Miracle-dz/UniFAR-FTUs huggingface dataset:
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

Aesthetic appeal and image quality are two important features of photographs, which play the dominant role when people clean their albums. Currently, the objective image quality assessment has been documented very well whereas the objective aesthetic appeal assessment algorithms are not developed well enough. This paper first subjectively evaluated image quality and aesthetic appeal separately of 339 photographs across different levels of depth of field. With the subjective data, the paper proposed two mathematical models to predict the subjective aesthetic appeal from subjective image quality. More specifically, depth of field, as a common photographic feature, was investigated to see how it influenced aesthetic appeal and image quality. 32 participants were asked to score for the aesthetic appeal and image quality. With these subjective scores, we used two methods linear regression and deep neural networks to build models separately to predict aesthetic appeal from image quality. We found that both models worked well on the valid dataset and the performance of the deep neural networks model was better than the linear regression model. INDEX TERMS Aesthetic appeal, image quality, depth of field, linear regression, deep neural networks.
Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach

Conventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the benefits of machine learning in solving secure relay selection problem from a data-driven perspective. We convert secure relay selection to a multiclass-classification problem and solve it by a decision-tree-based scheme, which is composed of three phases - preparing training data, building decision tree and predicting relay selection. To meet decision tree’s requirement that input features must take discrete values, a feature extraction method is proposed to generate discrete input by quantizing the accurate CSI of legitimate and eavesdropper channels. By this means, the decision-tree-based relay selection only requires quantized CSI feedback which takes substantially fewer bits in predicting phase. For the purpose of optimizing quantization parameters and enhancing decision tree prediction, we further derive three splitting criteria, i.e. information gain, information gain ratio and Gini index. Simulation results show that if the quantization parameters are set properly, the proposed decision-tree-based scheme can achieve satisfactory performance in terms of average secrecy rate while reducing computational complexity and feedback amount.
DOA-based Weighted Spatial Filter Design for Sum and Difference Composite Co-Array

This paper presents a weighted spatial filter (WSF) design method based on direction of arrival (DOA) estimation for sum and difference composite co-array. We have developed a novel array configuration “sum and difference composite co-array” which is based on the combination of sum and difference co-arrays. Our configuration enables us to realize higher degree of freedom (DOF) with the sum co-array part with lower calculation cost. To further enhance robustness on the proposed system, we design an optimal beam pattern by WSF based on DOA estimation. The performance of the proposed system and especially capability of close wave detection are evaluated through some computer simulations.
Exploiting Negative Impedance Converters to Extend the Bandwidth of LEDs for Visible Light Communication

This paper proposes, for the first time, an optically lossless light emitting diode (LED) bandwidth extension technique based on the adoption of negative impedance converters applied in the context of high capacity visible light communication systems. The proposed technique aims to achieve bandwidth extension by offsetting the bandwidth-limiting effect of the LED diffusion capacitance through the introduction of a parallel negative capacitance. With the proposed technique, we predict up to 200% improvement in the LED modulation bandwidth with studies based on a verified LED equivalent model.
Joint Sparse Channel Recovery With Quantized Feedback for Multi-User Massive MIMO Systems

Accurate channel state information (CSI) at the transmitter is an essential prerequisite for transmit beamforming in massive multiple input multiple output (MIMO) systems. However, due to a large number of antennas in massive MIMO systems, the pilot training and feedback overhead become a bottleneck. To resolve this issue, the research work presents a novel framework for frequency division duplex (FDD) based multi-user massive MIMO system. A 2-step quantization technique is employed at the user equipment (UE) and the CSI is recovered at the base station (BS) by applying the proposed compressed sensing (CS) based algorithms. The received compressed pilots are quantized by preserving 1 bit per dimension direction information as well as the partial amplitude information. Subsequently, this information is fed back to the BS, which employs the proposed quantized partially joint orthogonal matching pursuit (Q-PJOMP) or quantized partially joint iterative hard thresholding (Q-PJIHT) CS algorithms to recover the CSI from a limited and quantized feedback. Indeed, an appropriate dictionary and the hidden joint channel sparsity structure among users is exploited by the CS methods, resulting in the reduction of the feedback information required for channel estimation. Simulations are performed using singular value decomposition (SVD) and minimum mean square error (MMSE) beamforming utilizing the estimated channel. The results confirm that the proposed 2-step quantization approaches the system with channel knowledge without quantization, thus overcoming the training and feedback overhead problem. Moreover, the proposed 2-step quantization outperforms 1-bit quantization, at the cost of slightly higher complexity.
Auto-ORVNet: Orientation-Boosted Volumetric Neural Architecture Search for 3D Shape Classification

Recently, more and more 3D shape datasets have become publicly available and significant results have been attained in 3D shape classification with 3D volumetric convolutional neural networks. However, the existing 3D volumetric networks have a problem with balancing model scale and classification accuracy. To address this problem, neural architecture search (NAS) was introduced into 3D shape classification tasks to search for a model satisfying both requirements. Automatically generating neural networks under NAS has attracted increasing research interest in recent years. The models learned by NAS outperform many manually designed networks in several 2D tasks like image classification, detection and semantic segmentation. In this paper, the differentiable formulation of NAS is exploited to search for several repeatable computation cells. The introduction of many light-weight designs for 3D CNNs assists in the construction of deep models with fewer parameters. The loss for the classification task along with the loss for orientation prediction are combined to guide such search. Extensive experiments are designed to evaluate candidate models on three datasets. The results demonstrate that without any pretraining, our discovered model for 3D shape classification outperforms most manually designed networks with small parameter sizes, whilst also showing that our model achieves a balance between model scale and classification accuracy.
Noise-induced Performance Enhancement of Variability-aware Memristor Networks

Memristor networks are capable of low-power, massive parallel processing and information storage. Moreover, they have widely used for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. However, till today, one of the major drawbacks resulting to their commercial cumbersome growth, is the fact that the fabricated memristor devices are subject to device-to-device and cycle-to-cycle variability that strongly affects the performance of the memristive network and restricts, in a sense, the utilisation of such systems for real-life demanding applications. In this work, we put effort on increasing the performance of memristive networks by incorporating external additive noise that will be proven to have a beneficial role for the memristor devices and networks. More specifically, we are taking inspiration from the well-known non-linear system phenomenon, called Stochastic Resonance, which alleges that noisy signals with specific characteristics can positively affect the operation of non-linear devices. As such, we are now focusing on the utilisation of the phenomenon on memristor devices in a way that the negative effect of variability is reduced, thus the operation of the whole memristor network is assisted by the increased variability tolerance. The presented results of Bit Error Rate (BER) on a small ReRAM crossbar array sound promising and enable us to further investigate the exploitation of the described phenomenon by memristor-based networks and memories.
Extreme Image Coding via Multiscale Autoencoders with Generative Adversarial Optimization

We propose a MultiScale AutoEncoder (MSAE) based extreme image coding/compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "priors" at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network (GAN) with multiscale discriminators to perform the end-to-end trainable rate-distortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using High-Efficiency Video Coding (HEVC) based Intra Profile and JPEG2000 on the public Cityscapes, ADE20K and Kodak datasets, demonstrating the significant subjective quality improvement. However, objective measurements, such as PSNR, SSIM, etc, are often deteriorated by applying the generative adversarial optimization.
Recursive Estimation for Sensor Systems With One-Step Randomly Delayed and Censored Measurements

Censored measurements frequently occur in network systems involving censored sensors or saturated sensors. In addition, unreliable network characteristics can produce random measurement delays during signal transmission. In this paper, we investigate the state estimation problem for network systems with the simultaneous appearance of the aforementioned two measurement uncertainties. The occurrences of two random measurement phenomena are described by two Bernoulli random variables in which the censored variable is dependent on the delay variable. The probability of the process signal being uncensored is calculated by the local approximations using a priori and a posteriori of the state estimation. Then, a novel measurement model that incorporates both the censoring random matrix and the signal delay is established. Based on this model, an optimal recursive estimation method is proposed for systems with specified two uncertainties by making use of an innovation analysis approach and a weighted conditional expectation formula. The superior performance of our proposed method is verified through a typical oscillator simulation example.
Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture

Mammogram is an X-ray image of the breast. It plays an important role in the breast cancer early diagnosis. In recent years, computer aided detection (CAD) is used for breast cancer detection. Multi-view of mammograms are needed to achieve high accuracy of automatic detection. Since nipple is the only landmark on mammogram of different views (mediolateral oblique (MLO) and craniocaudal (CC) views), nipple detection becomes the first important step of many CAD systems. Researchers have developed different models to detect nipple in recent 20 years. Grey scale, geometric feature and breast edge's gradient are used to find the nipple on the mammogram. For most methods, MLO and CC views need to be tested separately, and obvious and subtle types of nipples also need different methods to detect. In this paper, a model with deep learning is designed to locate nipples on mammogram of both MLO and CC views. Both obvious and subtle types are used for experiment. Four convolutional neural network blocks are used to attain candidate blocks. Normalization layers are added to the proposed model in order to improve the domain adaptation. Based on the intersection of candidates, the model computes the final block of nipple. In this experiment, train set and test set are randomly attained from Digital Database for Screening Mammography (DDSM). Our proposed method achieved an overall nipple detection accuracy of 98.00%, which outperformed three comparative methods.
A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) – and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor’s importance-performance.
Integration of Measurement Model in Information System User Acceptance

User acceptance is an important factor in the success of an information system (SI). Utilization of SI in a company at the beginning was enforced, the user must still not be optimal. This affects employee performance. This study aims to bring together two Measurement Models for user acceptance of the application of SI. This quantitative research was conducted using the integration of the Technology Acceptance Model (TAM) model and the End User Computing Satisfaction (EUCS) model with analysis techniques using PLS-SEM with SmartPLS 3.0. Sampling is done using a purposive sampling technique. As a result, four out of ten proposed hypotheses were rejected.
Tomato Diseases Recognition Based on Faster RCNN

Plant diseases are a major challenge for the agricultural sector. Accurate and rapid detection of diseases in plants can greatly reduce economic losses. In this paper, we present a method based on Faster RCNN to detect tomato diseases. We combine Faster RCNN with different deep convolutional neural networks, including vgg16, resnet50, and resnet101. We trained and tested the collected tomato diseased dataset, which contained six diseased images and healthy tomato fruit images. The experimental results show that our proposed system can effectively identify different types of tomato diseases.
Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm

Brain tumor can be classified into two types: benign and malignant. Timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. One of the most practical and important methods is to use Deep Neural Network (DNN). In this paper, a Convolutional Neural Network (CNN) has been used to detect a tumor through brain Magnetic Resonance Imaging (MRI) images. Images were first applied to the CNN. The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67%. Also, the accuracy of the CNN is obtained with the Radial Basis Function (RBF) classifier 97.34% and the Decision Tree (DT) classifier, is 94.24%. In addition to the accuracy criterion, we use the benchmarks of Sensitivity, Specificity and Precision evaluate network performance. According to the results obtained from the categorizers, the Softmax classifier has the best accuracy in the CNN according to the results obtained from network accuracy on the image testing. This is a new method based on the combination of feature extraction techniques with the CNN for tumor detection from brain images. The method proposed accuracy 99.12% on the test data. Due to the importance of the diagnosis given by the physician, the accuracy of the doctors help in diagnosing the tumor and treating the patient increased.
Exploring Multi-loss Function Based Attention Framework for Vehicle Re-Identification

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts.
An exploration of STEM freshmen's attitudes, engagement and autonomous learning in introductory computer programming

Programming skills and computational thinking are fundamental concerns in professional training in Science, Technology’ Engineering and Mathematics (STEM) disciplines. In spite of this, in the Chilean higher education system, as in most parts of the world, teaching programming is a challenging endeavor, partly because the majority of freshmen students in STEM undergraduate programs have not been introduced to the fundamentals of the discipline and its applications in K12 education. Therefore, students' attitudes, engagement, and autonomous learning can make a difference in how students in STEM disciplines in higher education face their first programming course. In the present study, we carried out measurement of variables associated to these constructs involving a sample of 1694 freshmen of seventeen STEM programs in a Chilean research university. Relevant differences were found among different STEM programs, and by student gender. Implications of the results obtained and future research avenues are discussed.
EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

Background. The most common and successful technique for signal denoising with non-stationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experimentally set. Fortunately, the optimality of the combination of these parameters can be measured in advance by using the mean squared error (MSE) function. Method. In this paper, five powerful metaheuristic algorithms are proposed to find the optimal WT parameters for EEG signal denoising which are harmony search (HS), $\beta $ -hill climbing ( $\beta $ -hc), particle swarm optimization (PSO), genetic algorithm (GA), and flower pollination algorithm (FPA). It is worth mentioning that this is the initial investigation of using optimization methods for WT parameter configuration. This paper then examines which efficient algorithm has obtained the minimum MSE and the best WT parameter configurations. Result. The performance of the proposed algorithms is tested using two standard EEG datasets, namely, Kiern’s EEG dataset and EEG Motor Movement/Imagery dataset. The results of the proposed algorithms are evaluated using five common criteria: signal-to-noise-ratio (SNR), SNR improvement, mean square error (MSE), root mean square error (RMSE), and percentage root mean square difference (PRD). Interestingly, for almost all evaluating criteria, FPA achieves the best parameters configuration for WT and empowers this technique to efficiently denoise the EEG signals for almost all used datasets. To further validate the FPA results, a comparative study between the FPA results and the results of two previous studies is conducted, and the findings favor to FPA. Conclusion. In conclusion, the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy. Therefore, using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method.
Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition

Face recognition is an active research subject of biometrics due to its significant research and application prospects. The performance of face recognition can be affected by a series of uncontrollable factors, such as illumination, expression, posture and occlusion, which restricts its real-world applications. Therefore, improving the robustness of face recognition to environmental changes became an urgent problem. In this paper, a simplified deep convolutional neural network structure having high robustness under unlimited conditions is designed for face recognition. This structure can improve training speed and face recognition accuracy, and be suitable for small-scale data sets. Inception Module Incorporated Siamese Convolutional Neural Networks (IMISCNN) is developed based on effective reduction of external interference and better features extraction by adopting the Siamese network structure. A cyclical learning rate strategy is also introduced in IMISCNN for better model convergence. Compared to classical face recognition algorithms, such as PCA, PCA and SVM, CNN, PCANet, and the original SNN et al. The accuracy of IMISCNN in CASIA-webface and Extended Yale B standard face database is 99.36% and 99.21%, respectively. Its feasibility and effectiveness have been verified in our experiments.