Reviewerly/exHarmony huggingface dataset:
Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people's thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people's sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people's sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.
Transfer learning of fuzzy classifiers for optimized joint representation of simulated and measured data in anomaly detection of motor phase currents

The generation of simulation data from physical models trying to mimic parts of the real-world process as accurately as possible has received much attention in industry during the last years. A proper augmentation of simulation data with (available or recordable) real-measured data is an intrinsic challenge to increase the performance of machine learning models. In this paper, we propose a joint representation learning approach based on an optimized transfer of fuzzy classifiers from original simulated data (as source task) to real-measured data (as target task). This is particularly done for binary classification tasks under the scope of anomaly detection of phase currents in electrical machines, where only anomaly-free real-measured data can be collected in advance (so, only in the simulated data anomaly samples are present). Our approach combines distribution matching between simulated and real-measured data on the basis of fuzzy rule activation levels with a weighted error term representing the classification loss between anomaly-free and anomaly data. Both are realized on a local basis per rule, which yields good flexibility during optimization with respect to the actual local distributions and their positions in the target task and is particularly possible for fuzzy classifiers due to the local geometric interpretation of the rules. We demonstrate how to find a feasible initial solution for the numerical optimization process. Results on data sets from the application showed significantly improved classification accuracies of up to 25% compared to several conventional fuzzy classifiers training variants and also compared to various renowned machine learning classifiers, while a respectful (slightly worse) performance compared to the two top performers (SVMs and deep MLPs) were achieved. However, a large performance gap between conventional fuzzy classifiers and these ML-based top performers could nearly be closed and due to the local geometric interpretation of the internal fuzzy partitions, explainable insights into the model transfer could be yielded to experts.
Computing Competencies: Mapping CC2020 Dispositions to SFIA Responsibility Characteristics

In the past decade, academic computing curricular guidelines have shifted from specifying knowledge and occasionally technical skills to establishing the overall competence expected of graduates. For instance, Computing Curricula 2020 (CC2020) guidelines identify competency as knowledge, skills, and dispositions where &#x201C;dispositions&#x201D; correspond to the behavioral and professional characteristics driven by employer needs and captured by industry-driven frameworks, such as the Skills Framework for the Information Age (SFIA). Computing programs thus must also ensure that graduates have these characteristics to improve initial employment and long-term career prospects. This paper aims to understand and achieve consistency between academia and industry curricular frameworks. The CC2020 dispositions map to the responsibility characteristics for SFIA Level 3, the level appropriate for a new graduate. As the mapping is not one-to-one, the paper reviews the extent to which each SFIA responsibility characteristic requires and enables the CC22020 dispositions, identifying potential shortcomings and, conversely, the importance of each disposition as it supports the responsibility characteristics. The developed mapping is validated by relating the CC2020 dispositions to the SFIA behavioral factors, the principal 21st Century Skills and relevant competency-based educational frameworks. Thus, dispositions in competency-focused curricula map to the actual competencies sought by employers. Finally, the paper postulates that future computing curricula must further develop the CC2020 dispositions and relate them to SFIA to guide academic programs in their preparation of career-ready graduates to reduce the current &#x201C;skills gap&#x201D;.
An efficient feature selection method based on improved elephant herding optimization to classify high‐dimensional biomedical data

Abstract Machine learning algorithms are widely applied to biomedical data to classify the samples of patients and healthy persons. The high‐dimensional biomedical datasets contain a large number of features to represent a sample. However, such datasets may have redundant, noisy and irrelevant features, influencing machine learning algorithms' classification performance and increasing computation overhead. Therefore, data normalization and feature selection techniques are introduced to reduce the impact of noisy features and accurately identify the patterns in features to improve the predictive accuracy of diagnosis. This work proposes an efficient feature selection and parameter optimization method to classify high‐dimensional biomedical datasets. In the proposed method, a binary version of the improved elephant herding optimization (IEHO) algorithm is introduced to select features and optimize the C and γ parameters of the support vector machine classifier. Further, four variants of the proposed method are presented based on data normalization techniques: Z ‐score normalization (ZN), Pareto‐scaling (PS), tan h ‐based normalization (TN), and variant of tan h ‐based normalization (VTN). The proposed variants reduce the dominance of noisy features and explore the feature space to obtain the optimal feature set that maximizes the classification accuracy and minimizes the time complexity. The performance of the proposed variants is evaluated on 15 high‐dimensional biomedical datasets. Friedman's mean rank test is applied to check the statistical difference between proposed variants. Results show that the proposed Z ‐score normalization‐IEHO (ZN‐IEHO) variant performed significantly better than the other proposed variants for classification accuracy, false‐positive rate and f ‐score metrics. Moreover, the performance of the proposed ZN‐IEHO variant is compared with 18 state‐of‐the‐art feature selection methods. The experimental results expressed the effectiveness of the proposed ZN‐IEHO variant in finding the best combination of features and parameters to classify the biomedical datasets accurately.
Size matters: contextual factors in local policy translations of National School Digitalisation Policy

Abstract National policies on school digitalisation take shape in their local contexts. Consequently, to understand the outcome of national policy, the local translations must be set within a contextual perspective. This article explores how four contextually different municipalities in Sweden translate national school digitalisation policy. It draws on a comparative cross-case study with data gathered from interviews, and over 150 local documents dating from 2018 to 2020. The results show how contextual aspects affects responses to national policy, and that municipalities approach school digitalisation in two distinct ways. The first, general approach, emphasises competitiveness and the creation of an enabling environment for the teachers. This is manifested in the development of special support organisations, and generous access to digital technology. The second, specific approach, emphasises local consensus in policy translations along with unity in policy adherence. Here, critique of national policy is explicit. The two approaches exemplify how translational power may be distributed differently, the former prioritising individual translational precedency for teachers over a unifying policy translation controlled through local governance. The paper suggests that contextual factors matters in the choice of approaches, one in which size matters. The paper concludes that policy makers need to acknowledge contextual dimensions within governance by weighing translational power in relation to translational coherence.
A new approach for reduction of the noise from microscopy images using Fourier decomposition

In this paper, an efficient method based on the Fourier decomposition method (FDM) is presented for noise removal of medical microscopic images. We propose an adaptive thresholding technique based FDM for denoising of heavily degraded images. An accurate image deconvolution is done with variance stabilization transformation and multi-scale Wiener filtering as a pre-processing step. The different series of frequency intrinsic band functions (FIBF’s) obtained with FDM which are further separated into noise and signal-significant FIBF’s based on cosine similarity index. The FDM adaptive thresholding technique is used to filter-out the unwanted frequency coefficients related to mixed Poisson-Gaussian noise (MPG). The thresholded FIBF’s and signal significant FIBF’s are combined to obtained reconstructed output. Finally, the optimization is done using mixed noise unbiased risk estimate (MNURE). To evaluate the effectiveness of proposed scheme, we have compared the results of the proposed scheme with six different state-of-the-art techniques. The simulation results verify, the effectiveness of proposed method. The proposed algorithm achieves better performance in terms of four quantitative evaluation measures by reducing the effect of noise.
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation

Personalized recommendation is an important class of deep-learning applications that powers a large collection of internet services and consumes a considerable amount of datacenter resources. As the scale of production-grade recommendation systems continues to grow, optimizing their serving performance and efficiency in a heterogeneous datacenter is important and can translate into infrastructure capacity saving. In this paper, we propose Hercules, an optimized framework for personalized recommendation inference serving that targets diverse industry-representative models and cloud-scale heterogeneous systems. Hercules performs a two-stage optimization procedure &#x2014; offline profiling and online serving. The first stage searches the large under-explored task scheduling space with a gradient-based search algorithm achieving up to 9.0&#x00D7; latency-bounded throughput improvement on individual servers; it also identifies the optimal heterogeneous server architecture for each recommendation workload. The second stage performs heterogeneity-aware cluster provisioning to optimize resource mapping and allocation in response to fluctuating diurnal loads. The proposed cluster scheduler in Hercules achieves 47.7% cluster capacity saving and reduces the provisioned power by 23.7% over a state-of-the-art greedy scheduler.
Drivers and barriers in the transition to open science: the perspective of stakeholders in the Spanish scientific community

This paper presents the results of a research study whose objective was to identify the facilitating factors and barriers that, in the opinion of representatives of the Spanish scientific community, impact the implementation of the new open science model in four areas: open access, open research data, research assessment and open peer review. A qualitative study was designed in which information was obtained through interviews with researchers, editors of scientific journals, representatives of assessment agencies and vice-chancellors, and through a focus group of librarians with expertise in aspects of open science. The enabling factors and barriers identified were related to the researcher and the fruit of their research, as well as to the scientific ecosystem, which provides direct institutional support and backing (universities/research centres), the regulatory framework (management of the science system) and the science communication system (media). The results indicate that a shift in scientific practices toward the open science model can only be achieved if there is a policy framework that integrates all initiatives and links into the scientific assessment and reward system, and if the necessary funding is in place to support this transition.
Deep Neural Networks Based on Span Association Prediction for Emotion-Cause Pair Extraction

The emotion-cause pair extraction task is a fine-grained task in text sentiment analysis, which aims to extract all emotions and their underlying causes in a document. Recent studies have addressed the emotion-cause pair extraction task in a step-by-step manner, i.e., the two subtasks of emotion extraction and cause extraction are completed first, followed by the pairing task of emotion-cause pairs. However, this fail to deal well with the potential relationship between the two subtasks and the extraction task of emotion-cause pairs. At the same time, the grammatical information contained in the document itself is ignored. To address the above issues, we propose a deep neural network based on span association prediction for the task of emotion-cause pair extraction, exploiting general grammatical conventions to span-encode sentences. We use the span association pairing method to obtain candidate emotion-cause pairs, and establish a multi-dimensional information interaction mechanism to screen candidate emotion-cause pairs. Experimental results on a quasi-baseline corpus show that our model can accurately extract potential emotion-cause pairs and outperform existing baselines.
Educational Technology as a Support Tool for Students with Specific Learning Difficulties—Future Education Professionals’ Perspective

The research presented is framed in the context of educational technology (ET), and specifically in its use as a support tool for students with specific learning difficulties (SLD). This is descriptive quantitative research, the objective of which is to know what students know about the use of educational technology, the perceived usefulness of educational applications, and the use of educational technology as support for students with specific learning difficulties. In order to answer this question, a data-collection instrument was designed that included an ad hoc questionnaire made up of three blocks to evaluate the use of ET, the perceived usefulness of educational applications, as well as ET as a support for students with SLD. The participating sample is made up of students from different teacher training degrees of the Faculty of Education of the University of Burgos (n = 130). After the descriptive analysis was carried out, the results that were obtained allowed us to conclude that ET is an excellent proposal for the classroom, and that, due to its adaptability, it can, and should, be a frequent resource for achieving educational objectives, and especially as support for students with SLD.
GFSNet: Generalization-friendly siamese network for thermal infrared object tracking

The lack of large labeled training datasets hinders the usage of deep neural network for Thermal Infrared (TIR) tracking. Regular practice is to train a tracking network with large-scale RGB datasets and then retrain it to the TIR domain with limited TIR data. However, we observe that existing Siamese-based trackers can hardly generalize to TIR images though they achieve outstanding performance on RGB tracking. Therefore, the main challenge is the generalization problem: How to design a generalization-friendly Siamese tracking network and what affects the network generalization. To tackle this problem, we introduce the self-adaption structure into Siamese network and propose an effective TIR tracking model, GFSNet. GFSNet is successfully generalized to different TIR tracking tasks, including ground target, aircraft and high-diversity object tracking tasks. To estimate generalization ability, we present a notion of Growth Rate, the improvement of overall performance after retraining. Experimental results show that the Growth Rates of GFSNet exceed state-of-the-art SiamRPN++ by more than 7 times, which indicates the great power of GFSNet in generalization. In addition to experimental validations, we provide the theoretical analysis of network generalization from a novel perspective, model sensitivity. By performing some tests to analyze the sensitivity, we conclude that the self-adaption structure helps GFSNet converge to a more sensitive minimum with better generalization to new tasks. Furthermore, when compared with popular tracking methods, GFSNet maintains comparable accuracy while achieving real-time tracking with the speed of 112 FPS, 5 times faster than other TIR trackers.
Privacy protection scheme for mobile social network

In the recent years, popularity and number of users of mobile social networks have grown significantly. Due to different types of communication on these networks, users share a lot of information with each other. One of the most critical challenges of social networks is disclosure and unauthorized access to information, data, and communication between users that is a kind of violation of their privacy, which can be done by social network providers, especially unauthorized users. One way to protect one’s privacy is using encryption. Therefore, in the present paper, an improved secure design was presented for mobile social network using ciphertext-policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) that will encrypt users data in an end-to-end manner. In the proposed scheme by CP-ABE encryption, in addition to data privacy, the users’ desired access control is applied to the access of others to their shared data. As a result, unauthorized users will not be able to access and violate data privacy. Furthermore, it will prevent the unauthorized access of social network provider with the help of AES. This scheme provides new management to exchange encryption keys and manage network components for their information. Therefore, the keys are exchanged securely and some of them are generated outside reach of the social network server and by the user. The users' main keys are encrypted using the Rivest-Shamir-Adleman (RSA) method. Consequently, the proposed scheme will protect users privacy and prevent the unauthorized access to users’ data and information by the social network provider and unauthorized users by comparing results of the proposed scheme with other similar investigated schemes. In addition, it will be resistant to common attacks (man-in-the-middle attack and replay attack). Also, according to our findings, it is a safe and comprehensive scheme with high security and practicality while needing less time for both encrypting and decrypting the users messages.
A database of students’ spontaneous actions in the real classroom environment

Students’ actions in the classroom play a key role in their studying performance in class. This study focuses on the automatic recognition of students’ spontaneous actions in a real classroom environment. A database of students’ spontaneous actions is first established in this study. Our database consists of 4917 images of students’ spontaneous actions, including the actions of students in 10 learning states. Based on the characteristics of students’ actions in the classroom, we proposed a new 11-layer convolutional neural network algorithm based on an enhanced deep super resolution network. In this algorithm, a data augmentation method is introduced for data processing. The experimental results show that the proposed algorithm effectively improved the accuracy of action recognition on the database established in this paper. Such a spontaneous database proposed in this paper provides good data support for the study of students’ actions in the classroom.
A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments

Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial basis function neural network-convolutional neural network (RBFNN-CNN) model. In this research, we applied our approach to two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. To the best of our knowledge, this is the first work that addresses the use of an RBFNN-CNN model in speaker identification under stressful talking environments. Our speech identification models select the finest speech signal representation through the use of Mel-frequency cepstral coefficients (MFCCs) as a feature extraction method. A comparison among traditional classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN) and deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), was conducted. The results of our experiments show that speaker identification performance in stressful environments based on the RBFNN-CNN model is higher than that with the classical and deep machine learning models.
Improved linear regression prediction by transfer learning

Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While several studies address the problem of what to transfer, the very important question of when to answer remains mostly unanswered, especially from a theoretical point-of-view for regression problems. A new theoretical framework for the problem of parameter transfer for the linear model is proposed. It is shown that the quality of transfer for a new input vector depends on its representation in an eigenbasis involving the parameters of the problem. Furthermore, a statistical test is constructed to predict whether a fine-tuned model has a lower prediction quadratic risk than the base target model for an unobserved sample. Efficiency of the test is illustrated on synthetic data as well as real electricity consumption data.