Reviewerly/exHarmony huggingface dataset:
Estimation of Demographic Traits of the Deputies through Parliamentary Debates Using Machine Learning

One of the most impressive applications of the combined use of natural language processing (NLP), classical machine learning, and deep learning (DL) approaches is the estimation of demographic traits from the text. Author Profiling (AP) is the analysis of a text to identify the demographics or characteristics of its author. So far, most researchers in this field have focused on using social media data in the English language. This article aims to expand the predictive potential of demographic traits by focusing on a more diverse dataset and language. Knowing the background of deputies is essential for citizens, political scientists and policymakers. In this study, we present the application of NLP and machine learning (ML) approaches to Turkish parliamentary debates to estimate the demographic traits of the deputies. Seven traits were determined: gender, age, education, occupation, election region, party, and party status. As a first step, a corpus was compiled from Turkish parliamentary debates between 2012 and 2020. Document representations (feature extraction) were performed using various NLP techniques. Then, we created sub-datasets containing the extracted features from the corpus. These sub-datasets were used by different ML classification algorithms. The best classification accuracy rates were more than 31%, 27%, 35%, 41%, 29%, 59%, and 32% according to the majority baseline for gender, age, education, occupation, election region, party, and party status, respectively. The experimental results show that the demographics of deputies can be estimated effectively using NLP, classical ML, and DL approaches.
A Deep Learning Model for ERP Enterprise Financial Management System

With the advent of the information age, the need for information technology construction is beginning to be realized when working in corporate financial management. The application of ERP systems to financial management has become a major trend in the development of modern society. This can help companies collect financial information in real time and analyze and process the obtained information. This paper first gives the significance and models of the ERP financial management system. Then, a financial risk prediction model based on a deep learning model is designed. The method proposes an improved temporal convolutional network-long and short-term memory network (TCN_LSTM) structure and introduces an optimization algorithm to optimize the parameters of the deep learning model. Finally, several benchmark models and evaluation methods are used for comparative study. The experimental results show that the deep learning risk prediction model has significant superiority in prediction accuracy and stability. The proposed model can help enterprises organize their limited resources, realize the scientific allocation of enterprise resources, and create more benefits for enterprises.
Construction of Marine Professional Virtual Simulation Experiment Platform Based on Internet-of-Things Technology

In order to meet the needs of high-intensity underwater information confrontation training under complex battlefield conditions, military academies must integrate scientific issues related to environmental operations into classroom teaching through research-based teaching of marine environmental effects. By analyzing the research-oriented teaching requirements of marine environmental effects, the design and implementation of a virtual simulation laboratory based on a high-performance computing platform for signal-level marine environmental effects are discussed. Multilevel, research-based teaching provides effective technical support, which is of great significance to helping submarine professional students experience, recognize, and master the scientific laws behind combat problems, improve their practical training capabilities, and promote practical teaching. By the time the number of operations reaches 5, most students have an accurate grasp of the liquid chromatography procedures. After more than five times, the improvement of students is not obvious. Compared with liquid chromatography, the improvement of Bernoulli experiment is divided into two stages: 1 to 5 times belong to the process improvement stage, and students can master it quickly; 6 to 12 times belong to the detail improvement stage, and the improvement times are relatively slow; fully mastered the time required is much higher than the liquid chromatography virtual simulation experiment. Finally, combined with the actual teaching application, the research-based teaching group training mode and relevant implementation suggestions are summarized.
Spline quasi‐interpolation in the Bernstein basis and its application to digital elevation models

A nonstandard low‐cost spline approximation method for approximating bivariate functions is constructed. It is applied for Digital Elevation approximation and then its accuracy in the downscaling process is studied.
Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis

Ultrasound-guided needle biopsy based on artificial neural network, as a safe, effective, and simple preoperative pathological diagnosis technique, has been widely used in clinical practice. Ultrasound-guided needle biopsy based on artificial neural networks for suspicious breast lesions found in conventional ultrasound examinations is an effective method for preoperative diagnosis. The purpose of this article is to study the value of artificial neural network ultrasound in improving breast cancer diagnosis. This article summarizes the neuron model of PCNN by observing and studying its impulse synchronization phenomenon. Aiming at gray-scale images disturbed by mixed noise (impulse noise and the Gaussian noise), a comprehensive filtering algorithm based on the simplified PCNN model is proposed. In this paper, the benign and malignant breast masses were evaluated based on the two-dimensional and three-dimensional ultrasound imaging signs of the mass, and compared with the postoperative pathological results, a logistic regression model was established to analyze the shape, boundary, microcalcification, and posterior echo attenuation of the mass, values for keratinization or burrs, convergent signs, and blood flow classification in the differential diagnosis of benign and malignant. In this paper, a color ultrasound diagnostic device is used, Sonobi is used as a contrast medium, and the injection volume is 2.4 ml/dose. During the imaging process, the sound image performance of the lesion is dynamically observed, the original dynamic data are stored throughout the whole process, and the playback analysis is performed after the imaging is completed. Studies have shown that CDUS elastography (UE) combined with MRI can increase the sensitivity of breast cancer diagnosis, with a diagnostic accuracy rate of 92.4%.
QuatJND: A Robust Quaternion JND Model for Color Image Watermarking

Robust quantization watermarking with perceptual JND model has made a great success for image copyright protection. Generally, either restores each color channel separately or processes the vector representation from three color channels with the traditional monochromatic model. And it cannot make full use of the high correlation among RGB channels. In this paper, we proposed a robust quaternion JND Model for color image watermarking (QuatJND). In contrast to the existing perceptual JND models, the advantage of QuatJND is that it can integrate quaternion representation domain and colorfulness simultaneously, and QuatJND incorporates the pattern guided contrast masking effect in quaternion domain. On the other hand, in order to efficiently utilize the color information, we further develop a robust quantization watermarking framework using the color properties of the quaternion DCT coefficients in QuatJND. And the quantization steps of each quaternion DCT block in the scheme are optimal. Experimental results show that our method has a good performance in term of robustness with better visual quality.
Innovation and Digital Construction of Cultural Tourism Industry under the Background of Big Data and Internet of Things

In recent years, the concept of the Internet of Everything has gradually entered all walks of life, and the combination of big data technology and the Internet of Everything applied to other industries has become the current trend of industrial innovation. The integration between the tourism industry and the cultural industry is an inevitable trend for the sustainable development of the tourism industry. New science and technology are constantly innovating the way of life of human beings. If the tourism industry wants to maintain a state of rapid development, it must focus on the innovative development and digital construction of cultural tourism, develop new cultural tourism products, and propose new marketing innovation models. This promotes faster and better development of the tourism industry. Based on this, this paper designs a questionnaire and uses statistical methods to analyze the data obtained by the questionnaire, summarizes the current problems in the innovation and digital construction of the cultural tourism industry, and proposes solutions in a targeted manner, in order to improve the cultural tourism industry. Healthy development provides incremental contributions.
Determination of <scp>Ultra‐Trace</scp> Au, Ag, As, Pt and Re Mass Fractions in Volcanic Glasses and Rock Powders by <scp>LA‐ICP‐MS</scp>

Practical methods for determining the ultra‐trace abundances of precious metals in geological materials are needed for research into magmatic and hydrothermal processes and to expand the geochemical footprints of concealed ore deposits. This study presents a new protocol for determining Au, Ag, As, Pt and Re mass fractions in both volcanic glasses and in rock powders prepared as nano‐powder pellets, through the synthesis and refinement of published LA‐ICP‐MS methods. This matrix flexibility allows the method and its limitations to be rigorously assessed for the first time using different volcanic materials. High‐yield laser parameters, interference corrections and low oxide production rates facilitated by laser ablation sampling enabled accurate measurements without chemical pre‐separation. A key finding is that ablation‐remobilised system contamination must be quantified and corrected to make accurate ng g −1 ‐level Au determinations by LA‐ICP‐MS, resulting in a mean + 2 s quantification limit for Au of 0.38 ng g −1 . This approach is likely necessary for other ultra‐trace LA‐ICP‐MS analyses of certain elements. Following this correction, the protocol can be usefully applied to both in situ analysis of volcanic materials and efficiently integrated into methods for the determination of major and trace elements in nano‐powder pellets.
Deep Learning-Based Lung Cancer Detection

Cancer is considered one of the most dangerous diseases emerging in the human body cells. Lung cancer is found to be a type of cancer that has affected the majority of the population. Detection of lung cancer is a difficult task as there appears to be a very small growth called nodule once cancer affects the lung. Early detection of lung cancer can hence be a helpful aid for people affected by the disease and the doctors and medical personnel treating them. In this paper, a deep learning technique using long short-term memory (LSTM) with autoencoders is proposed for the detection of lung cancer.
Designing a Secure Smart Healthcare System with Blockchain

In medical service applications, IoT facilitates correspondence among specialists and patients as the later can be analyzed distantly in crisis situations through body sensor organizations and wearable sensors. Nonetheless, utilizing IoT in medical service frameworks can prompt infringement of the security of patients. Accordingly, security ought to be think about. Blockchain is one of the secure techniques and has been applied to most of the IoT situations, these days. Scarcely, the significant purposes behind utilizing the Blockchain in medical care frameworks are its conspicuous provisions, i.e., decentralization, immutability, security, privacy, and transparency. This design, in view of the blockchain, Ethereum permitting the patient just as the clinical and paramedical staff to have a protected admittance to wellbeing data. The Ethereum hub is carried out on an installed stage, which ought to give a productive, adaptable, and secure framework. Our primary intention is to give an appropriate, secure, and approved admittance to this critical information utilizing the Ethereum blockchain innovation. In this paper, we proposed a blockchain-based healthcare IoT system toward security of electronic health records (EHRs). This design is based on Ethereum that provides security to the critical clinical data of the patients. This Ethereum-based architecture provides an adoptable and secure framework of smart healthcare system. The implementation results show that our proposed technique implements security with very less computational overheads.
A comparative study of swarm intelligence and artificial neural networks applications in modeling complex reaction processes

This study investigated two artificial intelligence techniques, Swarm Intelligence(SI) and Artificial Neural Networks(ANN), aiming to overcome the difficulties of simulating complex processes with unknown reactions and intermediates. These techniques are incorporated in reaction modeling via mass balances and reaction kinetic models. The accuracy and the applicability of the resulting models from ANN and SI were compared in the trained semi-batch reactors and the new continuous flow reactors. The ANN-based model is recommended when the extrapolation is unnecessary, and the data is high in volume and variety at the applied space. In this case, no profound reaction knowledge is required. Otherwise, the SI-based model should be employed, which provides detailed information of the target process and is constrained by physical meaning parameters.
Enhanced engineering design behaviour using chatbots with user experience

In recent years, both industrialists and scholars have come to believe that involving users in the design process is the basis for ensuring design innovation and successful product development. However, practical restrictions of time, budget, and location may inhibit users’ long-term participation at teaching sites. With the development of emergent chatbot technology, the aim of this study is to investigate the feasibility that students practice interacting with chatbots who incorporate multiple real-life users’ experiences with products and product-specific knowledge in the classroom for exempting from time and accessibility constraints. We conducted an experiment using a design workshop to compare the student groups with respect to their interactions with chatbots, with target customers, and with no users while they generated design concepts. The quality of the three student groups’ design concepts and the differences of their design behaviours were compared. The outcomes of this study indicate that the chatbot group exhibited more significant transitions of engineering design behaviours and better design concept score than that the target-user and no-user group did. Chatbots incorporate user experience may provide students with opportunities to practice extracting information from users with the aim of promoting the success of the design process.
Frequency Effects in Chinese Learners’ Acquisition of the English Article Construction

The current study, built on the usage-based approach to language, investigated frequency effects in Chinese learners’ acquisition of English articles. We carried out type and token frequency analysis of article usages in academic written essays sourced from a written English corpus of Chinese learners. We adopted an existing usage-based article cue coding scheme, which allowed us to implement a refined frequency analysis of all form-function mappings in learner texts. Our findings suggested that learners’ article usage follows the Zipfian distribution in terms of token frequency. Learners show a heavier reliance on a very limited number of high-frequency cues than native speakers. Non-definites (indefinite article and zero article) outnumber definite articles in terms of token and type frequency in learner texts. Yet definite articles show a significantly higher type/token ratio than non-definites, suggesting that learners develop a more complex and heterogeneous profile of definite article usage. We argue for more research and pedagogical attention to frequency and complexity effects in the acquisition of articles.
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective

Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management.
Web Page Classification Based on Novel Black Widow Meta-Heuristic Optimization with Deep Learning Technique

As a search engine, web page classification gives useful information which may be used to build many various applications. For effective Internet use, spam filtering, and a host of other applications, categorizing web pages is helpful. Search engines must tackle the difficulty of swiftly finding relevant results from millions of online pages. During an automatic classification process, a significant variety of elements, such as XML/HTML and text content, should be taken into account. In addition to interfering with the routine operations of the website and crawling a large amount of worthless information, present methods for listing have many disadvantages. An important goal of this study is the reduction of features needed to upsurge the speed and web page classification accuracy by dropping the number of features that need to be employed. An NBW-MHO technique was utilized in this study to choose the best features, and a deep learning-based classification ideal was proposed to learn the best structures in each web page and aid search engine listing. The project was funded by the National Science Foundation. Using only link text, side information, and header text, our suggested algorithm gives an ideal classification for websites with a large amount of web pages. When employing NBW-MHO for feature selection, classification accuracy and runtime performance are improved. Information gain and chi-square feature selection approaches were compared to the NBW-MHO algorithm.
Revisiting power messaging in service failures: Pitfalls and proposed solutions

Abstract Past research shows that company messaging can inflate consumers' feelings of power, which in turn alleviate the negative effects of service failures. Extant research, however, has not examined how various types of power messaging can have a differential effect on consumers, with some leading to counterproductive consequences for companies. Across five experiments, we show that power messaging stressing that consumers have power over the company can backfire by increasing manipulative intent. Power messaging communicating that consumers will obtain a power boost from the service is more acceptable and less likely to be perceived as manipulative. Furthermore, we demonstrate that messaging eliciting power from the service is most effective when (1) targeted at consumers with low levels of skepticism, (2) delivered by an underdog brand, and (3) paired with co‐created recovery. Following a service failure, messaging communicating how customers will gain a power boost from the service experience increases identification with underdog brands. Our examination of power messaging as an overt communication strategy contributes to the literature on service failure and recovery. The research advances knowledge of the potential pitfalls of power messaging while proposing strategies to overcome risks associated with this communication strategy.
Enabling modality interactions for RGB-T salient object detection

Most existing RGB and thermal (RGB-T) salient object detection (SOD) techniques focus on investing multi-modality feature fusion strategies for capturing cross-modality complementary information within RGB and thermal images. However, most of these strategies do not allow explicitly extracting the interactions among the features of different modalities, thus leading to insignificant cross-modality complementary information exploitation. In this paper, we propose a novel RGB-T SOD model that alleviates this issue by leveraging a modality-aware and scale-aware feature fusion module. Such a module is capable of capturing the cross-modality complementary information by exploiting the interactions of single-modality features across modalities and the interactions of multi-modality features across scales. A stage-wise feature aggregation module is also proposed to thoroughly exploit the cross-level complementary information and reduce their redundancies for generating accurate saliency maps with sharp boundaries. To this end, a novel multi-level feature aggregation structure with two types of feature aggregation nodes is employed. Experimental results on several benchmark datasets verify the effectiveness and superiorities of our proposed model over some state-of-the-art models.