Reviewerly/exHarmony huggingface dataset:
A massively parallel speciation‐based differential evolution algorithm applied to the 3D‐AB protein structure prediction

Abstract One of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off‐lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D‐AB off‐lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation‐based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state‐of‐the‐art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78 .
Aspectos neurológicos en la Tutoría entre Iguales (TEI)

In the present study, it presents a theoretical review of the brain changes which has been associated with prosocial and cooperative behaviors, especially in childhood and adolescent neurovelopment. It has been shown that goodness behavior, empathy and altruism enhance the liberation of neurotransmitters such as serotonin, oxytocin (OXT) and glucocorticoid receptors, which help to generate a sense of well-being in people who carry out solidarity behaviors and who are in favor of helping others. The Infant Educators World Association (2018), warns that the main difficult to assure coexistence in schools, as well as the need to set up programs based on a shift towards values from the experiential point of view. According to this point, The Peer Tutoring (PT) program introduces changes in favor of empathy and kindness as a way of life. Tutor / tutored live enriching experiences as a result of carrying out activities and caring for peers, which could get consolidated in their neurodevelopment. Beyond protocols, it‘s fundamental work in the prevention of violent attitudes in school, by teaching how to live in peace through good behavior. What is more, it should be noted in this case, all the school community benefits from this program. To summarize, the school is considered as a source of social transformation as a fundamental part of the primary socialization besides the family. So, it is pretend to promote healthy development of children and adolescent and regain a baggage of values which allows them to develop in a healthy way when they become adults and transmit them to the new generations. The school as a source of social transformation.
A Study on Core Competency Level and Difference by Teaching Experience of Middle School Beauty Teacher

The purpose of this study is to analyze how the level of teacher core competency differs according to the teaching experience of a beauty school teacher. A survey was conducted to analyze the core competencies of beauty teachers. The analysis method is a one-way ANOVA. As a result of analyzing the core competency level of beauty teachers, ‘executive competency’ was the highest, followed by ‘attitude competency’, ‘knowledge competency’. In the case of knowledge, compared to ‘theoretical knowledge of the major and the major subject in charge’, ‘practical ability for the major and the major subject in charge’, ‘the ability to apply teaching and learning evaluation related to the major’, ‘the professor related to the major’, ‘Learning evaluation development ability’ was evaluated low, In the case of performance, the average was slightly higher than that of knowledge and attitude in all competencies, and the level of ‘the ability to instruct students to acquire a certificate related to a major’ was evaluated high. In terms of attitude, ‘the ability to communicate smoothly with peers’ was high. As a conclusion of this study, it was found that in order to respond to the internal and external beauty subject education environment and increase the quality change of beauty subject education, It is necessary to strengthen the competence of professional beauty teachers.
A Biological Test Questions Naming Entity Recognition Method for Fusion Triggers

Training a neural model for named entity recognition (NER) in a new field often requires additional human annotations (for example, a large number of tagged instances), which are often expensive and time-consuming to collect. Therefore, one of the key research problems is how to obtain the supervision effect in an economical and effective way. In this article, we use “entity triggers” for the biological domain, which can help explain how to effectively learn the label of NER's model. Entity triggers are defined as words or words in a sentence, which helps explain why people recognize entities in a sentence. For biological disciplines, according to the characteristics of the high school biology field test data, in a small, small amounts of annotation and trigger a small entity tagging of self-built corpora in the biology one experiment, the experimental results show that using “triggers the matching network can automatically learn” trigger said and soft matching module, which can be easily generalization for the tag will be invisible to the sentence, then use the two-way coding BiLSTM, finally with the airport marking label according to conditions. The experimental results show that this model has a better recognition effect than other algorithms, and can effectively solve the problems of entity recognition difficulty and low accuracy in the task of named entity recognition in the field of biological science, such as insufficient labeling data, and the special ranking of biological science
Research and Practice on Data Acquisition of Android-based Application

The Internet has become the main space for crime. Android based mobile devices have a high utilization rate and become one of the main tools of cybercrime. Under the framework of Android system, this paper obtains an automatic and efficient electronic data forensics method based on Android by using the practice of ADB tools and electronic data such as contacts, relationships and communication records in mobile communication software.
The price prediction of virtual currency base on improved support vector regression

With the emergence of a large number of virtual currencies represented by Bitcoin and Ethereum and the continuous rise of their prices, a large number of investors have been attracted to invest in them. The attention and research based on virtual currencies have aroused the wide concern of the social public. The price prediction of the virtual currencies is one of the research hotspots, obtaining and analyzing the historical data of virtual currency price for the prediction of future price. We find that some commonly used machine learning algorithms have a great deviation in the price prediction of virtual currency. To solve this problem, we propose a novel support vector regression (SVR) based on data segmentation, which can significantly improve the accuracy and effectiveness of price prediction of virtual currency. The experimental results show that our algorithm has obvious advantages over the traditional SVR algorithm and other eight classical machine learning algorithms.
XRSpectator: Immersive, Augmented Sports Spectating

In-stadium sports spectating delivers a unique social experience in a variety of sports. However, in contrast to broadcast delivery, it lacks the provision of real-time information augmentation, like game statistics overlaid on screen. In an earlier iteration, we developed ARSpectator, a prototypical, mobile system which can be brought to the stadium to experience both, the live sport action and situated infographics spatially augmented into the scene. In some situations it is difficult or often impossible to go to the stadium though, for instance because of limited stadium access during pandemics or when wanting to conduct controlled user studies. We address this by turning our ARSpectator system into an indirect augmented reality experience deployed to an immersive, virtual reality head-mounted display: The live stadium experience is delivered by way of a surrounding 360 video recording while maintaining and extending the provision of interactive, situated infographics. With our XRSpectator demo prototype presented here, users can have an ARSpectator experience of a rugby game in our local stadium.
A learning-aid based on augmented reality technology for the fundamentals of engineering design course

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Faiz Al Rasyid Tisyadi, R. Cahyadi Nugraha, Rispianda; A learning-aid based on augmented reality technology for the fundamentals of engineering design course. AIP Conf. Proc. 24 February 2023; 2772 (1): 070001. https://doi.org/10.1063/5.0117459 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Coupling a Generative Model With a Discriminative Learning Framework for Speaker Verification

The task of speaker verification (SV) is to decide whether an utterance is spoken by a target or an imposter speaker. In most studies of SV, a log-likelihood ratio (LLR) score is estimated based on a generative probability model on speaker features, and compared with a threshold for making a decision. However, the generative model usually focuses on individual feature distributions, does not have the discriminative feature selection ability, and is easy to be distracted by nuisance features. The SV, as a hypothesis test, could be formulated as a binary discrimination task where neural network based discriminative learning could be applied. In discriminative learning, the nuisance features could be removed with the help of label supervision. However, discriminative learning pays more attention to classification boundaries, and is prone to overfitting to a training set which may result in bad generalization on a test set. In this paper, we propose a hybrid learning framework, i.e., coupling a joint Bayesian (JB) generative model structure and parameters with a neural discriminative learning framework for SV. In the hybrid framework, a two-branch Siamese neural network is built with dense layers that are coupled with factorized affine transforms as used in the JB model. The LLR score estimation in the JB model is formulated according to the distance metric in the discriminative learning framework. By initializing the two-branch neural network with the generatively learned model parameters of the JB model, we further train the model parameters with the pairwise samples as a binary discrimination task. Moreover, a direct evaluation metric (DEM) in SV based on minimum empirical Bayes risk (EBR) is designed and integrated as an objective function in the discriminative learning. We carried out SV experiments on Speakers in the wild (SITW) and Voxceleb. Experimental results showed that our proposed model improved the performance with a large margin compared with state of the art models for SV.
Noise Detection from Single Image Based on Harris Operator

Noise detection has been a long-established research topic in modern digital image processing technology, since noise can reveal the quality degradation information of images. The fundamental premise of most existing algorithms is that the statistical distribution of noise is provided, while in practice it is unlikely to get the type of noise in advance. To solve this problem, an efficient noise detection method derived from the general Harris corner operator is proposed. The real corner and noise points are simultaneously extracted in the first stage. Then candidates are selected based on an adaptive threshold for different illumination regions and a Support Vector Machine is adopted to construct a pixel-level classifier. The final binary decision for a test image is made based on the ratio of the obtained noise points to all candidates. With the purpose of expanding the diversity of content variation, a challenging image dataset is established. Experiments on three different datasets show that the proposed method is robust to sophisticated texture without auxiliary information about noise types and presents great generalization performance.
Velocity Prediction for MIDI Notes with Deep Learning

This papers studies the use of a deep autoencoder to automatically generate dynamics for MIDI (Musical Instrument Digital Interface) files without velocity information. The experimental results show that the proposed approach outperforms existing methods. The subjective tests also show that a MIDI file with velocity values generated by the proposed model sounds more pleasant.
Noise Data Removal and Image Restoration Based on Partial Differential Equation in Sports Image Recognition Technology

With the rapid development of image processing technology, the application range of image recognition technology is becoming more and more extensive. Processing, analyzing, and repairing graphics and images through computer and big data technology are the main methods to obtain image data and repair image data in complex environment. Facing the low quality of image information in the process of sports, this paper proposes to remove the noise data and repair the image based on the partial differential equation system in image recognition technology. Firstly, image recognition technology is used to track and obtain the image information in the process of sports, and the fourth-order partial differential equation is used to optimize and process the image. Finally, aiming at the problem of low image quality and blur in the transmission process, denoising is carried out, and image restoration is studied by using the adaptive diffusion function in partial differential equation. The results show that the research content of this paper greatly improves the problems of blurred image and poor quality in the process of sports and realizes the function of automatically tracking the target of sports image. In the image restoration link, it can achieve the standard repair effect and reduce the repair time. The research content of this paper is effective and applicable to image processing and restoration.
Elementary Poetry. By Andrei Monastyrski. Edited and translated by Yelena Kalinsky and Brian Droitcour. New York: Ugly Duckling Presse, 2019; 329 pp. $28.00 paper.

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Algorithm integration model for computer aided engineering based on Backtracking Algorithm

research-article Algorithm integration model for computer aided engineering based on Backtracking Algorithm Share on Author: Zerui Li Rutgers University School of Engineering, USA Rutgers University School of Engineering, USAView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 531–535https://doi.org/10.1145/3482632.3482745Online:22 November 2021Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Pre-trained transformer-based language models for Sundanese

Abstract The Sundanese language has over 32 million speakers worldwide, but the language has reaped little to no benefits from the recent advances in natural language understanding. Like other low-resource languages, the only alternative is to fine-tune existing multilingual models. In this paper, we pre-trained three monolingual Transformer-based language models on Sundanese data. When evaluated on a downstream text classification task, we found that most of our monolingual models outperformed larger multilingual models despite the smaller overall pre-training data. In the subsequent analyses, our models benefited strongly from the Sundanese pre-training corpus size and do not exhibit socially biased behavior. We released our models for other researchers and practitioners to use.
An enhanced approach for optimizing mathematical and structural problems by combining PSO, GSA and gradient directions

In this paper, the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA) is enhanced by the first-order gradient method and a new optimization algorithm is introduced as GPSG. In metaheuristic methods, some search directions are randomly selected and the resulting information gradually progresses toward the optimal solution. Since along the gradient direction usually yields the largest decrease in the desired function, it is added to the GSA and PSO process to allow for faster and more accurate convergence. By integrating the metaheuristic methods with the gradient directions, a powerful method for optimizing functions has been made possible. A novel approach is introduced by merging two metaheuristic methods with the randomly generated steepest decent directions. Numerous examples of unconstrained problems of mathematical functions of CEC2005 and CEC2017 together with some constrained examples of stress and displacement structural design problems have been chosen to demonstrate the reliability and capability of the presented method. Comparison of the numerical results with some methods indicates that the average rank of the proposed technique is better.
Face De-Occlusion With Deep Cascade Guidance Learning

Occlusion is a challenging yet commonly seen problem for facial perception. Existing works resort to deep learning models and perform model training on synthesized data due to the lack of paired real-world data. As a result,they usually perform unsatisfactorily on real-world occluded faces because of domain gaps. In this paper, we decompose the face de-occlusion task into three stages, i.e., occlusion detection, face parsing, and face reconstruction, to alleviate this issue. We first perform occlusion detection and use its results as guidance for the second stage to conduct occlusion-free face parsing. As such, face de-occlusion is first performed on the face paring space with less difficulty. We can train these two stages on both synthesized and real-world images, hence can obtain accurate results for the latter. In the last stage, we use the domain-agnostic occlusion detection map and the face parsing map as the guidance to conduct face reconstruction, thus can reduce the impact of appearance information and improve the model performance on real-world data. Aiming at improving the model capacity of inferring occluded facial appearance, we also propose two types of reference modules to use relevant facial parts to enhance the reconstruction of occluded regions. Consequently, our proposed model achieves promising face de-occlusion results on real-world images.
Deep Learning Based Near-Orthogonal Superposition Code for Short Message Transmission

Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning based near-orthogonal superposition (NOS) coding scheme is proposed for reliable transmission of short messages in the additive white Gaussian noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional modulation (HDM), the NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector for transmission. The NOS decoder first estimates the information vectors and then performs a cyclic redundancy check (CRC)-assisted K-best tree-search algorithm to further reduce the packet error rate. The proposed NOS encoder and decoder are deep neural networks (DNNs) jointly trained as an auto-encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal codewords. Simulation results show the proposed deep learning-based NOS scheme outperforms HDM and Polar code with CRC-aided list decoding for short (32-bit) message transmission.
Classification of Untranscribed Handwritten Notarial Documents by Textual Contents

Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The amounts are so large that it is generally unfeasible for archivists to adequately tag most of the documents with the required metadata so as to allow proper organization of the archives and effective exploration by scholars and the general public. The class or “typology” of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The approach considered is based on “probabilistic indexing”, a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex notarial manuscripts from the Spanish Archivo Histórico Provincial de Cádiz, with promising results.
A Hybrid Multiclass Classifier Approach for the Detection of Malicious Domain Names Using RNN Model

Cybercrime has expanded in various areas as the number of websites and hosting services has grown significantly. The identification of hostile domain names has recently gained the interest of researchers in order to avoid cyber-attacks. In terms of network traffic and content, several attempts have been made to distinguish these names. The novel approach proposed in this paper utilizes the neural network-based algorithms to recognize the potentially hostile domain names. An LSTM network is created and prepared based on the dataset. The primary task is to first divide the URL into subdomain, domain, and domain-suffix. Then based on this, the proposed neural network is trained to classify the given data set as malicious or benign. The proposed system can perform well with a higher level of accuracy on the validation set.