Reviewerly/exHarmony huggingface dataset:
Job Candidate Rank Approach Using Machine Learning Techniques

Due to Covid19 Pandemic, all processes turn into a digital form that leads to online recruitment growth. Job portals are job recruiting systems. Human resources employees suffer from many job applications and the interviewing process and select the best candidate for the job vacancy. This paper presents the Job Candidate Rankmodel that analyzes the interviewers’ sentiment and calculates a rank for each job candidate to help Human resources employees make the hiring decision. The proposed approach analyses the interviewers’ sentiment and create a list of ranked keywords and then classified them using machine learning techniques. The results show that the Job Candidate Rank approach provides an accuracy of 93%.
A semantic approach to enable data integration for the domain of flood risk management

With so many things around us continuously producing and processing data, be it mobile phones, or sensors attached to devices, or satellites sitting thousands of kilometres above our heads, data is becoming increasingly heterogeneous. Scientists are inevitably faced with data challenges, coined as the 4 V’s of data - volume, variety, velocity and veracity. In this paper, we address the issue of data variety. The task of integrating and querying such heterogeneous data is further compounded if the data is in unstructured form. We hence propose an approach using Semantic Web and Natural Language Processing techniques to resolve the heterogeneity arising in data formats, bring together structured and unstructured data and provide a unified data model to query from disparate data sets.
SkelGAN: A Font Image Skeletonization Method

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.
Shortcut-based quantum gates on superconducting qubits in circuit QED*

Construction of optimal gate operations is significant for quantum computation. Here an efficient scheme is proposed for performing shortcut-based quantum gates on superconducting qubits in circuit quantum electrodynamics (QED). Two four-level artificial atoms of Cooper-pair box circuits, having sufficient level anharmonicity, are placed in a common quantized field of circuit QED and are driven by individual classical microwaves. Without the effect of cross resonance, one-qubit NOT gate and phase gate in a decoupled atom can be implemented using the invariant-based shortcuts to adiabaticity. With the assistance of cavity bus, a one-step SWAP gate can be obtained within a composite qubit-photon-qubit system by inversely engineering the classical drivings. We further consider the gate realizations by adjusting the microwave fields. With the accessible decoherence rates, the shortcut-based gates have high fidelities. The present strategy could offer a promising route towards fast and robust quantum computation with superconducting circuits experimentally.
Influence of virtual reality and 3D printing on architectural innovation evaluation based on quality of experience evaluation using fuzzy logic

In the last few years, the construction industry’s primary energydem and in developed economies accounted for 30%–40%, and technological innovation is considered to be an urgent need for the transformation and upgrading of the construction industry. New technological innovations are continually changing the way the construction industry is implemented. In the construction industry, artificial intelligence is beginning to change all aspects of the construction industry, in the field of equipment planning, overall layout, safety, management and other fields have caused many changes. The integration of new technologies has revolutionized the traditional construction industry, such as virtual reality technology and 3D printing technology. In this work, we propose a categorization for assessing Virtual reality and 3D printing of Architectural Innovation on the basis of quality of experience (QoE) metric Evaluation of virtual environment using Fuzzy Logic (FL) System. The simulation result is analysed based on the comparative analysis of user experience with Fuzzy logic estimation for perception of virtual environment. The result analysis validated that the proposed FL system replicates the user valuation for architecture innovation applications more precisely and accurately thus FL is efficient method for predicting the inclusive QoE of a virtual reality and 3D printing. This paper will recapitulate the expansion of the two techniques in the manufacture engineering, demeanor examination on the presentation of diverse skills in architectural innovation, as well as explain through real cases how these two techniques have an influence on the improvement of the manufacture engineering. Simultaneously, this paper discourses the limitations of technology mixing and put advancing opinions on the future development of the manufactureengineering.
Rollback Ensemble With Multiple Local Minima in Fine-Tuning Deep Learning Networks

Image retrieval is a challenging problem that requires learning generalized features enough to identify untrained classes, even with very few classwise training samples. In this article, to obtain generalized features further in learning retrieval data sets, we propose a novel fine-tuning method of pretrained deep networks. In the retrieval task, we discovered a phenomenon in which the loss reduction in fine-tuning deep networks is stagnated, even while weights are largely updated. To escape from the stagnated state, we propose a new fine-tuning strategy to roll back some of the weights to the pretrained values. The rollback scheme is observed to drive the learning path to a gentle basin that provides more generalized features than a sharp basin. In addition, we propose a multihead ensemble structure to create synergy among multiple local minima obtained by our rollback scheme. Experimental results show that the proposed learning method significantly improves generalization performance, achieving state-of-the-art performance on the Inshop and SOP data sets.
Relationship between social loneliness and creative ability in art students in Covid-19 times

The purpose of this study was to establish the relationship between social loneliness and creative capacity in plastic arts students from three regions of southern Peru. This is due to the fact that there is a series of painting artists throughout world history, who have reached exceptional levels of creativity in contexts of social loneliness. The study aims to validate a relationship model between these two variables, taking into account the context, characterized by social isolation and mediation with technologies in e-learning educational practice, using the LMS - Moodle. The study comprises of two phases; first, an exploratory factor analysis using the IBM-SPSS for the validation and reliability of the instruments; second, for the confirmatory analysis, the PLS-SEM methodology was used, a multivariate method called Modeling of Structural Equations with Partial Least Squares. The model was validated with a sample of 216 students from the Arequipa, Tacna and Cusco regions. The results have revealed that there is an almost nonexistent relationship between the study variables, such as social loneliness and creative ability. With the results obtained, it would force us to rethink that the painting artist does not need social solitude to get involved in an artistic creative process.
Classification of Full Text Biomedical Documents: Sections Importance Assessment

The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.
IMPACT OF COVID-19 ON ACADEMIC COMMUNITY SUSTAINABILITY

1 Al Furat Al Awsat Technical University (IRAQ)2 King Saud University (SAUDI ARABIA)3 University of Jordan (JORDAN)
A novel multi-loss-based deep adversarial network for handling challenging cases in semi-supervised image semantic segmentation

Image semantic segmentation is popular in computer vision and pattern recognition, since the high-level semantic understanding of images can be effectively realized. Based on whether and to what extent the training data should be labeled, most image semantic segmentation methods can be categorized into fully-supervised learning-based methods, weakly-supervised learning-based methods, and semi-supervised learning-based methods. Among them, semi-supervised image semantic segmentation receives increasing popularity recently, because of its flexibility and convenience in requiring partial training data to be labeled. Although semi-supervised image semantic segmentation is promising, its state-of-the-arts haven’t obtained satisfactory performance when handling challenging cases, including poor illumination, small-sized targets, multi-targets with the same semantics, etc. To tackle the above dilemmas, a novel multi-loss-based deep adversarial network is proposed in this paper. Technically, the more robust WGAN-GP model is utilized as the backbone of the novel network, instead of the conventional GAN model. Moreover, multiple losses including the cross entropy loss, the edge detection loss, the adversarial loss, and the semi-supervised loss, are all incorporated during the novel network’s training. Experimental analyses based on challenging cases shortlisted from the Pascal VOC 2012 dataset and the Cityscapes dataset suggest that, the novel network is capable to outperform state-of-the-arts in semi-supervised image semantic segmentation.
Fuzzy target detection algorithm based on improved SSD and transfer learning

Fuzzy target detection as an important task to reflect the detection ability of underwater robot, the artificial target recognition based on the image taken by underwater robot has been widely concerned. However, there is no open standard fuzzy underwater image data set, and in the harsh deep-water fuzzy environment, it is difficult to collect large-scale marked underwater fuzzy optical images. At the same time, it is also hoped that the detection model has the ability to learn quickly from small samples in the case of as few samples as possible. Therefore, combining depth learning and transfer learning, a new method based on improved SSD and transfer learning is proposed. Firstly, we design a more accurate SSD network (underwater SSD) which is suitable for fuzzy underwater target detection. The features extracted from the detection network are highly representative. Secondly, we use the Transfer learning method to train the underwater SSD network, which can only use the tags in the air to identify fuzzy underwater objects, and have strong robustness in both the air and fuzzy underwater imaging modes. Finally, soft NMS is used to detect the target. The experimental results of the simulation data show that the algorithm not only overcomes the difficulties of the known data set of underwater target, but also effectively improves the accuracy of underwater target detection compared with the traditional deep learning method, reaching 82.31%, showing better detection performance.
Gender-Specific Multi-Task Micro-Expression Recognition Using Pyramid CGBP-TOP Feature

Micro-expression recognition has attracted growing research interests in the field of compute vision. However, micro-expression usually lasts a few seconds, thus it is difficult to detect. This paper presents a new framework ... | Find, read and cite all the research you need on Tech Science Press
Analysis of necessary and sufficient conditions for quantum teleportation with non-Gaussian resources

Recent theoretical and experimental advances have demonstrated advantages of using non-Gaussian optical resources compared to the Gaussian ones in the context of quantum teleportation (QT), an important quantum information processing task. From both theoretical and experimental points of view the question of which attributes of the resources, besides entanglement, render them useful for QT is an important one. In this paper, we examine the question of whether two well-studied attributes of optical resources, viz., squeezed vacuum affinity (SVA) and Einstein-Podolsky-Rosen (EPR) correlation are necessary and/or sufficient for QT. The specific class of non-Gaussian resources that we have considered for this purpose are the two-mode entangled states generated by mixing nonclassical inputs with vacuum at the beam splitter (BS). Our analytical results show that SVA is not always nonzero and hence it cannot be considered to be a genuine attribute. Our numerical results show that there exist some BS-generated entangled states that do not give QT in spite of being EPR correlated, implying that EPR correlation is not sufficient for QT. In conjunction with the earlier observation in the literature to the effect that EPR correlation is not necessary for QT, our results lead to the conclusion that in general, EPR correlation is neither necessary nor sufficient for QT. Our results leave the question open as to what attributes, in general, may be necessary and/or sufficient for QT.
Dynamic Analysis and FPGA Implementation of a Kolmogorov-Like Hyperchaotic System

Chaotic systems have high potential for engineering applications due to their extremely complex dynamics. In the paper, a five-dimensional (5D) Kolmogorov-like hyperchaotic system is proposed. First, the hyperchaotic property is uncovered, and numerical analysis shows that the system displays the coexistence of different kinds of attractors. This system presents a generalized form of fluid and forced-dissipative dynamic systems. The vector field of the hyperchaotic system is decomposed to inertial, internal, dissipative and external torques, respectively, and the energies are analyzed in detail. Then, the bound of the 5D dissipative hyperchaos is estimated with a constructed spherical function. Finally, the system passes the NIST tests and an FPGA platform is used to realize the hyperchaotic system.
What You Do Predicts How You Do

Students using online learning environments need to effectively self-regulate their learning. However, with an absence of teacher-provided structure, students often resort to less effective, passive learning strategies versus constructive ones. We consider the potential benefits of interventions that promote retrieval practice – retrieving learned content from memory – which is an effective strategy for learning and retention. The goal is to nudge students towards completing short, formative quizzes when they are likely to succeed on those assessments. Towards this goal, we developed a machine-learning model using data from 32,685 students who used an online mathematics platform over an entire school year to prospectively predict scores on three-item assessments (N = 210,020) from interaction patterns up to 9 minutes before the assessment as well as Item Response Theory (IRT) estimates of student ability and quiz difficulty. These models achieved a student-independent correlation of 0.55 between predicted and actual scores on the assessments and outperformed IRT-only predictions (r = 0.34). Model performance was largely independent of the length of the analyzed window preceding a quiz. We discuss potential for future applications of the models to trigger dynamic interventions that aim to encourage students to engage with formative assessments rather than more passive learning strategies.
Virtual Assistant for Prediction of Fake Job Profile Using Machine Learning

To avoid fraudulent post for job in the internet, an android application using machine learning based classification techniques is proposed in the paper. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best fake job scam detection model. It helps in detecting fake job posts from an enormous number of posts. It is an android application that is used to conduct an online conversation via text and speech by using natural language processing (NLP) and predicts the fake job offers using machine learning techniques. The data we used contains real and fake job post. We cleaned and pre-processed our data. After we implemented the classifiers, we trained and evaluated them for prediction of fake job offers.
The role of national music in education of youth

This article identifies effective ways to nurture young people through national music. A good song is a good song with its aesthetic impact, social function, educational function, and vitality. The art of singing is inherently wide-ranging and wide-ranging. Music education and moral education through music also begins at school. Moral values are stable only when they are tested many times, and they motivate a person to act in life as a real support. The invitations, exhortations, and instructions in the thoughts and melodies of folk songs are the product of vital conclusions that have been re-examined and refined as a result of his centuries of experience.
Authorship Attribution of Microtext Using Capsule Networks

Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing leads to an increase in malicious and suspicious activities, and anonymity makes it difficult to find the suspect. AA helps to find the writer of a suspect text from a set of suspects. Different social media platforms, such as Twitter, Facebook, and Instagram, are used regularly by the users for sharing their daily life activities. Finding the writer of microtexts is considered the toughest task due to the shorter length of the suspect piece of text. We present a Capsule-based convolutional neural network (CNN) model over character <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -grams for performing the AA task. Capsule with kervolutional neural networks (KNNs) has also been utilized for this task. We also present different analyses of our developed system, which improves the interpretability of our developed system. Heat-maps for different models illustrate the relevant text fragments for the prediction task. A standard Twitter data set is used for evaluating the performance of the developed systems. The experimental evaluation shows that capsule-based CNNs and capsule-based KNNs perform competitively and are able to outperform previous methods. The source codes and the supplementary file are available here <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chanchalIITP/AuthorIdentification</uri> .
Reduced-Complexity Affine Representation for Takagi-Sugeno Fuzzy Systems

This paper presents a systematic approach to reduce the complexity of sector nonlinearity TS fuzzy models using existing linear dependencies between local linear submodels. The proposed approach results in a decrease of the fuzzy model rules from 2P to p + 1 rules while maintaining equivalence to the TS fuzzy model. An LMI formulation is presented to obtain conditions for stability analysis and stabilizing controllers design with some examples to offer a comparison between the two models. The main purpose of reduced-complexity models is to keep the design and the structure of the nonlinear control and observer schemes as simple as possible for real-time implementation, especially when dealing with highly nonlinear systems with a very large number of premise variables. Two real-world robotics examples are provided to highlight the interests and the curent limitations of the proposed approach.
Three-dimensional reconstruction Based on Tri-frequency Heterodyne Principle

The traditional 3d reconstruction method of stereo vision is based on image feature point extraction, but it is very inconvenient to extract feature points in more complex cases, and the reconstructed 3D information is not comprehensive. In this paper, a 3d reconstruction method based on three-frequency heterodyne four-step phase shift method is proposed. System parameters is obtained by binocular calibration algorithm first, and then using digital grating projector to the object under test of three groups of different frequency phase shift fringe and collecting images, using the four step phase-shift and three phase frequency heterodyne method to get the main value and get the absolute phase of continuous distribution, combining with the polar correction and phase matching reconstruction of 3 d point cloud data of the object to be tested, finally complete 3 d measurement in 3 d software. The experimental results show that the system can realize 3d dimension measurement of heterogeneous castings with high detection efficiency.
MONAH: Multi-Modal Narratives for Humans to analyze conversations

In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming.We introduce a system that automatically expands the verbatim transcripts of videorecorded conversations using multimodal data streams.This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability.Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.