Miracle-dz/UniFAR-FTUs huggingface dataset:
Homogeneous Transformation Based on Deep-Level Features in Heterogeneous Remote Sensing Images

Homogeneous transformation receives considerable attention in recent years as it is essential for change detection in heterogeneous images. However, most existing methods perform the homogeneous transformation based on low-level features. It leads to inaccurate homogeneous representations of the heterogeneous images and accordingly causes unsatisfied performance of change detection. To solve this problem, this paper presents a new model that utilizes deep- level features for homogeneous transformation instead of low-level features. Experimental results on real remote sensing data show that, the proposed method achieves an overall change detection accuracy of 95.91%, providing better performance than the existing methods based on low- level features.
Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style

The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver’s driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm.
Scaling Deep Learning Based Crop Classification on Modern Intel Xeon Processors

In this study, we explore deep learning based classification frameworks for state-wide crop classification using one meter resolution remote sensing imagery. We also studied the advantages of new Xeon processors (Xeon Gold 6134) in scaling deep learning models. Our study shows that deep learning models can indeed be scaled on CPUs, and can provide an easier solution over complex GPU implementations.
Comparative Study of Two Different Mooc Forums Posts Classifiers: Analysis and Generalizability Issues

Massive Open Online Courses (MOOCs) offer a wide range of opportunities for learning. Their growing popularity has resulted in a large amount of data being available for learning analytics purposes. A major problem of MOOCs is the overwhelming number of posts in their discussion forums. The forum is a key part of the learning process within a MOOC, so this information overload affects negatively the participants’ learning experience. Automatic classification of the posts can help searching of relevant information for both the learners and teaching assistants. In this study, we address this problem by building two multiclass classification models, using natural language processing techniques, that classify the posts according to a three-category coding scheme. Each model was created with data derived from a MOOC of different subject matter. The main goal was to evaluate each model’s accuracy along with its generalizability to courses of different subject matter. This study contributes to the line of research for automatic classification of forum discussions, ultimately aiming at the development of tools that may assist participants while searching in the forum. Furthermore it provides insights on the main issues that inhibit generalization of classifiers created for a specific subject matter and investigate how their linguistic features relate to this inhibition.
Validation of the Malay Version of Mini-IPIP among Substance Use Disorder Patients Attending Methadone Clinics in Malaysia

There has been an increasing interest in personality study over the years. This has led to the necessity for personality measures with good psychometric properties. However, good personality measures are usually too cumbersome to apply in real practical settings due to their length. This study aims to validate a commonly used short personality measure of the Big Five model, i.e., Mini-IPIP (Mini International Personality Item Pool), which has never been validated and used in the substance abuse population in the local setting. The participants were 239 individuals attending one of the six methadone clinics in Malaysia. Structural analysis was conducted using confirmatory factor analysis. Results showed a good model fit for Mini-IPIP when item-parcelling and adding-in correlated uniqueness items were applied (fit indices: Comparative Fit Index = 0.949, Standardised Root Mean Residual = 0.044). Our study supported the five-factor solution for the Mini-IPIP. It is valid and reliable to be used among individuals with drug abuse in Malaysia.
Automated Sea Ice Classification Using Sentinel-1 Imagery

Sentinel-1A and 1B operate in Extra Wide swath dual-polarization mode over the Arctic Seas, and the two-satellite constellation provides the most frequent SAR observation of the Arctic sea ice ever. However, the use of Sentinel-1 for sea ice classification has not been popular because of relatively higher level of system noise and radiometric calibration issues. By taking advantage of our recent development on Sentinel-1 image noise correction, we suggest a fully automated SAR image-based sea ice classification scheme which can provide a potential near-real time services of sea ice charting. The denoised images are processed into texture features and a machine learning-based classifier is trained by feeding digitized ice charts. The use of ice chart rather than manually classified reference image makes enable an automated training which minimizes the effects from biased human decision. The resulting classifier was tested over the Fram Strait area for an extensive dataset of Sentinel-1 constellation acquired from October 2017 to May 2018. The classification results are shown in comparison with the ice charts, and the feasibility of the ice chart-feeded automated classifier is discussed.
Establishing New Norms for Developmental Milestones

On the basis of >40 000 parent reports, we present normative data for young children’s developmental milestones and compare to CDC guidelines. BrightcoveDefaultPlayer10.1542/6138653314001PEDS-VA_2019-0374 Video Abstract BACKGROUND AND OBJECTIVES: Pediatric surveillance of young children depends on providers’ assessment of developmental milestones, yet normative data are sparse. Our objectives were to develop new norms for common milestones to aid in clinical interpretation of milestone attainment. METHODS: We analyzed responses to the developmental screening form of the Survey of Well-being of Young Children from 41 465 screens across 3 states. Associations between developmental status and a range of child characteristics were analyzed, and norms for individual questions were compared to guidelines regarding attainment of critical milestones from the Centers for Disease Control and Prevention (CDC). RESULTS: A contemporary resource of normative data for developmental milestone attainment was established. Lower developmental status was associated with child age in the presence of positive behavioral screening scores (P < .01), social determinants of health (P < .01), Medicaid (P < .01), male sex (P < .01), and child race (P < .01). Comparisons between Survey of Well-being of Young Children developmental questions and CDC guidelines reveal that a high percentage of children are reported to pass milestones by the age at which the CDC states that “most children pass” and that an even higher percentage of children are reported to pass milestones by the age at which the CDC states that parents should “act early. ” An interactive data visualization tool that can assist clinicians in real-time developmental screening and surveillance interpretation is also provided. CONCLUSIONS: Detailed normative data on individual developmental milestones can help clinicians guide caregivers’ expectations for milestone attainment, thereby offering greater specificity to CDC guidelines.
Pricing Game With Complete or Incomplete Information About Spectrum Inventories for Mobile Virtual Network Operators

In network virtualization, mobile virtual network operators (MVNOs) lease spectrum resources from mobile network operators (MNOs) and offer certain wireless services to end users. Each MVNO competes with others using optimal price and spectrum inventory so as to profit. However, the inventory of one MVNO is private information and maybe unknown to others, which makes pricing decisions difficult for MVNOs. In this work, we first study the pricing strategy given others’ inventory information. We model the pricing decision problem using the non-cooperative game theory, and develop an optimal price setting algorithm based on an ordinal potential game. Then we put forward three cooperation strategies for MVNOs and analyze the impacts of the coalitions structure on pricing decision. For the situation where the inventory of one MVNO is unknown to others, we use the Bayesian coalition formation game to formulate the pricing decision problem and propose an optimal price setting algorithm based on the Minimum Mean-Square Error to resolve the conflicts resulting from the uncertainty. Next we define a Belief Pareto Order to characterize the preferences of MVNOs regarding the coalition structures. Then we devise a distributed coalition formation algorithm with the proposed belief Pareto order to achieve a Bayesian-Nash stable coalition structure that enables each MVNO to maximize its own revenue. Finally, simulation results demonstrate comprehensive performance evaluation of the proposed game model and provide a guidance on pricing strategies for MVNOs.
Fuzzy C-means Based Model Simplification using Jaya optimization Algorithm

A new mixed approach of model order reduction (MOR) for the simplification of higher order linear time-invariant single-input single-output (SISO) systems is presented in this article by combining the benefits of Fuzzy C-means (FCM) along with Jaya optimization algorithm. The FCM is a pole clustering based method used for the determination of reduced order denominator polynomial while the numerator polynomial is calculated by the Jaya optimization algorithm. Further, two numerical examples are considered and their performance indices are computed to justify the superiority of the proposed approach.
IoT Network and Sensor Signal Conditioning for Meteorological Data and Photovoltaic Module Temperature Monitoring

The present paper describes the design, development, implementation and validation of an Internet of Things (IoT) network with sensors signal conditioning circuits for monitoring decentralized photovoltaic (PV) plants. The system intends to reduce costs of commercial dataloggers and sensing modules which require control and data storage using proprietary software. We applied our IoT monitoring network in three PV plants installed in Fortaleza and Maracanaú cities, Brazil, and in Cologne, Germany. The applied embedded solutions (namely, ESP 8266 and ESP 32) are based on free software and hardware and communicate with a Cloud server via Wi-Fi. A website, called Web Monitor, was developed in PHP for online and real-time data consultation in form of charts. The main objective of the proposal is to measure PV module temperature, providing data for the analysis of PV module’s efficiency and for fault detection in case of PV cells overheating. Solar irradiance, ambient temperature, relative air humidity and wind speed are also monitored, getting more data to analyze the effect of meteorological parameters on the PV module’s performance. The proposed system was designed in compliance with IEC 61724:1998 standard and the obtained charts are discussed in the results section considering different periods of the year. Finally, we validate the proposed temperature conditioning system with a commercial piece of equipment and a correlation of 0.9999 was achieved.
Modeling Landsat Clumping Index Basing On MODIS and Field Data: A Machine Learning Approach

Clumping index (CI) is an important vegetation structure parameter in the estimation of leaf area index (LAI) and the modeling of ecological and meteorological process. With the development of surface process modeling and remote sensing technology, high resolution CI product is urgently needed but no appropriate high resolution multi-angle reflectance satellite data is currently available to produce such product. In recent years, random forest algorithm has been widely used in the derivation of high resolution products from remote sensing data. In this study, the random forest algorithm was used to estimate Landsat CI basing on MODIS and field data. The developed predictive model was validated using 26 field measurements and the predicted CI shown a good consistency with the field CI (R2=0.63, bias=0.005, RMSE=0.10).
Maximum Membership Fraction Based Pure Pixel Assessment Approach for Hyperspectral Data Analysis Using Deep Learning

Land cover classification in the remote sensing has been done using various deep learning algorithms; and higher classification accuracies have been achieved. Such classification is based on the maximum membership fraction (MMF), when we use Convolutional Neural Network (CNN). MMF is basically the maximum probability fraction. A pixel under prediction has been assigned to that class which has maximum fraction out of the corresponding fractions for all land cover classes. Various methodologies exist for pure pixel extraction and used for hyperspectral unmixing. An assumption has been taken that MMF and abundance used in the case of unmixing are similar. Both MMF and abundance follow the rule of sum to one. In this paper, a classification method has been implemented using CNN to achieve better classification accuracy. Thereafter number of pure pixels extracted based on the various MMF thresholds.
Synthesis of Nanoscale Zerovalent Iron (nZVI) Supported on Biochar for Chromium Remediation from Aqueous Solution and Soil

Maize straw biochar-supported nanoscale zero-valent iron composite (MSB-nZVI) was prepared for efficient chromium (Cr) removal through alleviating the aggregation of zero-valent iron particles. The removal mechanism of MSB-nZVI was investigated by scanning electron microscopy with energy dispersive X-ray (SEM-EDX), X-ray diffractometry (XRD), and X-ray photoelectron spectroscopy (XPS). Cr(VI) removal from aqueous solution by MSB-nZVI was greatly affected by pH and initial concentration. The removal efficiency of Cr(VI) decreased with increasing pH, and the removal kinetics followed the pseudo-second-order model. XRD patterns of MSB-nZVI before and after reaction showed that reduction and precipitation/co-precipitation (FeCr2O4, Fe3O4, Fe2O3) occurred with the conversion of Cr(VI) to Cr(III) and Fe(0) to Fe(II)/Fe(III). The produced precipitation/co-precipitation could be deposited on the MSB surface rather than being only coated on the surface of nZVI particles, which can alleviate passivation of nZVI. For remediation of Cr(VI)-contaminated saline–alkali soil (pH 8.6–9.0, Cr 341 mg/kg), the released amount of Cr(VI) was 70.7 mg/kg, while it sharply decreased to 0.6–1.7 mg/kg at pH 4.0–8.0, indicating that the saline–alkali environment inhibited the remediation efficiency. These results show that MSB-nZVI can be used as an effective material for Cr(VI) removal from aqueous solution and contaminated soil.
Optimization of vehicle delay and drivers’ unhappiness at a signalized network: a multi-objective approach

In existing system-optimal traffic-responsive signal strategies, the individual driver’s interest is always neglected. In order to make a compromise between user equilibrium (individual delay) and system optimality (total delay), a traffic signal scheduling strategy with consideration of drivers’ un-happiness is firstly developed based on the cell transmission model (CTM). The exponential function is adopted to delineate the driver’s anxiety according to their waiting time, which leads to the assignment of the traffic signals is dominated by drivers’ waiting time but not the total volume demand. By adopting the discrete harmony search algorithm (DHS), numerical simulation results illustrate the effectiveness of our real-time traffic light scheduling. Secondly, in order to satisfy the trade-off between the proposed cost (drivers’ unhappiness) and the common traffic performance (network delay), a bi-objective urban traffic light scheduling problem by minimizing both the drivers’ unhappiness and the total network delay is proposed, which is solved by the non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated sorting harmony search algorithm (NSHS). Experiments are carried out to compare the efficiency of both algorithms.
Improving Hyperspectral Image Classification with Unsupervised Knowledge Learning

Recently, deep convolutional neural networks(DCNNs) based methods have shown pleasing performance in hyperspectral image(HSI) classification. However, due to extensive coefficients resulted by the deep structure, these methods are prone to be overfitting during training, especially when the labeled samples are limited. To address this problem, we propose to learn the unsupervised knowledge from both unlabeled and labeled samples to regularize the conventional supervised learning. Following this idea, we present a two-branch network, in which two branches are separately utilized to perform the clustering and classification based on a shared feature extraction module. Thanks to the shared structure, the crucial unsupervised information (e.g., intra-cluster similarity & inter-cluster dissimilarity, etc.) can be injected into the supervised learning procedure, and thus leads to improved generalization capacity. Experiments on two widely used HSI datasets show the superior performance of the proposed method.
A Fast Optimization Method for Automatic Train Stop Control

This paper studies a fast optimization method considering both stopping accuracy and riding comfort in Automatic Train Operation (ATO) systems. ATO system plays an important role in the operation of a train, but stopping accuracy is a difficult problem. We use a predictive model and minimize the stopping error under the constraint of gear switches. The braking acceleration is a function of velocity and gear. We propose a multi-step method which makes each step solvable with limited computing resources on trains. The method computes a sequence of action outputs at each time interval, and only adopts the first output as the gear action of the next time interval. The experiment shows that the stopping accuracy and riding comfort are better than the traditional PID method. The problem is motivated by a practical project for the automatic control of Dongguan–Huizhou intercity railway. Various experiments are conducted based on the real data and scenarios. It is demonstrated that this method is robust and easy to use in different railway lines.
Sequential Recurrent Encoders for Land Cover Mapping in The Brazilian Amazon Using Modis Imagery and Auxiliary Datasets

We explore a state-of-art architecture based on convolutional recurrent networks, designed to ingest and extract information of a sequence of satellite images, for large area LULC classification in the Brazilian Amazon biome. We fine-tune and evaluate it according to multiple combinations between MODIS archives at 250 m for a single year, providing surface reflectance time series data and derived indices, and external environmental data. Qualitative and quantitative differences between the trained models were evident according the input features assessed. The combination of surface reflectance and auxiliary data yield slightly better performance than only using surface reflectance data. In specific, the auxiliary data contributed to the accuracy of Mixed Forest, Savanna, Grassland and Cropland. In contrast, the arrangement including derived indices showed lower performance and therefore they negatively contribute to the classification task in the area of study.
Associations between tumor necrosis factor-α and interleukin-6 polymorphisms and unexplained recurrent spontaneous abortion risk

Abstract To evaluate the associations between Tumor necrosis factor-α (TNF-α)(-238G>A) and Interleukin-6 (IL-6)(-174G>C) polymorphism and risk of unexplained recurrent spontaneous abortion (URSA). Correlated case-control studies were collected by computer retrieval. A meta-analysis was conducted by Stata 12.0 software to analysis the strength of association between polymorphism of TNF-α -238G>A and IL-6 -174G>C and URSA. Twenty-one articles with twenty-two studies were included, of which 12 and 10 studies were respectively related to mutation of TNF-α -238G>A, IL-6 -174G>C and URSA. The integrated results showed that the TNF-α-238G>A gene mutation was significantly correlated with the risk of URSA under homozygote model (AA vs GG;OR 1.533,95% CI 1.022–2.301) and recessive model (AA vs GG+AG;OR 1.571,95%CI 1.050–2.350)(P < .05). There was no association between URSA and TNF-α -238G>A under heterozygote model (AG vs GG;OR 0.963,95% CI 0.816–1.137), dominant model (AA+AG vs GG; OR 1.031,95%CI 0.880–1.209) and additive model (A vs G;OR 1.046,95%CI 0.909–1.203)(P > .05). The results of subgroup analysis based on ethnicity showed that -238G>A was significantly correlated with the risk of URSA in Asians under all gene models except for heterozygote model (AG vs GG; OR 1.129,95% CI 0.857–1.487) (P < .05). In Caucasians, it was dominant model (AA+AG vs GG; OR 1.430,95%CI 1.040–1.965) (P < .05) rather than others that showed relationship with URSA. From the integrated results, association was manifested between -174G>C and URSA under all gene models (P < .05) except for recessive model (CC vs GG+CG, OR 1.166, 95%CI 0.938–1.449) (P > .05), which is identical to subgroup analysis based on ethnicity. It is of great guiding significance for screening out and preventing URSA among high-risk women to test on TNF-α -238G>A and IL-6 -174G>C under gene models mentioned above which are highly associated with the risk of URSA, which can act as biological markers for URSA.
Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.