Reviewerly/exHarmony huggingface dataset:
The Transformers’ Ability to Implement for Solving Intricacies of Language Processing

AI (Artificial Intelligence) tools have reached a surprising level of linguistic frequency. The best and biggest (little complex) of these are based on an architecture called the transformers. A transformer in AI is a tool that acts as a kind of training model to train the pre-trained models. Available pre-trained models can reduce computational cost, carbon foot print and saves time. A transformer model can be used for different modalities such as text, images, audio and even for multi-model including video classification and visual question answering. Latest version of transformers (Transformers 4.18.0) has been released by Pypi.org on 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Apr 2022 as part of the Hugging Face logo which claims SOTA (state-of-the-art), NLP (Natural Language Processing for Tensorflow 2.0 and PyTorch. This paper is one the first papers using this latest release of transformer tool for NLP (Natural Language Processing) in Python. Using latest Transformers this paper overviews, represents implements its powerful tools BERT (Bidirectional Encoder Representation from Transformers), GPT (Generative Pre-Trained), BART (Bidirectional Abstractive Representation from Transformers), & other utilities of highly efficient linear algebra libraries of Python for Natural Language Processing. So, this proposal implements Natural Language Processing introducing POS (Parts of Speech) Tags, Bigrams and implementing pipeline, summarizer, paraphrasing, sentiment-analysis using transformers. For sentiment Analysis multilingual (English & French) have been selected & matching sentiment results are 94% to 98 % for four test cases which are far better than conventional methods.
AutoKYC: Automation of Identity establishment and authentication in KYC process using Text extraction and face recognition

AutoKYC is a system designed for the organization who wants to verify the identity of the users while doing their KYC for example, a bank. These organizations face a big concern of fake account creation and thus portrays a false image in minds of stakeholders. So, AutoKYC would be a system which would allow these organizations to establish the identity of the users not only on the basis of the proof they have submitted but the face verification as well. AutoKYC follows the set of multi-step verification in order to establish the identity of person. The closed loop of identity verification for the users enables the system to be used in Public Distribution Sectors as well, where the data of the user and the proof can be a means to ensure the fair delivery and propose clarity in the system.
Stability and Convergence Analysis of 3D Feature-Based Visual Servoing

Visual control based on image features has received much attention for its inherent robustness against camera calibration errors, modelling uncertainties and the capability to keep the object in the Field of View (FoV) of the camera. Nevertheless, some drawbacks related to the basin of convergence and the existence of local minima, which make the camera to get stuck in undesired equilibrium points, are still worth being investigated. Nowadays, the availability of cheap and lightweight RGB-D cameras makes the use of three-dimensional features natural. By using an RGB-D camera in an eye-in-hand configuration, this letter proposes an in-depth stability and convergence analysis of 3D feature-based visual servoing. It will be proved that the visual control system is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">almost globally asymptotically stable</i> in the sense that the only trajectories not converging to the desired equilibrium point are those belonging to a zero Lebesgue measure set in the feature space. Moreover, a sufficient condition guaranteeing that the feature trajectories remain in the camera FoV is derived and an algorithm to prevent feature loss caused by violation of the camera FoV constraint is proposed.
A Study on English Translation Skills and Modes of Multimodal Network from the Perspective of Teaching Situation

With the in-depth research and continuous exploration of English translation skill patterns by multimodal networks, the scope of English translation skill analysis has been extended to other aspects besides words, and the traditional single-mode English translation skill analysis has gradually changed into a systematic English translation skill analysis that integrates language, images, music, and other symbols. From the perspective of situational teaching, this paper studies the skills and modes of English translation by multimodal network. The research shows that 30 books are randomly selected from preschool children’s books, primary school books, and junior high school books, among which the highest proportion of multimodal books is 82%, 57%, and 54%, respectively, and the proportion of multimodal network books among 120 literature books is 79%. Translators have a full understanding of the translated content in situational teaching, but in the actual process of translation, they will inevitably face the situation of language integration and word order adjustment. If translators have a certain understanding of the corresponding English translation skills, they can lay a good foundation for English translation skills and ensure the accuracy of translation.
An Efficient Intrusion Detection Method Based on Federated Transfer Learning and an Extreme Learning Machine with Privacy Preservation

Current network security is becoming increasingly important, and intrusion detection is an effective method to protect the network from malicious attacks. This study proposes an intrusion detection algorithm FLTrELM based on federated transfer learning and an extreme learning machine to improve the effect of intrusion detection, which implements data aggregation through federated learning and facilitates the construction of personalized transfer learning for all organizations. FLTrELM first builds a transfer extreme learning machine model to solve the problem of insufficient samples and probability adaptation, then uses the model to learn to protect data privacy without sharing training data under the federated learning mechanism, and finally obtains an intrusion detection model. Experiments on the NSL-KDD, KDD99, and ISCX2012 datasets verify that the proposed method can achieve better detection results and robust performance, especially for small samples and new intrusions, and protects data privacy.
Parameterized Two‐Qubit Gates for Enhanced Variational Quantum Eigensolver

The variational quantum eigensolver is a prominent hybrid quantum-classical algorithm expected to impact near-term quantum devices. They are usually based on a circuit ansatz consisting of parameterized single-qubit gates and fixed two-qubit gates. We study the effect of parameterized two-qubit gates in the variational quantum eigensolver. We simulate a variational quantum eigensolver algorithm using fixed and parameterized two-qubit gates in the circuit ansatz and show that the parameterized versions outperform the fixed versions, both when it comes to best energy and reducing outliers, for a range of Hamiltonians with applications in quantum chemistry and materials science.
Learning Identity-Consistent Feature for Cross-Modality Person Re-Identification via Pixel and Feature Alignment

RGB-IR cross-modality person re-identification (ReID) can be seen as a multicamera retrieval problem that aims to match pedestrian images captured by visible and infrared cameras. Most of the existing methods focus on reducing modality differences through feature representation learning. However, they ignore the huge difference in pixel space between the two modalities. Unlike these methods, we utilize the pixel and feature alignment network (PFANet) to reduce modal differences in pixel space while aligning features in feature space in this paper. Our model contains three components, including a feature extractor, a generator, and a joint discriminator. Like previous methods, the generator and the joint discriminator are used to generate high-quality cross-modality images; however, we make substantial improvements to the feature extraction module. Firstly, we fuse batch normalization and global attention (BNG) which can pay attention to channel information while conducting information interaction between channels and spaces. Secondly, to alleviate the modal difference in feature space, we propose the modal mitigation module (MMM). Then, by jointly training the entire model, our model is able to not only mitigate the cross-modality and intramodality variations but also learn identity-consistent features. Finally, extensive experimental results show that our model outperforms other methods. On the SYSU-MM01 dataset, our model achieves a rank-1 accuracy of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>40.83</mn> <mo>%</mo> </math> and an mAP of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>39.84</mn> <mo>%</mo> </math> .
Innovative Region Convolutional Neural Network Algorithm for Object Identification

Object identification is a part of the field of computer science, namely, image processing, whose research continues to innovate.Object identification describes an object based on the main characteristics of the object.Many research innovations related to object identification have been carried out to obtain optimal identification results.The convolutional neural network (CNN) is one of the algorithms that is widely used by researchers in the field of object identification or object recognition in digital images.The purpose of this study was to analyze the development of object identification in the search for the best algorithm in terms of the speed and efficiency of identification.The article data used were obtained from several sources, namely, Dimensions AI, Science Direct, and Google Scholar.The database search results obtained 1041 articles in the form of publications from 2010-2021.Through a systematic literature review based on the articles obtained, 32 articles were selected.The evaluation of the articles was carried out in the form of article data visualization, object identification algorithm development, and the research objects used.CNN's research innovation is growing rapidly, with improvements being made to the identification techniques in its algorithmic architecture.The use of the CNN algorithm in the identification of image objects, starting with the region CNN technique, is improved with Fast R-CNN, Faster-CNN, and Mask R-CNN.The object of research has developed from facial recognition and the identification of moving images to the introduction of ancient manuscripts that are useful for the development of history and tourism.The successful identification of ancient scripted texts will greatly assist the availability of such manuscripts in a digital format, which allows for further multidisciplinary research.The availability of ancient manuscripts in a digital format also helps the government to preserve culture and increase people's understanding of the culture they have.
Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.
Prediction of breast cancer metastasis by deep learning pathology

With the rapid development of social economy, the incidence of breast cancer is increasing year by year. Whether there is lymph node metastasis in frozen tissue sections during breast cancer surgery is of tremendous priority for breast cancer surgical decision-making. Therefore, it is very significant to diagnose the pathological sections of breast cancer quickly and accurately. In this study, a model which can quickly fine segmentation of lesion regions in high-resolution breast cancer pathology sections is proposed. Firstly, pathology sections are processed by pre-processing module; Secondly, the main lesion region in pathology sections can be quickly recognized by recognition module; Thirdly, the fine segmentation of lesion region can be accomplished by segmentation module. The dataset is selected from two medical institutions to evaluate the proposed model; it achieved the average recognition precision of 0.936 for region of interest in high-resolution pathology section, with an F1-score of 0.787; and the dice for lesion region segmentation is 0.8517. The proposed model outperforms several similar works, which can effectively improve the speed and precision of pathologist's diagnosis for high-resolution breast cancer pathology sections.
Successful Management of IT Projects in Healthcare Institutions after COVID-19: Role of Digital Orientation and Innovation Adaption

This research aims to examine specific issues that how healthcare institutions successfully manage IT projects after the deadly disease of COVID-19. The world's healthcare institute changed its traditional way of treatment to IT-based equipment after COVID-19. Hence, this study investigated the how digital orientation helps healthcare institutes for successful management of IT. Our study identifies the critical role of digital orientation and innovation adaption in the successful management of IT. The mediating role of innovation adaption in the association between digital orientation and successful management of IT was also investigated. In total, 456 questionnaires were used for the collection of data from eight different healthcare centers. We selected participants through random sampling. Findings on the healthcare institution showed that successful management of IT is predicted through digital orientation. This study's results proved that digital orientation impacts innovation adaption, and similarly, innovation adaption influences the successful management of IT. The outcomes show the mediating role of innovation adaption in the linkage between digital orientation and successful management of IT. Current research contributes to the existing literature through combined impacts of the digital orientation, innovation adaption, and successful management of IT through means of demonstrating how, when, and why digital orientation supports the successful management of IT. Moreover, innovation adaption performs a significant role in the extant digitalize world; thus, we chose innovation adaption as a mediator in this study.
Building a Closed-Domain Question Answering System for a Low-Resource Language

In recent years, the Question Answering System (QAS) has been widely used to develop many systems, such as conversation systems, chatbots, and intelligent search. Depending on the amount of information or knowledge that the system processes, the system can be applied in answering the questions in an open domain or closed domain. There are many approaches to solving the QA problem, but the neural network models have yielded impressive and promising results, especially the Machine Reading Comprehension approach. In this article, we build a closed-domain QAS for a low-resource language, Vietnamese—specifically, “The Postgraduate Admission of Ho Chi Minh City University of Food Industry, Vietnam.” In addition, we have created two datasets to serve our QAS: vi-SQuAD v1.1, which is automatically translated and edited from SQuAD (Stanford University Question Answering Dataset), and HUFI-PostGrad, which is manually collected. We use two main models for the system, including the Intent Classification model and the Machine Reading Comprehension model. Experimental results initially show that our QAS gives encouraging results.
Security and Usability of a Personalized User Authentication Paradigm: Insights from a Longitudinal Study with Three Healthcare Organizations

This article proposes a user-adaptable and personalized authentication paradigm for healthcare organizations, which anticipates to seamlessly reflect patients’ episodic and autobiographical memories to graphical and textual passwords aiming to improve the security strength of user-selected passwords and provide a positive user experience. We report on a longitudinal study that spanned over 3 years in which three public European healthcare organizations participated to design and evaluate the aforementioned paradigm. Three studies were conducted ( n = 169) with different stakeholders: (1) a verification study aiming to identify existing authentication practices of the three healthcare organizations with diverse stakeholders ( n = 9), (2) a patient-centric feasibility study during which users interacted with the proposed authentication system ( n = 68), and (3) a human guessing attack study focusing on vulnerabilities among people sharing common experiences within location-aware images used for graphical passwords ( n = 92). Results revealed that the suggested paradigm scored high with regard to users’ likeability, perceived security, usability, and trust, but more importantly it assists the creation of more secure passwords. On the downside, the suggested paradigm introduces password guessing vulnerabilities by individuals sharing common experiences with the end users. Findings are expected to scaffold the design of more patient-centric knowledge-based authentication mechanisms within today's dynamic computation realms.
Analysis of Software Engineering Skills Gap in the Industry

Many practitioners might struggle with becoming productive in different software engineering (SE) roles due to misalignment of the skills learnt during the university time with what is expected in the industry. Companies spend significant resources to train the personnel, whose academic backgrounds are not only based on “computing disciplines”. Hiring properly trained practitioners allows employers to spend less time while incorporating them more efficiently into the workforce; for employees, knowing the most important skillset is helpful to increase their chance of employability. On the other hand, for academia, understanding the necessary skillset is critical to make curriculum updates. To achieve these objectives, we conducted a survey, which was responded to by 628 software practitioners, who completed their undergraduate degree in Turkey, working in 13 countries. This paper sheds light on the most important (hard and soft) skills in the industry by presenting various cross-factor analyses as well as their coverage in the academic curriculum (mostly in Turkish universities). The results showed that the most important skills are related to various factors such as profiles of the practitioners (e.g., SE role(s), work experience) and the characteristics of the product developed by the practitioner. The findings revealed that both academia and industry should invest in skills improvement: academia can make necessary educational updates according to industrial needs; whereas industry can provide practical experiences to students. By creating the awareness of the expected skillset, both practitioners and academics will benefit from the results, which help close the gaps that can and should be achieved through more Industry Academia Collaborations (IACs) .
Inconclusives are not errors: a rejoinder to Dror

In Arkes and Koehler (2022), we proposed that signal detection theory provides a helpful framework for understanding when and why a forensic examiner may wish to call an ‘inconclusive’ rather than offer a binary source conclusion (i.e. exclusion or identification) when deciding whether two samples share a common source. As Dr. Itiel Dror notes in his thoughtful letter to the editor, inconclusive decisions by forensic examiners have important implications for both casework and error rate studies. If there is a higher rate of inconclusive decisions in error rate studies than in casework, then the studies probably underestimate the casework error rate when inconclusives are set aside. We agree with Dr. Dror on this point. We part company with Dr. Dror when he proposes that inconclusives should be scored as errors in test situations ‘when there is enough information to determine the state of nature’ (Dror, in press; see also Dror and Scurich, 2020). We disagree with this proposal for two reasons.
METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance

Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms.METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses.META-BOA is available at https://complimet.ca/meta-boa.Supplementary data are available at Bioinformatics online.