Miracle-dz/UniFAR-FTUs huggingface dataset:
Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

The deficiency of fine-resolution satellite-derived land surface temperature (LST) has impeded the meticulous exploration of the urban thermal environment at micro spatial scales. Although the LST downscaling methods are well-documented, the scale dependence between LST and environmental factors has been rarely considered in the regression establishment. This article proposes a new method (MGWRK) coupling multiscale geographically weighted regression (MGWR) and area-to-point kriging (ATPK) to generate fine-resolution LST maps over heterogeneous landscapes. Technically, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and albedo are selected using random forests (RF) as scale factors. First, the spatial and temporal robustness of MGWRK is evaluated by downscaling aggregated Landsat-8 Thermal Infrared Sensor images (1000-m) into finer resolutions of 500, 400, 300, 200, and 100-m in different seasons (spring, summer, and winter). Results reveal that MGWRK is superior to GWR and DisTrad methods for spatial details improvement (SSIM > 0.93) and LST fidelity (${\boldsymbol{R}^2}$ > 0.91) under all scales and in the seasons. Second, MGWRK is utilized to downscale the synchronous 1000-m Moderate Resolution Imaging Spectroradiometer (MODIS) LST map into 100-m. The quantitative and qualitative result substantiates that MGWRK obtains ideal performance when applied on MODIS data with few LST distortion (${\boldsymbol{R}^2}\; = \;0.915$, RMSE = 1.607 K) and obvious spatial information enrichment (SSIM = 0.903).
Comparison of Machine Learning Models in Prediction of Cardiovascular Disease Using Health Record Data

Cardiovascular diseases (CVDs) are disorders of heart and blood vessels. This is the leading cause of deaths worldwide. Early detection and diagnosis can help the patients. Machine learning can be used to create the predictive model using cardiovascular diseases risk factors like cholesterol level, glucose level and blood pressure. The aim of this study is to do comparison of machine learning models on prediction of cardiovascular disease using patients' cardiovascular risk factors. Source of data is kaggle machine learning competitions, the dataset consists of 70,000 patients records. The machine learning algorithms used in this study are Random Forest, Naïve Bayes, KNN and Logistic Regression. The results of comparison shows that Random Forest achieve high classification accuracy of 73 %, specificity of 65% and sensitivity of 80%. The model can be used in medical field for prediction of cardiovascular diseases.
A Physical Layer Security Communication Mechanism Based on End-to-End Learning

Due to the randomness of wireless channels, it is possible to achieve secure communications by designing communication systems according to the characteristics of wireless channels. In this work, a physical layer security communication mechanism based on end-to-end learning is introduced. The proposed mechanism achieves secure communications by designing the loss function of end-to-end learning. We evaluate secrecy performances of the proposed mechanism through block error rate (BLER) under three classic circumstances which different decoding models are applied by the eavesdropper. Self-training decoding model, the legitimate decoding model and unsupervised learning (K-means clustering) model are alternative for the eavesdropper. The experiments results show that K-means clustering model can get the best BLER performance for the eavesdropper with some prior information, but its BLER is still higher than 0.5. Even if the eavesdropper has a much higher than the legitimate users, there is no improvement in its BLER traditional physical layer security Es/N0. Compared to methods, the proposed mechanism can achieve secure communications without extra key extraction.
Evaluation of Domain Randomization Techniques for Transfer Learning

To address the challenge of resource-intensive data collection from real robotic environments, many deep learning applications use synthetic data to train their networks. This creates new problems when transferring the obtained knowledge from the simulated to the real world domain. Various aspects of the simulation, which do not influence the learning objective, can be randomized to enhance generalization to new domains. In this paper, we analyze the effect of these domain randomization techniques. To get an insight into their benefits, we apply them while training a grasp success classifier based on state-of-the-art CNN for an industrial robot as a showcase. We generated a large synthetic data set containing 1.44M RGB images with 48 permutations of 6 different randomizations and a base scenario as training data. The resulting networks, each trained on a different subset of this data set, are evaluated on 3k real world images of the robot performing grasps. We observed the effectiveness of randomization of perspective, distractors, lighting and the grasped box. Notably, we show that pretrained networks benefit from these techniques in particular.
Performance Evaluation of a Fleet of Photovoltaic Systems Across India and Southeast Asia

The growth of PV systems deployment continues to lead the rapid dissemination of solar photovoltaics as one of the primary sources of renewable electricity generation today across the globe. However, several solar technology entrants in new markets naturally mean that thousands of PV systems are being deployed and not necessarily performing at their highest potentials. The core location of new systems lies in Asia. Little information is also available on system performance across the scientific world and public databases with regards to large datasets of sites. This work aims to break that trend and expose the performance metrics of a considerable pool of distributed Asian PV systems (57 sites, ~42 MWp) based on a fleet of assets deployed in India and six other Southeast Asia countries. Challenges preventing optimal system operation are discussed and could be used as guidelines for higher-quality systems coming online in the years ahead in the region.
Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO's 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%.
DeepTriager: A Neural Attention Model for Emergency Triage with Electronic Health Records

As the first pass for emergency patients, triage is the most important factor affecting emergency department (ED) overcrowding. So it is crucial to develop a data-driven and evidence-based triage method to quickly identify acute and severe patients, and prevent the limited emergency resources from over-diagnosis. To address these challenges, we propose an attention based deep learning framework, named DeepTriager. Trained and tested on 70,918 clinical records, DeepTriager achieved highly accurate performance on assessment of acuity level I (endangered patients), with AUC of 0.98, which was 0.16 higher than the clinical scale method MEWS and NEWS, and 0.04 higher than traditional machine learning methods. In summary, we presented a new approach for clinical evidence based discovery using a cohort of Electronic Health Records (EHRs). This approach not only outperforms the traditional word segmentation methods but also provides evidence for interpreting the results.
SURFr: Algorithm for identification and analysis of ncRNA-derived RNAs

Noncoding RNAs (ncRNAs) regulate gene expression in essential cellular processes and play key roles in many human diseases. Small nucleolar RNAs (snoRNAs) are a relatively large group of ncRNAs. Many studies have identified significant alterations of different snoRNAs in prostate, breast, and lung malignancies and that many of these snoRNAs have been shown to be processed into microRNA-like molecules known as snoRNA-derived RNAs (sdRNAs). Similarly, several small RNAs have also recently been found to be excised from well characterized tRNAs to function in both normal cellular metabolism and disease. In this paper, we present a new computational methodology for the identification, analysis, and visualization of ncRNA-derived RNAs (ndRNAs) with linear time and space complexity (named SURFr for Short Uncharacterized R NA Finder). We provide the results of our algorithm's running time by analyzing several publicly available datasets and finally discuss future research directions to our work.
VaLeNS: Design of a Novel Variable Length Nested Soft Arm

Over the last decade, soft continuum arms (SCAs) have successfully demonstrated the compliance and dexterity needed to operate in unstructured environments and handle fragile objects. However, their inherent soft compliance limits their performance in situations where stiffness and force transfer is required. In this letter, we present a compact design architecture, which is a hybrid between soft arms and rigid links known as Variable Length Nested Soft (VaLeNS) arm. The design architecture involves a novel SCA nested inside a concentric rigid tube. The SCA can undergo a combination of spatial bending ($B$) and bidirectional axial twist ($R^2$), and can extrude out or retract back into the rigid tube with varying length. The resulting configuration is shown to modulate stiffness up to a factor of ten and exhibits enhanced workspace and dexterity. Furthermore, the VaLeNS arm mounted on a rigid robotic platform allows for bifurcation of the overall workspace into rigid and soft, and can achieve high reachability in constrained environments. The letter demonstrates the effectiveness of the VaLeNS arm system in manipulation tasks that require both the rigid and soft attributes. This design architecture is deemed useful in agricultural applications and in physical human robot interaction.
Learning from an Imbalanced and Limited Dataset and an Application to Medical Imaging

Chest X-rays (CXRs) are routinely acquired in medical imaging for the purpose of diagnosing lung diseases. But for many patients, accurate and timely radiologic interpretation of the acquired CXRs is not always feasible, due to limited medical personnel and resources. A computer aided diagnosis (CAD) system based on machine learning would be an effective solution to enhance the efficiency of disease diagnosis. However, obtaining a sufficiently large-scale, balanced, and annotated dataset of CXRs for effectively training a CAD system is challenging in practice. In this paper, we present a comprehensive comparative study on learning from imbalanced and limited CXRs to detect pneumonia, tackling two main questions: (1) Is data sampling an effective method for improving the performance of learning models? (2) Are there quantifiable differences between learning models with different sampling techniques? With respect to data sampling, we investigate two general categories of techniques that modify of an imbalanced data set to deliver a balanced data distribution: (i) undersampling the majority class; and (ii) oversampling/augmentation of the minority class. With respect to learning models, we focus on Support Vector Machine (SVM) and deep convolutional neural network (CNN). Using a publicly available CXR dataset, we demonstrate that SVM and CNN learning models both exhibit improved performance, with the proper selection of the data sampling strategies.
A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on SE(3)

Simultaneous trajectory estimation and mapping (STEAM) is a method for continuous-time trajectory estimation in which the trajectory is represented as a Gaussian Process (GP). Previous formulations of STEAM used a GP prior that assumed either white-noise-on-acceleration (WNOA) or white-noise-on-jerk (WNOJ). However, previous work did not provide a principled way to choose the continuous-time motion prior or its parameters on a real robotic system. This letter derives a novel data-driven motion prior where ground truth trajectories of a moving robot are used to train a motion prior that better represents the robot's motion. In this approach, we use a prior where latent accelerations are represented as a GP with a Matérn covariance function and draw a connection to the Singer acceleration model. We then formulate a variation of STEAM using this new prior. We train the WNOA, WNOJ, and our new latent-force prior and evaluate their performance in the context of both lidar localization and lidar odometry of a car driving along a 20 km route, where we show improved state estimates compared to the two previous formulations.
Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyper-parameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyper-parameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used.
Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

Recently, to address the astonishing capacity requirement of 5G, researchers are investigating the possibility of combining different technologies with ultra-dense networks (UDNs). However, the ultra-dense deployment of small cells in the coverage area of conventional macrocells known as UDNs introduces new technical challenges such as severe interference, unfairness in radio resource sharing, unnecessary handover, a significant increase in energy consumption, and degraded quality-of-service (QoS). To overcome these challenges and achieve the performance requirements in 5G, there is a need to combine UDNs with other 5G enabling technologies and then, design intelligent management techniques for better performance of the overall networks. Hence, in this paper, we present a comprehensive survey on different generations of wireless networks, 5G new radio (NR) standards, 5G enabling technologies and the importance of combining UDNs with other 5G technologies. Also, we present an extensive overview of the recent advances and research challenges in intelligent management techniques and backhaul solutions in the last five years for the combination of UDNs and other enabling technologies that offers the visions of 5G. We summarise the mathematical tools widely exploited in solving these problems and the performance metrics used to evaluate the intelligent management algorithms. Moreover, we classify various intelligent management algorithms according to the adopted enabling technologies, benefits, challenges addressed, mathematical tools and performance metrics used. Finally, we summarise the open research challenges, provide design guidelines and potential research directions for the development of intelligent management techniques and backhaul solutions for the combination of UDNs and other 5G technologies.
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

The Internet of Things (IoT) concept has been widely adopted and Internet connected devices enter more and more areas of our everyday lives. However, their limited security measures raise increasing concerns, especially in terms of users’ privacy. That is why, in this paper, privacy risks, focusing primarily on information leakage exposed by traffic fingerprinting attacks, on IoT devices are investigated. The considered attacks take advantage of the statistical network flows’ features and application of machine learning (ML) methods and can be utilized by a passive traffic observer. In this perspective, the first part of the research presented in this paper analyzes the feasibility of identifying individual devices in a victim’s home network. It considers smart environment setups of different scales and conditions, and it also includes a performance comparison of the different ML models applied. The second part introduces and validates a method for the devices’ state detection based on pattern recognition with ML. Finally, recommendations for mitigating the discussed privacy risks are also enclosed. INDEX TERMS Internet of Things, machine learning, network traffic fingerprinting, privacy, traffic analysis.
Input-Process-Output Dimensions for Measuring the Success of Online Tax Information Systems

Information System success is a new topic to study. It can be seen from the number of similar studies. The number of researchers in defining the success of information systems is still multi-definition. Then in determining indicators as gauges also differ for each researcher. It can be said to be reasonable because the viewpoint of the success of IS is different. The means that there is no agreement between researchers and practitioners. It is related to the use of partial or single dimensions in measurements so that researchers do not get a clear picture of the use of measures. This study is intended to provide an alternative method of the input-process-output (IPO) dimension in making measurements. Then in developing the model carried out by adoption, adaptation, a combination of previous models. Delone and McLean, Davis's IPO is a model developed by researchers. From the measurement results, the researcher used eight variables and 35 indicators. The use of variables and indicators is only a concept, but the development of the model is by the consistency of measurement of the success of the information system.
Wireless Communication System For Monitoring Heart Rate In The Detection And Intervention Of Emotional Regulation

Based on data from the Indonesian Child Protection Commission (KPAI) cases of violence against children from 2010 to 2015 continued to increase which from 2010 only 171 cases increased to 2015 as many as 6006 which means that every year cases of violence against children continue to increase at least 1000 cases each year. Changes in heart rate in humans can be known through the flow of blood that flows in blood vessels. When the heart beats, the flow in the blood vessels will move so that's when the heart rate can be measured. In this study, a wireless heart rate condition data collection system will be developed and a heart rate condition sensing device that has the same capabilities as devices used in general medical activities. From the results of testing, the system designed the process of sending data goes well where of the 20 devices that send data all of that can be sent properly with a time interval of 1 second but for the sensor reading process some problem need to be solved such as interruptions in the data collection process when participants doing activities and some constraints on the choice of devices used on the server.
CU-Net: Cascaded U-Net Model for Automated Liver and Lesion Segmentation and Summarization

One of the leading causes of cancer death is liver cancer. The most common type of primary liver cancer in adults is Hepatocellular carcinoma (HCC), and it is also the most common cause of death among people suffering from chronic liver diseases. In clinical practices, accurate and automated liver and tumor segmentation mechanisms are highly desired to help oncologists with early diagnosis and treatment of HCC. Manual segmentation of tumor and liaisons, from abdominal computerized tomography (CT) scans, is time-consuming, requires domain expertise, and is prone to inaccuracies in many cases. Automated segmentation, on the other hand, is a challenging task due to the nature of medical images such as heterogeneous modalities and formats, the minimal labeled training data, and the high-class imbalance in the labeled data. To solve these problems, we propose a 2-dimensional network consists of two CNN models, called CU-Net. To perform double segmentation, the first model segments the liver from the CT scan of the abdominal cavity and second detects the tumor on the liver. Our model also goes one step further and provides useful information for interpretation purposes and reports on the number of tumors, size of each tumor, size of the liver, and percentage of the liver that is tumorous, which is not provided by any other liver-lesion segmentation model. We have used the Liver Tumor Segmentation (LiTS) challenge data for training and validation purposes, and with this data, we have achieved a Dice score of 0.894 for liver segmentation and 0.595 for tumor detection.
Energy Efficient Routing using Sectors Based Energy-Hole Reduction in WSNs

In wireless sensor networks (WSNs), the sensor nodes have restricted battery source. If unbalanced load is generated in the network, it creates energy-hole problem, and affects the network lifespan. In this paper, the sector based energy-hole reduction (SEHR) scheme is proposed for WSNs. The network is sectorized into eight parts, and each parts contains a fixed number of sensor nodes with a dynamic selection of forwarder nodes (FNs). Further, each part is divided into small segments to balance the load of network and reduction of energy consumption. The sectorization of network reduces energy-hole creation problem and improves network lifespan. The simulation results validate that the proposed scheme reduces the energy-hole creation and enhances the network lifespan in comparison of DR scheme.