formulations of these routes. Index Terms – diabetes mellitus, insulin delivery, insulin, novel drug delivery
PERFORMANCE ANALYSIS OF THE IOPES SEAMLESS INDOOR-OUTDOOR POSITIONING APPROACH

Tracking the members of civil protection or emergency teams is still an open issue. Although outdoors tracking is routinely performed using well-seasoned techniques such as GNSS, this same problem must be still solved for indoors situations. There exist several approaches for indoor positioning, but these are not appropriate for tracking emergency staff in real-time: some of these approaches rely on existing infrastructures; others have not been tested in light devices in real-time; none offers a combined solution. The IOPES project seeks to solve or at least alleviate this problem by building a portable, unobtrusive, lightweight device combining GNSS for outdoor positioning and visual-inertial odometry / SLAM for the indoors case. This work, the third of the IOPES series, presents the analysis of the performance results obtained after developing and testing the first IOPES prototype. To do it, the operational aspects of the prototype, the real-life scenarios where the tests took place and the actual results thus obtained are described.
THE IMPACT OF MOBILE BANKING APPLICATION ON CLIENT INTERACTION WITH YEMEN BANKS

Purpose: The purpose of the study is to analyse the impact of a mobile banking application on client interactions with banks in Yemen. Methodology: Data were collected using structured questionnaires. The Statistical Package for the Social Sciences (SPSS) was used for statistical analysis. Descriptive statistics and correlation values were used to report the results. This paper coverers the barriers and the factors that might affect badly on the performance of the mobile banking services in the Yemen banks industry. Findings: The findings of this study show that the traditional method of banks is changing; the entire system of banks has been changed from the traditional old way to the advanced way, and The traditional way is a paper-based banking provider while the new way is to use the technology as a method to enhance the performance of the banks such as the usage of mobile banking. In addition, the result of this paper is that there is a link between the user stratification and supportive access factor, and they are related to mobile banking. However, recently mobile banking is being adopted scientifically in recent years as most of the systems are become computerized and mobile phones are being in the market more than computers. Although, the emerging new technology to the banks' services market is exposed to barriers to the adoption of mobile banking services. The results of this current study will provide insights into the factors that affect the successful uptake of mobile banking services among clients of Yemeni banks.
Credibility Detection on Twitter News Using Machine Learning Approach

Social media presence is a crucial portion of our life. It is considered one of the most important sources of information than traditional sources. Twitter has become one of the prevalent social sites for exchanging viewpoints and feelings. This work proposes a supervised machine learning system for discovering false news. One of the credibility detection problems is finding new features that are most predictive to better performance classifiers. Both features depending on new content, and features based on the user are used. The features' importance is examined, and their impact on the performance. The reasons for choosing the final feature set using the k-best method are explained. Seven supervised machine learning classifiers are used. They are Naïve Bayes (NB), Support vector machine (SVM), Knearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Maximum entropy (ME), and conditional random forest (CRF). Training and testing models were conducted using the Pheme dataset. The feature's analysis is introduced and compared to the features depending on the content, as the decisive factors in determining the validity. Random forest shows the highest performance while using user-based features only and using a mixture of both types of features; features depending on content and the features based on the user, accuracy (82.2 %) in using user-based features only. We achieved the highest results by using both types of features, utilizing random forest classifier accuracy(83.4%). In contrast, logistic regression was the best as to using features that are based on contents. Performance is measured by different measurements accuracy, precision, recall, and F1_score. We compared our feature set with other studies' features and the impact of our new features. We found that our conclusions exhibit high enhancement concerning discovering and verifying the false news regarding the discovery and verification of false news, comparing it to the current results of how it is developed.
A FRAMEWORK FOR GENERIC SPATIAL SEARCH IN 3D POINT CLOUDS

Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.
Deep Learning-based mitosis detection in breast cancer histologic samples

This is the submission for mitosis detection in the context of the MIDOG 2021 challenge. It is based on the two-stage objection model Faster RCNN as well as DenseNet as a backbone for the neural network architecture. It achieves a F1-score of 0.6645 on the Preliminary Test Phase Leaderboard.
Lorentzian quantum cosmology goes simplicial

We employ the methods of discrete (Lorentzian) Regge calculus for analysing Lorentzian quantum cosmology models with a special focus on discrete analogues of the no-boundary proposal for the early universe. We use a simple four-polytope, a subdivided four-polytope and shells of discrete three-spheres as triangulations to model a closed universe with cosmological constant, and examine the semiclassical path integral for these different choices. We find that the shells give good agreement with continuum results for small values of the scale factor and in particular for finer discretisations of the boundary three-sphere, while the simple and subdivided four-polytopes can only be compared with the continuum in certain regimes, and in particular are not able to capture a transition from Euclidean geometry with small scale factor to a large Lorentzian one. Finally, we consider a closed universe filled with dust particles and discretised by shells of three-spheres. This model can approximate the continuum case quite well. Our results embed the no-boundary proposal in a discrete setting where it is possibly more naturally defined, and prepare for its discussion within the realm of spin foams.
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form of task specification, as they are already grounded in the robot's observation space. However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks. Natural language provides a convenient and flexible alternative for task specification, but comes with the challenge of grounding language in the robot's observation space. To scalably learn this grounding we propose to leverage offline robot datasets (including highly sub-optimal, autonomously collected data) with crowd-sourced natural language labels. With this data, we learn a simple classifier which predicts if a change in state completes a language instruction. This provides a language-conditioned reward function that can then be used for offline multi-task RL. In our experiments, we find that on language-conditioned manipulation tasks our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%, and is able to perform visuomotor tasks from natural language, such as"open the right drawer"and"move the stapler", on a Franka Emika Panda robot.
EMOTIONAL MATURITY AND SELF ESTEEM AS PREDICTORS OF MARITAL ADJUSTMENT AMONG HOMEMAKERS AND WORKING WOMEN

The present study was done to investigate emotional maturity and self esteem as predictors of marital adjustment among home makers and non-working women. Emotional maturity can be defined as how well people are able to respond to different situations, manage their emotions and respond in an adult-like manner while dealing with others. It has a relationship with the individual’s adjustment levels. One’s esteem is their level of self-value; it is how we perceive our value to the world and how valuable we think we are to others. It affects our trust in others, our relationships, our work nearly every part of our lives. Positive self gives us the strength to take charge of our lives and grow from our mistakes without the fear of rejection. The sample consisted of 100 working and 100 non working in the age range of 30 to 45 years. There was a minimal level of difference among the scores obtained by the home makers and working women . Emotional maturity and self esteem levels were higher for the case of home makers than that of working women. But the both the variables together were able to predict a higher level of marital adjustment for working women that accounted for 32% in comparison of 22% obtained in case of home makers. Three tools were administrated to the participants of data collection. The scales used are Rosenberg Self-Esteem Scale (RSES;1960), Emotional Maturity Scale (EMS; Singh and Bhargava, 1990) ,Marital Adjustment Scale (MAS; Locke and Wallace ,1959).
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation

Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English<->German and English<->Chinese) verify the effectiveness and superiority of the proposed approach.
Spin-
S
 impurities with XXZ anisotropy in a spin-
12
 Heisenberg chain

Ayushi Singhania,1, ∗ Masahiro Kadosawa,2 Yukinori Ohta,2 Sanjeev Kumar,1 and Satoshi Nishimoto3, 4 Department of Physical sciences, Indian Institute of Science Education and Research Mohali, Sector 81, S.A. S. Nagar, Manauli PO 140306, India Department of Physics, Chiba University, Chiba 263-8522, Japan Department of Physics, Technical University Dresden, 01069 Dresden, Germany Institute for Theoretical Solid State Physics, IFW Dresden, 01069 Dresden, Germany (Dated: September 3, 2021)
Solving Inverse Problems with Conditional-GAN Prior via Fast Network-Projected Gradient Descent

The projected gradient descent (PGD) method has shown to be effective in recovering compressed signals described in a data-driven way by a generative model, i.e., a generator which has learned the data distribution. Further reconstruction improvements for such inverse problems can be achieved by conditioning the generator on the measurement. The boundary equilibrium generative adversarial network (BEGAN) implements an equilibrium based loss function and an auto-encoding discriminator to better balance the performance of the generator and the discriminator. In this work we investigate a network-based projected gradient descent (NPGD) algorithm for measurement-conditional generative models to solve the inverse problem much faster than regular PGD. We combine the NPGD with conditional GAN/BEGAN to evaluate their effectiveness in solving compressed sensing type problems. Our experiments on the MNIST and CelebA datasets show that the combination of measurement conditional model with NPGD works well in recovering the compressed signal while achieving similar or in some cases even better performance along with a much faster reconstruction. The achieved reconstruction speed-up in our experiments is up to 140-175.
Bimodule coefficients, Riesz transforms on Coxeter groups and strong solidity

In deformation-rigidity theory it is often important to know whether certain bimodules are weakly contained in the coarse bimodule. Consider a bimodule $H$ over the group algebra $\mathbb{C}[\Gamma]$, with $\Gamma$ a discrete group. The starting point of this paper is that if a dense set of the so-called coefficients of $H$ is contained in the Schatten $\mathcal{S}_p$ class $p \in [2, \infty)$ then the $n$-fold tensor power $H^{\otimes n}_\Gamma$ for $n \geq p/2$ is quasi-contained in the coarse bimodule. We apply this to gradient bimodules associated with the carr\'e du champ of a symmetric quantum Markov semi-group. For Coxeter groups we give a number of characterizations of having coefficients in $\mathcal{S}_p$ for the gradient bimodule constructed from the word length function. We get equivalence of: (1) the gradient-$\mathcal{S}_p$ property introduced by the second named author, (2) smallness at infinity of a natural compactification of the Coxeter group, and for a large class of Coxeter groups: (3) walks in the Coxeter diagram called parity paths. We derive several strong solidity results. In particular, we extend current strong solidity results for right-angled Hecke von Neumann algebras beyond right-angled Coxeter groups that are small at infinity. Our general methods also yield a concise proof of a result by T. Sinclair for discrete groups admitting a proper cocycle into a $p$-integrable representation.
Morphological characterization of Terminalia chebla (Aralu) and their propagation techniques

Terminalia chebula (Aralu) is a valuable medicinal plant, which has an amazing power to combat various kinds of ailments.The plant becomes endangered due to its poor natural resurgence, unsustainable harvesting together with over exploitation. The conventional propagation of Terminalia chebula is through seeds and their germination is very low.This study was focused on morphological characterization of Terminalia chebula germplasm and to select suitable vegetative propagation method. A field survey was carried out in Galle and Matara districts, in Southern province of Sri Lanka for germplasm characterization. Ten plants were selected from each district. Information on growing environment, plant, leaf and fruit characters were collected and compared with the herbarium of Royal Botanical Garden. Data were subjected to a cluster analysis and two sample t test. In subsequent experiments, grafting, budding and air layering were practiced and they were carried out at the medicinal plant garden, Pinnaduwa, Galle. One-year old healthy rootstocks of Terminalia catappa (kottamba) and Terminalia bellerica (bulu) were used for wedge grafting and patch budding while keeping Terminalia chebula as the scion. Completely randomized design in factorial arrangement was used with 9 replicates. Air layering was carried out using two hormone types together with their three different concentrations including 500,700 and 900 ppm. Randomized complete block design was used with three replicates. Six different categories of Terminalia chebula were identified by cluster analysis. Both Terminalia catappa and Terminalia bellerica rootstocks can be used for vegetative propagation of Aralu. The highest survival percentages of 78% and 67% were recorded respectively in wedge grafted Terminalia chebula using Terminalia catappa root stock and patch budding with Terminallia bellerica. Types and concentrations of hormones used were not effective for Terminalia chebula air layering. Different categories of Aralu reported in study provide clues for its existing considerable genetic variability. Chemical compounds present in these six different categories warrant further investigation.
Generative Models for Multi-Illumination Color Constancy

In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color constancy method. GANs are exploited to model the illumination estimation problem as an image-to-image domain translation problem. Additionally, a novel multi-illumination data augmentation method is proposed. Experiments on single and multi-illumination datasets show that our methods outperform sota methods.