Reviewerly/exHarmony huggingface dataset:
Nursing Comiittee to Coping with COVID-19 in Bahia

ABSTRACT Objective: To report the experience of the Nursing Committee for Coping with COVID-19 in Bahia. Methods: The experience report describes motivation, objectives, representatives, organization, working groups, activities and impact of the Committee actions. Results: The Committee consists of educational institutions and class representation. It accepts demands, questions and complaints from nursing workers, acts in favor of safe care and inspection of health and safety conditions at work. Five working groups and six technical support groups were formed. These groups address Communication, Review of Health Services Contingency Plans, Assistance to Long-Term Institutions, Epidemiology and External Activities. An Instagram account was created for quick and reliable access to information, and also an email to meet demands and monitor COVID-19 cases. Conclusion: The results of the Committee work contribute to guide, support, value and defend nursing workers in coping with COVID-19
Robust URL Phishing Detection Based on Deep Learning

Phishing websites can have devastating effects on governmental, financial, and social services, as well as on individual privacy.Currently, many phishing detection solutions are evaluated using small datasets and, thus, are prone to sampling issues, such as representing legitimate websites by only high-ranking websites, which could make their evaluation less relevant in practice.Phishing detection solutions which depend only on the URL are attractive, as they can be used in limited systems, such as with firewalls.In this paper, we present a URL-only phishing detection solution based on a convolutional neural network (CNN) model.The proposed CNN takes the URL as the input, rather than using predetermined features such as URL length.For training and evaluation, we have collected over two million URLs in a massive URL phishing detection (MUPD) dataset.We split MUPD into training, validation and testing datasets.The proposed CNN achieves approximately 96% accuracy on the testing dataset; this accuracy is achieved with URL schemes (such as HTTP and HTTPS) removed from the URL.Our proposed solution achieved better accuracy compared to an existing state-of-the-art URL-only model on a published dataset.Finally, the results of our experiment suggest keeping the CNN up-to-date for better results in practice.
Analysis K-Nearest Neighbors (KNN) in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction

Pulmonary tuberculosis is an infectious disease caused by Microbacterium tuberculosis, which is one of the lower respiratory tract disease, which is largely in the pulmonary tissue of the lung infection and then undergoes a process known as the primary focus of Ghon. Because the disease is difficult and takes a long time to decide the patient is affected by the disease Tuberkolusis, then the detection of the patient affects Tuberkolusis by utilizing the K-NN method as a classification and HOG as feature extraction. Results of the classification of positive diagnosis with a total of 234 samples from 330 samples or successfully recognizable Sebasar 70.90%, while the classification result is a negative diagnosis with the amount of 240 samples from 330 samples or successfully identified by 72.72%. The results of the study showed the image classification of the X-ray Set Tuberculosis using the method K-NN and HOG feature with cross-validation 5 folds with 71.81% accuracy.Keyword : tuberculosis, K-NN, HOG.
A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods

Recently, most background modeling (BM) methods claim to achieve real-time efficiency for low-resolution and standard-definition surveillance videos. With the increasing resolutions of surveillance cameras, full high-definition (full HD) surveillance videos have become the main trend and thus processing high-resolution videos becomes a novel issue in intelligent video surveillance. In this article, we propose a novel content-adaptive resizing framework (CARF) to boost the computation speed of BM methods in high-resolution surveillance videos. For each frame, we apply superpixels to separate the content of the frame to homogeneous and boundary sets. Two novel downsampling and upsampling layers based on the homogeneous and boundary sets are proposed. The front one downsamples high-resolution frames to low-resolution frames for obtaining efficient foreground segmentation results based on BM methods. The later one upsamples the low-resolution foreground segmentation results to the original resolution frames based on the superpixels. By simultaneously coupling both layers, experimental results show that the proposed method can achieve better quantitative and qualitative results compared with state-of-the-art methods. Moreover, the computation speed of the proposed method without GPU accelerations is also significantly faster than that of the state-of-the-art methods. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nchucvml/CARF</uri> .
An Efficient DCT-SVD Steganographic Approach Applied to JPEG Images

To prove the origin of images in social media, this work proposes an efficient JPEG image steganography approach. After structuring the cover image into blocks of 8*8 pixels, Discrete Cosine Transform is applied to each block of pixels. The latter are quantified using a quantization
Investigating the Mediation and Moderation Effect of Students’ Addiction to Virtual Reality Games: A Perspective of Structural Equation Modeling

Virtual reality (VR) games construct an immersive context for the youth’s entertainment and learning. VR game addiction (VAD) occurs simultaneously and brings about psychological issues. The current research employed a maximum likelihood estimation method to construct a structural equation model, by which learners’ VR consumption behaviors were examined. The bootstrapping approach was adopted to explore further the mediation of VR realism (VRR) and the moderation of VR affordance on learners’ VAD, which potentially leads to distressing behavioral and psychological circumstances. Data were collected from 575 students recruited in the survey, which was followed by a test of reliability and validity through confirmative factor analysis. Results concluded that VRR and consumers’ cognitive satisfaction played a significant role as mediators while conceptualizing addictive behavior based on VR-perceived experience of consumers, self-efficacy, and interactivity. Moreover, this study conceptualized addictive behavior toward VR games through a structural equation model in a media-rich environment and suggested practical implications to reduce addictive behavior.
Experimenting with Attention Mechanisms in Joint CTC-Attention Models for Russian Speech Recognition

The paper presents an investigation of attention mechanisms in end-to-end Russian speech recognition system created by join Connectional Temporal Classification model and attention-based encoder-decoder. We trained the models on a small dataset of Russian speech with total duration of about 60 h, and performed pretraining of the models using transfer learning with English as non-target language. We experimented with following types of attention mechanism: coverage-based attention and 2D location-aware attention as well as their combination. At the decoding stage we used beam search pruning method and gumbel-softmax function instead of softmax. We have achieved 4% relative word error rate reduction using 2D location-aware attention.
A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the requirements of receivers and the facilities of servers. One of the challenges while assigning web services is how to select appropriate server to reduce faults. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the neural fuzzy system. While considering a threshold for the rejuvenation of each web service, we completed the training based on the features of the service providers as well as the requirements of the receivers. The results of simulations revealed that our strategy can decrease the failure rate in comparison with state-of-the-art strategies and improve system availability in web services.
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles

Prediction intervals are a machine- and human-interpretable way to represent predictive uncertainty in a regression analysis. In this paper, we present a method for generating prediction intervals along with point estimates from an ensemble of neural networks. We propose a multi-objective loss function fusing quality measures related to prediction intervals and point estimates, and a penalty function, which enforces semantic integrity of the results and stabilizes the training process of the neural networks. The ensembled prediction intervals are aggregated as a split normal mixture accounting for possible multimodality and asymmetricity of the posterior predictive distribution, and resulting in prediction intervals that capture aleatoric and epistemic uncertainty. Our results show that both our quality-driven loss function and our aggregation method contribute to well-calibrated prediction intervals and point estimates.
Violence Detection In Surveillance Videos Using Deep Learning

Nowadays computer technologies are flowering especially the artificial intelligence field. It lives its prosperous years. Recently it closes the gap between humans and machines with the facilitation of supporting decisions. One of these gaps is the surveillance cameras labors' attentiveness and the lack of instantaneous detection of violence actions on the scenes of such cameras. In this paper we present an end to end deep neural network to detect the violence scenes in the surveillance cameras, the proposed system composed of set of phases. It extracts a set of selectively distributed frames of the video clip and preforms a spatio-temporal features and passes them to a fully connected neural to classify they video to violence or non-violence action. The model is evaluated on different datasets; like Real Life Violence Situations aka RLVS and Hockey Fight Detection datasets. The accuracy was 92% and 94.5% respectively, which outperformed the previous related works.
Locally Masked Convolution for Autoregressive Models

High-dimensional generative models have many applications including image compression, multimedia generation, anomaly detection and data completion. State-of-the-art estimators for natural images are autoregressive, decomposing the joint distribution over pixels into a product of conditionals parameterized by a deep neural network, e.g. a convolutional neural network such as the PixelCNN. However, PixelCNNs only model a single decomposition of the joint, and only a single generation order is efficient. For tasks such as image completion, these models are unable to use much of the observed context. To generate data in arbitrary orders, we introduce LMConv: a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image. Using LMConv, we learn an ensemble of distribution estimators that share parameters but differ in generation order, achieving improved performance on whole-image density estimation (2.89 bpd on unconditional CIFAR10), as well as globally coherent image completions. Our code is available at this https URL.
Preservation of Individuals’ Privacy in Shared COVID-19 Related Data

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Cyber Digital Twin Simulator for Automatic Gathering and Prioritization of Security Controls’ Requirements

The scale and complexity of cyber threats in digital enterprises hamper operators' ability to gather, prioritize and rationalize which security controls requirements should be handled first to achieve rapid risk reduction. This paper presents a cyber digital twin, based on attack graph analytics, that automatically gathers and prioritizes security controls requirements at scale over active networks. The first-of-a-kind twin collects information about the computer network, associates it with attack tactics, measures the efficiency of implemented security controls requirements and automatically detects missing security controls. The twin also evaluates a cyber risk value using the attack graph and proposes prioritization of the detected requirements to rapidly reduce risk within existing system constraints. The cyber digital twin simulator offers several new risk reduction methods for automatically selecting security controls requirements. The necessary data for constructing a contextual cyber digital twin is defined, including the relationship between security controls and attack tactics. The paper illustrates the calculations used for ranking security controls' risk impact, the algorithm for security controls' requirements prioritization, and finally demonstrates successful results using a field experiment conducted via an active network.
Semi-Supervised Speech-Language Joint Pre-Training for Spoken Language Understanding

Spoken language understanding (SLU) requires a model to analyze input acoustic signals to understand its linguistic content and make predictions. To boost the models' performance, various pre-training methods have been proposed to utilize large-scale unlabeled text and speech data. However, the inherent disparities between the two modalities necessitate a mutual analysis. In this paper, we propose a novel semi-supervised learning method, AlignNet, to jointly pre-train the speech and language modules. Besides a self-supervised masked language modeling of the two individual modules, AlignNet aligns representations from paired speech and transcripts in a shared latent semantic space. Thus, during fine-tuning, the speech module alone can produce representations carrying both acoustic information and contextual semantic knowledge. Experimental results verify the effectiveness of our approach on various SLU tasks. For example, AlignNet improves the previous state-of-the-art accuracy on the Spoken SQuAD dataset by 6.2%.
Transmission Dynamics and Contact Tracing Assessment of COVID-19 in Chattogram, Bangladesh and Potential Risk of Close Contacts at Different Exposure Settings

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Contextual Semantic Interpretability

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpretable attributes that are present in the scene. We call such an intermediate layer a semantic bottleneck. Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision. In this paper, we look into semantic bottlenecks that capture context: we want attributes to be in groups of a few meaningful elements and participate jointly to the final decision. We use a two-layer semantic bottleneck that gathers attributes into interpretable, sparse groups, allowing them contribute differently to the final output depending on the context. We test our contextual semantic interpretable bottleneck (CSIB) on the task of landscape scenicness estimation and train the semantic interpretable bottleneck using an auxiliary database (SUN Attributes). Our model yields in predictions as accurate as a non-interpretable baseline when applied to a real-world test set of Flickr images, all while providing clear and interpretable explanations for each prediction.
Optimization of Naïve Bayes Classifier By Implemented Unigram, Bigram, Trigram for Sentiment Analysis of Hotel Review

The information needed in its development requires that proper analysis can provide support in making decisions.Sentiment analysis is a data processing technique that can be completed properly.To make it easy to classify hotels based on sentiment analysis using the Naїve Bayes Classifier algorithm.As a classification tool, Naїve Bayes Classifier is considered efficient and simple.In this study consists of 3 stages of sentiment analysis process.The first stage is text pre-processing which consists of transform case, stopword removal, and stemming.The second stage is the implementation of N-Gram features, namely Unigram, Bigram, Trigram.The N-Gram feature is a feature that contains a collection of words that will be referred to in the next process.Next, the last click is the hotel review classification process using Na menggunakanve Bayes Classifier.OpinRank Hotels Review dataset on Naїve Bayes Classifier using N-Gram namely Unigram, Bigram, Trigram with research results that show Unigram can provide better test results than Bigram and Trigram with an average accuracy of 81.30%.
Volumetric Transformer Networks

Existing techniques to encode spatial invariance within deep convolutional neural networks (CNNs) apply the same warping field to all the feature channels. This does not account for the fact that the individual feature channels can represent different semantic parts, which can undergo different spatial transformations w.r.t. a canonical configuration. To overcome this limitation, we introduce a learnable module, the volumetric transformer network (VTN), that predicts channel-wise warping fields so as to reconfigure intermediate CNN features spatially and channel-wisely. We design our VTN as an encoder-decoder network, with modules dedicated to letting the information flow across the feature channels, to account for the dependencies between the semantic parts. We further propose a loss function defined between the warped features of pairs of instances, which improves the localization ability of VTN. Our experiments show that VTN consistently boosts the features’ representation power and consequently the networks’ accuracy on fine-grained image recognition and instance-level image retrieval.
The NTNU System at the Interspeech 2020 Non-Native Children’s Speech ASR Challenge

This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA. This ASR shared task is made much more challenging due to the coexisting diversity of non-native and children speaking characteristics. In the setting of closed-track evaluation, all participants were restricted to develop their systems merely based on the speech and text corpora provided by the organizer. To work around this under-resourced issue, we built our ASR system on top of CNN-TDNNF-based acoustic models, meanwhile harnessing the synergistic power of various data augmentation strategies, including both utterance- and word-level speed perturbation and spectrogram augmentation, alongside a simple yet effective data-cleansing approach. All variants of our ASR system employed an RNN-based language model to rescore the first-pass recognition hypotheses, which was trained solely on the text dataset released by the organizer. Our system with the best configuration came out in second place, resulting in a word error rate (WER) of 17.59 %, while those of the top-performing, second runner-up and official baseline systems are 15.67%, 18.71%, 35.09%, respectively.
Development of Supply Air Temperature Prediction Model for Optimal Control Algorithm of Containment Data Center

Purpose: This study aimed at developing a temperature prediction model for a containment data center. The predictive model must be guaranteed with stability and accuracy in order to be used for real-time control. Therefore, statistical evaluation was conducted to verify the prediction performance of the proposed model. Method: The predictive models were developed using four representative machine learning algorithms. A thermodynamic based containment data center and cooling system were modeled by MATLAB & Simulink software. The initial and optimized models were evaluated by R² and Cv(RMSE), and the model with the highest performance was applied to the simulation. Result: In the initial models, RF and ANN presented highest accuracy on R² (0.89) and Cv(RMSE) (17.85%), respectively. After the optimization, ANN presented the best prediction performance on both R² (0.99) and Cv(RMSE) (0.94%). The result supports the accuracy and stability of the ANN model to be used for real-time control, and based on which the optimal control algorithm will be developed on further study.
WITHDRAWN: Urban Garden Landscape Design Based on VR Technology and Internet of Things System

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Knowledge-and-Data-Driven Amplitude Spectrum Prediction for Hierarchical Neural Vocoders

In our previous work, we have proposed a neural vocoder called HiNet which recovers speech waveforms by predicting amplitude and phase spectra hierarchically from input acoustic features. In HiNet, the amplitude spectrum predictor (ASP) predicts log amplitude spectra (LAS) from input acoustic features. This paper proposes a novel knowledge-and-data-driven ASP (KDD-ASP) to improve the conventional one. First, acoustic features (i.e., F0 and mel-cepstra) pass through a knowledge-driven LAS recovery module to obtain approximate LAS (ALAS). This module is designed based on the combination of STFT and source-filter theory, in which the source part and the filter part are designed based on input F0 and mel-cepstra, respectively. Then, the recovered ALAS are processed by a data-driven LAS refinement module which consists of multiple trainable convolutional layers to get the final LAS. Experimental results show that the HiNet vocoder using KDD-ASP can achieve higher quality of synthetic speech than that using conventional ASP and the WaveRNN vocoder on a text-to-speech (TTS) task.