Reviewerly/exHarmony huggingface dataset:
Dual-Teacher Distillation for Low-Light Image Enhancement

Low-light enhancement studies based on the Retinex theory have been widely conducted. Previous studies show that enhancing low-light images through adjustment of the reflectance and illumination components is one of the promising approaches. In this paper, we extended the conventional Retinex-Net with dual-teacher distillation and attention mechanisms for further improvement. The attention mechanism is adopted in auto-encoder architecture to maximize enhancement performance. Also, we create our own dataset with different exposure times which is suitable for effectively training the proposed network. Experimental results show that the proposed dual-teacher knowledge distillation outperforms Retinex-Net and other existing methods, particularly for ultra low-light scenes.
UGAN: Semi-supervised Medical Image Segmentation Using Generative Adversarial Network

Semi-supervised medical image segmentation is well known to solve the cost problem of medical data labelling. However, most methods are proposed for solving specific tasks, which means that a well-designed model is difficult to migrate to other datasets. It is a challenge to design a semi-supervised model adaptive to different datasets. We propose UGAN, i.e., generative adversarial network based on U-Net. Especially, the segmentation network can adjust itself to different tasks based on the signature of the dataset and obtain good segmentation results. We designed the discriminator to distinguish the ground truth from the segmentation results of the U - Net segmentation network. The results on the dataset ASOCA show the effectiveness of our network.
Hyperparameter Optimization for Convolutional Neural Networks with Genetic Algorithms and Bayesian Optimization

Deep learning (DL) has emerged as a leading approach to solving complex problems in multiple areas. Its success is mainly due to technological development, large volumes of data, and various algorithms based on artificial neural networks (ANN). However, their functioning and performance are sensitive to the configuration of hyperparameters, which result from trial and error processes based on the knowledge and creativity of experts. In this sense, to reduce subjective associated with human intervention, an approach suggests tackling the tuning of hyper-parameters as an optimization problem. Here, the objective is to find a configuration that maximizes some performance metrics. The present work presents an implementation and comparison of three optimization strategies (Random Search, Bayesian Opti-mization, and Genetic Algorithm) for image classification in the CIFAR-10 dataset using convolutional neural networks, showing that algorithm performance and their solutions are tightly bound with the kind of architecture and search space.
Distance-Based Dynamic Weight: A Novel Framework for Multi-Source Information Fusion

Belief theory have been widely used in multi-source information (MSI) fusion due to its advantages in modeling uncertainty. However, combining highly conflicting MSI still remains a big challenge for belief theory. In this paper, we present a novel dynamic weight-based fusion framework, called DwFuse, which can effectively deal with conflicting data by dynamically integrating different evidences at the decision level. Specifically, we first formulate a dynamic weight generation function to explore the optimal contribution which uses evidence distance as a conflict measurement. Unlike most previous work only focusing on linear association of weight and distance, we develop a nonlinear function by joint distance and weight variables, which can better characterize the dynamic properties of complex systems. Then, by integrating the obtained dynamic weights into the Dempster-Shafer combination rule, we propose a general multimodel-based belief generation architecture and extensive experiments have been done on large corpus. The results demonstrated that our framework is more robust and effective compared with others on real datasets.
Video Human Action Recognition Algorithm Based on Double Branch 3D-CNN

The traditional action recognition algorithm based on manual feature extraction is relatively complex and has low recognition accuracy. This paper presents a video action recognition algorithm based on double branch convolutional neural network, which includes two separate convolutional neural networks in sequence. The training network effectively extracts spatio-temporal features through 3D convolution and GRU layers. Then, the features extracted from the training network are input into the test network for classification. The accuracy of the proposed algorithm is 95.0% on UCF-101 dataset. By comparing with other benchmark methods, the accuracy and effectiveness of this method are verified.
Optimal scheduling of regional integrated energy system based on cloud energy storage

In the context of smart grid, the penetration rate of distributed energy resources is increasing, but the disadvantages of high energy storage cost and low energy utilization rate are revealed. As a new energy use method, cloud energy storage has attracted the attention of researchers due to its high energy efficiency and excellent economic performance. This paper applies the cloud energy storage model to the integrated energy system. Firstly, we propose the overall system framework, and analyze the energy storage and energy consumption behaviors of each participant in the framework. Secondly, from the perspective of users, the interior point method is used to solve the energy consumption scheme that minimizes the energy consumption cost of each user, from the perspective of cloud energy storage aggregators, PSO(Particle Swarm optimization) is used to solve the scheduling scheme that maximizes the benefits of the aggregators. Finally, through case analysis, it is verified that the cloud energy storage model can be applied to the regional electric heating integrated energy system to achieve the purpose of reducing energy costs, and the participating entities can achieve mutual benefit and win-win results, which verifies the feasibility and superiority of the cloud energy storage model.
Image Classification Using Vision Transformer for EtC Images

In this paper, we propose an image classification method for compressible encrypted images called encryption-then-compression(EtC) images, where the accuracy can be preserved without any degradation. The method focuses on the structure of the Vision Transformer(ViT). We first train a ViT model by using plain images, and then encrypt the trained ViT model with a series of encryption keys. On another front, test images are also encrypted with the same series of encryption keys as the model encryption. We employ an EtC method for image encryption, so the encrypted images have high compression performance. When we classify the encrypted images through the encrypted trained model, the classification accuracy is exactly equal to that when plain images are applied to models trained with plain images. In our experiments, the key space is further calculated in order to indicate that the encrypted images are robust against brute-force attacks.
Punctuation Restoration for Singaporean Spoken Languages: English, Malay, and Mandarin

This paper presents the work of restoring punctuation for ASR transcripts generated by multilingual ASR systems. The focus languages are English, Mandarin, and Malay which are three of the most popular languages in Singapore. To the best of our knowledge, this is the first system that can tackle punctuation restoration for these three languages simultaneously. Traditional approaches usually treat the task as a sequential labeling task, however, this work adopts a slot-filling approach that predicts the presence and type of punctuation marks at each word boundary. The approach is similar to the Masked-Language Model approach employed during the pre-training stages of BERT, but instead of predicting the masked word, our model predicts masked punctuation. Additionally, we find that using Jieba <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/fxsjy/jieba instead of only using the built-in SentencePiece tokenizer of XLM-R can significantly improve the performance of punctuating Mandarin transcripts. Experimental results on English and Mandarin IWSLT2022 datasets and Malay News show that the proposed approach achieved state-of-the-art results for Mandarin with 73.8% F1-score while maintaining a reasonable F1-score for English and Malay, i.e. 74.7% and 78% respectively. Our source code that allows reproducing the results and building a simple web-based application for demonstration purposes is available on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://github.com/AetherPrior/Multilingual-Sentence-Boundary-detection.
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing

Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions. The search of optimal weights in the linear combination is computationally heavy. It is accomplished by probabilistic search in original SLM. The acceleration of SLM by PSO requires 10–20 times fewer iterations. Second, we leverage parallel processing in the SLM implementation. Experimental results show that the accelerated SLM method achieves a speed up factor of 577 in training time while maintaining comparable classification/regression performance of original SLM.
Graph Evolving and Embedding in Transformer

This paper presents a novel graph representation which tightly integrates the information sources of node embed-ding matrix and weight matrix in a graph learning representation. A new parameter updating method is proposed to dynamically represent the graph network by using a specialized transformer. This graph evolved and embedded transformer is built by using the weights and node embeddings from graph structural data. The attention-based graph learning machine is implemented. Using the proposed method, each transformer layer is composed of two attention layers. The first layer is designed to calculate the weight matrix in graph convolutional network, and also the self attention within the matrix itself. The second layer is used to estimate the node embedding and weight matrix, and also the cross attention between them. Graph learning representation is enhanced by using these two attention layers. Experiments on three financial prediction tasks demonstrate that this transformer captures the temporal information and improves the Fl score and the mean reciprocal rank.
Specific Emitter Identification at Different Time Based on Multi-domain Migration

Specific emitter identification technology aims to identify the signal emitted by a specific emitter from multiple signals. Existing techniques for specific emitter identification are faced with the problem that the identification performance decreases with the passage of time. In this paper, we propose a method for specific emitter identification at different time. The method treats the signals of radiation sources at different time as signals of separate domains, and resolves the influence of time on specific emitter identification by eliminating the factor of different domains. The experimental results show that the method has better performance on both data corresponding to the time involved in training and data corresponding to the time not involved in training compared with both domain-free migration and ordinary domain migration methods. At the same time, the proposed tagging flexible loss function is more efficient in the face of new time tag free data.
A Study on Quantum Machine Learning for Accurate and Efficient Weather Prediction

Recently Quantum Computing has gained much attention in the field of data science and computational problem solving. It is expected that the quantum machine learning will help researchers to find solutions for many complex problems in areas such as weather forecasting, data science, computational biology, energy management, secure communication, and many others. This paper presents a study on quantum machine learning techniques, challenges and applications of these techniques in climate change prediction, and weather forecasting towards future research in Quantum Machine Learning and Quantum Computing. It also discusses the latest developments and trends in Quantum machine Learning and presents practical examples to understand how Quantum Machine Learning considerably improves the performances of existing machine learning approaches.
Substation meter detection and recognition method based on lightweight deep learning model

With the advancement of robotics, intelligent robots are widely used in substation inspections. In view of the problem that the parameters of the deep learning model are too large, and the performance of embedded devices is limited, this paper proposes a meter detection and recognition method based on a lightweight deep learning model, which provides support for deploying the model to the substation intelligent inspection robot. First perform target detection on the input image to detect the position frame of the dashboard; then extract the target area, perform semantic segmentation in the target area, segment the mask of the pointer and scale, and convert the mask into two-dimensional by scanning the image is converted into a one-dimensional array, and the position of the pointer and scale is predicted through peak detection, and finally the scale is calculated according to the scale and range. The invention applies the lightweight method of runing and knowledge distillation based on the YOLOv7-tiny model in the target detection stage, so that the model is greatly compressed while maintaining the prediction accuracy; in the semantic segmentation stage, a lightweight method based on depth-wise separable convolution is used. The lightweight U2NetP model replaces the U2Net model, which greatly reduces the amount of model parameters. The experimental results show that the lightweight method used in this paper can compress the original YOLOv7-tiny model by 95.7%, the average accuracy rate can reach 90.5%, the original U2NetP model can be compressed by 76.8%, the average IOU can reach 88.7%, and the average pixel accuracy rate can reach 99.4%.
Data Augmentation Methods for Low Resolution Facial Images

Regularization techniques are useful in alleviating the problem of overfitting in super-resolution using deep learning. In this paper, multiple kinds of augmentation techniques such as CutOut, CutMix, Cutblur, Blend and Mix of augmentation techniques (MOA) are applied to CELEBA database. Both MOA and CutBlur methods improve the performance of face super resolution method. The first set of experiments are conducted without applying augmentation methods. Next set of experiments are conducted applying both MOA augmentation and CutOut, CutMix, CutBlur, Blend methods. The experiments are carried out for different values of patch size and CutBlur ratio. CELEBA dataset is used for the experiment. The effectiveness of the application of data augmentation technique is tested in EDSR model. The results shows that data augmentation techniques improve the performance of super resolution methods. CutBlur technique performs the best among all the methods.
Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning

Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of CRC and shape the patients' follow-up plans. This study evaluated the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. To this end, a deep neural network was developed on a training set of 655 whole slide images of digitized colorectal resection slides from a tertiary medical institution; and the network was evaluated on an internal test set of 234 slides, as well as on an external test set of 606 adenocarcinoma slides from The Cancer Genome Atlas database. The model achieved an overall accuracy, sensitivity, and specificity of 95.5%, 91.0%, and 97.1%, respectively, on the internal test set, and an accuracy and sensitivity of 98.5% for adenocarcinoma detection task on the external test set. Results suggest that such deep learning models can potentially assist pathologists in grading colorectal dysplasia, detecting adenocarcinoma, prescreening, and prioritizing the reviewing of suspicious cases to improve the turnaround time for patients with a high risk of CRC. Furthermore, the high sensitivity on the external test set suggests the model's generalizability in detecting colorectal adenocarcinoma on whole slide images across different institutions. Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of CRC and shape the patients' follow-up plans. This study evaluated the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. To this end, a deep neural network was developed on a training set of 655 whole slide images of digitized colorectal resection slides from a tertiary medical institution; and the network was evaluated on an internal test set of 234 slides, as well as on an external test set of 606 adenocarcinoma slides from The Cancer Genome Atlas database. The model achieved an overall accuracy, sensitivity, and specificity of 95.5%, 91.0%, and 97.1%, respectively, on the internal test set, and an accuracy and sensitivity of 98.5% for adenocarcinoma detection task on the external test set. Results suggest that such deep learning models can potentially assist pathologists in grading colorectal dysplasia, detecting adenocarcinoma, prescreening, and prioritizing the reviewing of suspicious cases to improve the turnaround time for patients with a high risk of CRC. Furthermore, the high sensitivity on the external test set suggests the model's generalizability in detecting colorectal adenocarcinoma on whole slide images across different institutions.
Network cards: concise, readable summaries of network data

The deluge of network datasets demands a standard way to effectively and succinctly summarize network datasets. Building on similar efforts to standardize the documentation of models and datasets in machine learning, here we propose network cards, short summaries of network datasets that can capture not only the basic statistics of the network but also information about the data construction process, provenance, ethical considerations, and other metadata. In this paper, we lay out (1) the rationales and objectives for network cards, (2) key elements that should be included in network cards, and (3) example network cards to underscore their benefits across a variety of research domains. We also provide a schema, templates, and a software package for generating network cards.
Enhanced dual branches network for arbitrary‐scale image super‐resolution

Deep convolutional neural networks (CNNs) are of great improvement for single image super-resolution (SISR). However, most existing SISR pre-trained models can only perform single image restoration and the upscale factors cannot be non-integers, which limits its application in real-world scenarios. In this letter, an enhanced dual branches network (EDBNet) in upsampling network is proposed to generate arbitrary-scale super-resolution (SR) images. Specifically, the authors design a scale-guidance upsampling module (SGU) by adding the scale factors and pixel-level features to guide the weights of convolution. The SGU module performs discriminant learning for each instance in the same batch. Extensive experiments on four benchmark datasets show that the proposed method can achieve superior SR results.
Diagnosis of dairy cow diseases by knowledge-driven deep learning based on the text reports of illness state

Expert system is the most commonly used method for auxiliary diagnosis of dairy cow diseases, which is complex to build and usually difficult for non-professional farmers to operate. Moreover, it cannot discover the implicit knowledge hidden in the observed symptoms. To address these problems, we proposed a knowledge-driven deep learning model for efficient diagnosis of dairy cow diseases. The model first selected the explicit features from the text reports of illness state. Then, our model employed a professional knowledge graph of dairy cow diseases for extracting implicit features. Both the explicit and implicit features were furtherly fed into a BiLSTM-CNN hybrid network to make a diagnosis. The experimental results showed that the F1 value of our model reached 94.89%, which was 9.53% and 2.49% higher than that of the best machine learning model XGBoost and the neural network model DE-CNN, respectively. Our model can accurately diagnose dairy cow diseases, especially those with similar or common symptoms, and it will provide a new idea for the auxiliary disease diagnosis of other animals.
WITHDRAWN: Mammogram Pectoral Muscle Removal Using Fuzzy C-Means ROI Clustering and MS-CNN Based Multi Classification

Mammography is crucial in determining breast cancer's benign, malignant, and expected stages in its earliest detection stages. Micro calcifications, masses, architectural deformities, and bilateral asymmetry are only some of the abnormalities that fall into four distinct categories when classifying malignant mammography using a multi-classification approach. To aid the radiologist in making an accurate diagnosis of the many types of breast lesions, this work proposes Enthalpy-based fuzzy c-means ROI clustering and a Median Support Value-Based Convolution Neural Network (MS-CNN). The proposed layout consists of four primary parts. To reduce noise in the photos, we first apply a fuzzy filtering method during the preprocessing phase. Next, we apply histogram equalization to the cleaned-up images. A second step involves slicing the pectoral muscle using fuzzy c-means region of interest clustering informed by Enthalpy. In the third stage, breast cancer is detected and classified based on the extracted hybrid features, including DCT, DWT, Gabor filter, Multifractal Analysis, and directional and morphological features. The final stage is applying the extracted MS-CNN classifier to perform precise classification. Using experimental results, the MIAS Dataset's proposed accuracy is 99.37%. DDSM and Lakeshore datasets are also at a perfect 100%[100% is not correct, for both data sets, it is 99%]. Approximately the same as the 95% accuracy of the current ECNN and the 92% accuracy of the CNN. The K-NN and MSVM had less Accuracy58%, 57%.