Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Evaluation Service Quality with Fuzzy - Banking Service Quality in East Java, Indonesia

The research aimed ad analyzing and evaluating the banking service quality in East Java. The research to know the service standard which is suitable for the customer's desire and arranges an improvement priority of service quality that should be given to the customers. The method used to measure quality is FuzzyBanking Service Quality. Service quality analysis by using fuzzy Banking Service Quality which is aided by using the MATLAB application. Based on the research results, customers valuation average toward the bank customer level is good. The services given have fulfilled the customer expectation. There is service which becomes the priority to be improved that is e-chanel, e-Banking, sms banking and ATM, competitive interest and parking lot for customers. Keywords—fuzzy-banking service quality; e-channel; ebanking; sms banking
Tumor progression is accompanied by significant changes in the levels of expression of polyamine metabolism regulatory genes and clusterin (sulfated glycoprotein 2) in human prostate cancer specimens.

Using Northern blotting, the expression levels of the genes for polyamine metabolism regulatory proteins and clusterin have been measured in a series of 23 human prostate cancers (CaPs) dissected from radical prostatectomy specimens. Patient matched, nontumor tissue was dissected from benign areas of the gland. The results indicate that transcripts encoding ornithine decarboxylase (ODC), ODC antizyme, adenosylmethionine decarboxylase, and spermidine/spermine N 1 -acetyltransferase (SSAT) were significantly higher, whereas clusterin (sulfated glycoprotein 2) mRNA was significantly lower in tumors compared with the benign tissue. All mRNA levels were compared with those of histone H3 and growth arrest-specific gene 1 , markers of cell proliferation and cell quiescence, respectively, and glyceraldehyde 3-phosphate dehydrogenase , a housekeeping gene. In poorly differentiated and locally invasive CaPs and in tumors with unfavorable prognosis or total prostate-specific antigen (PSA) levels >10.0 ng/ml at diagnosis, an overall increase in the levels of H3 mRNA and a decrease in growth arrest-specific gene 1 mRNA was detected, indicative of higher proliferation activity, whereas the differences in expression levels for the polyamine metabolism and clusterin genes were higher. ODC and SSAT changes were positively correlated in normal tissue but not in high-grade cancer, whereas ODC antizyme and SSAT changes were positively correlated in more malignant CaPs but not in normal tissue. Tumor classification based on the changes in expression levels of all of the genes studied could be correlated to differentiation grade and local invasiveness classification systems in 72.2 and 83.3% of the cases, respectively. In a 1-year follow-up period, three patients whose CaPs ranked as less aggressive according to clinical staging, but classified as advanced cancers with the proposed molecular classification, showed increases in total PSA levels, indicative of tumor relapse. Thus, molecular classification, based on gene expression, may enhance the available prognostic tools for prostate tumors.
Spectrum Allocation and Power Control in Full-Duplex Ultra-Dense Heterogeneous Networks

A full-duplex ultra-dense network (FDUDN) has been envisioned as a promising network paradigm for spectrum efficiency enhancement. This paper presents a novel joint spectrum and power management scheme, which maximizes the total capacity of the FDUDN, under given quality-of-service and cross-tier interference constraints. The proposed scheme is decomposed into inter-cell and intra-cell allocation. A novel approach, which performs initial capacity-maximization allocation and successive adjustment based on the constraints, is proposed for the allocation of the inter-cell subchannels. The inter-cell power control is formulated as a non-convex optimization problem, and variable substitution is used to transform it into a convex one. Furthermore, we solve this problem through a low-complexity heuristic scheme, which utilizes the water-filling theorem in inter-cell power allocation. Finally, a time-sharing relaxation method is adopted to solve the intra-cell subchannel allocation problem and reduce the computation complexity. The Computer simulation demonstrates the enhancement effect of the proposed scheme in terms of the capacity, spectrum efficiency, and power efficiency.
Adenoid nasopharyngeal ratio in children suffering from otitis media with effusion

Introduction: Otitis media (OM) is a major health problem that usually results from adenoid hypertrophy. The diagnosis is given based on symptoms and imaging studies, such as mouth breathing and lateral neck radiography (LNR), respectively. However, there are huge controversies regarding the LNR role in the management of patients with OM. This study aimed to evaluate adenoid nasopharyngeal ratio (AN ratio) in children suffering from OM with effusion (OME) and related
Enhanced answer selection in CQA using multi-dimensional features combination

: Community Question Answering (CQA) in web forums, as a classic forum for user communication, provides a large number of high-quality useful answers in comparison with traditional question answering. Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects, and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multi-dimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks: (a) classifying answers as good, bad, or potentially associated with a question, and (b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is signiﬁcantly more efﬁcient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.
Performance Study of Convolutional Coded OFDM

Orthogonal Frequency Division Multiplexing (OFDM) is a widely known modulation scheme used for high speed data transmission (like video) on multipath fading channels. The multipath components of these channels cause a frequency selective fading that seriously degrade the OFDM transmission, due to the erroneous exist on the received data. Time guard-interval is used to reduce intersymbol interference (ISI) and Intercarrier interference (ICI). Convolutional coding protects digital data from errors by selectively introducing redundancies in the transmitted data. In this paper a study of a combined use of Convolutional coding and OFDM technique is presented. The effect of coding rates and constraint lengths of the Convolutional code and the time guard interval of the OFDM symbols are studied under frequency selective environment through computer simulation. Recommendations for design issues are also discussed.
Cytotoxic, Antioxidant and Antifatigue Activities of 8 Cultivated Varietiesmyricarubra Kernel and Residue Extracts

The development of bayberry product has been long restricted to the under utilised residue and the waste of the kernels. In this paper, we aimed to investigate whether the waste part of bayberry produced during juice processing may possess potent beneficial role to use for our health promotion. In fruit residue and kernels parts from eight different cultivars, determination of cyaniding-3-O-glucoiside (C3G) levels using HPLC (high performance liquid chromatography), various functional assays including antioxidant, and antifungal assays in vitro, cytotoxic assays and also antifatigue assays in vivo using reducing testes, rectangular cantilever method, small microtiter-plate method and mice loaded swimming test, respectively. The results of the HPLC method showed the extracts of residue were rich in cyaniding-3-O-glucoside, especially in Myricarubra Sieb. Et Zucc. cv. Biqi (212.8 mg·g-1 from Ninghai and 185.7 mg·g-1 from Yuyao). The results of the activity experiments showed the TE values of the extract from Biqi were between 334 and 506 mmol·g-1 detected by different methods. The duration of loaded swimming to exhaust prolonged from 614±158s to 1012±205s. The MIC (minimal inhibitory concentration) values of different species of kernels extracts against 8 different fungi ranged from 0.5000 to 0.0625 mg·mL-1. The LC50 were from 49-316 μg·mL-1. In conclusion, the waste part of bayberry produced during juice processing, the residue and kernels, is of potential application value. However, further study is required.
A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.
A study of the factors contributing to the success of industrial cluster implementation in Thailand

Title of Dissertation A Study of the Factors Contributing to the Success of Industrial Cluster Implementation in Thailand Author Ariyaporn Suranartyuth Degree Doctor of Philosophy (Development Administration) Year 2010 This research studies the factors contributing to the success of industrial cluster implementation in Thailand. The objectives of this research are: 1) to study the criteria of effectiveness and efficiency, 2) to identify the factors that lead to successful industrial cluster implementation in Thailand from an implementation perspective, and 3) to receive suggestions for improving the implementation of the industrial clusters. The research is conducted at the enterprise level of analysis. Secondary data from questionnaires belonging to the Department of Industrial Promotion (DIP) were used for the research. The respondents were enterprises that joined the industrial cluster project implemented by the DIP in fiscal year 2007. During that year, the DIP implemented 22 industrial clusters in various areas in Thailand. The questionnaires that were responded to were collected randomly for each industrial cluster with an expectation of 10 answered questionnaires per industrial cluster. Therefore, the questionnaires represented 220 enterprises. A Likert scale was used for the questionnaires but four kinds of Likert scales appeared in the research: 1) a 10-point Likert scale from level 1 to level 10; 2) a 5-point Likert scale from level 0 to level 5 and level 1 to level 5; 3) a 3-point Likert scale from level 1 to level 3; and 4) the decision to say “yes” or “no.” The different levels ranged from strongly disagree at level 1 to higher levels of agreement; level 0 meant “no opinion. ” For the research methodology, cross-tabulation was used to study such characteristics of fundamental statistics as frequency distribution and percentage. The
Financial Restructuring in Fresh‐Start Chapter 11 Reorganizations

We find that firms substantially reduce their debt burden in “fresh-start” Chapter 11 reorganizations, yet they emerge with higher debt ratios than what is typical in their respective industries. While cross-sectional regressions reveal that post-reorganization debt ratios are more in line with the predictions of the static-tradeoff theory, they also reveal that pre-reorganization debt ratios affect post-reorganization debt ratios. Collectively, these results suggest that impediments in Chapter 11 prevent firms from completely resetting their capital structures. We also find that firms that reported positive operating income leading up to Chapter 11 emerge faster, suggesting that it is quicker to remedy strictly financial distress than economic distress.
Chordal replacement versus quadrangular resection in degenerative posterior mitral leaflet repair.

Background As an alternative to quadrangular resection (QR), little is known of the potential of chordal replacement (CR) for treating posterior mitral leaflet (PML) prolapse when comparing these two techniques. This study aimed to assess mid- to long-term outcomes of CR versus QR for isolated degenerative PML (idPML) repair. Methods We reviewed 112 consecutive patients using CR or QR for idPML repair from 4/2010 to 12/2015. Outcomes were compared before and after propensity score matching. Results CR was more used through the minimally invasive approach (CR 59.4% vs. QR 9.4%, P<0.001). At discharge mitral regurgitation (MR) was successfully rectified to a mild or less degree in both groups (CR P<0.001, QR P<0.001; between groups: P=0.337). Group CR showed much shorter postoperative time (CR 9.9±4.0 vs. QR 14.0±8.3 days, P<0.004) and higher event-free survival rate between matched patients [56 months, CR 85.7% vs. QR 30.8%, P (log-rank) =0.017], however QR showed better freedom from above-mild recurrent MR (MR ≥2.5+) during follow-up [60 months, CR 50.2% vs. QR 96.3%, P (log-rank) =0.061]. Cox regression analysis might suggest that CR technique was a risk factor for recurrent MR [CR over QR, hazard ratio (HR) 2.149; 95% CI: 0.974-4.744; P=0.058; adjusted for surgical approach, gender, age, preoperative MR and ejection factor (EF)]. Conclusions CR is more often used with the minimally invasive approach with less complications and shorter hospital stay. Nonetheless, CR is associated with recurrent MR development over time. Retaining of MV competence after CR demands attention and further investigation.
Inbreeding and fertility in Egyptian clover, Trifolium alexandrinum

Selection for manual tripping (MT) was effective in improving self-fertility (SF) in all populations and generations over original parents. Selection for MT was accompanied by improving fertility of open pollinated (OP) populations. Self-seed set (ST) was very poor. Some individual selections set seeds up to 88% in MT materials. The enigma of fertility, sterility and absence of inbreeding depressions in berseem was discussed. This was explained as inbreeding tolerance, due to selection for good vigor accompanying inbreeding. The results indicated that selection for high MT seed set could be accompanied by selection for good forage yield. An approach was suggested to develop new composite varieties characterized by high seed fertility and high forage yield. Improved selections for seed production was mainly due to profuse flowering and more flowers/ inflorescence, whereas improved forage yield was mainly due to more tillers / plant and to less degree of plant height. An approach was outlined to produce new composites with high seed setting and good forage. T. alexandrinum is self-compatible, but needs tripping to stimulate self seeds. This is mainly due to the relative position of male and female organs and the presence of bubbles on stigma. Molecular characterization revealed polymorphic differences between Io and selected inbred.
Understanding platform-based product development : a competency-based perspective

In recent years, the competition in product development and innovation has intensified through increased demand heterogeneity and shorter product life cycles. An increasingly popular strategy to meet the mentioned challenges is the use of a platform-based approach to create a successful product family for the purpose of increasing variety, shortening lead-times and reducing costs. However, unlike the wellpublished benefits of platform-based product development, a clear gap in literature still exists when it comes to understanding how to implement and manage product families and their successive platforms. We do not know enough about the key attributes of platform-based product development which can contribute to a competitive advantage, which in turn leads to the success of a platform. In addition, the impacts of a turbulent environment on platform-based product development remain largely unknown. Given these limitations, our research is directed at building a framework to better manage platform-based product development from a competency perspective and specifically, we want to addresses the following research question: How can firms improve their platform-based product development performance, from a competency-based perspective? Based on existing literature and the interviews in four leading technology-driven companies, we propose the concept of product platform competency, and identify its antecedents. We hypothesize that such competency directly affects the performance of platform-based product development. However, these effects are moderated by the turbulence of the environment. To test these hypotheses, a large-scale survey is conducted in the United States. After analyzing the data by the means of structural equation modeling using LISREL 8.7 and hierarchical multiple regression using SPSS 15.0, we find sufficient empirical evidences to support most of the hypotheses. The results lend support to the concept of product platform competency which comprises reusability of subsystems, compatibility of subsystem interfaces and extensibility of platform-based products. Our results show that a formalized development process, design knowledge dissemination across platform-based products, continuity of platform-based product development team and existence of a champion in platform-based product development significantly affect product platform competency. Additionally, our findings further suggest that in a high technologically turbulent environment, some of these factors have even greater impact on product platform competency. Based on the results of this study, product platform competency can be considered as the underlying cause of high performance of platform-based product development. Therefore, managers are strongly encouraged to apply the aforementioned four management practices to improve their product platform competency, especially in a high technologically turbulent environment. This in turn should lead to reduction in the development cost and time.
Reinforcement Learning Meets Information Seeking: Dynamic Search Challenge

We introduce the Dynamic Domain Track, which was a challenge held in Text REtrieval Conference (TREC) from 2015-2017. The challenge aims to improve search engine for interactive scenarios, where we believe reinforcement learning would play an important role. We share our design ideas of the challenge and discuss what we have learned from it. This paper has been published in the Machine Learning Competitions for All (CiML) Workshop at NeurIPS 2019.
Improving Outfit Recommendation with Co-supervision of Fashion Generation

The task of fashion recommendation includes two main challenges: visual understanding and visual matching. Visual understanding aims to extract effective visual features. Visual matching aims to model a human notion of compatibility to compute a match between fashion items. Most previous studies rely on recommendation loss alone to guide visual understanding and matching. Although the features captured by these methods describe basic characteristics (e.g., color, texture, shape) of the input items, they are not directly related to the visual signals of the output items (to be recommended). This is problematic because the aesthetic characteristics (e.g., style, design), based on which we can directly infer the output items, are lacking. Features are learned under the recommendation loss alone, where the supervision signal is simply whether the given two items are matched or not. To address this problem, we propose a neural co-supervision learning framework, called the FAshion Recommendation Machine (FARM). FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information. FARM enhances visual matching by introducing a novel layer-to-layer matching mechanism to fuse aesthetic information more effectively, and meanwhile avoiding paying too much attention to the generation quality and ignoring the recommendation performance. Extensive experiments on two publicly available datasets show that FARM outperforms state-of-the-art models on outfit recommendation, in terms of AUC and MRR. Detailed analyses of generated and recommended items demonstrate that FARM can encode better features and generate high quality images as references to improve recommendation performance.
Detection, Recognition and Pose Estimation of Tabletop Objects

—The problem of cleaning a messy table using Deep Neural Networks is a very interesting problem in both social and industrial robotics. This project focuses on the social application of this technology. A neural network model that is capable of detecting and recognizing common tabletop objects, such as a mug, mouse, or stapler is developed. The model also predicts the angle at which these objects are placed on a table,with respect to some reference. Assuming each object has a ﬁxed intended position and orientation on the tabletop, the orientation of a particular object predicted by the deep learning model can be used to compute the transformation matrix to move the object from its initial position to the intended position. This can be fed to a pick and place robot to carry out the transfer.This paper talks about the deep learning approaches used in this project for object detection and orientation estimation.
Single Currency in the ECOWAS Region: Does it Make Sense?

This study investigated empirically the possibility of forming an Optimum Currency Area (OCA) among member countries of Economic Community of West African States (ECOWAS) region. Under OCA, member countries would share a common currency (like, the euro) while foregoing their autonomy with respect to their use of monetary policy instruments. We observed that the countries were good candidates for forming an OCA, given the existence of a long run relationship in their trend (permanent) output component. Hence, the plausibility for forming an OCA.