Reviewerly/exHarmony huggingface dataset:
System for the unification of medical records using Blockchain

A patient’s medical record contains his entire medical history in sequential order, information whose confidentiality, integrity and availability must be guaranteed. There are some APIs and medical records management systems that propose the use of blockchain technology to achieve a high degree of security. In this work, a proof of concept based on three test cases was performed to evaluate the success factors of the solution with security aspects. It was found that the solution has high information security, respecting the main security aspects such as integrity, confidentiality and availability.
Digital Self-Efficacy Predictors in Students from a Private University in Peru: A Diagnostic Study in the Context of Virtual Education

The present study aims to analyze the predictive factors of the digital self-efficacy in students from a private university of Lima (Peru) in the context of the Covid-19 pandemic. The sample was 102 university students (92 women (90.2%) and 10 men (9.8%)) between 19 and 40 years old (M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age</inf> = 26.39, SD = 7.75). The results show in the analysis with the Student’s T the digital self-efficacy is higher in provinces students (Mean = 5.12, p <.01) and lower in the capital city students (Mean = 4.65, p <.05). Then, the correlation analysis shows age, origin place, academic performance and cognitive engagement are related with student digital self-efficacy. Finally, the analysis of multiple lineal regressions between age, origin place, academic performance and cognitive engagement shows only the age predicts positively the digital self-efficacy (β =.28, p<.01).
Deterministic Policy Gradient-based Reinforcement Learning for DDR5 Memory Signaling Architecture Optimization considering Signal Integrity

In this paper, we propose the deterministic policy gradient-based reinforcement learning for DDR5 memory signaling architecture optimization considering signal integrity. We convert the complex DDR5 memory signaling architecture optimization to the Markov decision process (MDP). The key limitation factor was found through the analysis of the hierarchical channel, and MDP was configured to solve it. The deterministic policy is essential for optimizing high-dimensional problems that have many continuous design parameters. For verification, we compare the proposed method with conventional methods such as random search (RS) and Bayesian optimization (BO) and other reinforcement learning algorithms such as the advantage actor-critic (A2C) and proximal policy optimization (PPO). RS and BO could not be properly optimized even after 10000 iterations of 1000 times, respectively, and A2C and PPO failed to optimize. As a result of comparison, the proposed method has the highest optimality, low computing time, and reusability.
Improving Image Quality Using Deep Learning Based Super Resolution

Super resolution is one of the important computer vision tasks. A low-definition image can be changed to a high-definition image through super resolution. Likewise, it can be applied to a video. Especially, the popularization of smart devices and the inundation of video-based contents cause a gradual increase in the importance of super resolution tasks. However, the super resolution task is an ill-posed problem without only one correct answer. The reason is that given a low-definition image, there is no only one answer sheet corresponding to the high-definition version of the corresponding image with possible multiple answer sheets. Namely, super resolution is the technology greatly contributing to society but has an ill-posed problem. Hence, super resolution is a very invaluable subject from the research perspective. The study performs the recurrence of the existing methodology for super resolution and presents the new deep learning model called the Boosted Super Resolution Generative Adversarial Nets (BSRGAN) by improving the methodology.
Why Rectified Linear Neurons: Two Convexity-Related Explanations

At present, the most efficient machine learning technique is deep learning, in which non-linearity is attained by using rectified linear functions $$s_0(x)=\max (0,x)$$ . Empirically, these functions work better than any other nonlinear functions that have been tried. In this paper, we provide a possible theoretical explanation for this empirical fact. This explanation is based on the fact that one of the main applications of neural networks is decision making, when we want to find an optimal solution. We show that the need to adequately deal with situations when the corresponding optimization problem is feasible—i.e., for which the objective function is convex—uniquely selects rectified linear activation functions.
A Machine Learning Approach for Human Action Recognition

Research on automatic human action recognition is gaining more popularity among researchers with the explosion of tremendous amount of video data. The goal of HAR is to deduce one or more people’s actions given a series of observations. There are various applications like surveillance systems, retrieval of video, human and computer interactions, gaming environment, entertainment environment, healthcare system, etc., which require the method of recognizing the human activities in various scenarios. The framework is presented to recognize the actions performed by humans on KTH dataset using spatial–temporal interest points-based detector and the KNN classifier.
Semantic Segmentation of Cardiac Structures from USG Images Using Few-Shot Prototype Learner Guided Deep Networks

This article proposed a method for semantic segmentation of ultrasound cardiac images using Deep network guided by learned prototype with few-shot learning approach. The main aim of the proposed method is to reduce the requirement for bulk training images to learn a supervised Deep neural network for semantic segmentation of cardiac structure. The proposed framework consists of two components, namely, a prototype learner and a supervised segmentor. The prototype learner generates a prototype for each type (class) of the cardiac structure, and the segmentor uses a U-Net architecture for segmentation and labeling of such structures. The prototype learner updates the parameters of feature extractor module by classifying the query set using K-nearest neighbor classifier and the support set. The output of the prototype learner (i.e., prototype) provides prior information about the classes. This output is fused with the output of the encoder of U-Net with an expectation that such a fusion will catalyze the learning process of the U-Net segmentor. Through training of the segmentor, the parameters of the encoding layer of U-Net get fine-tuned in a way so that it gradually aligns with the annotated segmentation maps. Probabilistic fusion model is used to amalgamate the prototypes with the generated features obtained from the encoder layer of the U-Net. To show the effectiveness of the proposed method, experiment was carried out on CAMUS dataset of 450 patients and performance is compared with those of four state-of-the-art techniques. To evaluate the performance of each of the methods, DICE index of endocardium, epicardium and atrium have been considered. Experimentation shows promising results obtained from the proposed technique.
Digi-CANE—An IoT Savior for Visually Sensitive

In this paper, researchers have projected the use of IoT devices for visually impaired ones. With the usage of this device, the visually challenged person will be able to deal with all real-time problems, he/she would face while walking down the lane. The project emphasizes on making such challenged people independent while traveling from source to destination. The major challenge to them is figuring out their way in crowded place. The project comprises of smart shoe and a stick whose purpose is to alert the visually impaired ones over the forthcoming obstacles in their path so that the collision is prevented. The stress point of this research paper is to demonstrate the well-grounded solution that includes a smart cane and a smart shoe, which communicates with each other and guides the user from the obstacle.
Accuracy Measures and the Convexity of ROC Curves for Binary Classification Problems

In this work, we offer a theoretical explanation of the convexity of the ROC (receiver operating characteristic) curves for rational binary classifiers, and show some natural important inequalities relating different measures of accuracy.
Clothing Classification Using Shallow Convolutional Neural Networks

The online fashion industry is continuously growing with more requirements from applications such as 3D printing, digital clothing, and the Internet of Things to provide outstanding support for buyers. Many customers expect an algorithm capable of recognizing clothes may help clothing merchants better understand the profile of potential customers, focus sales targeting specific niches, design campaigns based on consumer preferences, and improve user experience. Therefore, artificial intelligence methods that detect and categorize human clothes are necessary to increase sales or better understand customers. Recent advancements in deep learning have sparked a slew of computer vision-based commercial applications. Object identification is used in various industries to speed up business processes where deep learning tools and techniques are leveraged in a variety of ways to improve service quality. One of them is clothing classification in the clothing business. The trained deep learning model can predict the name of any clothing by displaying an image of it, and this process can be repeated at a much quicker pace to tag thousands of items in a short amount of time with high accuracy. This study has examined shallow convolutional neural network architectures conducted from VGGNet architecture but in more compact forms with some stacks of layers, including convolutional, max pooling, and dropout layers. The proposed architecture has reached 0.9359 in classification accuracy on dataset Fashion-MNIST with 70000 samples, including images of the shirt, bag, and boot. The shallow convolutional neural networks architecture can outperform the classic machine learning method and better performance than VGGNet in classifying small-size images.
CORAA ASR: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese

Abstract Automatic Speech recognition (ASR) is a complex and challenging task. In recent years, there have been significant advances in the area. In particular, for the Brazilian Portuguese (BP) language, there were around 376 h publicly available for the ASR task until the second half of 2020. With the release of new datasets in early 2021, this number increased to 574 h. The existing resources, however, are composed of audios containing only read and prepared speech. There is a lack of datasets including spontaneous speech, which are essential in several ASR applications. This paper presents CORAA (Corpus of Annotated Audios) ASR with 290 h, a publicly available dataset for ASR in BP containing validated pairs of audio-transcription. CORAA ASR also contains European Portuguese audios (4.6 h). We also present a public ASR model based on Wav2Vec 2.0 XLSR-53, fine-tuned over CORAA ASR. Our model achieved a Word Error Rate (WER) of 24.18% on CORAA ASR test set and 20.08% on Common Voice test set. When measuring the Character Error Rate (CER), we obtained 11.02% and 6.34% for CORAA ASR and Common Voice, respectively. CORAA ASR corpora were assembled to both improve ASR models in BP with phenomena from spontaneous speech and motivate young researchers to start their studies on ASR for Portuguese. All the corpora are publicly available at https://github.com/nilc-nlp/CORAA under the CC BY-NC-ND 4.0 license.
Sentinel

Serverless computing is a new computing paradigm that enables application developers to focus on the core service logic of their applications. To protect the host kernel from attacks, serverless service providers need to ensure isolation between application sandboxes while keeping the startup latency and memory usage low. Existing architectures provide the warm start functionality to alleviate the startup latency. However, as warm starts are achieved at the cost of high memory usage, handling all requests as warm starts is virtually impossible. Therefore, mitigating the cold start overhead is key to improving startup latency.
Federated split GANs

Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated learning (FL) and split learning (SL) to improve the protection of user's data privacy. However, SL often relies on server(s) located in the edge or cloud to train computationally-heavy parts of an ML model to avoid draining the limited resource on client devices, potentially resulting in exposure of device data to such third parties.
Evaluation model of college English teaching effect based on particle swarm algorithm and support vector machine

This paper studies the evaluation model of higher vocational English teaching effect based on particle swarm optimization and support vector machine classification. Based on the relevant theories, the model constructs a multi index evaluation system of English teaching effect in higher vocational colleges, which takes teachers and students as the main body. This model improves the accuracy and efficiency of English teaching evaluation in higher vocational colleges and meets the requirements of English teaching evaluation in higher vocational colleges. The experimental results show that this method has a good effect on the evaluation of English teaching in higher vocational colleges. The average evaluation accuracy is 97.1%, the evaluation time is short, and the test time is as low as 6ms.
Automatic segmentation of thyroid nodule from ultrasound images using spatial-channel attentive U-Net

The ultrasound image is a commonly used imaging modality to treat thyroid nodules due to its rapid imaging speed and the ability for multiple anatomical and soft-tissue visualization. However, considering the variability of the position, shape, size and intensity of nodules, the accurate segmentation of thyroid nodules is challenging. In this paper, we proposed an automatic segmentation network of thyroid nodules by combining fast-RCNN and spatial-channel attentive U-Net. In our model, fast-RCNN is firstly utilized to identify the rough position of nodules. The predicted boxes are then utilized to crop the image patches containing only nodules and then build a new database. Next, a spatial-channel attentive U-Net is designed and trained to realize the nodule segmentation. By comparing the proposed network with other state-of-the-art models, we can gain superior results in discarding similar non-nodule structures and preserving the boundary information of nodules.
Music melody generation and LIF supervised training based on spiking neural network

With the help of neuroscience research, the neural network model based on the pulse generation time can be accurately established, which is called Spiking Neural Network (SNN). This new type of neural network uses spike coding, which allows for more information and greater computing power to be obtained by obtaining the precise timing of the spikes. The application of SNN in music melody generation has a good application prospect. In this study, the Cell of Long Short-Term Memory (LSTM) in the generative model was replaced by Leaky Integrate-and-Fire (LIF), and two different variants of LIF were proposed based on Cell. Although the LIF variant achieved better results than the original LIF, it was not better than Cell in comprehensive comparison, and replacing Cell with LIF in the generative model could not improve the quality of the generated melody. The simple strategy used in the experiments to solve the supervised training problem of LIF was the main reason for such results. For SNN to be widely used, it is urgent to solve its supervised training problem.
Enhanced multi-objective evolutionary algorithm for workflow scheduling problem

With the development and application of distributed cloud computing, the problem of assigning workflow tasks to computational resources has become more and more prominent. It involves multiple constraints and optimization objectives, and is a typical NP-hard problem. Existing evolutionary algorithms face local optimum and premature convergence problems. Considering these facts, we proposed a multi-objective evolutionary algorithm with elitism strategy (MOEAES) in this paper. To avoid local optimum, MOEAES uses a new crossover operator called Random Sub-Sequence Exchange Crossover (RSSEX), and it introduces a multi-population-based elitism strategy to accelerate the algorithm. Finally, experimental validation is carried out, which shows that MOEAES achieves performance improvement in terms of solution quality and convergence speed comparing to other methods.
Research and design of image super-resolution algorithm based on attention mechanism

Image super-resolution algorithm is a technology that changes the image from low-resolution to high-resolution. It is widely used in various fields, including but not limited to image compression technology, satellite remote sensing imaging, urban traffic monitoring, medical imaging and so on. At present, the method based on deep learning has a very good effect on the super-resolution processing of low-resolution images, but some algorithms still have some problems, such as the loss of detail texture of the reconstructed image, and the large gap between the reconstruction results and the computational resources. To solve the above problems, this paper proposes a network model based on channel attention mechanism. The network model consists of three parts: the first part is a shallow feature extraction block, which is composed of a convolution layer and an activation layer, which is mainly used to extract the low-level features of the input image. The second part is the deep extraction block based on the channel attention mechanism, and the depth separable convolution is added to the local residual block of this part to effectively reduce the huge parameters generated by training. This module mainly extracts the high-level features of the input image. The third part is the reconstruction module, which is used to fuse the original image features extracted from the previous two parts and output the reconstructed image. Finally, the experimental results show that using this method will effectively improve the peak signal-to-noise ratio and structural similarity index of the reconstructed image.
Drone detection and classification model based on deep attention by using the RF signals

The illegal flight of drones have occurred frequently, which pose a great threat to the public security, and there is an urgent demand to develop related detection and identification technology to prevent such illegal behavior. In this paper, we analyze the principle and influencing factors of drone RF recognition, and propose a drone RF recognition method based on the deep attention mechanism. In our work, a fully convolutional network with the encoder-decoder architecture is adopted, and the residual network is used as the backbone network for feature extraction. Moreover, an RF channel attention aggregation (FCA) module is specifically designed for recognition. Our model was trained and verified on a public dataset (DroneRF Dataset), and achieved 99.895% accuracy for drone detection, more than 98.61% accuracy for drone recognition, and more than 99.33% accuracy for drone working mode recognition. On the self-test dataset, the performance of the RF recognition method proposed in this paper was further verified using the transfer learning approach.
Self2Align: a self-supervised denoising framework for single-scene images

In recent years, driven by the development of deep neural networks, image denoising technology has made great progress. One of the most representative technologies is the deep neural network denoiser based on supervised learning. The denoiser uses noise-clean image pairs as the input and output of a deep neural network, and trains the deep neural network to achieve the denoising goal. However, collecting many high-quality noise-clean image pairs is extremely challenging, which is mainly because (1) it is difficult to collect true and clean images; and (2) changes in motion and lighting make it impossible to align collected image pairs, which limits the widespread application of supervised learning denoising techniques. To solve the above problems, this paper proposes a simple and effective method Self2Align to train a deep neural network denoiser. First, we proposed an efficient deep network model for inter-image alignment. For the collection of original images, we only use noisy images and collected multiple images for different scenes. The trained alignment network was then used to align the original image pairs automatically. Second, the aligned image pairs generated in the first stage were used as training image pairs for the training of the denoising network. In addition, we introduced a new training strategy so that the network can obtain better performance. The proposed Self2Align architecture eliminates the reliance on noise-clean image pairs and reduces the acquisition difficulty of training image pairs in terms of self-supervised training of the network. We explained the feasibility of our proposed method through theoretical analysis and obtained competitive results through experimental verification.
Tibetan named entity recognition based on BiLSTM-CRF

According to the characteristics of Tibetan named entity itself, this paper analyzes the text characteristics and recognition difficulties of named entity, and puts forward BiLSTM-CRF model. The combination of the two models not only makes use of the advantages of bidirectional LSTM model to save context information, but also reduces the influence of CRF layer from sentence level to consider before and after annotation, so as to achieve the effect of learning from each other's strengths and complementing each other's weaknesses, which is more effective to solve the problem of Tibetan named entity recognition. The experimental results show that the proposed recognition method has good performance, and the f-value can reach 88.7%.
Mining spatiotemporally invariant patterns

Discovering patterns that represent key spatial or temporal dependencies among data is a well-known exploratory data mining task. However, prior works either separately analyze spatial and temporal dependencies or discover joint spatiotemporal properties of specific trajectories observed over a region of interest. With the goal of generalizing the information provided by spatiotemporal patterns, in this paper we extract sequences of discrete events showing spatiotemporally invariant properties. We seek patterns whose corresponding instances in the source data differ only due to an invariant spatiotemporal transformation. We denote such a new type of patterns as SpatioTemporally Invariant. We also propose an efficient algorithm to mine STInvs and validate its efficiency and effectiveness on real data.