Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Based On Deep Residual Bidirectional Gated Recurrent Unit Neural Networks

Human beings have rich emotions, in which positive emotions need to be constantly maintained, while negative emotions need to be regulated since most mental diseases are caused by the long-term persistence of negative emotions. Aiming at emotion classification in sentiment analysis tasks, a deep residual BiGRU (Bidirectional Gated Recurrent Unit) neural network model is introduced to improve the classification effect and solve the problem of long-distance dependence between layers of deep RNN (Recurrent Neural Network) by using a recurrent correlation channel connects all layers and a loss function with CRF (Conditional Random Fields) technique. This model provides a preferable method and outperforms other commonly used models in extracting sentiment features and recognizing emotions expressed in texts. Experiments on simplified Chinese sentiment analysis data set show that it classifies and predicts emotions more accurately than stacked neural network models built on RNN, TextCNN (Text Convolutional Neural Network), BiLSTM (Bidirectional Long Short-Term Memory), or non-neural network models built on NB (Naïve Bayesian) classifier and LR (Logistic Regression) method.
Lightweight Deep Network for the Classification of Breast Cancer Histopathological Images

Out of all cancers among female patients, breast cancer is on top in terms of prevalence. Many deep learning approaches have been investigated for the classification of histopathological images of breast cancer, however most tend to be either too complex or too large to adopt at a clinical level. In this study we propose a lightweight network, based on knowledge distillation, which performs almost the same as the teacher network but with significantly fewer parameters. Our student network consists of inverted residual blocks of MobileNetV2 and the ghost module. We performed our experiments on BreakHis, BACH and Kaggle breast cancer histopathological imaging datasets. The results show that different versions of our proposed lightweight student architecture perform with similar accuracy levels compared with the teacher network, while using significantly fewer parameters.
Visual Perception Based Blind Stereoscopic Omnidirectional Video Quality Assessment

Stereoscopic omnidirectional videos (SOVs) can provide users with continuous immersive visual experience, but the distortions introduced in the process of its processing, coding transmission, visualization bring great challenges to its quality assessment. In this paper, a blind SOV quality assessment method including spatial perception model (SPM) and temporal perception model (TPM) is designed based on the spatio-temporal separability of neurons in the region 18 of visual cortex, and the characteristics of stereoscopic and omnidirectional perception are considered. In the SPM, the fractal dimension is introduced to design the pixel domain feature extraction module of single viewpoint; based on the theory of binocular summation and binocular difference to regulate binocular behavior, the two-stage gain control and maximum response model are combined to construct the binocular perception model, and the orientation feature in transform domain of the binocular perception map is extracted to achieve the complementary role of content perception of single viewpoint and binocular. For the TPM, considering the motion perception of the middle temporal region and the correlation between the left and right viewpoints, a binary statistical model for temporal information extraction is constructed to assist SPM to form functional complementarity. Experimental results show that the proposed method has good quality assessment performance, and has a better consistency with human visual perception.
Adaptive Pixel Pair Evaluation Method for Image Matting

Image matting is an ill-posed problem that aims to extract the opacity of foreground objects in an image. Pixel-pair-optimization-based (PPO-based) image matting approaches are widely adopted in natural image matting, whereby the alpha value is estimated by choosing the optimal pixel pair according to a pixel pair evaluation (PPE) function. Multiple PPE criteria are employed to improve the accuracy of PPE, resulting in the weight setting problem of PPE criteria. Existing PPE functions use fixed weight PPE criteria, which cannot provide the accuracy of PPE on the little transparent images due to the satisfaction degree of PPE criteria related to the type of the image. To address this shortcoming, in this work, an adaptive weight criteria PPE method is presented, which adaptively adjusts the contribution of chromatic distortion and spatial closeness criteria to the PPE function by analyzing the type of the image. Experimental results show that the proposed adaptive weight criteria PPE method provides accurate PPE compared with existing PPE methods, especially on the little transparent Images.
Fast Signal Completion Algorithm with Cyclic Convolutional Smoothing

Recently, signal completion methods using delay-embedding transforms (DT) have been actively studied. Since the DT is an operation to transform a signal into a Hankel matrix, the high computational cost associated with the increase in data size is an issue. In this study, we consider modeling smooth signals based on inverse delay-embedding instead of delay-embedding. We propose a new algorithm that incorporates the properties of the delay-embedding-based methods while reducing the computational cost. The proposed algorithm takes advantage of the inverse delay-embedding being a cyclic convolution, and the computational complexity can be reduced to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(NlogN)$</tex> by transforming the optimization problem to Fourier space. Numerical experiments with typical signals and audio data show the effectiveness of the proposed algorithm in signal declipping and completion problems.
Multi-Branch Network for Few-shot Learning

Few-shot learning aims provide precise predictions for unseen data through learning from only one or few labelled samples of each class. However, it often suffers from the overfitting problem because of insufficient training data. In this paper, we propose a novel metric-based few-shot learning method, multi-branch network (MBN), with a new data augmentation module to improve the generalization ability of the model. Specifically, we generate different types of noise contaminated data through multiple branches in the network to simulate the real-world scenarios when noisy images are obtained. Following this novel data augmentation module, the feature embedding and similarities between the support and query samples are learned simultaneously through the embedding and metric modules, respectively. Moreover, to consider more details in the feature maps, we propose to utilize the average-pooling layer in the metric module rather than the commonly adopted max-pooling layer. The network is trained from end to end by the Kullback- Leibler (KL) divergence, to minimize the difference between the distributions of the ground truths and predictions. Extensive experiments on Standford-Dogs, Standford-Cars, CUB-200-2011 and mini-ImageNet in the 1-shot and 5-shot tasks demonstrate the superior classification performance of MBN.
An image encryption method based on random number matrix iterations

There is a demand for more image encryption techniques due to the safety of image transmission and archiving. so a means of encrypting images based on random number matrix iterations is suggested. In the encryption process, the algorithm uses two random number matrices with values ranging from 1 to 256, which are randomly generated. Use two matrices to iterate. During the iteration, take out the elements of the first matrix, then take out the elements in the same position of the second matrix, combining the element values of the two to form a new position index, and get the value of the new position index in the first matrix finally. Iterate over the value multiple times, using it to replace the element value of the current first matrix, and then continue to iterate the other position elements of the matrix. After the iteration of all elements is completed, the first random number matrix will be converted into a new matrix, and it will be used as an encrypted sequence. A plaintext image is XOR with the sequence to generate a ciphertext image. The testing findings demonstrate the algorithm's superior security and encryption performance. It features a big key spacing, strong key sensitivity, and good diffusion and obfuscation capabilities, and are resistant to conventional assaults including differential and brute force assaults.
Development of a Virtual Telecommunication System Research Laboratory

Researchers are utilizing cutting-edge technology to accelerate the discovery of solutions to stop the COVID-19 pandemic. Virtual reality (VR) also provides a vital role in the operation against this outbreak. Daily tasks have remained unchanged since life has entirely halted. The method for recovering people's lives and enabling them to work from home is described in this article. The digital Telecom Lab at Chulalongkorn University was developed using virtual 3D technology. Several methods were employed gradually to build this lab. The VR lab was developed using Unity, a cross-platform game engine, to deliver the greatest experience. The actual outcome, which was created with the expertise of Blender and Unity, is incredibly immersive.
Boolean Binary Grey Wolf Optimizer

Several binary swarm algorithms use the identical continuous proposal, adding a transfer function to mapping from continuous to binary space. It has been shown that binary operators are more appropriate and efficient for binary optimisation. Based on it, we proposed the Boolean Binary Grey Wolf Optimizer, a new version of Grey Wolf Optimizer to solve binary optimisation. Our proposal uniquely operates in the binary space using binary vector and boolean operators, precisely AND, OR, XOR, and NOT gates. We used OneMax, ZeroMax and 0-1 Knapsack to compare our proposal with bGWO1, bGWO2 and BPSO. In general, BBGWO outperformed the other swarm-based algorithms in different scenarios. Furthermore, analysing the swarm hamming distance and the unique candidate solutions through optimisation, we found that the proposal can overcome premature convergence, and most of the time, wolves are in different positions.
Generation of English Question Answer Exercises from Texts using Transformers based Models

This paper studies the use of NLP techniques, in particular, neural language models, for the generation of question/answer exercises from English texts. The experiments aim to generate beginner-level exercises from simple texts, to be used in teaching ESL (English as a Second Language) to children. The approach we present in this paper is based on four stages: a pre-processing stage that, among other basic tasks, applies a co-reference resolution tool; an answer candidate selection stage, which is based on semantic role labeling; a question generation stage, which takes as input the text with the resolved co-references and returns a set of questions for each answer candidate using a language model based on the Transformers architecture; and a post-processing stage that adjusts the format of the generated questions. The question generation model was evaluated on a benchmark obtaining similar results to those of previous works, and the complete pipeline was evaluated on a corpus specifically created for this task, achieving good results.
Research on path planning of electric tractor based on improved ant colony algorithm

The emergence of electric tractors and related control technologies has accelerated the development of modern agriculture, but the path planning problem of electric tractors affects their operating range to a certain extent. In this paper, an improved ant colony algorithm is proposed to address the problems that the basic ant colony algorithm in the path planning of electric tractors is prone to local optimal solutions and slow convergence speed. Based on the idea of “Newton’s cooling law”, the pheromone volatility factor and state transfer probability function are improved to enhance the ability of global search in the early iteration of the algorithm and accelerate the convergence speed in the middle and late iteration; the early termination strategy of the algorithm iteration is introduced to reduce the iteration redundancy and shorten the running time of the algorithm; based on the kinematic model of the electric tractor, a mathematical model of energy loss is established to shorten the running time of the algorithm. Based on the kinematic model of electric tractor, the mathematical model of energy loss is established, and the evaluation index of the optimal path is established. The simulation results show that compared with the literature algorithm and the basic ant colony algorithm, the energy loss of the electric tractor is reduced by 18.31% and 28.96%, the optimal path length is shortened by 0.81% and 0.97%, and the running time is reduced by 20.13% and 18.43%, respectively. The comprehensive performance of the improved algorithm in this paper is excellent, which verifies the optimization effect.
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis

We present a comprehensive empirical study for per-sonalized spontaneous speech synthesis on the basis of linguistic knowledge. With the advent of voice cloning for reading-style speech synthesis, a new voice cloning paradigm for human-like and spontaneous speech synthesis is required. We, therefore, focus on personalized spontaneous speech synthesis that can clone both the individual's voice timbre and speech disfluency. Specifically, we deal with filled pauses, a major source of speech disfluency, which is known to play an important role in speech generation and communication in psychology and linguistics. To comparatively evaluate personalized filled pause insertion and non-personalized filled pause prediction methods, we developed a speech synthesis method with a non-personalized external filled pause predictor trained with a multi-speaker corpus. The results clarify the position-word entanglement of filled pauses, i.e., the necessity of precisely predicting positions for naturalness and the necessity of precisely predicting words for individuality on the evaluation of synthesized speech.
Analysis Method for Motion Factors Related to Joint Contact Forces at the Knee during Walking Using Grad-CAM

Walking is an essential physical activity in an increasingly aging society. However, sustained walking with an incorrect posture may cause excessive stress on the knee joint, causing knee abrasion. The elderly are particularly vulnerable to this issue. Walking motion features can be analyzed to reduce knee load and lead to sustainable living. This study proposes a method for analyzing the motion of walking that affects the load on the knees using a motion capture system, ground reaction force measurement, a convolutional neural network (CNN) model, and a visual explanation technique for CNN. We trained a CNN model to classify the joint contact forces at the knee using gait measurement data. The heat maps were calculated using gradient-weighted class activation mapping from the trained CNN model. The experiment analyzed the motion factors affecting the joint contact forces at the knee using heat maps.
Deep Learning Acceleration Design Based on Low Rank Approximation

Recently, artificial intelligence applications require large resources for training and inferencing. Therefore, intensive computation or large memory requirements often become the bottleneck of AI. This paper proposes the singular value decomposition (SVD) low-rank approximation (LRA) method applied to the CNN model. By exploiting the fact that redundancy exists between different channels and filters, the SVD matrix decomposition is used to estimate the most informative parameters in deep CNNs, and by reducing the convolutional layer parameters in this way, a special structure of the convolutional layers is designed to accelerate the trained neural network, to control the accuracy degradation within 2% but to greatly reduce the data storage and the number of operations. The design process is based on algorithm/architecture co-design, and the analysis of the number of operations and data storage.
Feature Distillation Network for Multi-Band NIR Colorization

Unlike an RGB image, an NIR image has been widely used for night vision and surveillance because it is less noisy and can keep details and textures preserved even in low-light environments. However, the NIR image is useless for human cognitive system or computer vision algorithms. This is because NIR is invisible and has no color information. Several recent studies have tried to solve this problem by so-called NIR colorization, and the recent deep learning techniques achieve superior performances. However, there still exist fundamental limitations to NIR colorization. It is quite challenging to successfully restore the original color information from NIR with no color. In other words, NIR-to-RGB conversion is a significant ill-posed problem, and this inspired us to concentrate on effective priors. In this paper, we propose a novel network architecture which transfers the conversion knowledge from 'RGB+NIR’ to RGB in the teacher network to the student by the distillation loss. Also, we built a new dataset which includes NIR multi-band images with the corresponding RGB ground truth. Comparison results show that NIR multi-band and feature distillation can contribute to higher quality NIR colorization.
Restoring Edge and Color using Weighted Near-Infrared Image and Color Transmission Maps for Robust Haze Removal

In recent years, various haze removal methods based on atmospheric scattering models have been proposed. Most methods target strong haze images in which light is scattered equally in all color channels. This paper proposes a haze removal method using near-infrared (NIR) images for weak haze images. The proposed method first restores the edges of color images by fusing weighted NIR images. Second, it estimates transmission maps for all color channels based on a wavelength-dependent scattering model and restores the color of the edge-restored image using the estimated transmission maps. Finally, the edge- restored and color-restored images are blended. Qualitative and quantitative evaluations demonstrate that the proposed method can restore edges and colors more naturally in weak haze images than conventional methods.
Comparative Analysis on Different DeepFake Detection Methods and Semi Supervised GAN Architecture for DeepFake Detection

The use of deep learning results in solving a wide range of real-world problems and applications, but there are some drawbacks along with this positive side. One of the most recent and advanced problems among them is the wide use of deepfakes. Deepfakes are digital tampered images or videos created using different deep learning methods. In a deepfake, the face of a targeted person is superimposed on a source image so that this digital tampered data can be used for digital frauds, blackmailing, pornography etc. With the developments in the deep learning field, it is becoming challenging to distinguish between real and fake manually. So it is essential to do research and development in the area of deepfake detection. In this paper, an extensive discussion and timely overview on different deepfake detection methods are done under the classification of feature-based, temporal-based, and deep feature-based deepfake detection. The comparison study is mainly done based on the key features used, face detection architecture, deep learning architecture, video-based or image-based, the dataset used, frames size, and dataset size used. Along with the comparison, a semisupervised GAN architecture is also proposed and developed to detect the deepfake images.
Detection of insulator defects based on improved YOLOv7 model

Inspection for the electric transmission system has great significance for powerline maintenance, in which defects of insulators are needed to be found in time to preserve the safety of the whole system. To improve the accuracy and efficiency of insulator defect detection, computer vision techniques are employed. However, since insulator defects on the insulator strings are small objects and usually works in complex environment, it is challenging to get satisfactory detection results. In order to solve this issue, we proposed an insulator defect detection method based on YOLOv7 which is one of the state-of-the-art object detection methods. By introducing coordinate attention mechanism into the backbone network and redesigning the feature pyramid network (FPN) to have bi-directional FPN like structure, we successfully adapt the original model to the insulator defect detection task. We used an open-source dataset called CPLID to train our model. Experiments demonstrate that our method achieve good performance for insulator defect detection and have better average precision comparing with other methods. Ablation study were also designed to verify the effectiveness of the improved component.
An image retargeting method based on saliency map optimization and seam carving

A good image retargeting method can retain the important information of an image while changing its size. Image retargeting has been widely used in multi-size device displays and software thumbnail images. The existing image retargeting methods have some defects when they are used to process important regions of a large area and linear elements in the image. In this paper, an improved Seam Carving method is developed through optimizing the saliency map determination and operation flow. The saliency map is determined by Canny edge detection with adaptive threshold, Hough transforms for detecting straight lines, Yolo neural network and flood fill for sensitive area detection, etc. With these methods, the expression of essential information in pictures is improved. In the algorithm running process, the SC algorithm based on average energy and the similar simulated annealing algorithm based on seam neighborhood penalty are used to improve the running process of Seam Carving. Finally, experimental results on the open-source data set RetargetMe indicate that the proposed method achieves better performance in comparison with Seam-carving (SC), Shift-map (SM), Scale-and-stretch (SNS), and Warping methods.
Neural network acceleration methods via selective activation

Abstract The increase in neural network recognition accuracy is accompanied by a significant increase in the scales of networks and computations. To make deep learning frameworks widely used on mobile platforms, model acceleration has become extremely important in computer vision. In this study, a novel neural network acceleration method based on selective activation is proposed. First, as the algebraic basis for selective activation, mask general matrix multiplication is used to reduce matrix multiplication calculations. Second, to screen and remove activated neurons and reduce the number of calculations, we introduce an Activation Management Unit that includes two different strategies, Selective Activation with Primary Weights (SAPW) and Selective Activation with Primary Inputs (SAPI). SAPW greatly reduces the number of calculations of the fully connected layer and self‐attention and better guarantees detection accuracy. SAPI has the best performance on convolutional architectures, which can significantly reduce the amount of convolutional computation while maintaining the image classification accuracy. We present result of extensive experiments on computational and accuracy tradeoffs and show strong performance for CIFAR‐10 classification and Pascal VOC2012 detection. Compared with the dense method, the proposed selective activation method significantly reduces the number of neural network calculations with equal accuracy.