Miracle-dz/UniFAR-FTUs huggingface dataset:
Customer Churn Prediction with Feature Embedded Convolutional Neural Network: An Empirical Study in the Internet Funds Industry

In this paper, we investigated the customer churn prediction problem in the Internet funds industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) method that can automatically learn features from both the dynamic customer behavioral data and static customer demographic data and can utilize the advantage of convolutional neural networks to automatically learn features that capture the structured information. Our results show that our FE-CNN model outperforms the other traditional machine learning models with hand-crafted features, such as logistic regression (LR), support vector machines (SVM), random forests (RF) and neural networks (NN) in terms of accuracy, area under the receiver operating characteristics curve (AUC) and top-decile lift. Furthermore, we found that after adding the demographic data feature to the basic CNN model, the performance of the FE-CNN model improved. Overall, we found that the FE-CNN is the most powerful way to solve the problem of customer churn prediction in the Internet funds industry. Our FE-CNN method can also be applied to other fields that have both dynamic data and static data.
Effects of FABP2 Ala54Thr gene polymorphism on obesity and metabolic syndrome in middle-aged Korean women with abdominal obesity.

OBJECTIVES Asians (including Chinese, Japanese and Koreans), who generally have a relatively smaller body size and a lower mean body mass index (BMI), have a relatively higher risk of developing android-type obesity than westerners. Substitution of alanine for threonine (Ala54Thr) on the FABP2 gene (rs 1799883) is related to insulin resistance and obesity. However, few studies have examined this substitution in Koreans, and the number of Korean subjects in those studies is limited. For this reason, we investigated the differences between the FABP2 Ala54Thr polymorphism and obesity, hemodynamic variables, blood lipid profile results, and insulin resistance among middle-aged Korean women with abdominal obesity. METHODS We studied 243 middle-aged community-dwelling Korean women with abdominal obesity from Gyeonggi Province, Republic of Korea, who had no history of taking chronic medications. We examined each subject (n = 243) for the presence of FABP2 Ala54Thr polymorphism using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Subjects were also examined for obesity hemodynamic variables (n = 243), lipid profiles (n = 142), and insulin resistance (n = 142). RESULTS Of the 243 subjects, 117 had AA ("normal") homozygotic genotype, 100 had AT heterozygotic genotype, and 26 had TT homozygotic genotype for the FABP2 Ala54Thr polymorphism. The AT heterozygotic individuals had a significantly higher mean waist-to-hip ratio, abdominal fat area, and visceral fat area than individuals with other genotypes. TT homozygotic individuals had higher mean triglyceride and fasting glucose levels than individuals with other genotypes. CONCLUSIONS The results of this study show that the FABP2 Ala54Thr polymorphism was associated with central obesity and obesity-related metabolic syndrome among middle-aged Korean women.
Camera Tampering Detection using Generative Reference Model and Deep Learned Features

An unauthorized alteration in the viewpoint of a surveillance cameras is called tampering. This involves comparing images from the surveillance camera against a reference model. The reference model represents the features (e.g. background, edges, and interest points) of the image under normal operating conditions. The approach is to identify a tamper by analysing the distance between the features of the image from surveillance camera and from the reference model. If the distance is not within a certain threshold, the image is labeled as a tamper. Most methods have used images from the immediate past of the surveillance camera to construct the reference model. We propose to employ a generative model that learns the distribution of images from the surveillance camera under normal operating conditions, by training a generative adversarial network (GAN). The GAN is capable of sampling images from the probability density function, which are used as reference. We train a Siamese network that transforms the images into a feature space, so as to maximize the distance between the generated images and tampered images (while minimizing the distance between generated and normal images). The distance between the generated and the surveillance camera image is classified as either normal or tampered. The model is trained and tested over a synthetic dataset that is created by inducing artificial tampering (using image processing techniques). We compare the performance of the proposed model against two existing methods. Results show that the proposed model is highly capable of detecting and classifying tampering, and outperforms the existing methods with respect to accuracy and false positive rate.
Research of Image Stitching Method Based on Graph Cuts and Poisson Fusion

This paper considers the problem of ghosting artifacts and stitching seam caused by image stitching and fusion directly, which influenced by the registration and geometric transformation, and the differences between the two images overlap region. This paper proposed maximum flow / minimum cut in graph cut algorithm to obtain the optimal stitching line. Considering the relationship between color difference and structural changes of pixels in the neighborhood, achieving costs or energy division minimize, thereby eliminating the stitching seams. And using the Poisson fusion technology, further eliminate ghosting artifacts caused by the brightness and color differences or moving objects in the image fusion process. The experiment proved that the algorithm of searching optimal stitching line and Poisson fusion algorithm in this paper, can eliminate ghosting artifacts and also be able to maximize avoid stitching seams appears, it presents good performance in applications.
Sparse Representation based Satellite Image Restoration Using Adaptive Reciprocal Cell

Recently, an emerging method called image sparse representation has attracted more attentions. The method has been proved to be effective in various image processing applications. It is important to note that few sparse representation methods fail to analyze the aliasing in satellite image restoration. To address the problem, firstly, we employ adaptive reciprocal cell as a image quality estimation tool, which can analyze the satellite image degradation factors including aliasing, blur and noise. Then, with the help of the powerful tool, the estimation about the satellite image quality is introduced into the sparse representation model . Experiment results show that our method can produce good quality restored results.
Using Textual Similarity and Sentiment Analysis in Discussions Forums to Enhance Learning

Web 2.0 technologies have changed the learning process and have affected the unidirectional relationship obtaining between the teacher and the student. Web 2.0 has given rise to new bidirectional learning strategies in which the learner is both an actor and active member in the learning process. Given the wide use of these technologies as educational tools, our objective in this article is to analyze learners’ comments in discussion forums, which are popular platforms amongst students. For this purpose, we use the Latent Semantic Analysis (LSA), a statistical model that can allow us to establish the relationship between the set of messages published by learners and course content that students need to master. We also use sentiment analysis techniques to identify the students’ opinions about educational issues that are problematic. The implementation of both methods has revealed that the data available in educational discussion boards can improve the quality of learning and teaching.
The Lottery Ticket Hypothesis: Training Pruned Neural Networks

Recent work on neural network pruning indicates that, at training time, neural networks need to be significantly larger in size than is necessary to represent the eventual functions that they learn. This paper articulates a new hypothesis to explain this phenomenon. This conjecture, which we term the "lottery ticket hypothesis," proposes that successful training depends on lucky random initialization of a smaller subcomponent of the network. Larger networks have more of these "lottery tickets," meaning they are more likely to luck out with a subcomponent initialized in a configuration amenable to successful optimization. This paper conducts a series of experiments with XOR and MNIST that support the lottery ticket hypothesis. In particular, we identify these fortuitously-initialized subcomponents by pruning low-magnitude weights from trained networks. We then demonstrate that these subcomponents can be successfully retrained in isolation so long as the subnetworks are given the same initializations as they had at the beginning of the training process. Initialized as such, these small networks reliably converge successfully, often faster than the original network at the same level of accuracy. However, when these subcomponents are randomly reinitialized or rearranged, they perform worse than the original network. In other words, large networks that train successfully contain small subnetworks with initializations conducive to optimization. The lottery ticket hypothesis and its connection to pruning are a step toward developing architectures, initializations, and training strategies that make it possible to solve the same problems with much smaller networks.
Levelized Recommendation Method in Internet of Things Environment

The Internet of Things (IoT) is one of the most promising technologies for the changing the world. With the advent of IoT environment, new technologies are needed in recommendation methods. The existing recommendation methods provide recommendation using users and items, but they do not consider the characteristics of IoT. The recommendation methods need to incorporate IoT characteristics into it. This paper presents a levelized recommendation method in IoT environment. Our method integrates the traditional recommendation method with the social relationships between objects in IoT environment. The recommendation method is proposed, a scenario is described, and then several experiments are performed. The experimental results show that the proposed method's recommendation performance is better than other existing method in IoT environment.
Design of the control circuit of automatic dryer

Keywords: the temperature and humidity of the received signal after processing is sent to the display shows the number, when the value reaches the initial settings required the relay to control the motor, closed, the dryer to stop working, the GSM module and the remote user information exchange, automatically send text messages that have been drying clothes. The design of remote control system to overcome the distance to and understanding the working state and the password protection dryer faults, scalability and use value has a good.
Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Clustering‐Based Resource Allocation (CBRA) is a relatively recent approach applied in some communication network technologies that executes resource allocation rules by following patterns discovered in the user and traffic flow information. It is a useful option for classifying traffic and detecting the most suitable Quality‐of‐Service parameters to perform improved resource allocation, especially in mobile systems such as Long Term Evolution Advanced, which are growing in user demand. An open issue in the CBRA approach is the necessity to perform feature engineering manually (feature selection), which may cause loss of relevant system information and consequent deterioration of performance. In this research article, we present a novel CBRA mechanism that implements an auto‐encoder to perform feature learning, thus avoiding feature selection. The auto‐encoder, deployed at the early stages of the CBRA mechanism, transforms the user and traffic flow data into a shortened representation, preventing the curse of dimensionality while capturing the nonlinear correlations of the data. The simulation results indicate better cluster formation and performance improvement for real‐time video applications using the proposed mechanism.
Comparative Assessment of the Image Divide and Link Algorithm in Different Color Spaces

Abstract In this paper, a comparative assessment of the Image Divide and Link Algorithm (ID&L) in different color spaces is presented. This, in order to show the significance of choosing a specific color space when the algorithm computes the dissimilarity measure between adjacent pixels. Specifically, the algorithm procedure is based on treating a digital image as a graph, assigning a weight to each edge based on the dissimilarity measure between adjacent pixels. Then, the algorithm constructs a spanning forest through a Kruskal scheme to order the edges successively while partitions are obtained. This process is driven until all the pixels of the image are segmented, that is, there are as many regions as pixels. The results of the algorithm which have been compared with those generated using different color spaces are shown.
miR-31-5p Is a LIPUS-Mechanosensitive MicroRNA that Targets HIF-1α Signaling and Cytoskeletal Proteins

The roles of low-intensity pulsed ultrasound (LIPUS) and microRNAs (miRNAs) on hMSCs commitments have already been investigated; however, the effects of the application of their co-treatments in an in vitro cell model are still unknown. Our previous studies demonstrated that (i) LIPUS modulated hMSCs cytoskeletal organization and (ii) miRNA-675-5p have a role in HIF-1α signaling modulation during hMSCs osteoblast commitment. We investigated for the first time the role of LIPUS as promoter tool for miRNA expression. Thanks to bioinformatic analysis, we identified miR-31-5p as a LIPUS-induced miRNA and investigated its role through in vitro studies of gain and loss of function. Results highlighted that LIPUS stimulation induced a hypoxia adaptive cell response, which determines a reorganization of cell membrane and cytoskeleton proteins. MiR-31-5p gain and loss of function studies, demonstrated as miR-31-5p overexpression, were able to induce hypoxic and cytoskeletal responses. Moreover, the co-treatments LIPUS and miR-31-5p inhibitor abolished the hypoxic responses including angiogenesis and the expression of Rho family proteins. MiR-31-5p was identified as a LIPUS-mechanosensitive miRNAs and may be considered a new therapeutic option to promote or abolish hypoxic response and cytoskeletal organization on hMSCs during the bone regeneration process.
Design of a Self-Complementary Frequency Selective Surface With Multi-Band Polarization Separation Characteristic

According to the principle of Babinet, this paper designs a novel frequency selective surface (FSS) with high performance. Using the Babinet principle, an asymmetric Jerusalem cross patch structure and its complementary aperture structure are mixed on one side of the dielectric substrate, namely, self-complementary FSS (SCFSS). The theoretical analysis and experimental results show that SCFSS has excellent multi-bands polarization separation characteristics in different frequency ranges. In each passband, the insertion loss is less than 0.5 dB, and the out-of-band suppression level is relatively high. By individually adjusting the size of a certain structural parameter of SCFSS, the position of polarization separation frequency band can be adjusted in a specific frequency band range, thus meeting the requirements of the practical application. By rotating the surface, its filtering characteristics for polarized waves of a certain mode can be mechanically tuned from bandpass to bandstop. Furthermore, the SCFSS has an excellent incident angle stability in a wide range from 0° to 60°. Therefore, when designing polarization separation structure and polarization wave generator for the wireless communication system (such as the satellite communication system), the novel SCFSS structure proposed in this paper can provide an excellent reference.
A Trace Mechanism of Change History in the Presence of Refactoring

Nowadays, changes in software development are inevitable. Therefore, understanding and analysis of changes considered more important than anything else. The existing version control systems, which are used for effective change management, are impossible to trace changes in the presence of refactoring. There have been a number of approaches proposed for trace changes using operation-based mechanisms or rule-based mechanisms. However, it has many limitations with excessive recording or not sufficient coverage. In this approach, we define four rules based on M.Flower’s refactoring techniques and find an origin method matching the pattern of changed source code with the pre-defined rules. The case study shows that the proposed mechanism is effective Also, case study looking for open source project code changes about the origin method show the proposed mechanism’s effectiveness.
Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems

The industrial internet of things (IIoT) known as industry 4.0, is the use of internet of things technologies, via the Wireless Sensor Network (WSN), to enhance manufacturing and industrial processes. It incorporates machine learning and big data technologies, to allow machine-to-machine communication that have existed for years in the industrial world. Therefore, it is necessary to propose a robust and functional communication architecture that is based on WSNs, inside factories, in order to show the great interest in the connectivity of things in the industrial environment. In such environment, propagation differs from other conventional indoor mediums, in its large dimensions, and the nature of objects and obstacles inside. Thus, the industrial medium is modeled as a fading channel affected by an impulsive and Gaussian noise. The objective of this paper is to improve robustness and performances of multi-user WSN architecture, based on Discrete Wavelet Transform, under an industrial environment using conventional channel coding and an optimal thresholding receiver.
Multiuser Interference Cancellation for GFDM With Timing and Frequency Offsets

In uplink multiuser systems, different timing offset (TO) and/or carrier frequency offset (CFO) of multiple users cause multiuser interference (MUI) to a desired user. In this paper, we propose two different MUI cancellation methods, namely weighted parallel interference cancellation (WPIC) and adaptive interference cancellation filter (AICF), for uplink transmission with generalized frequency division multiplexing (GFDM) waveform over Rayleigh fading channel given the existence of TO and CFO of multiple users. The proposed WPIC performs MUI estimation using the detected data symbol of other users and obtains the optimal weight to maximize the signal-to-interference ratio (SIR) of the desired user. We also propose the AICF that aims to maximize the SIR of the desired user. Based on our SIR expression derived in closed-form in the presence of TO and CFO, the AICF is obtained by solving the Rayleigh quotient problem. Lastly, the impact of multiple antennas at the receiver on MUI cancellation is also analyzed to improve reliability. The simulation results show that the proposed WPIC and AICF effectively eliminate MUI and that the AICF can outperform the WPIC in terms of the SIR and spectral efficiency. In addition, the multiple receiver antennas effectively cancel MUI as the number of antennas goes to infinity.
A scientometric analysis of BIM studies in facilities management

Purpose The purpose of this paper is to statistically classify and categorize Building Information Modelling (BIM)-Facility Management (FM) publications in order to extract useful information related to the adoption and use of BIM in FM. Design/methodology/approach This study employs a quantitative approach using science mapping techniques to examine BIM-FM publications using Web of Science (WOS) database for the period between 2000 and April 2018. Findings The findings guide the researchers who are interested in the BIM-FM model by providing visual maps analysis of that area in a simple, easy and readable way. In addition, they help the researchers to understand which authors and journals to consider when dealing with BIM-FM topics. Finally, knowledge gaps in this domain can be identified easily using the findings of the Scientometric analysis. Research limitations/implications First, the results of the analysis depend on the database that has been extracted from WOS, and therefore it carries any of WOS’s limitations in terms of how much it covers the published studies. Another limitation is that the study is based on exploration of “what” questions, rather than “how” and “why”. These limitations represent the hot topics to be addressed in future research. Originality/value This research is the first to conduct the Scientometric Analysis of BIM-FM topics, in which 68 top-ranked publications were systematically examined using a Science Mapping method through VOSviewer software.
Resilience in medical doctors: a systematic review

Introduction Resilience can be difficult to conceptualise and little is known about resilience in medical doctors. Aims This systematic review discusses the existing literature on influences on resilience levels of medical doctors. Methods The bibliographic databases PubMed, MEDLINE, EMBASE and PsycINFO were searched from 2008 to November 2018 using keyword search terms resilience* AND (“medical physician*” OR doctor* OR surgeon* OR medical trainee* or clinician*). Results Twenty-four studies were deemed eligible for inclusion. A narrative synthesis was performed. The following influences on resilience in doctors were identified: demographics, personality factors, organisational or environmental factors, social support, leisure activities, overcoming previous adversity and interventions to improve resilience. Conclusions Resilience is not limited to a doctor’s own personal resource. Published studies also highlight the influence of other modifiable factors.
Effects of obstacle avoidance to LQG-based motion planners

Motion planning under uncertainty is critical for robust autonomy. The Partially Observable Markov Decision Process (POMDP) is a principled and general framework for solving such problems. Although solving a POMDP problem exactly is computationally intractable, in the past decade, many practical methods have been proposed to approximate solution of POMDPs that represent motion planning under uncertainty problems. However, the problem remains relatively open when it involves robots with complex non-linear dynamics. Recently, linearization-based methods that are derived from the Linear Quadratic Gaussian (LQG) controller have been shown to perform well in some planning under uncertainty problems with non-linear robot dynamics. However, it is not clear what the effect of linearization to motion planning under uncertainty is. The control and estimation results have clearly indicated that linearization performs well only when the non-linear dynamics is “weak”. These results will deﬁnitely apply to the LQG-based derived methods for solving motion planning under uncertainty problems, too. However, a signiﬁcant difference between motion planning and control is in the presence of obstacles. And it is not clear how the presence of obstacles affect the effectiveness of linearization and Gaussian simpliﬁcation for solving motion planning problems. This paper presents a preliminary study in understanding this effect, by performing comparison studies via simulation.