Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieval-oriented E2E ASR Modeling for Improved Query-by-example Spoken Term Detection

Query-by-example Spoken Term Detection (STD) systems can make effective use of automatic speech recognizer (ASR), especially in situations with high recognition accuracy. However, the conventional DNN-HMM ASR has a problem of low recognition accuracy for out-of-vocabulary (OOV) words. This problem has a serious impact on the performance of STD-based speech retrieval and can lead to false retrieval. In a recent study, End-to-end (E2E) ASR has achieved comparable performance compared to DNN-HMM ASR. Since E2E ASR can learn direct speech-to-character mappings without knowledge of word or phonetic pronunciation, it can reduce the impact of the OOV problem. In this paper, we propose STD method using an ASR system adapted to spoken word retrieval and acoustic similarity at the sub-phone level. Our proposed method employs E2E ASR modeling with Kana as the output unit, which can be easily converted to phoneme sequence. To improve the recognition and retrieval accuracy of words that are likely to be searched for, we propose E2E modeling that adds optional tag information to the output unit. Experiments using the NTCIR-12 SpokenQuery&Doc-2 task show that the E2E ASR-based STD method significantly improves the retrieval performance over the STD method using DNN-HMM ASR. This is due to the fact that the proposed E2E ASR was able to reduce the OOV problem for spoken documents and spoken queries.
Improved Mask R-CNN Method for Intelligent Monitoring of Helmet in Power Plant

Traditional helmet wearing detection algorithms frequently encounter the problems of low accuracy and poor robustness during the processing of power plants monitoring image with complex background, low image quality, target occlusion, and small targets. A helmet wearing detection algorithm based on improved Mask R-CNN was proposed. By improving the backbone network structure of Mask R-CNN algorithm and increasing the number of anchors, the detection accuracy and robustness were improved correspondingly. Meanwhile, Multi-NMS algorithm was proposed to remove redundant labels and masks; The helmet information was bound to the head as the detection target and the Soft-NMS was introduced to reduce the interference of occlusion and handheld the helmet. Through the analysis of 500 evaluation data sets, the results yield the Mean Average Precision (mAP) of the algorithm of 97.1%, the Precision (P) of 98.0%, and the Recall (R) of 97.4%, which can meet the requirements of intelligent monitoring in different scenarios of power plants.
Multi-speaker TTS system for low-resource language using cross-lingual transfer learning and data augmentation

We propose a multi-speaker text-to-speech (TTS) system for use with low-resource languages when only a very small amount of target language data is available. We investigate the effects of using high-resource language datasets and augmented data during model training, and compare different strategies for fine-tuning the model. After using a combination of cross-lingual transfer learning, a small amount of target language data and augmented target language data for training our low-resource TTS model, we then fine-tuned the pre-trained model using the original and augmented target language data. Our experimental results show that by sequentially training the model with high-resource language data, target language data and augmented target language data, followed by gradual fine-tuning using the original and augmented target language data, our system was able to achieve the most natural speech after text-to-speech conversion, achieving a native speaker mean opinion score of 3.50 on a scale of 0 to 5.
New Digital Infinite Impulse Response Filters on Atomic Function ha(x)

Digital infinite impulse response low pass filters on atomic functions $\text{h}_{a}$(x) are first proposed. Due to its compactly support and the presence of interval where it is constant, atomic function $\text{h}_{a}(x)$ can play the role of an ideal low pass filter magnitude response. Since $\text{h}_{a}(x)$ is an infinitely differentiable function its spectrum decay is plenty fast. The synthesis of new infinite impulse response filters is performed by using analogue filter prototypes. Herewith the recently designed construction method for analogue low pass filter with magnitude response approximating function $\text{h}_{a}(x)$ is applied. This method is based on representation of truncated entire function using Cauchy integral formula. To carry out the approximation the partial Fourier sums of $\text{h}_{a}(x)$ function are used in the formula. Rational fractions which approximate atomic function $\text{h}_{a}(x)$ can be found by replacement of integral with Riemann sum. Then, the problem of integration contour optimization need to be solved. The obtained rational functions have the convenient form of representation with sum of partial fractions. After receiving the nonnegative rational fraction which defines the frequency response of analogue filter the transfer function of digital infinite impulse response filter is obtained using standard transforms. New digital filters are stable since the poles of their transfer functions are situated inside the unit circle. The magnitude responses of new digital infinite impulse response filters approximates the infinitely smooth atomic function $\text{h}_{a}(x)$. This property makes the new low pass filters substantially different from classical low pass infinite impulse response filters on Butterworth, Chebyshev and elliptical analogue prototypes which magnitude responses with order increase are approaching to discontinuous rectangular function. Due to the shape of new filters magnitude responses their impulse responses are fast decaying functions.
Network Intrusion Detection with Improved Feature Representation

This paper presents new feature representation methods for the network intrusion detection. Like conventional work, our method is based on neural networks. However, rather than focusing on the network architecture search, we improve the performance by developing effective feature representation methods. First, we apply the embedding method to categorical features in the network data. Although embedding has been commonly used in natural language processing and recommen-dation systems, categorical features in network problems were often ignored or simply used in a one-hot-encoding vector form. By applying the embedding method to categorical features, we can effectively exploit them. Second, we apply a robust scaler to numerical features. Numerical features are concentrated in a few clusters with a small portion of outliers, and we can effectively remove outliers with a robust scaler. Finally, we augment feature vectors with categorical representations of numerical values. These categorical representations (e.g., high, medium, and low) help to discover simple logical rules and facilitate the intrusion detection. We have applied our method to two scenarios: a normal/attack classification and an unsupervised learning of anomaly detection. Experimental results have shown that the proposed method outperforms conventional methods on public benchmark datasets.
Image Captioning Based on An Improved Transformer with IoU Position Encoding

The task of image captioning aims to automatically generate descriptive sentences for a given image. Most existing works use recurrent neural network as language decoder. In this paper, we use a transformer structure to generate descriptive captions. When applied in the task of image captioning, the transformer network exists two problems. The first is the disappearance of the query vector information in stacking network. The second is the lacking of spatial information between objects in the decoding process. To solve these problems, we propose an improved Transformer with IoU Position encoding model, i.e., TIP. We improve the transformer from two aspects. First, we pro-pose an intra-modal attention mechanism to alleviate the problem of vanishing query vectors. Second, we propose an Intersection-over-Union (IoU) spatial position encoding method to enhance the semantic information of images. Extensive experiments on MS-COCO datasets demonstrate the effectiveness of our model.
Multi-feature Fusion for Epileptic Focus Localization Based on Tensor Representation

Epileptic focus localization based on intracranial electroencephalogram (iEEG) signal is a key task before the patient's surgery, which is time-consuming by visual diagnosis of clinical experts and its accuracy directly affects the effectiveness of surgery. Recently, many machine learning methods have been applied to automatic epileptic focus localization. However, most studies have performed feature extraction and classification based on single type of features, leading to less robustness to noises and degraded performance. In this paper, inspired by making a diagnosis from multiple angles in practical situation, we proposed a multi-set feature fusion strategy by using tensor representation technique. As compared to the existing linear fusion strategy, our proposed tensor fusion approach can significantly enhance the expressive power of the model by taking into account the feature interaction information. We integrate tensor fusion strategy into deep convolutional neural network, yielding a new method for automatic epileptic focus localization. Experimental results demonstrate that our proposed model based on multi-set tensor fusion can achieve the best performance among single type features based models and the linear fusion model.
Application of Fractional Calculus in Voltage Source Converters

Over the past several decades, Fractional Calculus (FC) arose as an excellent realistic approach. It is utilized broadly in numerous applications for modeling real objects more precisely than integer-order models. In this paper, frequency domain analysis on the mathematical model of fractional-order Voltage Source Converter (VSC) has been done. The modeling of fractional order VSC is done in a d-q domain. A stability analysis has been performed to validate the application of fractional-order VSC in the power system. The stability of the converter is checked using a bode plot, polar plot, and step response. The comparative study demonstrates a better stability response of fractional order VSC as compared to integer order VSC.
Realizing 5G Network Slicing Provisioning with Open Source Software

5G has gradually been commercialized in countries around the world, but for most telecom companies, network slicing is still in the development stage and has not been applied to appropriate scenarios. Automated provisioning of network slicing is even more challenging for development. The purpose of this research is to implement a complete, open source, and automatically deployable 5G network slicing architecture. We use OpenStack as the platform required to realize virtualization, Tacker module for slicing environment deployment, free5GC as the core network of the 5G system, and UERANSIM as the role of simulating UE and gNB. Through the proposed architecture, an automatic slicing service with specific functions can be created, the slice can be registered into the 5G network with the support of the core network, and the simulated UE can be used to connect the related slice. Experimental results show that the proposed open-source-based architecture is feasible and the QoS is guaranteed for each slice.
Smart Facial Skincare Products Using Computer Vision Technologies

Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of skin. In view of this, this work proposes the use of computer vision technology to realize a new business model of facial skin care products based on the concept of unmanned stores. Skin care products recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skin care products that improve skin issues of consumers finally. Experimental results show that the comparison of the skin type classification accuracy is the highest.
Characterization of fractional order for high-frequency bandwidth model of dielectric ferroelectrics

Fractional operators are particularly well adapted for modeling dielectric losses of a ferroelectric material. Common integer derivative operators are generally limited to a relatively weak frequency bandwidth, whereas an approach based on fractional derivatives gives rise to good accuracy between measured hysteresis and simulated loops beyond the frequency bandwidth of classical piezoelectric systems. This article demonstrates the relation between the fractional operator used for the modeling of hysteresis plots (high electric field amplitude, relatively low frequency) and the fractional behavior of a ceramic characterized by the frequency analyzer characterization (low electric field amplitude but high frequency) bandwidth measurements obtained using impedance spectroscopy. In both cases, it was concluded that the dynamic losses had the same physical origin and could consequently be modeled using the same operator and parameters. This notion is particularly interesting as it enables to limit the piezoceramic characterization to the impedance analyzer and to anticipate the high-level excitation behavior in simulation. Finally, it confirms that the fractional operator provides a good view of the dynamic behavior and broadens its domain application to the dynamic variation of the well-known piezoelectric coefficients.
Alphabetic Image Recognition Based on Multi-channel Diffractive Deep Neural Network

Machine learning has made remarkable achievements in image classification in recent years. It has also inspired the emergence of optical machine learning frameworks as an effective combination of optics and artificial intelligence. Diffractive deep neural network (D2NN) is a recently proposed optical neural network framework, which is designed and trained by computers, and then fabricated by the usage of 3D printing technology. It can recognize handwritten digital dataset and fashion product dataset with only light source, and the recognition effect is comparable to that of electronic neural network. We propose a diffractive light neural network structure is improved on the basis of the traditional diffractive deep neural network (D2NN), providing a multi-channel design model, while the distribution of neuron nodes on the network layer is adjusted in such a way that different network layers correspond to incoming light waves of different frequencies. The neuron nodes on the network layer are arranged in a concentric circle pattern to reduce the parameters generated during network training, and in the output layer we get channels of different frequencies and then merge these channels to obtain the final results. It is experimentally verified that the diffractive optical neural network with multiple channels has a large improvement of 95.44% recognition accuracy compared to the 89.92% recognition accuracy of the traditional D2NN for recognition of 10 different alphabetic image datasets. The proposed network improves the recognition accuracy by increasing the number of channels while reducing the parameters, which provides a guidance to design optical networks with reduced cost and improved efficiency.
Self-supervised Visual Transformers for Breast Cancer Diagnosis

Owing to the growing incidences, breast cancer is considered the world's most prevalent cancer both in terms of morbidity and mortality rates. While breast cancer treatment is quite effective, higher mortality is caused due to the delayed diagnosis. Mammogram image processing using artificial intelligence techniques can be promising in the early diagnosis and Convolutional Neural Networks (CNN) have shown some potential in image classification and semantic segmentation. However, working with unlabeled image data poses challenges and while the manual labeling is not efficient, pre-trained CNN s also perform poorly on the actual medical images. In this paper, we propose to use transformer-based networks in the field of computer vision. Previously published research also suggests a predominance of transformer-based vision models over the convolutional models. Bootstrap Your Own Latent (BYOL), which is Self-supervised learning (SSL) algorithm, is proposed in this paper to process mammograms images for diagnostic purposes. BYOL can handle unlabeled image data and improve the deep learning mechanism for data analytics. UNet networks with Xavier initializations have been considered for the baseline to compare with their performances when implemented with and without BYOL. It is shown that the overall network performance improves when they are trained using the proposed BYOL algorithm.
An Ultra-wideband Fractal Antenna with Circular Rings Based on Coplanar Waveguide

As the rapid development of wireless communication technology, the antenna has become a key part in communication systems. In various antennas, the fractal antenna was ever thought of as very promising. A fractal structure refers to a geometric body with self-similarity in structure. Its mathematical foundation is fractal geometry. At present, there are few studies on sub-type antennas, and the current research status of sub-type broadband antennas is that the fractal structure is limited and more complicated. An ultra-wideband fractal antenna is proposed in the paper. The antenna is composed of four rings with different radii and 8 short-circuit stubs with an angle difference of 45°. Through the nesting of the rings, the current path is changed, and the miniaturization is realized. The antenna is feed by a coplanar waveguide (CPW) and the ultra-wideband feature can be achieved. The frequency band of the final antenna with S11 < -10 dB is 4.03–13.42 GHz, and the antenna has good radiation characteristics and gain.
Deriving a Compact Analytical Model for Camera Response Functions with Application to Chartless Radiometric Calibration

Radiometric calibration (RC) is an essential pre-processing step to correct the non-linearity of camera output images. The chartless RC is a novel RC approach that does not need a color checker to do the calibration and attracts intensive research interests. A challenging issue in the chartless RC is how to reveal the parameters of the camera response function (CRF) more effectively from limited camera images. In this work, we take a general strategy for this issue by deriving more compact parametric CRF models. Firstly, a novel exponential exponent gamma curve (EEGC) model is proposed as a more compact superset to characterize the functional space of CRF. Then the general criteria for monotonic EEGCs (MEEGC) are derived. Fi-nally, the analytical solution to low-order MEEGCs (AMEEGC) is proposed to get some nice constraint-free low-order analytical CRF models. The curve fitting experiments showed that the proposed models gave a more compact representation for real-world CRFs when compared to some existing models. We also demonstrated how to improve the efficiency of chartless RC algorithms with the 2nd-AMEEGC model by revisiting Taka-matsu's noise-based method, and the results proved our method's superiority.
A Reconfigurable Parallelization of Generative Adversarial Networks based on Array Processor

Aiming at the intensive calculations of convolution and the invalid calculations caused by “zero” inserted of deconvolution in Generative Adversarial Network (GAN), which makes difficulties of accelerated by hardware. Through analyzing of network structure and calculation flows of GAN, a paralleling scheme of reconfiguration for convolution and deconvolution is proposed in this paper. Based on the Dynamic Programmable Reconfigurable Array Processor (DPRAP), on a 4×4 processing elements (PEs) array, the flexible switching of the two convolution modes are driven by a H-tree controlled reconfiguration mechanism. The proposed scheme is verified based on the DPRAP. The experimental results show that, compared with other FPGA schemes, the resource occupation can be reduced by up to 90% at a working frequency of 150MHz. Performance has been significantly improved.
Uncertainty estimation in automatic pronunciation assessment with pseudo samples based on deep kernel learning

A reliable and accurate automatic pronunciation assessment is of great help for second language (L2) learners. Commonly, a Gaussian process (GP)-based model is utilized to output pronunciation scores and their uncertainty. However, the application of GPs is limited by computational intractability when data are sufficiently numerous. In this paper, we propose an improved GP-based model for uncertainty prediction in au-tomatic pronunciation assessment utilizing a small set of pseudo samples derived from the full training set. To further exploit more expressive information from data, we optimize the network based on deep kernel learning with deep features derived from an automatic speech recognition (ASR) system. Experimental results based on one spoken test show its superiority compared with the GP-based baselines with the full training set and standard kernels. With a small number of pseudo samples, which is only 25% compared to the full training set, we can match the full GP performance.
Dual-Band Gysel Power Divider with Wide Frequency Ratios and Flexible Impedance Transformation

ABSTRACT A compact coupled-line circuit is proposed in this article to design a kind of dual-band Gysel power divider (Gysel, 1975). This circuit inherently consists of three pairs of coupled lines, an open stub, two output extension lines, and two grounded isolation resistors. Besides the high power-handling capability, this Gysel power divider features dual-band operating performance, wide frequency ratios, flexible impedance transformation, etc. Using the transmission-line theory and rigorous even- and odd-mode analysis, closed-form solutions for the electrical parameters are obtained. A practical Gysel power divider, which works at 0.92 and 2.45 GHz, simultaneously, is designed and fabricated for radio frequency identification application. Good agreement between the simulated and measured results verify the correctness of the design theory.
Edge Map-guided Scale-iterative Image Deblurring

Blind image deblurring aims to remove the blur generated from camera motion or moving objects. Advance of deep end-to-end learning methods showed superiority in removing non-uniform motion blur, but there still exists unsharp blurriness due to the pixel-wise loss in a restored image using deep deblurring methods. Therefore, we propose a simple and effective iterative edge map guidance to restore spatial details. The proposed method extracts the edge map of the blurred input image prior to the image deblurring process and restores it to a clear edge map. We use the edge map information for image deblurring task. Therefore, unlike conventional methods, the proposed method performs deblurring by considering the edge information of the clear image, which leads to restored images with sharper edges. Furthermore, the proposed method iteratively reconstructs sharp images and edge maps on multiple scales. This iterative scheme can further improve performance due to the advantages of weight-sharing with multi-scale training. We found that the proposed method showed 0.26dB higher PSNR compared to the original method for GoPro dataset.