Reviewerly/exHarmony huggingface dataset:
The Impacts of E-Learning Readiness in Higher Education during COVID 19 Pandemic

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 in early 2020 had global impacts, including on universities. As a preventive measure to halt the spread of the virus, the Kingdom of Saudi Arabia (KSA) closed all its higher education institutions in March 2020 and imposed a nationwide curfew. This substantially affected teaching activities, which were primarily still being conducted face-to-face. Against this backdrop, this paper explores the readiness of Saudi higher education, as exemplified by King Abdulaziz University, to shift to online learning and explores the challenges experienced by students and teachers during the process. Specifically, it examines the main factors influencing the usage of the e-learning system and the challenges facing its implementation during the COVID-19 pandemic, drawing on the e-learning readiness assessment framework to assess the readiness of the university for e-learning in the crisis. To this end, the study conducted a survey of 547 students and 213 teachers at King Abdulaziz University to examine their perspectives on various aspects of e-learning. The findings highlight the importance of certain dimensions of e-learning readiness in relation to students, staff, and infrastructure, including acceptance of e-learning, technical skills, and the right cultural mindset, in the event of a crisis like the COVID-19 pandemic. Disciplinary: Higher Education (Education Technology, Elearning). (C) 2021 INT TRANS J ENG MANAG SCI TECH.
Machine Learning in Databricks

Organizations and developers that are seeking to leverage the power of machine learning (ML) and AI spend a significant amount of time building ML models and are seeking a method for streamlining their machine learning development lifecycle to track experiments, package code into reproducible runs, as well as build, share, and deploy ML models.
SSGD: A Safe and Efficient Method of Gradient Descent

With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent method plays an important role in solving various optimization problems, due to its simple structure, good stability, and easy implementation. However, in multinode machine learning system, the gradients usually need to be shared, which will cause privacy leakage, because attackers can infer training data with the gradient information. In this paper, to prevent gradient leakage while keeping the accuracy of the model, we propose the super stochastic gradient descent approach to update parameters by concealing the modulus length of gradient vectors and converting it or them into a unit vector. Furthermore, we analyze the security of super stochastic gradient descent approach and demonstrate that our algorithm can defend against the attacks on the gradient. Experiment results show that our approach is obviously superior to prevalent gradient descent approaches in terms of accuracy, robustness, and adaptability to large-scale batches. Interestingly, our algorithm can also resist model poisoning attacks to a certain extent.
Augmented Reality and Its Influence on Cognitive Thinking in Learning

Augmented reality (AR) is a medium of transforming K-12 and higher education. The ability to overlay multimedia onto the real world for viewing using web-based environments and technology-enabled devices such as tablets, desktop computers, mackintosh, and phones affords students the ability to access information and resources any place and time they need it. Effective use of technology-based environments and augmented reality reduces cognitive load by scaffolding information and lessons' contents. Augmented reality can readily help in cognitive thinking and enhancing cognitive thinking processes. An increase in patients' cognitive processes using augmented reality in the medical industry where patients use augmented reality tasks to regain cognition, mobility, and ability to perform basic human tasks. The critical literature review will review augmented reality in higher education and its impact on increasing cognitive thinking. Challenges of using augmented reality will be reviewed, including its benefits. There will be a discussion on the gaps in research on augmented reality in higher education and best practices on the use of augmented reality.
Towards Making Virtual Human-Robot Interaction a Reality

For robots deployed in human-centric spaces, natural language promises an intuitive, natural interface. However, obtaining appropriate training data for grounded language in a variety of settings is a significant barrier. In this work, we describe using human-robot interactions in virtual reality to train a robot, combining fully simulated sensing and actuation with human interaction. We present the architecture of our simulator and our grounded language learning approach, then describe our intended initial experiments.
Emotion Recognition from Facial Expressions Using a Genetic Algorithm to Feature Extraction

In this paper, we present a new method for emotion recognition from facial expressions. The proposed algorithm concentrates on only two specific areas (eyes and mouth), reducing features and descriptors and focusing only on these areas. The algorithm extracts characteristics from these two regions of the face and, in a subsequent process, eliminates the less significant characteristics or those that introduce noise into the classifier. The system allows obtaining a reduced set of features to improve the performance of the classification. In the experiments carried out, we obtain precisions of 99.56%. We evaluated the proposed algorithm on two benchmark datasets; we find that SVM consistently outperforms traditional machine learning techniques.
A linear programming approach to multiple instance learning

Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of a bag is mapped to a pseudo-class membership estimate and these estimates are aggregated to obtain the bag-level class membership in an optimization framework. A simple linear mapping enables handling various MIL assumptions with adjusting instance contributions. Our experiments with instance-dissimilarity based data representations verify the effectiveness of the proposed MIL framework. Proposed mathematical models can be solved efficiently in polynomial time.
Anti-Hong-Ou-Mandel effect with entangled photons and lossy beamsplitter

Classical Hong-Ou-Mandel (HOM) effect – two-particle interference on a lossless beamsplitter, reveals the fundamental difference between bosonic and fermionic particles. As a result of such interference, pairs of bosons coalesce while pairs of fermions anti-coalesce. Here we report an observation of the anti-HOM effect where bosons anti-coalesce and fermions show coalescent-like behavior when interfere on a lossy beamsplitter. By exploiting two-photon entangled states, we provide an experimental demonstration of the anti-HOM effect for both bosonic and fermionic spatial wavefunctions of the photons. This fundamental phenomenon may enrich quantum information and metrology protocols where states of entangled photons are dynamically converted.
Traffic Lights and Traffic Signs Detection System Using Modified You Only Look Once

Traditional image processing methods used for detecting traffic lights and traffic signs are replaced by the recent enhancements of the deep learning method by the success of building a Convolutional Neural Network (CNN). In this research, a traffic lights and traffic signs detection system using a modified You Only Look Once (YOLO) has been proposed. The system processes an image captured by a camera sensor and provides the results in the form of detecting traffic lights and traffic signs contained in the image. The CNN architecture used is a modified Cross Stage Partial YOLOv4 (YOLOv4-CSP). The experiments were carried out using a self-constructed dataset consisting of1360 training data and 340 testing data with 6 types of traffic lights and 39 types of traffic signs. The network is built using the Darknet framework and the result shows 79,77% of the mean Average Precision at the 0,5 Intersection over Union threshold (mAP at 0,5 IoU threshold) and 29 frames per second (FPS) of inference speed tested on a single NVIDIA Tesla T4 Graphics Processing Unit (GPU).
Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity

To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.
Dual-frequency fringe for improving measurement accuracy of three-dimensional shape measurement

A new phase unwrapping method based on dual-frequency fringe is proposed to improve both high accuracy and large measurement range of three-dimensional shape measurement by synthesizing the projected dual-frequency fringes obtaining higher and lower frequencies. The lower-frequency one is their phase difference, which can help unwrap the wrapped phase of the higher-frequency one from their phase sum. In addition, the relationship between the measuring accuracy and the frequencies of the projected fringes is studied to guide the frequency selection in actual measurement. It is found that the closer the two frequencies are, the higher the measurement accuracy will be. The computer simulation and experiment results show the viability of this method.
Design of Efficient Human Head Statistics System in the Large-Angle Overlooking Scene

Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose a human head statistics system that consists of head detection, head tracking and head counting, where the proposed You-Only-Look-Once-Head (YOLOv5-H) network, improved from YOLOv5, is taken as the head detection benchmark, the DeepSORT algorithm with the Fusion-Hash algorithm for feature extraction (DeepSORT-FH) is proposed to track heads, and heads are counted by the proposed cross-boundary counting algorithm based on scene segmentation. Specifically, Complete-Intersection-over-Union (CIoU) is taken as the loss function of YOLOv5-H to make the predicted boxes more in line with the real boxes. The results demonstrate that the recall rate and mAP@.5 of the proposed YOLOv5-H can reach up to 94.3% and 93.1%, respectively, on the SCUT_HEAD dataset. The statistics system has an extremely low error rate of 3.5% on the TownCentreXVID dataset while maintaining a frame rate of 18FPS, which can meet the needs of human head statistics in monitoring scenarios and has a good application prospect.
Prospecting the Effect of Topic Modeling in Information Retrieval

Enormous records and data are gathered every day. Organization of this data is a challenging task. Topic modeling provides a way to categorize these documents, where high dimensionality of the corpus affects the result of topic model, making it important to apply feature selection or information retrieval process for dimensionality reduction. The requirement for efficient topic modeling includes the removal of unrelated words that might lead to specious coexistence of the unrelated words. This paper proposes an efficient framework for the generation of better topic coherence, where term frequency-inverse document frequency (TF-IDF) and parsimonious language model (PLM) are used for the information retrieval task. PLM extracts the important information and expels the general words from the corpus, whereas TF-IDF re-estimates the weightage of each word in the corpus. The work carried out in this paper improved the topic coherence measure to provide a better correlation among the actual topic and the topics generated from PLM.
Towards Quality in Distance Education: The First Lessons Learned by Schools During COVID-19 Pandemics

During the initial COVID-19 breakout and related lockdown Lithuanian schools created new pathways of organizing distance education under unprecedented circumstances. The qualitative research focuses on the experiences of Lithuanian schools in organizing distance education during COVID-19 pandemic in March 2020 – October 2020, searching for ways to ensure the quality of teaching and learning. The 8 school activity areas need changes during transition to distance teaching and learning: strategy, management and administration; infrastructure of information technologies; digital learning content; digital competencies and continuous professional development; teaching, learning and assessment in the digital environment; a support system for pupils and teachers; partnership, cooperation and networking; and quality assurance. Changes in all the indicated areas lead to the quality of distance teaching and learning. The qualitative research data (expert interviews and focus groups) revealed the importance of school community agreements regarding the choice of technologies and environments, and the need for a national digital curriculum to be created. Quality assurance of the learning process, improvement of digital competences and school community collaboration were among the most successfully changing areas. IT infrastructure, support system, and teaching and learning process were based on very different school or teacher decisions and led to different experiences.
Prediction of Software Effort in the Early Stage of Software Development: A Hybrid Model

The key challenge that project managers face during software development is the accurate prediction of the software effort. Improper prediction leads either to overestimation or underestimation of the software effort, which can have disastrous consequences for the stakeholders. This article attempts to design a model that gives an accurate prediction of effort in the initial phase of the software development lifecycle. The proposed model uses multilayer perceptron (MLP) and the generalized linear model (GLM) with the ensemble technique for the learning purpose. The model is trained and validated using the ISBSG dataset. The proposed model is compared for performance with two baseline models: MLP and GLM. The results show that the proposed model outperforms most of the baseline models against different performance metrics.
Improving domain adaptation in de-identification of electronic health records through self-training

De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain.We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the target domain. We validate the effectiveness on 4 standard de-identification datasets. In each experiment, we use a pair of datasets: labeled data from the source domain and unlabeled data from the target domain. We compare the proposed self-training framework with supervised learning that directly deploys the model trained on the source domain.In summary, our proposed framework improves the F1-score by 5.38 (on average) when compared with direct deployment. For example, using i2b2-2014 as the training dataset and i2b2-2006 as the test, the proposed framework increases the F1-score from 76.61 to 85.41 (+8.8). The method also increases the F1-score by 10.86 for mimic-radiology and mimic-discharge.Our work demonstrates an effective self-training framework to boost the domain adaptation performance for the de-identification task for electronic health records.
Quantum neuron with real weights

This paper proposes a new model of a real weights quantum neuron exploiting the so-called quantum parallelism which allows for an exponential speedup of computations. The quantum neurons were trained in a classical–quantum approach, considering the delta rule to update the values of the weights in an image database of three distinct patterns. We performed classical simulations and also executed experiments in an actual small-scale quantum processor. The results of the experiments show that the proposed quantum real neuron model has a good generalisation capacity, demonstrating better accuracy than the traditional binary quantum perceptron model.
Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation.
The Internet of Things: Description, Applications, Development, Challenges

This overview describes not only the nature and applications of the Internet of things (IoT) but also some challenges and open research issues.Some of the established working definitions are presented, and the state of the IoT system is shown, incl.its reference model is based on a multi-layered architecture.Different characteristics of IoT are described and the communication technologies that are most often used are compared.Various applications of IoT and some challenges to the development of IoT innovations are shown.Based on the analysis of IoT, guidelines for future development of research in the field, which form the development of technology, are summarized.
Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification

Automatic computer security inspection of X-ray scanned images has an irresistible trend in modern life. Aiming to address the inconvenience of recognizing small-sized prohibited item objects, and the potential class imbalance within multi-label object classification of X-ray scanned images, this paper proposes a deep feature fusion model-based dual branch network architecture. Firstly, deep feature fusion is a method to fuse features extracted from several model layers. Specifically, it operates these features by upsampling and dimension reduction to match identical sizes, then fuses them by element-wise sum. In addition, this paper introduces focal loss to handle class imbalance. For balancing importance on samples of minority and majority class, it assigns weights to class predictions. Additionally, for distinguishing difficult samples from easy samples, it introduces modulating factor. Dual branch network adopts the two components above and integrates them in final loss calculation through the weighted sum. Experimental results illustrate that the proposed method outperforms baseline and state-of-art by a large margin on various positive/negative ratios of datasets. These demonstrate the competitivity of the proposed method in classification performance and its potential application under actual circumstances.
Simulation Design and Implementation of Voice Landscape Quantification in Virtual Reality Based on Cloud Computing

Traditionally, the recognition of sound mainly focuses on the source of sound, such as level and quality. Now, the sound, the environment, and the listeners have begun to study the landscape structure, composition, and characteristics of the acoustic environment. The purpose of this paper is to study the simulation design of virtual reality voice landscape quantification based on cloud computing. Firstly, the definition and characteristics of cloud computing are described, and the key technologies of cloud computing are analyzed. Combined with the basic principles of technology selection, the virtualization technology is emphatically analyzed. By selecting 7 acoustic elements, such as traffic sound, water flow sound, fountain sound, birdsong, wind sound, rippling sound, beach sound, and seabird sound, the possible acoustic elements in a given park environment are simulated for subjective evaluation. The experimental results show that when the traffic sound is 60 dB, the evaluation result of the superimposed sound type is the same as that when the traffic sound is 50 dB. For the superimposed sound level, 30 dB and 40 dB are significantly different from 60 dB and 70 dB, respectively, 50 dB is only significantly different from 70 dB, while 60 dB is only not significantly different from 50 dB, and 70 dB evaluation is significantly different from each sound level. However, 60 dB can be regarded as the turning point of the evaluation result. When the sound level of the added sound is greater than 60 dB, the evaluation result is obviously worse.