Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors related to survival discharge in trisomy 18: A retrospective multicenter study

Infants with trisomy 18 (T18) previously had a poor prognosis; however, the intensive care of these patients has markedly diversified the prognosis. We investigated the current situation of patients with T18, clarified factors for survival discharge, and surveyed actual home healthcare. A total of 117 patients with T18 admitted to nine institutions between 2000 and 2015 were retrospectively investigated. After excluding four patients whose outcomes were unclear, we divided 113 patients into two groups—the survival discharge group (n = 52) and the death discharge group (n = 61)—and compared maternal factors, perinatal factors, neonatal factors, and therapeutic factors between the groups. In addition, home healthcare, readmission, utilization of respite care and home nursing, and cause of death among the survival group were surveyed. Fifty‐two (44%) patients with T18 survived at discharge and their 1‐year survival rate was 29%. The survival group had a longer gestation period, larger physique, and longer survival time, compared to the death group. Independent factors associated with survival discharge were the absence of an extremely low birthweight infant (ELBWI), the absence of esophageal atresia and patent ductus arteriosus, and cardiovascular surgery. All surviving patients required some home healthcare. The most frequent cause of death was a respiratory disorder. We recommend discussing the treatment strategy with families in the presence of neonatologists or pediatric surgeons, who can explain differences in prognosis, based on the gestation period, birthweight, severity of cardiovascular disease, and cardiovascular surgery.
Converting Utility Meters from Analogue to Smart based on Deep Learning Models

In this paper, we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition, it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN), where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter.
A Bayesian Approach for Fault Diagnosis in an Irrigation Canal

In this paper, we present a fault diagnosis approach based on a probabilistic model, more specifically a Bayesian one, while relying on a distributed Multi-Agent Systems paradigm. First, a networked control water system benchmark is introduced, then a Bayesian approach for fault diagnosis is provided where inference and time dependencies between operating modes of systems are tackled. It is suggested that a Multi-Agent System, where entities interact and cooperate in a distributed architecture while expressing subjective knowledge of the system, is relevant regarding fault handling and offers more flexibility in the implementation and data collection.
Unmanned Surface Vehicle Collision Avoidance Trajectory Planning in an Uncertain Environment

Unmanned surface vehicles (USVs) can encounter undetected moving obstacles while sailing along a planned global path. USVs need to plan collision avoidance trajectories for moving obstacles. In this paper, an algorithm based on the Gaussian mixture model (GMM) and Gaussian process regression (GPR) is proposed to predict the motion of moving obstacles and estimate the uncertainty of the prediction. A nonlinear finite-time velocity obstacle (NLFVO) method is developed for obstacle avoidance. The NLFVO method analyzes the velocity of the USV and the predicted uncertain velocity vectors of the moving obstacles and selects a collision-free velocity for the USV and minimizes the objective function. To enable the actual navigation of USVs, the International Regulations for Preventing Collisions at Sea (COLREGs) are considered in addition to the NLFVO method. The simulation results show that the prediction algorithm can effectively predict the trajectory of moving obstacles, and the NLFVO method can obtain a collision-free trajectory for a USV.
GAMMA: Automating the HW Mapping of DNN Models on Accelerators via Genetic Algorithm

DNN layers are multi-dimensional loops that can be ordered, tiled, and scheduled in myriad ways across space and time on DNN accelerators. Each of these choices is called a mapping. It has been shown that the mapping plays an extremely crucial role in overall performance and efficiency, as it directly determines the amount of reuse that the accelerator can leverage from the DNN. Moreover, instead of using a fixed mapping for every DNN layer, research has revealed the benefit of optimizing per-layer mappings. However, determining the right mapping, given an accelerator and layer is still an open question. The immense space of mappings (or map-space) makes brute-forced exhaustive search methods unapproachable. In this paper, we propose a domain-specific genetic algorithm-based method, GAMMA, which is specially designed for this HW-mapping problem. In contrast to prior works that either target simple rigid accelerators with a limited map-space or choose from a restricted set of mappings, we construct an extremely flexible map-space and show that GAMMA can explore the space and determine an optimized mapping with high sample efficiency. We quantitatively compare GAMMA with many popular optimization methods and observe GAMMA consistently finds better solutions.
A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

The detection and recognition of human emotional states have raised recent research interests for various applications from e-learning to chronic health conditions prevention. In this paper, we proposed an emotion recognition system based on the electrocardiogram (ECG) and photoplethysmogram (PPG) signals as objectives data input sources. Three emotion states (positive, neutral, negative) were defined as classification outputs. The training and validation data were collected by Kaohsiung Medical University (KMU) from 47 participants aged from 30 to 50 years old diagnosed with chronic cardiovascular health conditions. A convolution neural network (CNN) was built to efficiently map the subject's emotions with the extracted features from both ECG and PPG signals. This emotion recognition system achieved an accuracy of 75.4% for 3 classes outputs higher or similar than other models used in other works.
Uncommon hepatic macrophagic foamy‐cell nodules in Iberian barbel (Luciobarbus bocagei Steindachner, 1864) from the Vizela River (Portugal)

The main intent of this work (after the by chance finding, in archived histological slides) is to characterize one previously non‐described liver lesion of the Iberian barbel from the Vizela River (Portugal). This ran through a textile and dyeing industrial region. The lesion type was made of groups of foamy cells (presumptive macrophages), which appear either as a “smaller non‐nodular form,” without a connective tissue capsule and displaying an irregular profile, or as a “bigger nodular form,” presenting a thin capsule and a circular profile. The nodular forms could appear multi‐layered, resembling “cross‐sectioned onions”. The lesions number, dimension, and structural complexity were greater in bigger fish, appearing only after a history of poor water quality. In extreme cases, the lesions slightly protruded the liver surface. Special histological staining proved the connective tissue nature of the capsule (and its eventual septa), the presence of proteins, glycoproteins, lipofuscin, melanin, iron (putative hemosiderin), and copper, in a variable number of foamy cells within the lesions. At times, degenerating hepatocytes appeared at the border of the lesions. It is proposed that this lesion type incorporates both macrophages and degenerating hepatocytes, looking as one (unpublished) form of a macrophage aggregate. The term “foamy‐cell nodules” was advanced for this abnormality. The lesion could have been induced by pollution, because: no parasites were ever associated with the lesion; there was co‐existence of the lesion with a water quality status scored as “bad”; such lesion neither existed in fish sampled after mitigation and remediation measures nor in reference fish.
Digital Transformation of Indonesia Banking Institution: Case Study of PT. BRI Syariah

Competition in the financial services industry has now entered the digital era, where banks are competing to carry out digital transformation. The survey results from PwC in 2018 stated that of the banks surveyed, including regional development banks, joint venture banks, and local banks, as many as 66% had indicated the digital strategy as part of the company's strategy. Digitalization can result in savings in human resources and all costs associated with them, which increases competition. This research discusses the digital transformation carried out by one of the banks in Indonesia, what is the role and impact of IT on digitalization carried out and the extent to which digitalization has been carried out in the bank. The results of the research show that IT has a big role in the digital transformation that exists in organizations, but that does not yet make an organization a digital bank.
Decision Trees for Very Early Prediction of Student's Achievement

The prediction of students' academic achievement is crucial to be conducted in a university for early detection of students at risk. This paper aims to present data mining models using classification methods based on Decision Trees (DT) algorithms to predict students' academic achievement after preparatory year, and to identify the algorithm that yields best performance. The students' academic achievement is defined as High, Average, or Below Average based on graduation CGPA. Three classifiers (J48, Random Tree and REPTree) are applied on a newly created dataset consisting of 339 students and 15 features, at the College of Computer Science and Information Technology (CCSIT). The outcome showed the J48 algorithm had an overall superior performance compared to other algorithms. Feature selection algorithms were used to reduce the feature vectors from 15 to 4 resulting in improvements in performance and computational efficiency. Finally, the results obtained help to pinpoint the preparatory year courses that impact graduation CGPA. Timely warnings, and preemptive counseling towards improving academic achievement is possible now.
Nonlinear Observability for Polynomial Systems: Computation and Examples

Controllability and observability are important system properties in control theory. For nonlinear state space systems it is difficult to check whether these properties are fulfilled or not. This paper deals with the examination of polynomial systems with respect to their observability. We present local and global observability criteria. Our methods are illustrated on several example systems.
WYE-354 restores Adriamycin sensitivity in multidrug-resistant acute myeloid leukemia cell lines

Multidrug resistance (MDR) is a major reason for the failure of acute myeloid leukemia (AML) therapy. Agents that reverse MDR and sensitize AML cells to chemotherapy are of great clinical significance. The present study developed Adriamycin (Adr)-resistant cell lines, namely K562/Adr200 and K562/Adr500, which exhibited MDR. The upregulation of ATP-binding cassette subfamily B member 1 (ABCB1) was confirmed as the mechanism of resistance by reverse transcription-quantitative polymerase chain reaction and western blot analyses. Subsequently, the role of the mammalian target of rapamycin (mTOR) kinase inhibitor, WYE-354, in sensitizing the K562/Adr200 and K562/Adr500 cell lines to Adr was evaluated. At sub-cytotoxic concentrations, WYE-354 increased Adr cytotoxicity in the K562/Adr200 and K562/Adr500 cells. WYE-354 restored Adr sensitivity in the resistant cells by inhibiting ABCB1-mediated substrate efflux, thereby leading to an accumulation of Adr, an increase in Adr-mediated G2/M cell cycle arrest and the induction of apoptosis. Furthermore, WYE-354 stimulated the ATPase activity of ABCB1, which was consistent with in silico predictions using a human ABCB1 mouse homology model, indicating that WYE-354 is a potent substrate of ABCB1. WYE-354 did not regulate the expression of ABCB1 at the concentrations used in the present study. These findings indicate that WYE-354 may be a competitive inhibitor of ABCB1-mediated efflux and a potential candidate in combination with standard chemotherapy for overcoming MDR. Further clinical investigations are warranted to validate this combination in vivo.
BayesCap: A Bayesian Approach to Brain Tumor Classification Using Capsule Networks

Convolutional neural networks (CNNs), which have been the state-of-the-art in many image-related applications, are prone to losing important spatial information between image instances. Capsule networks (CapsNets), on the other hand, are capable of leveraging such information through their routing by agreement process, making them powerful architectures for small datasets, such as medical imaging ones. Within the domain of medical imaging problems, brain tumor classification is of paramount importance, due to the deadly nature of this cancer and the consequences of the tumor misclassification. In our recent works, we showed potentials of developing CapsNet architecture for the task of brain tumor type classification. Similar to other deep learning models, however, CapsNets do not capture prediction uncertainty (coming from the uncertainty in the model weights, which is significantly important in keeping the human experts in the loop, by returning the uncertain samples. In this paper, we propose a Bayesian CapsNet framework, referred to as the $\text{BayesCap}$, that can provide not only the mean predictions, but also entropy as a measure of prediction uncertainty. Results show that filtering out the uncertain predictions can improve the accuracy, confirming that returning the uncertain predictions is an appropriate strategy for improving interpretability of the network.
The role of Mobile Skillfulness and User Innovation toward Electronic Wallet Acceptance in the Digital Transformation Era

The age of digital transformation has profound effects on the businesses’ operations and consumer behavior. Payment through electronic wallet, which is one of the solutions to digitize consumer payables and corporate receivables, is increasingly popular globally. This study had enriched the Technology Acceptance Model (TAM) to explore the role of mobile skillfulness and user innovation in the electronic wallet acceptance of consumers in the digital transformation era. Both qualitative and quantitative research methods were used to achieve the research goal. Research results affirmed the TAM model applied toward e-wallet service, besides, showed the positive effect of mobile skillfulness and user innovation on consumers’ intention to accept the e-wallet in daily transactions. Some managerial implications were also suggested for regulators at e-wallet service providers.
An Empirical Study: Computer-Mediated Communication and Collaboration among Government Employees during Flexible Working Arrangements

During the Covid-19 pandemic, the implementation of flexible working arrangements (FWA) became a trend and necessity in many work forces, including government offices. The success of FWA is affected by many factors; one of them is the effective computer-mediated communication and collaboration. This study examines the patterns and the effectiveness of computer-mediated communication and collaboration among government employees, cases in the Ministry of Communication and Informatics, during their working experience with flexible arrangement. Using quantitative approach, the data is collected through a survey to 745 employees, in June 2020. The results show that all employees have used instant messaging and online meeting applications to communicate during flexible working. Moreover, majority of the employees also have used office e-mail and document sharing tool to correspondence and collaborate remotely. Even though most employees consider that communication and coordination through information and communication technology is easy and clear, it has not been able to replace fully face-to-face meetings. Some recommendations are given to result in more effective computer-mediated communication and collaboration among government employees to support flexible working arrangements.
Dynamic IR-Drop ECO Optimization by Cell Movement with Current Waveform Staggering and Machine Learning Guidance

Excessive dynamic IR-drop degrades the circuit performance and may lead to functional failure. Existing IR-drop fixing techniques at the placement stage do not consider the time-variant property and thus cannot handle dynamic IR-drop hotspots well. In current practice, designers perform Engineer Change Order (ECO) to move out these hotspot cells based on their experience. In this paper, we present a novel dynamic IR-drop ECO optimization and prediction framework by wise cell movement. We first spread high demand current cells in a global view to stagger their current waveforms. Then, we further move IR hotspot cells close to power/ground (PG) vias for minimizing the resistance from PG pads to their PG pins. Moreover, we propose an accurate machine learning-based dynamic IR-drop prediction model to guide the final cell movement. The features of our model capture power ground network characteristics, timing information, and cumulative current drawn by cells, thus leading to a general model applicable to ECO. Experimental results show that our proposed model precisely predicts dynamic IR-drop after cell movement, and our optimization scheme can substantially alleviate dynamic IR-drop without timing degradation.
Clothing Classification using Unsupervised Pre-Training

Deep Learning has changed the way computer vision tasks are being solved in recent times. Deep Learning based approaches have achieved outstanding results in computer vision tasks including image classification, segmentation, object detection. Most of this success has been achieved by training deep neural networks on labelled data. In general, the more labelled data is fed to a deep learning model, the more accurate the model will be. However, labelling is time consuming and sometimes even impossible.Fashion and e-commerce are domains where a large amount of unlabelled data is available. There is a huge need to leverage these data without labels. The aim of this paper is to explore and evaluate the possibility and effectiveness of using massive amount of unlabelled data to build deep learning models. We compare the performance of these models with the performance of models built with labelled data. Specifically, we compare fully supervised deep learning with two deep learning methods with unsupervised pre-training. Our pre-trainings are based on clustering of features called DeepCluster and rotation as a self-supervision task. The comparison is performed on the DeepFashion dataset.Our experimental results have shown that using unsupervised pre-training can attain comparable classification accuracy ($\sim$1-4 % difference) on image classification comparing to fully supervised models. Furthermore, we have shown that our models uses five times less labelled data during the fine-tuning phase and still achieves comparable accuracy ($\sim$3-4 % difference) comparing to fully supervised models. These results demonstrate the potential of using unsupervised pre-training approaches in achieving comparable results to fully supervised models.
90% Efficient Two-Stage Multibeam Klystron: Modeling and Design Study

The efficient generation of microwave power has recently gained broad attention in the accelerator community because energy consumption is one of the major concerns for future large-scale high-energy accelerator projects. For the first time, we present a concept of the two-stage multibeam klystron with annular beams (TS MABK). It allows for a remarkably high RF power production efficiency (90%), using a compact design and delivering a cost-effective electro-mechanical solution. The design is for a 1-GHz, 20-MW klystron. The performance of the tube is validated using particle-in-cell computer simulations. In this article, we focus on technological novelties and on mitigation of specific issues.
A New Nonparametric Tukey MA-EWMA Control Charts for Detecting Mean Shifts

Control charts are a type of statistical tool used to control a production process in order to obtain the quality products that can fulfill the demands of both the manufacturer and the consumers. In this paper, we propose the Tukey Moving Average-Exponentially Weighted Moving Average control chart (MME-TCC) to detect the change of average of the process with symmetric and asymmetric distribution and to compare the efficiency in detecting the change of the MME-TCC to the MA, MME, MEM, MA-TCC and MEM-TCC at the various change levels of the parameter. The criteria to measure the efficiency were average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) which evaluated by using Monte Carlo simulation (MC), The research results showed that the proposed control chart has the highest efficiency in detecting the change when the change level was at $-0.75\le \delta \le 0.75$ . However, if the change of parameter increased ( $\delta \ge 1.00$ ), the MME had more efficiency. In the case where the observation was logistic distributions, the MA-TCC had more efficiency to detect the change. Moreover, from applying the proposed control chart to two sets of real data, the mine explosion period in the UK during 1875–1951 and data of diameter of the workpiece from an industrial factory, it was found that the MME-TCC was able to more quickly detect the change than the other control charts.
On Change Detection in the Complexity of the Time Series with Multiscale Renyi Entropy Processing

The objective of the paper refers to change detection in the structure of time series data, with direct application in fault detection of industrial processes and their components. The detection criterion is based on a combination of multiscale analysis technique with Renyi entropy on each scale, followed by a cumulative sum evaluation to estimate the point of change. Two case studies are considered. One considers a synthetized mixed signal, by mixing a deterministic signal with one having random components. The other one uses real vibration signals generated by some faults in the bearings of rotating machines. The method generates different values of the entropy for different structures of the time series and allows change detection of the incipient faults in bearings, i.e. small amplitude of the vibration signals, and changes in the structure and complexity of these signals. From fault detection point of view, the results are at the same level of qualitative performance with other approaches based on multiscale analysis and other type of entropies, e.g. Multi Scale Entropy (MSE). From change detection in the structure of the signals, the proposed method is superior, being more sensitive to random components and smoother. The proposed method could be adapted to other categories of difficult signals or processes, as those from medical area.