Reviewerly/exHarmony huggingface dataset:
Exploration on Construction of Mobile Communication Experimental Teaching Based on Virtual-Real Combination

Aimed to adapt the experimental teaching to keeping up with the actual application of the experimental scene, an experimental teaching based on virtual-real combination for mobile communication was proposed in this article, which realized the collaborative mode of combining online virtual operations and real operations. The virtual experiments were complementary to real experiment operation and on-the-spot experience through sharing virtual software, instruments and equipment and remote equipment control with automatic logging for experimenter's data contained knowledge learning, experimental operation process and experimental results so as to evaluate the experiment. Compared with regular sessions, the degree of curriculum achievement in experimental class has improved significantly and the distribution of scores was more reasonable according to tracking the whole process of students. The system with virtual-real combination enriches the practical strategies and improves the quality of mobile communication experiment studying effectively.
Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Human Activity Recognition (HAR) has become a subject of concern and plays an important role in daily life. HAR uses sensor devices to collect user behavior data, obtain human activity information and identify them. Markov Logic Networks (MLN) are widely used in HAR as an effective combination of knowledge and data. MLN can solve the problems of complexity and uncertainty, and has good knowledge expression ability. However, MLN structure learning is relatively weak and requires a lot of computing and storage resources. Essentially, the MLN structure is derived from sensor data in the current scene. Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules, MLN structure can be obtained. To this end, we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning. Such a rulebase can reduce the time required for MLN structure learning. We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time. In addition, we evaluate the parameters of the rulebase building scheme to check its stability.
A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning

Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).
Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson

[This corrects the article DOI: 10.2196/43960.].
SISTEM INFORMASI AKADEMIK BERBASIS LARAVEL UNTUK REGISTRASI ULANG DAN PENGAJUAN CUTI MAHASISWA

Hasnur Polytechnic is a private educational institution located in Barito Kuala Regency. Currently Hasnur Polytechnic already has an information system that is intended to improve operational service activities such as HR Systems, Academic Systems, Payroll Systems, Internship Systems, Web Profile Systems. However, in this case, it is necessary to improve the quality of academic services in terms of re-registration and application for student leave, which was implemented before the existence of the system required a lot of time because management was carried out in an unsystematic manner. In designing this system, the Waterfall method is applied, starting from the communication stage to the construction stage. As for the process of communication stages of collecting the required data, it is requested directly from the relevant parties, namely the Academic Administration Section of the Hasnur Polytechnic. In the system design stage described using DFD. At the construction stage the system is carried out using the Laravel framework, and applying programming languages (PHP, HTML, JavaScript, and MySql Queries). In the final stage in the system aspect using Blackbox Testing, with 100% successful test results according to the design of functional requirements and running according to its function.
The impact of hyper-realistic virtual spokesperson characteristics on brand attitude

In the highly technological and intelligent era, big data and artificial intelligence technologies are increasingly integrated into people's lives. Under this trend, many brands choose to adopt virtual images with high simulation as brand spokespersons to attract consumers' attention. This paper takes hyper-realistic virtual spokesperson as the research object, and mainly explores the interaction between hyper-realistic virtual spokesperson characteristics, brand trust, brand attitude and adult playfulness. The study found that: (1) hyper-realistic virtual spokesperson relevance and attractiveness characteristics have a positive effect on brand attitude, and interactivity characteristics do not have a significant effect on brand attitude. (2) Brand trust partially mediates between the relevance and attractiveness characteristics of hyper-realistic virtual spokespersons and brand attitude. (3) Adult playfulness plays a moderating role between hyper-realistic virtual spokesperson characteristics and brand trust.
Breaking Down These Barriers and Paving the Way for Blockchain Adoption in Dentistry

Blockchain technology can potentially transform the dentistry sector by facilitating safe communication between dental practitioners and offering secure and efficient administration of patient information. Nevertheless, using this technology in dentistry confronts various barriers, including regulatory and legal obstacles, a lack of technical skills, and a lack of standardization. To overcome these challenges, dental practitioners, industry stakeholders, and regulators must work together to develop a legislative framework that encourages the use of blockchain technology in dentistry. Moreover, education and training programs must equip dental practitioners with the skills and expertise to properly incorporate and use blockchain technology. The use of blockchain technology in dentistry has the potential to greatly improve patient outcomes while also increasing the efficiency and security of the dental business.
Transformer Vibration Signal Feature Extraction Based on Compressed Sensing and Wavelet Packet

Abstract Transformers are important equipment in the power system. Aiming at the collection, transmission, storage, and processing of massive high-dimensional vibration data in the process of transformer vibration online monitoring, this paper proposes a transformer vibration signal feature extraction method based on compressed sensing and wavelet packets. Firstly, the PartHadamard measurement matrix is used to compress the transformer vibration signal, and then the characteristics of the transformer vibration signal are extracted based on the wavelet packet decomposition and normalized wavelet information entropy. The proposed method completes feature extraction on the measured vibration data of the transformer, and three classification algorithms are applied to carry out simulation experiments. The results show that the proposed feature extraction method can effectively extract the vibration characteristics reflecting the operating state of the transformer while greatly reducing the dimension of vibration data, which provides a reference for the online monitoring of transformer vibration.
Corrigendum: Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections

[This corrects the article DOI: 10.3389/fonc.2022.1022967.].
Secure Voice Enabled Text Chat Application

Both the written and audio files can be uploaded to the chat programme. Everyone carries a cellphone in the current world, however not all users have the most recent models, making the majority of native programmes incompatible with those systems. Any device, including computers and handsets, can access the online application because it is a text- and audio-enabled chat system. You can also form a group called "channel" where everyone is able to chat simultaneously in text and audio. In contrast to other chat programmes, the channel does not impose any restrictions on its users. Still, we have applications that allow for an unlimited number of members on a specific channel. but they may need some amount of money to utilise the feature or enable the feature. The application has a feature of speech-to-text conversion, so that the speech can be converted into text. The project is secured with end-to-end encryption, so the security of the application is also provided.
Gender Facial Affirmation Surgery

Fuzzy Rule-Based Systems, FRBSs, are powerful tools to address regression problems. They can model the relationship between inputs and outputs by linguistic concepts. However, those FRBSs which are based on the conventional Type-1 fuzzy sets may not be able to handle some difficulties of real-world applications. In such situations, using novel representations of fuzzy sets seems like a good idea. Different extensions of fuzzy sets usually help to provide more precise models in the real-world problems. In this study, the influence of using fuzzy extensions in improving the efficiency of linguistic fuzzy rule-based regression models is investigated. For this purpose, a conventional Type-1 Mamdani FRBS is adapted to the three extensions of fuzzy sets, namely Interval Type-2, Intuitionistic, and Interval Type-2 Intuitionistic fuzzy sets. A two-pass method is proposed to define membership (non-membership) functions of these fuzzy sets; this method is based on the 3-tuples representation of the standard Type-1 membership functions. Wang and Mendel’s rule learning method is adapted to extract fuzzy rules from regression data. In order to tune the membership functions up to different extents, three evolutionary extensions are also presented for each type of the proposed FRBSs. Individual, internal, and external comparisons of the proposed FRBSs were done using 22 real-world regression datasets and statistical tests. Experimental results confirm that all the three proposed FRBSs outperform the classical Type-1 framework; furthermore, the Interval Type-2 Intuitionistic FRBS is the superior system so that an appropriate tuning of its parameters makes it the most accurate model.
Optimization on large interconnected graphs and networks using adiabatic quantum computation

In this paper, we demonstrate that it is possible to create an adiabatic quantum computing algorithm that solves the shortest path between any two vertices on an undirected graph with at most [Formula: see text] qubits, where [Formula: see text] is the number of vertices of the graph. We do so without relying on any classical algorithms, aside from creating an ([Formula: see text]) adjacency matrix. The objective of this paper is to demonstrate the fact that it is possible to model large graphs on an adiabatic quantum computer using the maximum number of qubits available and random graph generators such as the Barabási–Albert and the Erdős–Rényi methods which can scale based on a power law.
Effectiveness of Pre-Trained Language Models for the Japanese Winograd Schema Challenge

This paper compares Japanese and multilingual language models (LMs) in a Japanese pronoun reference resolution task to determine the factors of LMs that contribute to Japanese pronoun resolution. Specifically, we tackle the Japanese Winograd schema challenge task (WSC task), which is a well-known pronoun reference resolution task. The Japanese WSC task requires inter -sentential analysis, which is more challenging to solve than intra -sentential analysis. A previous study evaluated pre-trained multilingual LMs in terms of training language on the target WSC task, including Japanese. However, the study did not perform pre-trained LM-wise evaluations, focusing on the training language-wise evaluations with a multilingual WSC task. Furthermore, it did not investigate the effectiveness of factors (e.g., model size, learning settings in the pre-training phase, or multilingualism) to improve the performance. In our study, we compare the performance of inter-sentential analysis on the Japanese WSC task for several pre-trained LMs, including multilingual ones. Our results confirm that XLM, a pre-trained LM on multiple languages, performs the best among all considered LMs, which we attribute to the amount of data in the pre-training phase.
Design of Computer-Aided Platform for College Students' Mental Health Education Based on Wisdom Test Algorithm

This paper proposes an algorithm framework for generating pairs of combined test case sets based on particle swarm optimization.When generating test cases, the framework uses particle swarm optimization to try to generate test cases with strong combined coverage capability, and studies the search space.The network environment is described.Undergraduate mental health education system.The system has a clear process and strong practicability, which can reduce the burden of teachers, improve work efficiency, and provide effective support for college students' mental health education.Various analysis methods are used to extract eigenvalues from signal analysis and modeling.The evaluation system has been completed, and the psychological stress evaluation system adapted to the clinical needs of the existing hospital has been completed.
On the number of ℤp-double cyclic codes and quasi cyclic codes

In this paper, we study the class of [Formula: see text]-double cyclic codes of length [Formula: see text] We give a closed formula for the number of [Formula: see text] -double cyclic codes of length [Formula: see text] for any integers [Formula: see text] and [Formula: see text] that are relatively prime to [Formula: see text] Moreover, we give a closed formula for the number of quasi-cyclic (QC) codes of length [Formula: see text] and index [Formula: see text] We also provide formulas for the number of separable and non-separable [Formula: see text]-double cyclic codes of length [Formula: see text] In order to illustrate the results, we calculate the number of some codes with different [Formula: see text] and [Formula: see text]. Moreover, we list optimal parameter [Formula: see text]-double cyclic codes for specific values of [Formula: see text] and [Formula: see text].
Secret Communication Using Multi-Image Steganography with Face Recognition and OTP System

Abstract: Our Proposed system is to develop a Web operation for hiding information in any image train to ensure the safety of the exchange of data between different military parties and give better security during communication transmission. We use the LSB fashion. The proposed approach uses a steganography algorithm to bed data in the image lines for military operations. For security purposes, we used modules face Recognition fashion with the AES algorithm for Strong Security purposes. And we use the cover channel fashion as an information-hiding fashion that can be exploited by a process to transfer information in a manner that violates the system security programs. Image steganography is the main aspect of information caching where the ciphertext is bedded into an image called a cover image which is coming to insolvable for the interferers to see with their naked eyes. The retired information can be any textbook, images, audio, or indeed videos inside a cover image. The abstract description ofMulti-image Steganography is that the secret law is divided into multiple corridors and is etched into multiple cover images. So we proposed two image steganography ideas to make it veritably grueling for the hackers to conceal the data. This paper proposes the Least significant bit( LSB) fashion of Steganography and the Advanced Encryption Standard( AES) fashion of Cryptography to make a safe and secure system. Then the sender and the receiver use the same key to encrypt and decipher the data which is popularly nominated a symmetric crucial Keywords AES Algorithm, LSB Algorithm, and Steganography.
To Analyse Human Sentiments on Social Media Platforms and Their Interest Inference by Using Deep Learning

Abstract: In the modern environment, human emotions are complicated and multifaceted. People are exposed to a wide range of information, ideas, and experiences as a result of the quick development of technology and globalisation, which can affect how they feel and how they view the world. from multiple social network platforms that users engaged in. User interest exhibits dualheterogeneities: it is complementarily and multiple social networks reflects them comprehensively; interest of user are not independent of each other rather inter-correlated in a nonuniform way. Although past approaches towards these problems had great success that considers the dual heterogeneities simultaneously, consistency of sources, uses multi-sourced and multi-tasked learning scheme. To address this, a web application coupled with machine learning technique like linear regression to predict the human sentiments by inviting them to attempt the survey. The answers to the survey are per-processed to identify the relationship between different independent variables and a dependent variable that represents a particular sentiment. A database utilising MYSql is used to record the user personal information, the responses, and the quiz results of users who have registered while unknown users are first requested to sign up. Pre-trained data and Supervised Learning algorithms can be used to detect sentiments.
SIGN LANGUAGE RECOGNITION: A DEEP CONVOLUTIONAL NEURAL NETWORK APPROACH FOR ACCURATE ALPHABET IDENTIFICATION

Sign language recognition has drawn the attention of researchers due to its potential impact on communication within the large and diverse community of approximately 466 million deaf individuals worldwide.However, sign languages exhibit variations across different languages and communities, posing a complex challenge for developing effective recognition models.Previous studies have explored various sign languages, including the Sign Language MNIST dataset.Classifiers such as support vector machines, random forests, and multilayer perceptrons have been utilized in the past for sign language recognition.More recently, shallow convolutional neural networks (CNNs) and Capsule Networks have shown improved performance.In our research, we propose a deep CNN model that accurately identifies sign language alphabets.Our model achieved an impressive overall accuracy of 97.62 percent.Furthermore, a comparative analysis with previous studies demonstrates that our proposed model outperforms all previously introduced models.
About complex intelligent technologies for techno-ecological events control in the water area

Aspects of the important task solution of creating complex intelligent decision-making support technologies for the identification of techno-ecological event (TEE) and the optimal selection of the sequence of available measures to reduce the life cycle of this TEE in the water area in order to minimize material losses are considered ("CONTROL_TEE" system). In the V.M. Glushkov Institute of Cybernetics of NAS of Ukraine, Concern "BaltRobotics" (Ukraine-Poland), NTU of Ukraine "Igor Sikorsky KPI" study of the possibility of theoretical development, research and practical implementation of methods and tools that make up the information technology of research design (informational, mathematical, algorithmic, software, technical, organizational support) of robots intended for reconnaissance and neutralization of TEE in a number of environment. For the classifying waves, mathematical models of the propagation of both running and standing waves in the sea area were obtained and solved. The structure of the information storage of the situation center has been developed. In order to create a database wave classification and mathematical and computer modeling were carried out. The deterministic process of sound propagation in a flat waveguide in the homogeneous mode is considered. Special boundary value problems and Cauchy problems are solved for the two-dimensional wave equation and, accordingly, for the Helmholtz equation. Calculation formulas for sound pressure and corresponding to its velocities are obtained in an analytical closed form. In the general case, the tangent and normal components of the velocity vector and the hydrodynamic potential are calculated in the form of Fourier series by the methodology of the works of Bilonosov, Ovsienko, Li, Zinchenko, Zinchenko, Nogin.
Analysis of customer behaviour using web usage mining

This paper attempts to figure out the concealed data and establish user Behaviour online by exploiting the online as a source for data collection.With the assistance of this information, user Behaviour can be predicted very easily.Free tool like similar web and page score were used to analyze user pattern on the web site of the particular establishment.These tools attempt to conduct mining on the web in an independent manner.The experimental result emphasizes a clear way to surf through the web and enhance the design of the web.This paper discusses the elaborate results of website in a particular education domain application.The main objective of conducting this study on websites of the different polytechnics in Federal Republic of Nigeria is to discern the pattern of Behavioural expressions and user characteristics so that institutional establishments can enhance their website structure and may transfer additional information in an applicable and timely manner.