Reviewerly/exHarmony huggingface dataset:
Higher Education Teaching Practices Experience in Mexico, During the Emergency Remote Teaching Implementation due to COVID-19

Higher education teaching practices experience in Mexico changed from face-to-face teaching to the emergency remote teaching derived from the health contingency by COVID-19. The change from face-to-face to an online modality in the Mexican education system represented a great challenge for teachers of all educational levels. In Mexico, the federal government declared on Tuesday March 24, 2020, Phase 2 of the plan to address the country’s Covid 19 pandemic. Governments in at least ten states decided to suspend their activities and services as of March 17, including the education system. On April 13, the Mexican education system began activities in the online modality exclusively; however, derived from the country’s technical adequacy and digital connectivity conditions, a large proportion of the educational campuses, academic programs, and the teaching staff were not prepared for this situation. This cross-sectional study was carried out to analyze the faculty members experiences about the change from face-to-face to an online modality in the health emergency context due to the COVID-19 outbreak. We designed a study to gather information on practice in the new online learning scenario. For this purpose, we collected data on the previous experience in the conduct of online courses, the technological tools used, the barriers faced while driving online courses, the current conditions of use of educational and technological tools, the vision for the future, and some indicators of physical and mental health.In a sample of 341 faculty members, those working in public institutions were on less favorable terms than their peers attached to private universities. In contrast, lecturers recorded better conditions for dealing with modality change than full-time teachers. Likewise, lecturers more often mentioned having their infrastructure to teach courses from their homes than full-time teachers, which indeed responds to less access to the universities facilities in which they teach. Another important aspect to highlight is the increased proportion of teachers in public institutions and lecturers who have other jobs, so their workload can be more intense than those of their peers.
A New Hybrid Model for Hourly Solar Radiation Forecasting Using Daily Classification Technique and Machine Learning Algorithms

Photovoltaic power generation depends significantly on solar radiation, which is variable and unpredictable in nature. As a result, the production of electricity from photovoltaic power cannot be guaranteed permanently during the operational phase. Forecasting global solar radiation can play a key role in overcoming this drawback of intermittency. This paper proposes a new hybrid method based on machine learning (ML) algorithms and daily classification technique to forecast 1 h ahead of global solar radiation in the city of Évora. Firstly, several comparative studies have been done between random forest (RF), gradient boosting (GB), support vector machines (SVM), and artificial neural network (ANN). These comparisons were made using annual, seasonal, and daily testing sets in order to determine the best ML algorithm under different meteorological conditions. Subsequently, the daily classification technique has been applied to classify the original training set into sunny and cloudy training subsets in order to enhance the forecasting accuracy. The evaluation of the proposed ML algorithms was carried out using the normalized root mean square error (nRMSE) and the normalized absolute mean error (nMAE). The results of the seasonal comparison show that the RF model performs well for spring and autumn seasons with nRMSE equaling 22.53% and 23.42%, respectively. While the SVR model gives good results for winter and summer seasons with nRMSE equaling 24.31% and 8.41%, respectively. In addition, the daily comparison demonstrates that the RF model performs well for cloudy days with nRMSE = 41.40%, while the SVR model yields good results for sunny days with nRMSE = 8.88%. The results show that the daily classification technique enhances the forecasting accuracy of ML models. Furthermore, this study demonstrates that the forecasting accuracy of ML algorithms depends significantly on sky conditions.
CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication

In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that the master node needs to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering the structure of the matrices is crucial to reduce the recovery threshold. In CodedSketch, we use count-sketch, as a hash-based compression scheme, on the rows of the first and columns of the second matrix, and a structured polynomial code on the columns of the first and rows of the second matrix. This arrangement allows us to exploit the gain of both in reducing the recovery threshold. To increase the accuracy of computation, multiple independent count-sketches are needed. This independency allows us to theoretically characterize the accuracy of the result and establish the recovery threshold achieved by the proposed scheme. To guarantee the independency of resulting count-sketches in the output, while keeping its cost on the recovery threshold minimum, we use another layer of structured codes. The proposed scheme provides an upper-bound on the recovery threshold as a function of the required accuracy of computation and the probability that the required accuracy can be violated. In addition, it provides an upper-bound on the recovery threshold for the case that the result of the multiplication is sparse, and the exact result is required.
Exploring the Readiness for Digital Transformation in a Higher Education Institution towards Industrial Revolution 4.0

Assessing the impact of digital transformation in the fundamental areas of higher education at a specific university, i.e., Hanoi University of Science and Technology (HUST) will show us the readiness for the digital university. Therefore, in this paper, we deeply analyze the characteristics of the typical model of the digital university (University 4.0) to identify the criteria to be surveyed for the assessment of the availability of HUST in digital transformation process. The analysis of survey results, thereby, discloses the effects achieved by practical implementation of digital transformation solutions at HUST. The results of this study allow us to introduce a readiness framework for digital transformation as a reference for the other higher education institutions. In addition, we can partly confirm that the trend of digital transformation is indispensable for universities in Vietnam, contributing to the improvement of higher education quality and international integration in the context of industrial revolution 4.0.
Bayesian neural architecture search using a training-free performance metric

Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settings. The architecture optimization of RNNs is a time-consuming task, where the search space is typically a mixture of real, integer and categorical values. To allow for shrinking and expanding the size of the network, the representation of architectures often has a variable length. In this paper, we propose to tackle the architecture optimization problem with a variant of the Bayesian Optimization (BO) algorithm. To reduce the evaluation time of candidate architectures the Mean Absolute Error Random Sampling (MRS), a training-free method to estimate the network performance, is adopted as the objective function for BO. Also, we propose three fixed-length encoding schemes to cope with the variable-length architecture representation. The result is a new perspective on accurate and efficient design of RNNs, that we validate on three problems. Our findings show that (1) the BO algorithm can explore different network architectures using the proposed encoding schemes and successfully designs well-performing architectures, and (2) the optimization time is significantly reduced by using MRS, without compromising the performance as compared to the architectures obtained from the actual training procedure.
Kannada to English Machine Translation Using Deep Neural Network

In this paper, we focus on the unidirectional translation of Kannada text to English text using Neural Machine Translation (NMT).From studies, we found that using Recurrent Neural Network (RNN) has been the most efficient way to perform machine translation.In this process we have used Sequence to Sequence (Seq2Seq) modelled dataset with the help of Encoder-Decoder Mechanism considering Long Short Term Memory (LSTM) as RNN unit.We have compared our result concerning to Statistical Machine Translation (SMT) and obtained a better Bi-Lingual Evaluation Study (BLEU) value, with an accuracy of 86.32%.
Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Artificial intelligence and fifth generation (5G) technology are widely adopted to evaluate the classroom poses of college students, with the help of campus video surveillance equipment. To ensure the effective learning in class, it is important to detect and intervene in abnormal behaviors like sleeping and using cellphones in time. Based on spatiotemporal representation learning, this paper presents a deep learning algorithm to evaluate classroom poses of college students. Firstly, feature engineering was adopted to mine the moving trajectories of college students, which were used to determine student distribution and establish a classroom prewarning system. Then, k-means clustering (KMC) was employed for cluster analysis on different student groups, and identify the features of each group. For a specific student group, the classroom surveillance video was decomposed into several frames; the edge of each frame was extracted by edge detection algorithm, and imported to the proposed convolutional neural network (CNN). Experimental results show that our algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.
DESIGNING VIRTUAL WORKPLACE USING UNITY 3D GAME ENGINE

Designing a workplace may be a challenging task. It is important to make sure that the new workplace will prevent unnecessary resource waste, but also create a safe working environment for employees. Therefore, creating a virtual copy of the workplace before its real-life implementation may help to eliminate design shortcomings. This article presents a methodology of creating a virtual workplace using a game engine – Unity 3D. The methodology describes basic principles and methods used for the creation of virtual workplace, from initial analysis to utilization. The user then can use a VR head-mounted device to see small details and possible shortcomings. Effectiveness of methodology was then evaluated by using it to visualize a bar-processing workplace.
The Image Classification Method with CNN-XGBoost Model Based on Adaptive Particle Swarm Optimization

CNN is particularly effective in extracting spatial features. However, the single-layer classifier constructed by activation function in CNN is easily interfered by image noise, resulting in reduced classification accuracy. To solve the problem, the advanced ensemble model XGBoost is used to overcome the deficiency of a single classifier to classify image features. To further distinguish the extracted image features, a CNN-XGBoost image classification model optimized by APSO is proposed, where APSO optimizes the hyper-parameters on the overall architecture to promote the fusion of the two-stage model. The model is mainly composed of two parts: feature extractor CNN, which is used to automatically extract spatial features from images; feature classifier XGBoost is applied to classify features extracted after convolution. In the process of parameter optimization, to overcome the shortcoming that traditional PSO algorithm easily falls into a local optimal, the improved APSO guide the particles to search for optimization in space by two different strategies, which improves the diversity of particle population and prevents the algorithm from becoming trapped in local optima. The results on the image set show that the proposed model gets better results in image classification. Moreover, the APSO-XGBoost model performs well on the credit data, which indicates that the model has a good ability of credit scoring.
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation

Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).However, the presence of overlap between the train and test datasets can lead to inflated results, inadvertently evaluating the model's ability to memorize and interpreting it as the ability to generalize.In addition, such data sets may not provide an effective indicator of the performance of these methods in real world scenarios.We identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction, and study them to assess the impact of that leakage on the model's ability to memorize versus generalize.Urease activity, judged as the amount of ammonia production from urea, could be measured at 25 ng per tube (S/N = 1.5) with Jack bean meal urease.100.00
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition

In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR). Our method automatically extracts and transcribes target speaker's utterances from a monaural mixture of multiple speakers speech given a short sample of the target speaker. The proposed auxiliary loss function attempts to additionally maximize interference speaker ASR accuracy during training. This will regularize the network to achieve a better representation for speaker separation, thus achieving better accuracy on the target-speaker ASR. We evaluated our proposed method using two-speaker-mixed speech in various signal-to-interference-ratio conditions. We first built a strong target-speaker ASR baseline based on the state-of-the-art lattice-free maximum mutual information. This baseline achieved a word error rate (WER) of 18.06% on the test set while a normal ASR trained with clean data produced a completely corrupted result (WER of 84.71%). Then, our proposed loss further reduced the WER by 6.6% relative to this strong baseline, achieving a WER of 16.87%. In addition to the accuracy improvement, we also showed that the auxiliary output branch for the proposed loss can even be used for a secondary ASR for interference speakers' speech.
Demonstrating Shareability of Multipartite Einstein-Podolsky-Rosen Steering

Einstein-Podolsky-Rosen (EPR) steering, a category of quantum nonlocal correlations describing the ability of one observer to influence another party's state via local measurements, is different from both entanglement and Bell nonlocality by possessing an asymmetric property. For multipartite EPR steering, the monogamous situation, where two observers cannot simultaneously steer the state of the third party, has been investigated rigorously both in theory and experiment. In contrast to the monogamous situation, the shareability of EPR steering in reduced subsystems allows the state of one party to be steered by two or more observers and thus reveals more configurations of multipartite EPR steering. However, the experimental implementation of such a kind of shareability has still been absent until now. Here, in an optical experiment, we provide a proof-of-principle demonstration of the shareability of EPR steering without the constraint of monogamy in a three-qubit system. Moreover, based on the reduced bipartite EPR steering detection results, we verify the genuine three-qubit entanglement results. This work provides a complementary viewpoint for understanding multipartite EPR steering and has potential applications in many quantum information protocols, such as multipartite entanglement detection, quantum cryptography, and the construction of quantum networks.
Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons

Sharing genome data in a privacy-preserving way stands as a major bottleneck in front of the scientific progress promised by the big data era in genomics. A community-driven protocol named genomic data-sharing beacon protocol has been widely adopted for sharing genomic data. The system aims to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. However, beacon protocol was recently shown to be vulnerable against membership inference attacks. In this paper, we show that privacy threats against genomic data sharing beacons are not limited to membership inference. We identify and analyze a novel vulnerability of genomic data-sharing beacons: genome reconstruction. We show that it is possible to successfully reconstruct a substantial part of the genome of a victim when the attacker knows the victim has been added to the beacon in a recent update. In particular, we show how an attacker can use the inherent correlations in the genome and clustering techniques to run such an attack in an efficient and accurate way. We also show that even if multiple individuals are added to the beacon during the same update, it is possible to identify the victim's genome with high confidence using traits that are easily accessible by the attacker (e.g., eye color or hair type). Moreover, we show how a reconstructed genome using a beacon that is not associated with a sensitive phenotype can be used for membership inference attacks to beacons with sensitive phenotypes (e.g., HIV+). The outcome of this work will guide beacon operators on when and how to update the content of the beacon and help them (along with the beacon participants) make informed decisions.
Continuance Intention of Augmented Reality Textbooks in Basic Design Course

The Basic Design course can help students understand design principles and visual art elements It is a compulsory basic course for the department of art and design in many universities. In recent years, Augmented Reality (AR) technology has found its way into the field of design education and has become a popular textbook tool in teaching courses. There are not many pieces of research on the application of AR in design courses. Therefore, this study attempts to explore the factors that affect students’ acceptance of AR textbooks in Basic Design course and their continuance intention for AR textbooks. This study first focused on students with experience in using AR textbooks. Open-ended questions were used to collect factors that allow AR textbooks to be used in the design curriculum; then questionnaire surveys and factor analysis were conducted and the research hypotheses are presented. Then, the research hypotheses were verified through reliability and validity as well as structural equation modeling. Three factors and 15 items for students to accept AR textbooks in the Basic Design course were summarized in the research results, including the three factors named “Visual Attraction”, “Knowledge-ability”, and “Situational Experience”. The analysis of differences showed significant differences in gender among these three factors and no significant differences between grades and departments. Also, it was found from the analysis results of the structural equation model that the factors of “Visual Attraction” and “Knowledge-ability” both had a positive effect on the continuance intention, of which “Knowledge-ability” was the most important influencing factor. The results of this study can help the application and development of AR textbooks in the Basic Design course.
Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem (FJSP), which is NP-hard, widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm (MSCGA) based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
Combining Task and Motion Planning: Challenges and Guidelines

Combined Task and Motion Planning (TAMP) is an area where no one-fits-all solution can exist. Many aspects of the domain, as well as operational requirements, have an effect on how algorithms and representations are designed. Frequently, trade-offs have to be made to build a system that is effective. We propose five research questions that we believe need to be answered to solve real-world problems that involve combined TAMP. We show which decisions and trade-offs should be made with respect to these research questions, and illustrate these on examples of existing application domains. By doing so, this article aims to provide a guideline for designing combined TAMP solutions that are adequate and effective in the target scenario.
Efficient Byzantine Consensus Mechanism Based on Reputation in IoT Blockchain

Blockchain technology has advanced rapidly in recent years and is now widely used in a variety of fields. Blockchain appears to be one of the best solutions for managing massive heterogeneous devices while achieving advanced data security and data reputation, particularly in the field of large-scale IoT (Internet of Things) networks. Despite the numerous advantages, there are still challenges while deploying IoT applications on blockchain systems due to the limited storage, power, and computing capability of IoT devices, and some of these problems are caused by the consensus algorithm, which plays a significant role in blockchain systems by ensuring overall system reliability and robustness. Nonetheless, most existing consensus algorithms are prone to poor node reliability, low transaction per second (TPS) rates, and scalability issues. Aiming at some critical problems in the existing consensus algorithms, this paper proposes the Efficient Byzantine Reputation-based Consensus (EBRC) mechanism to resolve the issues raised above. In comparison to traditional algorithms, we reinvented ways to evaluate node reliability and robustness and manage active nodes. Our experiments show that the EBRC algorithm has lower consensus delay, higher throughput, improved security, and lower verification costs. It offers new reference ideas for solving the Internet of Things+blockchain+Internet court construction problem.