Reviewerly/exHarmony huggingface dataset:
The relationship between sentiment score and COVID-19 cases in the United States

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders, and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19 related tweets. We connect users' sentiment with COVID-19 cases across the USA and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.
Study of Convective Heat Transfer Characteristics of Nano Fluids in Circular Tube

In this Paper the Study investigated Thermal and physical properties of Nano fluid composites are synthesized and characterized. SiO$_{\mathbf{2}}$-water and SiO$_{\mathbf{2}}$-EG Nano fluids are measured at different volume of Sio2 particles and based fluid like EG, Water and oils. The Mixture of Nano particle and based fluid deposition of small amount of Sio2 surface the outcome in considerable improve in thermal properties of the based liquid. Performed experiments to find laminar heat Exchange and pressure penalty of Nano fluids Al-water and Al/ H2O-EGl) under the inlet of a annular tube. The Nano fluids had more thermal properties and nano fluid viscosity in contrast to base fluids and this enhanced with enhancing nanoparticles loading. Measured the convective coefficient of heat transfer of Sio2/H$_{\mathbf{2}}$O Nano fluids. The flow was taken laminar. At 0.02 vol. % of Nano fluid and Re= 1860, the maximum augmentation observed in thermal properties, co-efficient of heat transfer was 10.3% and 14.2%. Water fluid contains less than 2% of modified nanocoposites can increases thermal conductivity of water up to 12 %. The increase on thermal conductivity of EG with equal amount of nanoparticles was 12.5% (temperature 25°C). A water Nano fluid contains less than 2% of modified Sio2 can raise the thermal properties of based up to 11%. The rises on thermal properties of the EG with the equal amount of Sio2 was about 11.5% (temperature $25^{\circ}\text{C}$).
Segmentation of thyroid nodules from ultrasound images using convolutional neural network architectures

Thyroid nodule segmentation is an indispensable part of the computer-aided diagnosis of thyroid nodules from ultrasound images. However, it remains challenging to segment the nodules from ultrasound images due to low contrast, high noise, diverse appearance, and complex thyroid nodules structure. So, it requires high clinical experience and expertise for proper detection of nodules. To alleviate the doctor’s tremendous effort in the diagnosis stage, we utilized several convolutional neural network architectures based on Encoder-Decoder architecture, U-Net architecture, Res-UNet architecture. To handle the complexity of the residual blocks, we also proposed three hybrid Res-UNet architectures by reducing the number of residual connections. The experimental analysis of the segmentation models proves the viability of residual learning in the U-Net architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to Res-UNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and U-Net can accurately delineate nodules without human intervention. This model helps to reduce dependencies on operators and acts as a decision tool for the radiologist.
Long depth-range measurement for fringe projection photogrammetry using calibration method with two reference planes

Using a calibration method with two reference planes, this study proposes a simple method to extend a measurement range in an optical direction for a fringe projection technique. The proposed method only inserts a baseline to the conventional fringe pattern projected onto an object surface and computes an exact phase alignment in the extended area concerning the baseline. The correction coefficient for the fundamental equation calculating 3D position coordinates of the object surface is developed to prevent accuracy degradation caused by extending the measurement range. Regardless of the extension of the measurement range, the accuracy verification test demonstrates that the proposed fringe projection method provides a measurement result with the same accuracy as the conventional method. Furthermore, through the surface shape measurement of the stationary parabola antenna reflector having a diameter of 1.5 m, a pendulum motion of the flat plate with a large amplitude, and a complicated deployment behavior of the flexible membrane structure, effectiveness and applicability of the proposed method for actual structures are demonstrated. This study offers a fringe projection photogrammetry applicable to capture dynamic responses of the object surface with a large amplitude and the large-sized stationary object surface with high spatial resolution.
Research on USV trajectory tracking method based on LOS algorithm

In order to study the trajectory tracking control of unmanned surface vehicle (USV) in real water environment and improve the tracking accuracy under the influence of wind, waves and currents, a USV trajectory tracking control method based on line-of-sight (LOS) guidance algorithm is proposed. First, according to the structural characteristics of the USV, a nonlinear mathematical model is established. Then, the LOS guidance algorithm is used to transform the position tracking problem of the USV into a heading control problem, and the heading angle of the USV is made to converge to the desired heading angle by designing a nonlinear heading controller. Finally, multiple desired heading points are set in the real water environment and the tracking experiments are conducted, and good results are obtained, further verifying the engineering.
A graph sequence neural architecture for code completion with semantic structure features

Abstract Code completion plays an important role in intelligent software development for accelerating coding efficiency. Recently, the prediction models based on deep learning have achieved good performance in code completion task. However, the existing models cannot avoid three drawbacks: (i) In the existing models, the code representation loses the information (parent–child information between nodes) and lacks many effective features (orientation between nodes). (ii) The known code structure information is not fully utilized, which will cause the model to generate completely irrelevant results. (iii) Simple sequence modeling ignores repeated patterns and structural information. Besides, previous works cannot capture the characteristics of correlation and directionality between nodes. In this paper, we propose a C ode C ompletion approach named CC‐GGNN, which is graph model based on G ated G raph N eural N etworks (GGNNs) to address the problems. We introduce a new architecture to obtain the effective code features from code representation. In order to utilize the known information, we propose Classification Mechanism, which classifies the representation of the node using the known parent node and constructs training graph in the model. The experimental results show that our model outperforms the state‐of‐the‐art methods MRR@5 at most 9.2% and ACC at most 11.4% in datasets.
Recognizing Emotions from Texts Using an Ensemble of Transformer-Based Language Models

The use of ensembles has given rise to improved performance in various machine learning tasks. Following the performance of major transformer-based language models in detecting emotions from written texts, the paper investigates the ensemble's performance of the RoBERTa and XLNet transformer-based language models in recognizing emotions from the ISEAR dataset. Finally, the results obtained outperformed the F1-scores of current works in literature with a higher F1-score of 0.75 in detecting emotions from the ISEAR text data.
Concatenative Text-to-Speech Synthesis System for Communication Recognition

Text-to-speech (TTS) synthesis is one of the rapidly emerging areas of computer-to-human interaction technology. Human-like speech is replicated by the computer with the introduction of input text which is usually very natural. Real-life applications of TTS synthesis technique make users task hassle-free. For example, reading book for the visually impaired people, paying electricity bill through automated call-centre, announcing train information at the railway station, etc. Traditionally, rule-based speech synthesis methods are deployed which find difficulties in obtaining optimal rules, resulting in lack of naturalness in the generated synthesized speech. Alternatively, to meet the desired quality of experience (QoE) of users while using these applications, this paper designs and develops a simple and robust TTS synthesis system for English language using the concatenative speech synthesis method and its variants and finds its suitability in intelligible and/or natural speech production. Various steps involved in processing text for speech production through TTS system are described. Results demonstrate that the speech generated by the concatenative speech synthesis method, in particular, unit-selection technique is smoother and natural, sounding like human voice. This is supported by informal listening test of the generated synthesized speech.
Hybrid Architecture for Gender Recognition Using Smartphone Motion Sensors

Motion sensor data in smart devices can be used as a side-channel to capture user's behavioral biometrics. In this paper, we investigate the feasibility of using smartphone motion sensors to detect the gender of the user. The main idea behind our study is based on behavioral differences of male and female users in touching and holding the smartphones. In order to implement our method, we collect data from 100 subjects while they are performing different activities like sitting, standing and walking. Our experiments point out that tapping behaviors are very discriminative for gender recognition but their significance decreases while users are walking. To address this issue, we also implement user activity detection and propose a hybrid model to predict gender in different user activities. In this context, user activity is firstly detected and then gender is predicted correspondingly. Consequently, we show that gender recognition is implicitly performed with a success rate of 85% in sitting and standing activities, whereas 83% success rate is acquired in walking scenario.
Research on Online Education Curriculum Resources Sharing Based on 5G and Internet of Things

Information technology has brought great changes to China’s education. 5G technology provides a better guarantee for the sharing of curriculum resources, facing the extreme shortage of educational resources in China. The contradiction between limited educational resources and unlimited development needs of higher education has become increasingly prominent. How to effectively realize resource sharing among universities has become a problem that must be considered in the talent development of universities. In order to solve this problem, universities must improve the utilization rate of resources, maximize resource sharing, and establish a more perfect resource sharing mechanism under the background of 5G and Internet of Things. This paper analyzes the current situation of research at home and abroad, the current situation of resources development, and the application of online courses under the background of Internet of Things, thus constructing an overall framework of curriculum resource sharing mode. According to effective experiments, the offline curriculum education resource sharing and traditional resource sharing schemes in the background of 5G and Internet are compared, and the necessity and importance of applying 5G Internet of Things are verified.
Source-Style Transferred Mean Teacher for Source-data Free Object Detection

Unsupervised cross-domain object detection transfers a detection model trained on a source domain to the target domain that has a different data distribution from the source domain. Conventional domain adaptation detection protocols need source domain data during adaptation. However, due to some reasons such as data security, privacy and storage, we cannot access the source data in many practical applications. In this paper, we focus on source-data free domain adaptive object detection, which uses the pre-trained source model instead of the source data for cross-domain adaptation. Due to the lack of source data, we cannot directly align domain distribution between domains. To challenge this, we propose the Source style transferred Mean Teacher (SMT) for source-data free Object Detection. The batch normalization layers in the pre-trained model contain the style information and the data distribution of the non-observed source data. Thus we use the batch normalization information from the pre-trained source model to transfer the target domain feature to the source-like style feature to make full use of the knowledge from the pre-trained source model. Meanwhile, we use the consistent regularization of the Mean Teacher to further distill the knowledge from the source domain to the target domain. Furthermore, we found that by adding perturbations associated with the target domain distribution, the model can increase the robustness of domain-specific information, thus making the learned model generalized to the target domain. Experiments on multiple domain adaptation object detection benchmarks verify that our method is able to achieve state-of-the-art performance.
STV-BEATS: Skip Thought Vector and Bi-Encoder based Automatic Text Summarizer

A novel text summarization framework referred to as Skip-Though Vector and Bi-encoder Based Automatic Text Summarization (STV–BEATS) is proposed in this paper. STV–BEATS utilizes — (a) skip-though vector to generate sentence-based embedding; and (b) Long Short-Term Memory (LSTM) based deep autoencoder to reduce dimensions of skip thought vectors. STV–BEATS works in the conjunction of extractive and abstractive summarization models to enhance the overall quality of the results. For each sentence, relevance and novelty metrics are calculated on the intermediate representation of the deep autoencoder to generate the final sentence score. The highly scored sentences are selected to generate an extractive summary. On the other hand, the abstractive part is composed of two encoders and a decoder which works as — (a) the first GRU-based bi-directional encoder and decoder work as basic sequence-to-sequence model on the extractive summary; and (b) the second GRU-based unidirectional encoder is used for fine encoding. Extensive computer experiments are conducted to determine the effectiveness of the STV–BEATS. Three standard benchmark datasets, namely, CNN/Daily Mail, DUC-2004, and DUC-2002 are used during experiments. Further, recall-oriented understudy for gisting evaluation (ROUGE) is used for validation of the STV–BEATS. Result reveals that the proposed STV–BEATS is capable of effective text summarization and achieves substantially better results over the state-of-the-art models.
Fundamental Noise — the Key to Recovering Your Signal: an Integrated Workflow for Seismic Survey Design

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Latent Dirichlet Allocation (LDA) Based on Automated Bug Severity Prediction Model

The software systems prevail various types of bugs reported by users. On the basis of severity levels of bugs, it is decided which bug needs to be fixed first. If the bug is crucial, then it is fixed immediately, and if the bug is minor, then its fixing procedure can be postponed. This research seeks to present an automated bug severity prediction model by using topic modelling and classification techniques. The summary of historical bug reports of the Eclipse project is pre-processed to extract features. Later feature extraction is done by using term frequency and latent Dirichlet allocation (LDA): a topic modelling technique. The topics extracted from LDA are then used to train the classifiers. Our approach also endorses the severity level for the newly disclosed bug. After training the model, the results actively exhibit admissible accuracy scores.
Using a Machine Learning Approach for Analysis of Polling Systems with Correlated Arrivals

The paper investigates stochastic polling systems using machine learning. M/M/1 and MAP/M/1-type polling systems with cyclic polling, as well as M/M/1-type polling systems with adaptive cyclic polling are considered. To train a machine model of a M/M/1-type polling system, we used the results of analytical calculations, and for other considered systems that do not allow exact analysis, we used the simulation results. Numerical examples are given, and it is shown that the results of machine learning are close enough to the results of analytical or simulation calculations.
Co-Attentive Span Network with Multi-task learning for Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BioNER) is often modeled as a sequence labeling task, which assigns the predefined label to each token in given input sequence. Although these sequential labeling models achieve significant achievements, they often fail to give precise boundaries of the named entity. In addition, a vast amount of work focuses more on textual sequence representation but ignores label information. To tackle these problems, in this paper, we directly model span-level named entity recognition, specifically, we treat the BioNER as a joint task of boundary detection and span classification under a multitask framework. In order to enhance boundary supervision, we introduce an entity type label as an additional guide and propose a co-attentive interactive mechanism to improve the span representation. Extensive experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on four benchmark datasets demonstrate that our proposed method obtains competitive results, achieving 90.26%, 78.04%, 90.21%, and 86.58% on BC5CDR, JNLPBA, NCBI, and BC2GM datasets, respectively, in terms of F1 score.
DistilledCTR: Accurate and scalable CTR prediction model through model distillation

Accuracy and scalability are critical to the efficiency and effectiveness of real-time recommender systems. Recent deep learning-based click-through rate prediction models are improving in accuracy but at the expense of computational complexity. The purpose of this study is to propose an accurate and scalable click-through rate (CTR) prediction model for real-time recommendations. This study investigates the complexity, accuracy, and scalability aspects of various CTR models. This work ensembles top CTR models using a gated network and distill into a deep neural network (DNN) using a knowledge distillation framework. Distilled DNN model is more accurate and 20x scalable than any of the individual CTR models. The low latency of distilled model makes it scalable and fit for deployment in real-time recommender systems. The proposed distillation framework is extensible to integrate any CTR models to the ensemble and can be distilled to any neural architecture.
Systematic Advancement of Yolo Object Detector For Real-Time Detection of Objects

This paper explicates the systematic advancements that were observed from the inception of the YOLO (You Only Look Once) object detector to the most recent version 4. Since its introduction in late 2015, YOLO has recorded tremendous implementation as well as improvements and applications. In this work, a brief survey of the YOLO network is presented considering the introduction that was made to each version that succeeded each preceding version and the advancement on how the model performed with detection. We used the latest version of the network (YOLOv4) to train 50 classes of objects that we considered popular objects for real-time detection. The model trained obtained an mAP of 64.80% @IoU of 0.5 and when deployed for real-time detection, it achieved a 43FPS speed of detection.
Multi-Label kNN classifier with Online Dual Memory on data stream

Due to an ever-increasing demand for analyzing the large volumes of information issuing from high-speed data streams, multi-label stream classification is replacing the traditional offline multi-label classification system and has thus become a focal point in recent years. In this paper, we propose a new algorithm for multi-label stream classification that relies on an Online Dual Memory (ODM). It uses a short-term memory in order to adapt quickly to changes within the data stream, and employs a long-term memory for recurring concepts. Furthermore, via a modification of Learning Vector Quantization, ODM is not only capable of classifying efficiently but also of maintaining a low computational complexity. Experiments were conducted so as to establish a comprehensive overview of its performance, which was achieved by comparing four methods on two different kinds of datasets (nine stationary datasets and four datasets with concept drift). Our results confirm the high accuracy and low time complexity of ODM, as well as its ability to cope with concept drift.
Interactive image segmentation based on the appearance model and orientation energy

Tang et al. (2013) proposed a graph-based image segmentation model by minimizing the distance between the object and background appearance overlap models. This model is very effective for interactive image segmentation. However, it is prone to isolated nodes when the colors or other appearances characteristic of the object and background are very similar. To improve the performance of this algorithm and related algorithms, we add new spatial distance and contour orientation energy terms to the energy function. Accordingly, we modify the construction of the energy graph. We add terminal nodes S and T and add prior constraints. Finally, we use the pseudoflow algorithm proposed by Hochbaum to calculate the maximum flow of the new energy graph. A large number of experiments on the MSRA dataset, BSD dataset and GrabCut dataset show that the results of the proposed method are better than those of many recently proposed image segmentation methods. The code is available at https://github.com/powerhope/AMOE.