Reviewerly/exHarmony huggingface dataset:
A modified Bayesian neural network integrating stochastic configuration network and ensemble learning strategy

In this paper, a stochastic configured Bayesian neural network (SCBNN) is proposed for solving regression and classification problems. Firstly, stochastic configuration network (SCN) is applied to extract feature. Then, the stochastic configured scheme is applied to Bayesian neural network (BNN) for obtaining the appropriate structure. The extracted features are combined with the original features to compute the output of the network. Further, an integration strategy of the Bayesian model average (BMA) is considered to improve the performance of the network. Some experimental results demonstrate the validity of the proposed method.
A Comparison of Performance of Rough Set Theory with Machine Learning Techniques in Detecting Phishing Attack

Phishing is a deceptive social engineering trick that lures online users to disclose personal and confidential information to the fake websites disguising as legitimate ones in electronic communication. Several machine learning methods have been proposed to detect phishing websites. The major limitation of assumption of a peculiar distribution of the data plagues analysis with machine learning (ML) especially when the number of data points are limited in number. This paper applies classical rough sets analysis (CRSA) for detecting phishing websites from a collection of legitimate and fake websites sourced from an open UCI Repository. The work focuses primarily on comparing the classification performance of CRSA with the traditional ML tools. CRSA does not require any distributional assumption for analysis and does not need any user inputs for the parameters in the analysis unlike the traditional ML tools. The comparison revealed no significant difference in the classification performances of the two sets of algorithms. The precision of CRSA was 95.6% which is comparable and in some cases better than the other ML tools. Similarly, high ROC and f-measure places CRSA at par with the statistical ML tools. The relaxation in considering the statistical properties of data clearly gives CRSA edge over other data analysis tools. The CRSA based techniques provide a robust method of detecting phishing websites. The generalization of the analysis makes CRSA a quick-to-use framework for detection of phishing websites without consideration of statistical properties of the data.
Detection of Microcalcifications in Mammograms Using Edge Detection Operators and Clustering Algorithms

Mammography is a popular modality used by the radiologists to detect the affected areas in human breast. Microcalcification detection is not an easy task because of their small size. This paper proposes a methodology for enhancement of low contrast mammograms and then applying three edge detection operators with K-means clustering and fuzzy C-means clustering to find out microcalcification clusters in a mammogram taken from MIAS database. To validate the work, the proposed methodology is tested on different mammographic images, and the results obtained are compared on the basis of different metrics such as accuracy, mean square error (MSE) and peak signal-to noise ratio (PSNR).
Research on Semantic Segmentation and Object Grasping Strategy Generation Based on Deeplab Algorithm

The Semantic segmentation is another important research direction in the field of machine vision after object recognition. And the contour segmentation of objects will be realized at the pixel level, which will provide great help for robot object grasp strategy generation and driverless driving. The algorithm of deeplab series proposed by Google innovatively use dilated convolution kernels instead of traditional solid convolution kernels to achieve precise object semantic segmentation and perform well on multi-scale objects. Firstly, this paper studies the principle of deeplab algorithm from a mathematical point of view, and discusses its excellent performance in semantic segmentation; Secondly, this paper applies the advantages of deeplab algorithm in semantic segmentation to the research of object grasp strategy generation, which makes a beneficial exploration for robot grasp and promotes the wider application of robot; Finally, the core parameters of deeplab algorithm are optimized, and the experimental results are analyzed in detail to improve the accuracy of the model.
Research and Implementation of Automatic Composition System Based on DLGN

The LSTM (Long Short-Term Memory) network is a commonly used network in the field of artificial neural network composition. However, in order to generate high-quality music, it is necessary to use the LSTM network correctly and reasonably so that it can give full play to its advantages. This paper proposes a network model called Double LSTM-GRU Network (DLGN) to realize automatic composition. This model is composed of two LSTM-GRU networks in series. Then the model is compared with the model that only contains two layers of LSTM network through experiments. The experimental results show that the DLGN model performs better in many aspects than the model composed of only two layers of LSTM network. For example, the DLGN model has a higher output per unit time, a lower repetition rate of notes in the generated music, and a higher fluency in the generated music.
Effects of Game Perspectives Differences on Immersion Using Eye Tracking

The current development of science and technology has led to more and more expressions of video games. To solve the problem that whether the differences of game perspectives in video games will cause the difference of immersion, a method of measuring immersion by combining eye tracking and immersion scale was proposed. The setting of the experiment consisted of participants playing a game from one of the perspectives and recording their eye movement behavior, then filling out an immersion scale. The results showed that, in the absence of narrative guidance, players were more immersed in the third person than in the first person. In addition, the experimental data also showed that previous gaming experience had an impact on the difference in immersion caused by perspective. In future work, we will try more types of games and explore their differences in immersion.
Shift-Sub Modular Multiplication Algorithm and Hardware Implementation for RSA Cryptography

RSA public-key cryptography requires modular exponentiation and modular multiplication on large numbers. Montgomery Modular Multiplication is a fast method for performing modular multiplication. The modular exponentiation can be performed by repeatedly calling Montgomery Modular Multiplication. Transformations to the Montgomery Domain are required before the calculations, and a transformation back to the normal domain is also required to get the final result. The domain transformations require a value that is calculated by costly modular arithmetic. Many hardware RSA implementations use precomputed values for such domain transformations. As a result, the flexibility to use different public keys is lost. This paper introduces a Shift-Sub Modular Multiplication (SSMM) algorithm for calculating such values in fields. The algorithm does not require modular arithmetic and precomputed values. Instead, it uses shift and addition/subtraction calculations. The SSMM algorithm can also be used directly for RSA public-key cryptography. We give the hardware implementation of RSA public-key cryptography using SSMM in Verilog HDL and compare the cost and performance to that of the RSA public-key cryptography implementation using Montgomery Modular Multiplication. The results show that the performance of the two implementations is about the same, but the implementation using SSMM uses fewer hardware resources (55% to 59% adaptive logic modules and 69% to 85% flip-flops) because it does not require domain transformations.
Design of Multifunctional pedal-actuated wheelchair for locomotion and rehabilitation training

Patients with lower limb disabilities have caused a great burden both to society and themselves. The combination of wheelchair and cycling achieves both locomotion and rehabilitation training at the same time, which meets their needs. This paper presents a new multifunctional electric wheelchair with a rehabilitation training module based on our previous research. We remove the exoskeleton and replace it with the leg structure of the user to form a crank-rocker mechanism. The rehabilitation module can now be adjusted forward and backward or hidden under the wheelchair completely. We also replace the force sensors with software to detect users’ intentions. A new friendly human-computer interface is designed to enable users to control the wheelchair easier. Experiments are conducted on several volunteers. Meanwhile, more than 100 people in a hospital and a nursing home have tried our product, which obtains a high praise rate. Experiment data is presented and analyzed to test the functions of the new design, and results show that our design can meet the need for rehabilitation and locomotion of different people.
On Data Wastage When User Becomes Idle in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) has become the fundamental technology of nowadays' online video streaming services to maintain the user's Quality of Experience. Such an ability of the HAS is powered by its Adaptive Bitrate Algorithm (ABR), which focuses on maximizing both the visual and audio quality of the video with regards to the user's network condition. However, not all users equally care about both visual and audio quality concurrently. Our subjective assessment on viewing behaviors shows that, for videos with audio-focused content, most of the participants let the video run in the background and move on to other works for a certain time. As a result, maintaining a high visual quality within this period, which current ABRs are doing, becomes unnecessary and thus causes wastage of data. To tackle this problem, this paper proposes a novel algorithm called Idle-Aware, which adjusts video quality based on the idle behavior of the user. The experimental results showed that our solution not only reduces data wastage but also diminishes the rebuffering events under congested networks.
Progressive Guidance Categorization Using Transformer-Based Deep Neural Network Architecture

In the era of advanced technology, the internet is one of the most indispensable necessities in our daily liveliness. Usually, people search for various things on a search engine, from daily needs to dream utilities. From the ample amount of searching styles, the prefix with “How To" is one of the exploited prominent styles. According to this search style with the mentioned prefix, people mainly find out the guidelines or instructions for solutions. However, there is no work has been conducted on guidelines or instructions classification in the past. In this research work, we identify the category of guidelines or instructions from the summary of “How To” articles. We employ some deep neural architectures, such as LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and BERT (Bidirectional Encoder Representations from Transformers) to classify guidelines precisely and swiftly. We have conveyed this work with a novel dataset comprising 11,121 observations from the WikiHow. In this classification process, we have manifested accuracy, precision, recall, and f1-score to appraise our proposed architectures. The outcomes have brought a wit of our applied BERT architecture that achieves notably with 87% accuracy.
Improving Robustness In Analog Neural Networks By Introducing Sparse Connectivity

Sparsity in the connectivity of an analog neural network can both reduce the amount of energy needed for multiply-accumulate operations as well as mitigate the effects of noise and random variations within the network. This paper gives an example for how sparsity can decrease the uncertainty in noisy, low-precision networks and compares common techniques for generating sparse network architectures. This work also presents a better method for generating small sparse neural networks.
Dual Path Residual Network for Speaker Verification

Current speaker verification technology relies on the use of neural networks to extract the representation of the speaker. In this work, we present a novel dual path network, SE-Shrinkage-ResNet. The basic module of the network, SE-Shrinkage, is a parallel connection of the SE-ResNet block and the DRSN block, which jointly contributes to the generation of the speaker-discriminative embedding. SE-ResNet blocks explicitly model channel interdependencies, while DRSN blocks employ channel-shared and channel-wise thresholds to mitigate the effect of high noise signals. Experimental results on the VoxCelebl dataset show that the proposed SE-Shrinkage-ResNet achieves superior performance compared to SE-ResNet and DRSN, and obtains a lower equal error rate compared to the latest ECAPA-TDNN with fewer parameters.
Quantum Coins for Non-Power-of-Two States - Implementation and Performance on Quantum Walk and Quantum Walk Search Algorithms

Quantum coin is an essential element of the discrete-time quantum walk, the quantum analog of random walk. Most existing papers relating to quantum coin discussed the implementation and performance of the coin on power-of-two states and degrees of graphs. In this paper, I proposed and analyzed three alternative coins–binary state coin, W state coin, and Walsh-Hadamard coin–that possessed the property to perform on all states and degrees of graphs. I discussed the implementation and the performance of the three coins in the application of Quantum Walk and Quantum Walk Search. I compared and discussed the advantages, disadvantages, and errors of the three coins on a quantum simulator and a NISQ computer (i.e. IBM Q). In the end, I presented further applications for the three coins in quantum-walk-based algorithms and other potential topics in quantum computing.
Kernel-based Class-specific Broad Learning System for software defect prediction

With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction.
Wave-Shape-Tolerant Photonic Quantum Gates

Photons, acting as "flying qubits" in propagation geometries such as waveguides, appear unavoidably in the form of wave packets (pulses). The actual shape of the photonic wave packet as well as possible temporal and spectral correlations between the photons play a critical role in successful scalable computation. Currently, unentangled indistinguishable photons are considered a suitable resource for scalable photonic circuits. Here we show that using so-called coherent photon conversion, it is possible to construct flying-qubit gates which are not only insensitive to wave shapes of the photons and temporal and spectral correlations between them but which also fully preserve these wave shapes and correlations upon the processing. This allows the use of photons with correlations and purity in a very broad range for a scalable computation. Moreover, such gates can process entangled photonic wave packets even more effectively than unentangled ones.
Application of Internet Remote Video Technology in Learning Motivation Intensity under Smart Cloud Platform Service

Cloud services are an increasing, using, and delivery model of Internet-based related services, which usually involve the provision of dynamic, easy-scalable and often virtualized resources through the Internet. In this study, researchers discuss students’ learning motivation intensity in cohort study under the smart cloud platform service and the motivation-behavior gap that influence Chinese students’ second language learning in smart cloud platform service.
Insulator image data enhancement based on BIG-GAN

The data set of power grid equipment is difficult to obtain on a large scale due to the complex working environment of the power grid and many high-altitude operation scenarios, resulting in scarce data in many scenarios. Aiming at the problem of lack of image data of insulators on transmission lines, a data augmentation network based on BIG-GAN network is proposed for the first time. This network combines convolutional neural networks with batch standardization and sampling truncation techniques added to the convolutional neural network, which effectively improve the stability of the GAN network training process and the reconstruction effect of the generative model.
A Monopulse Radar Anti-jamming Method Based on Labeled GM-PHD

Monopulse tracking radar can achieve high tracking and measurement precision and is widely used in space exploration, target measurement and weapon control areas. The anti-jamming of monopulse radar is an important issue. When jamming is treated as one or more false targets, radar anti-jamming can be regarded as a multiple targets tracking problem. This paper proposed a monopulse radar anti-jamming method based on labeled Gaussian Mixture Probability Hypothesis Density (GM-PHD) filtering. Firstly, random finite set (RFS) theory, GM-PHD filtering and labeled GM-PHD are introduced, then labeled GM-PHD filtering is used to distinguish jammer from the real target by four steps: prediction, update, cut and merge, and state estimation. Finally delay-retransmit jamming simulation was performed to verify the proposed method.
Non-Convex and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

In this paper, a zeroing neural network model with properties of non-convex activation and noise suppressing (NANSZNN) is proposed for finding the square root of a time-varying matrix. In comparison with the existing zeroing neural network models, the proposed NANSZNN model relieves the limit of convex constraint and makes a breakthrough in noise suppressing. Furthermore, theoretical analyses and strict proofs are provided in details for the global convergence and noise-suppressing performance of the proposed NANSZNN model under the circumstances of various noises by using Lyapunov stability theory. Finally, numerical experiments and comparative analyses are offered to further illustrate the availability, effectiveness, and robustness against different noises of the proposed NANSZNN model.
Provable Data Clustering via Innovation Search

This paper studies the subspace clustering problem in which data points collected from high-dimensional ambient space lie in a union of linear subspaces. Subspace clustering becomes challenging when the dimension of intersection between subspaces is large and most of the self-representation based methods are sensitive to the intersection between the span of clusters. In sharp contrast to the self-representation based methods, a recently proposed clustering method termed Innovation Pursuit, computed a set of optimal directions (directions of innovation) to build the adjacency matrix. This paper focuses on the Innovation Pursuit Algorithm to shed light on its impressive performance when the subspaces are heavily intersected. It is shown that in contrast to most of the existing methods which require the subspaces to be sufficiently incoherent with each other, Innovation Pursuit only requires the innovative components of the subspaces to be sufficiently incoherent with each other. These new sufficient conditions allow the clusters to be strongly close to each other. Motivated by the presented theoretical analysis, a simple yet effective projection based technique is proposed which we show with both numerical and theoretical results that it can boost the performance of Innovation Pursuit.
Design of integrated photonic controlled-phase gate with programmable phase for quantum applications

Photons are preferable carriers of quantum information having little interaction with environment. Most crucial elements of photonic quantum information processing are two qubit gates. Although usage of controlled-not gates for photonic two qubit operations is common, controlled-phase gates provide more versatile utilization by enabling desired phase difference to be generated between qubits. Ability to determine the phase after fabrication yields extra benefit. We demonstrate a design of integrated photonic controlled-phase gate with programmable phase difference. We used a unique and practical polarization rotator which is ring resonator with a single notch.
Optimal Transport for Super Resolution Applied to Astronomy Imaging

Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport and entropy for super resolution applications. We prove that the reconstruction is accurate when sparsity is known and noise or distortion is small enough. We prove that the optimizer is stable and robust to noise and perturbations. We compare this method to a state of the art convolutional neural network and get similar results for much less computational cost and greater methodological flexibility.
Application of DatasetGAN in medical imaging: preliminary studies

Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging. DatasetGAN is a recently proposed framework based on modern GANs that can synthesize high-quality segmented images while requiring only a small set of annotated training images. The synthesized annotated images could be potentially employed for many medical imaging applications, where images with segmentation information are required. However, to the best of our knowledge, there are no published studies focusing on its applications to medical imaging. In this work, preliminary studies were conducted to investigate the utility of DatasetGAN in medical imaging. Three improvements were proposed to the original DatasetGAN framework, considering the unique characteristics of medical images. The synthesized segmented images by DatasetGAN were visually evaluated. The trained DatasetGAN was further analyzed by evaluating the performance of a pre-defined image segmentation technique, which was trained by the use of the synthesized datasets. The effectiveness, concerns, and potential usage of DatasetGAN were discussed.