Miracle-dz/UniFAR-FTUs huggingface dataset:
W-net: the Convolutional Network for Multi-temporal High-resolution Remote Sensing Image Arable Land Semantic Segmentation

Aiming at the problem that manual arable land semantic segmentation is time-consuming, inefficient, segmentation results rely heavily on the staff’s experience and traditional methods cannot meet the requirements of land segmentation, this paper first proposes a new neural network W-net to realize multi-temporal high-resolution remote sensing image arable land semantic segmentation. We use some data augmentation methods, build the W-net neural network, train with the training set, and test the performance of the neural network with the test set. In this paper, the results of manual labeling are used as the reference. In the case of maintaining the same training set, verification set and test set, FCN, SegNet and W-net are used for control-experiments. The experimental results show that the segmentation accuracy of W-net is higher than that of FCN and SegNet, and the edge segmentation is flatter. W-net has higher accuracy of arable land semantic segmentation for multi-temporal high-resolution remote sensing images than single-temporal high-resolution remote sensing images. W-net neural network can effectively fulfil the task of arable land semantic segmentation.
Visualizing Student Opinion Through Text Analysis

Contribution: An automated methodology that provides visualizations of students’ free text comments from course satisfaction surveys. Focusing on sentiment, these visualizations reveal learning and teaching aspects of the course that either may require improvement or are performing well. They provide educators with a simple, systematic way to monitor their courses and make pedagogically sound decisions on teaching strategies. Background: Student course satisfaction surveys often solicit free text comments. This feedback can provide invaluable insights for educators, but because these comments often contain a large amount of data, they cannot easily be acted upon. Existing visualization methods are not suitable for this application, and needed additional capabilities. Research Questions: How can large quantities of student satisfaction data be summarized and visualized? How can these visualizations be used to learn meaningful information about courses? What are the recurring themes across semesters? Methodology: Several methods based on machine learning and text analysis techniques were used to visualize student satisfaction comments. The latent Dirichlet allocation (LDA) statistical method was used to identify aspects of student opinion of a course. The sentiment of the student comments were also identified. This information was then presented visually for educators in a case study that gives examples of these visualizations. Findings: The visualization methods explored provide educators with an overview of aspects and their associated sentiment. The summary visualizations allow easy comparison to be made between courses, or between teaching periods in the same course.
Training Performance Measurement with Schema and Mental Rotation Test

Training was a crucial technique for skill development and basic foundation of learning in manufacturing while the traditional way of training selection was to trial-and-error those training methods to see its performance which take cost and time. The purpose of present study was to preliminary investigate the training performance measurement using cognitive schema and mental rotation test in camera assembly tasks with basic training and physical training condition for production staffs. 180 novice participants performed in this study. Results revealed that both cognitive schema and mental rotation test could significantly distinguish training performance between basic and physical training condition in the same direction. The implication of this study was on the measurement that could shorten the time and reduce cost in estimating the performance of each training method which could be used to support decision making. Discussion was provided.
Agronomic and Anatomical Indicators of Dwarfism and Graft Incompatibility in Citrus Plants

Poncirus trifoliata var. monstrosa (T. Ito) Swingle is a rootstock used in Brazilian citriculture for its potential to induce dwarfism and the viability of densified crops. Its recommendation is still restricted due to lack of research on the performance and compatibility of different types of scion under specific conditions of soil and climate. The aim of this study was to correlate plant size and productive efficiency with anatomical indicators of dwarfism or graft incompatibility between citrus scion cultivars and the ‘Flying Dragon’ trifoliate orange (FD) and ‘Rangpur Lime’ (RL) as rootstocks. The experimental design was of randomised blocks with split plots, with the experiment conducted in an orchard from the fourth to the sixth year of cultivation, under localised irrigation. The treatments consisted of two rootstocks (FD and RL) in the plots and five scion cultivars (Tahiti acid lime, and Natal, Navel, Lima Sorocaba and Pera oranges) in the sub-plots, with four replications per treatment. The agronomic performance of the plants was evaluated, together with the characteristics of samples containing the cambial zone and secondary xylem below, above and within the graft region. The FD rootstock gives the smallest size and greatest productive efficiency. The ‘Navel’ orange has good anatomical plasticity, adjusting itself more efficiently to the rootstocks. Visual symptoms of incompatibility between the ‘Pera’ orange grafted onto the FD can be seen at 64 months after planting. Vessel size and frequency, as well as the size and disposition of the cambial zone, are related to dwarfism and graft incompatibility.
Feature Generation Using Machine Learning from Large Sparse Financial Data

The modeling of customer features has become a core component in modern financial analytics. There are several difficulties in adopting conventional machine learning (ML) methodologies to finance domain: distributional asymmetry in the observations, class imbalance in the training labels, and data sparsity resulting from infrequent occurrence. In this study, we try to address the statistical challenges of financial data. Then, we test feature processing using multiple machine learning approaches in combination with established methods. We evaluate separate feature selection results as part of a prediction pipeline, and show how they differ across models. The empirical implications of the feature transformation and selection on the prediction outcomes are discussed.
Exploring a P300 Brain-Computer Interface Based on Three Different RSVP Paradigms

A BCI Speller is a typical Brain-Computer Interface (BCI) system for communication purpose. This technology can provide users with severe motor disability with an assistive device controlled by brain activity. In the present preliminary study we investigated, in five subjects, the performance and the Information Transfer Rate (ITR) based on three different Rapid Serial Visual Presentation (RSVP) paradigms to control a BCI speller. The variants of the three paradigms were the stimuli presented: letters, images and famous faces. These preliminary results showed that performance can increase when using an RSVP paradigm based on images, and ITR can improves when using the images and the famous faces paradigms.
Measurement Techniques and Characterization of High Speed Moving Objects

Response to fast speed stimuli of non linear system is difficult to predict without a proper model whose parameter has been characterized in condition closer to the working ones, because assumptions and simplifications on component stiffness and damping, such as their linearity in the frequency domain, are no longer valid. To characterize a two degrees of freedom spring-mass-damper system reacting to a high speed impact a set of statistical techniques has been developed and applied to a set of experimental measurements obtained thanks to a high speed camera, a piezoelectric load cell and a set of piezoaccelerometers. Photogrammetric position accuracy has been improved using a least square estimator of a single position from different markers, therefore position and acceleration data have been fused together being weighted on the reciprocal of their uncertainties. Starting from load, position, velocity and acceleration data, spring-mass-damper systems with different degrees of freedom have been characterized in different configurations using genetic algorithms to minimize the square deviation of prediction from measured speed, although the term to be minimized has been again weighted on reverse of uncertainty to avoid false results. Eventually the whole process has been validated and automated in order to provide a new and efficient mean of characterizing dynamic parameters such as stiffness, damping, trigger length of elastic systems impacted by high speed objects.
Application of Moth-fire Optimization Algorithm in Parameter Identification of Photovoltaic System

In order to deeply study the influence of PV on the stability of power system, it is necessary to accurately reflect the external dynamic response characteristics of the PV system, and accurate PV system parameters are the key factors. For this reason, the moth-fire optimization algorithm is introduced to solve the PV system parameters value. This paper models the PV system and find out the key parameters of it. The active power and reactive power response curves of the grid are measured to identify the parameters of the PV based on the moth-fire optimization algorithm. The results of the example show that the curve fitting effect is better, and the feasibility of the proposed identification algorithm is verified.
A model for improved performance prediction using ensemble-based hybrid classification approach on a multivariate student dataset

Classification techniques have sensed substantial attention in Information Engineering and Technology for the performance prediction and optimisation since few decades. The discovered accuracy of the Classification Model helps the institutional practices and student’s performances. In this paper, a novel Ensemble-based Hybrid Classification Approach (EHCA) has been proposed to be managed to produce improved performance prediction. The mining process with new attributes based on student behaviours has also been incorporated, since it creates a great impact on their academic performances. Moreover, the performance of the students is analysed with a set of classifiers in Educational Data Mining (EDM) namely, Naive Bayesian, Support-Vector-Machine (SVM) and J48. Futuristic-bound Ensemble approach is employed for enhancing the classifier performances. Here, the futuristic methods of ensembles of Bagging, Classification Boosting and Stacking are used for optimising the results with more precision. Further, the process of Ensemble-based Hybrid Classification is analysed and tested with the dataset collected from Kerala Technological University-SNG College of Engineering (KTU_SNG). The results obtained are compared with the results obtained for utilized single classifiers and the EHCA on the basis of performance efficiency and classification accuracy. The work evidence the efficiency of the pro-posed approach and proves its reliability in Profound Performance Prediction and Optimisation.
Decomposition Method for Belief Reliability Analysis of Complex Uncertain Random Systems

Belief reliability is a new proposed reliability metric considering both aleatory and epistemic uncertainty. In belief reliability theory, system reliability analysis is a key component. Traditional system belief reliability theory for systems with random and uncertain components is based on a complex belief reliability formula, which is not understandable and efficient enough in engineering practise. In this paper, we put forward a novel system belief reliability analysis method, called decomposition method to cope with the problem. An algorithm of this method is proposed according to the properties of the cut sets of systems and the complexity of the algorithm is analyzed and compared with that of the reliability formula method. Finally, the effectiveness and efficiency of this method is further illustrated with a comparative case study.
A NEW TARGET-TRACKING APPROACH IN FLIR IMAGERY USING MONOCULAR THERMAL IMAGE SEQUENCE

In this paper, we present a new approach for tracking a moving target in forwardlooking infrared (FLIR) imagery taken from an anti-tank moving platform. First, the target image is captured in a frame with resolution 128×128 pixels by using IR camera, and then the image is enhanced by using a non uniformity correction algorithm to reduce the main dominant noises associated with infrared images. Second, the existing moving target is detected with time ( ) within the image by using 2-D Gabor filter kernels. Next, Gabor filter with 4 orientations applying on the window created with resolution 32×32 pixels around the predicted target center using Kalman filter estimator. Subtraction process is performed between the predicted center using Kalman filter estimator and the center of the detected target using Gabor filter, which is used to predict the next center by using Kalman filter, and to guide the detection of the target location in the tracking window of the next frame respectively. The proposed technique fasts and reduces the detection time into 1/16 of with high performance of tracking. The experiments performed on several real image data of an existing infrared imaging system. Results show the robustness of the proposed method, which combines high speed of detection and good performance of tracking even with strong ego-motion.
Khasi Language As Dominant Part-of-Speech(POS) Ascendant in NLP

Part-Of-Speech(POS) tagging is the pre-processing technique or text processing in Natural Language Processing (NLP). The POS tagger is a system that generates the tags of each word as output from the given input sentence. POS tagging in India is a challenging task as Indian languages are morphologically rich. The most difficult challenge of POS tagging is ambiguity because a single word can be used in multiple senses depending on the context it is used in. Therefore, words or items of the language structure can only be disambiguated based on the speech contexts. This paper presents the grammatical POS and the designed POS tag-sets of Khasi language. Khasi is an Austro-Asiatic language spoken in the central and eastern parts of the state of Meghalaya, India. Though Khasi is mostly isolating in morphology, some words are derived through certain morphological processing.
An intelligent gastric cancer screening method based on convolutional neural network and support vector machine

The continuous development of modern information technologies such as Internet and cloud computing leads to unprecedented explosive growth of global data volume. The rapid convergence of massive multi-source heterogeneous data provides a driving force for the continuous development of artificial intelligence. The application of big data and artificial intelligence in the medical health field can not only greatly improve efficiency of medical health management services but also effectively promote the renewal and development of medical knowledge and technology. However, intelligent screening of gastric cancer is less concerned in this study. In view of this, this paper proposes an intelligent gastric cancer screening method based on convolutional neural network and support vector machine, and makes empirical analysis of gastroscopy data from the First Affiliated Hospital of Anhui Medical University, further conforming good diagnosis accuracy of this method.
Time Series Imputation via Integration of Revealed Information Based on the Residual Shortcut Connection

Recovering missing values plays a significant role in time series tasks in practical applications. How to replace the missing data and build the dependency relations from the incomplete sample set is still a challenge. The previous research has found that residual network (ResNet) helps to form a deep network and cope with degradation problem by shortcut connection. Gated recurrent unit (GRU) can improve network model and reduce training parameters by update gate which takes the place of forgetting gate and output gate in long short-term memory (LSTM). Inspired by this finding, we observe that shortcut connection and mean of global revealed information can model the relationship among missing items, the previous and overall revealed information. Hence, we design an imputation network with decay factor for shortcut connection and mean of the global revealed information in GRU, called decay residual mean imputation GRU (DRMI-GRU). We introduce a decay residual mean unit (DRMU), which takes full advantage of the previous and global revealed information to model incomplete time series; and the decay factor is applied to balance the previous long-term dependencies and all non-missing values in the sample set. In addition, a mask unit is designed to check the missing data existing or not. An extensive body of empirical comparisons with other existing imputation algorithms over real-world data and public dataset with different ratio of missing data verifies the performance of our model.
Hardware Efficient Architecture for 2D DCT and IDCT Using Taylor-Series Expansion of Trigonometric Functions

This paper presents a hardware architecture for $8\times 8$ 2D Discrete Cosine Transform (DCT) and Inverse DCT (IDCT) using Taylor-series expansion of trigonometric functions. The processing of DCT/IDCT is modified to eliminate the need for: 1) partitioning the input image into fixed-size blocks and 2) use of transpose buffer for storing intermediate results, achieving low clock cycle time compared to existing techniques. The values obtained from Taylor-series are approximated by fixed-point numbers that reduce the hardware complexity at the cost of acceptable loss in quality of the output image. Based on the fixed-point approach, 18-bit, 24-bit, and 32-bit DCT and IDCT architectures are proposed. This DCT architecture considers the implementation of all 64 coefficients and hence, it is named as DCT64. An important observation about 2D DCT is that most of the compaction energy is concentrated on the low-frequency DCT coefficients which are sufficient to reconstruct the original image. Taking this point into consideration, three DCT architectures, namely, DCT15, DCT10, and DCT6 with 15, 10, and 6 coefficients are designed which are compared with the DCT64. The percentage improvement in the area and performance utilization is obtained for DCT15, DCT10, and DCT6 against DCT64. A gradual increase in the percentage is observed from DCT15 through DCT6 with a marginal decline in the quality of the output image at each step which can be seen from the measured PSNR and SSIM values. Regarding IDCT, the proposed architecture achieved a low percentage of area utilization and similar performance to that of the DCT. The field-programmable gate array (FPGA) implementation of the proposed DCT architecture operates at a higher frequency, achieving lower values of dynamic power, slice reg, slice LUTs metrics that outperforms the processing capabilities of existing Algebraic Integer (AI) and fixed-point implementations.
A Survey on Data Models for Internet of Things

— In Internet of Things (IoT), heterogeneous objects are connected as networks to facilitate the exchange of goods and services. These objects, varying from electronic devices to physical entities, rely on data models to be accessed and achieve their digital functionality. Nevertheless, we observe a divergence of data models in current IoT environments which has been influencing data exchange, share and integration from different sources. In this paper, we present a survey on IoT data models and provide background for a common understanding. Firstly, we introduce the concept of IoT and its emergence; and then we discuss IoT data models according to data structure and management operations to show current achievements and limitations; at last we illustrate the challenges and prospects to shed light on future work.
Global Transition Rules for Translating Land-use Change (LUH2) To Land-cover Change for CMIP6 using GLM2

Abstract. Information on historical land-cover change is important for understanding human impacts on the environment. Over the last decade, global models have characterized historical land-use changes, but few have been able to relate these changes with corresponding changes in land-cover. Utilizing the latest global land-use change data, we make several assumptions about the relationship between land-use and land-cover change, and evaluate each scenario with remote sensing data to identify optimal fit. The resulting transition rule can guide the incorporation of land-cover information within earth system models.
A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.
Multiscale Road Extraction in Remote Sensing Images

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.
D2D Computation Offloading Optimization for Precedence-Constrained Tasks in Information-Centric IoT

This paper proposes a computation offloading scheme for precedence-constrained tasks in a base station-assisted device-to-device (D2D) scenario for the information-centric Internet of Things (IC-IoT). When specified precedence among subtasks cannot be described as simple sequential or parallel relations in a task, the selection of task execution helper for subtasks offloading becomes complex due to the constraints of latency and resources. We define this type of precedence and aim to minimize the time and financial cost of computation task offloading for the user by optimizing subtask-helper pairs. This problem is modeled as a dynamic generalized multi-resource-constrained assignment problem. The optimal offloading policy is offered by searching minimum weight matchings in a bipartite graph. Computer simulations indicate the effectiveness of the proposed approach compared with the random helper selection and priority-based offloading scheme.