Reviewerly/exHarmony huggingface dataset:
The Relationship between Emotional Intelligence and Educators’ Performance in Higher Education Sector

The significance of emotions in the classroom has been thoroughly explored, but discussions on educators’ abilities to recognize, regulate, and manage their emotions are still ongoing. This paper aims to look at the concept of emotional intelligence (EI) and how professors in higher education can use it to achieve better results in the form of emotional intelligence competencies (EIC). A total of 312 educators from 25 higher education institutes in the United Arab Emirates (UAE) participated in this study. In sampling the Emotional Intelligence Competencies for this study, we adopted Costa and Faria’s (2015) EQ test, administered to the respondent. The Reuven Bar-On emotional intelligence scale was created and standardized to gather data. Using structural equation modeling, the validity and utility of a proposed model for EI-based teaching competencies and their relationship to critical strengths were evaluated (SEM). The findings show that EIC significantly impacts educator behavior, which in turn improves student success. In order to ensure successful instruction and remarkable performance, the study provides valuable recommendations to higher education institutes about the importance of recruiting new instructors with high skills in EI and providing training sessions for existing educators to improve their EI skills.
Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors’ knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.
Causality Inspired Representation Learning for Domain Generalization

Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The mainstream is to leverage statistical models to model the dependence between data and labels, intending to learn representations independent of domain. Nevertheless, the statistical models are superficial descriptions of reality since they are only required to model dependence instead of the intrinsic causal mechanism. When the dependence changes with the target distribution, the statistic models may fail to generalize. In this regard, we introduce a general structural causal model to formalize the DG problem. Specifically, we assume that each input is constructed from a mix of causal factors (whose relationship with the label is invariant across domains) and non-causal factors (category-independent), and only the former cause the classification judgments. Our goal is to extract the causal factors from inputs and then reconstruct the invariant causal mechanisms. However, the theoretical idea is far from practical of DG since the required causal/non-causal factors are unobserved. We highlight that ideal causal factors should meet three basic properties: separated from the non-causal ones, jointly independent, and causally sufficient for the classification. Based on that, we propose a Causality Inspired Representation Learning (CIRL) algorithm that enforces the representations to satisfy the above properties and then uses them to simulate the causal factors, which yields improved generalization ability. Extensive experimental results on several widely used datasets verify the effectiveness of our approach. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at “https://github.com/BIT-DA/CIRL”.
Foundations of Educational Technology: Integrative Approaches and Interdisciplinary Perspectives

This paper reviews the book “Book Review: Foundations of Educational Technology. Integrative Approaches and Interdisciplinary Perspectives”, written by J. Michael Spector (published in 2016 by Routledge, 2nd edition, first edition in 2012, 253 p.).
A Social Network Based Approach to Personalized Recommendation of Participatory Media Content

Given the rapid growth of participatory media content such as blogs, there is a need to design personalized recommender systems to recommend only useful content to users. We believe that in addition to producing useful recommendations, certain insights from media research such as simplification and opinion diversity in recommendations should form the foundations of such recommender systems, so that the behavior of the systems can be understood more closely, and modified if necessary. We propose and evaluate such a system based on a Bayesian user-model. We use the underlying social network of blog authors and readers to model the preference features for individual users. The initial results of our proposed solution are encouraging, and set the agenda for future research.
Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View

In this paper, we study the Differentially Private Empirical Risk Minimization (DP-ERM) problem with non-convex loss functions and give several upper bounds for the utility in different settings. We first consider the problem in low-dimensional space. For DP-ERM with non-smooth regularizer, we generalize an existing work by measuring the utility using ℓ2 norm of the projected gradient. Also, we extend the error bound measurement, for the first time, from empirical risk to population risk by using the expected ℓ2 norm of the gradient. We then investigate the problem in high dimensional space, and show that by measuring the utility with Frank-Wolfe gap, it is possible to bound the utility by the Gaussian Width of the constraint set, instead of the dimensionality p of the underlying space. We further demonstrate that the advantages of this result can be achieved by the measure of ℓ2 norm of the projected gradient. A somewhat surprising discovery is that although the two kinds of measurements are quite different, their induced utility upper bounds are asymptotically the same under some assumptions. We also show that the utility of some special non-convex loss functions can be reduced to a level (i.e., depending only on log p) similar to that of convex loss functions. Finally, we test our proposed algorithms on both synthetic and real world datasets and the experimental results confirm our theoretical analysis.
Binary image steganography based on joint distortion measurement

Most state-of-the-art binary image steganography methods depend on the content of the image to determine where to embed secret messages, which is capacity-limited and indicates that their distortion measurement may be not precise enough. In this paper, we propose a kind of distortion measurement that is not only based on the discrimination effects after flipping the pixels but also depends on the visual effects of flipping corresponding pixels, which is called joint distortion measurement. Instead of selecting suitable position to embed secret messages, we then employ the syndrome-trellis code to minimize the embedding distortion and get messages embedded. And experimental results have demonstrated that the proposed distortion measurement has higher performance and the steganography scheme can achieve stronger statistical security with high capacity and image quality.
Ethereum will cut back its absurd energy use

Bitcoin soaks up most of the hype and the opprobrium heaped on cryptocurrencies, leaving its younger and smaller sibling Ethereum in the shadows. But Ethereum is anything but small. Its market capitalization was roughly US $10 billion at press time, and it has an equally whopping energy footprint.
Pervasive Learning and Technology Usage for Creativity Development in Education

This paper’s aim is to investigate the role of creativity and pervasive learning in a modern education paradigm. The research was conducted by relevant literature review along with reflective analysis on sub-context such as creativity, educational development, pedagogical methods, important factors behind of creativity development and technology-learning systems affects. Various issues may become a supportive factor or barrier for creativity development in educational processes. Technology in an education field provides many opportunities for creativity enhancement, among which supports to enhance curiosity to develop some skills to improve student's cognitive processes and engagement as well as to increase intrinsic motivation, self-regulation. Authors attempted to consider all the benefits, challenges and risks related to enhancing creativity with the help of technology in modern educational paradigm. Many studies have been analyzed in order to answer main research question: How technology-pervasive learning environment can enhance stimulation and development of creativity among students?
Challenging the Boundaries of Unsupervised Learning for Semantic Similarity

The semantic analysis field has a crucial role to play in the research related to text analytics. Calculating the semantic similarity between sentences is a long-standing problem in the area of natural language processing, and it differs significantly as the domain of operation differs. In this paper, we present a methodology that can be applied across multiple domains by incorporating corpora-based statistics into a standardized semantic similarity algorithm. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. When tested on both benchmark standards and mean human similarity dataset, the methodology achieves a high correlation value for both word (r = 0.8753) and sentence similarity (r = 0.8793) concerning Rubenstein and Goodenough standard and the SICK dataset (r = 0.83241) outperforming other unsupervised models.
Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series

In today’s information age, the development of hot events is timely and rapid under the influence of the powerful Internet. Online social media, such as Weibo in China, has played an important role in the process of spreading public opinions and events. Sentiment analysis of social network texts can effectively reflect the development and changes of public opinions. At the same time, prediction and judgment of public opinion development can also play a key role in assisting decision-making and effective management. Therefore, sentiment analysis for hot events in online social media texts and judgment of public opinion development have become popular topics in recent years. At present, research on textual sentiment analysis is mainly aimed at a single text, and there is little-integrated analysis of multi-user and multi-document in unit time for time series. Moreover, most of the existing methods are focused on the information mined from the text itself, while the feature of identity differences and time sequence of different users and texts on social platforms are rarely studied. Hence, this paper works on the public opinion texts about some specific events on social network platforms and combines the textual information with sentiment time series to achieve multi-document sentiment prediction. Considering the related features of different social user identities and time series, we propose and implement an effective time+user dual attention mechanism model to analyze and predict the textual information of public opinion. The effectiveness of the proposed model is then verified through experiments on real data from a popular Chinese microblog platform called Sina Weibo.
Assessment of the Key-Reuse Resilience of NewHope

NewHope is a suite of two efficient Ring-Learning-With-Error based key encapsulation mechanisms (KEMs) that has been proposed to the NIST call for proposals for post-quantum standardization. In this paper, we study the security of NewHope when an active adversary takes part in a key establishment protocol and is given access to an oracle, called key mismatch oracle, which indicates whether her guess of the shared key value derived by the party targeted by the attack is correct or not. This attack model turns out to be relevant in private key reuse situations since an attacker may then be able to access such an oracle repeatedly – either directly or using faults or side channels, depending on the considered instance of NewHope. Following this model we show that, by using NewHope recommended parameters, several thousands of queries are sufficient to recover the full private key with high probability. This result has been experimentally confirmed using Magma CAS implementation. While the presented key mismatch oracle attacks do not break any of the designers’ security claims for the NewHope KEMs, they provide better insight into the resilience of these KEMs against key reuse. In the case of the CPA-KEM instance of NewHope, they confirm that key reuse (e.g. key caching at server side) should be strictly avoided, even for an extremely short duration. In the case of the CCA-KEM instance of NewHope, they allow to point out critical steps inside the CCA transform that should be carefully protected against faults or side channels in case of potential key reuse.
Multi-Scale Proposal Generation for Ship Detection in SAR Images

The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.
Opinion mining on newspaper headlines using SVM and NLP

&lt;p&gt;Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.&lt;/p&gt;
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media

The availability of large-scale online social data, coupled with computational methods, can help us answer fundamental questions relating to our social lives, particularly our health and well-being.The #MeToo trend has led to people talking about personal experiences of harassment more openly.This work attempts to aggregate such experiences of sexual abuse to facilitate a better understanding of social media constructs and to bring about social change.It has been found that disclosure of abuse has positive psychological impacts.Hence, we contend that such information can be leveraged to create better campaigns for social change by analyzing how users react to these stories and can be used to obtain a better insight into the consequences of sexual abuse.We use a three-part Twitter-Specific Social Media Language Model to segregate personal recollections of sexual harassment from Twitter posts.An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis explores the merit of our proposed model.* denotes equal contribution. 1https://www.who.int/news-room/factsheets/detail/violence-against-women
Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.
The Utility of Sparse Representations for Control in Reinforcement Learning

We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learning is limited to sparse coding where extracting representations for new data can be computationally intensive. Here, we begin by demonstrating that learning a control policy incrementally with a representation from a standard neural network fails in classic control domains, whereas learning with a representation obtained from a neural network that has sparsity properties enforced is effective. We provide evidence that the reason for this is that the sparse representation provides locality, and so avoids catastrophic interference, and particularly keeps consistent, stable values for bootstrapping. We then discuss how to learn such sparse representations. We explore the idea of Distributional Regularizers, where the activation of hidden nodes is encouraged to match a particular distribution that results in sparse activation across time. We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.
MARESye: A hybrid imaging system for underwater robotic applications

This article presents an innovative hybrid imaging system that provides dense and accurate 3D information from harsh underwater environments. The proposed system is called MARESye and captures the advantages of both active and passive imaging methods: multiple light stripe range (LSR) and a photometric stereo (PS) technique, respectively. This hybrid approach fuses information from these techniques through a data-driven formulation to extend the measurement range and to produce high density 3D estimations in dynamic underwater environments. This hybrid system is driven by a gating timing approach to reduce the impact of several photometric issues related to the underwater environments such as, diffuse reflection, water turbidity and non-uniform illumination. Moreover, MARESye synchronizes and matches the acquisition of images with sub-sea phenomena which leads to clear pictures (with a high signal-to-noise ratio). Results conducted in realistic environments showed that MARESye is able to provide reliable, high density and accurate 3D data. Moreover, the experiments demonstrated that the performance of MARESye is less affected by sub-sea conditions since the SSIM index was 0.655 in high turbidity waters. Conventional imaging techniques obtained 0.328 in similar testing conditions. Therefore, the proposed system represents a valuable contribution for the inspection of maritime structures as well as for the navigation procedures of autonomous underwater vehicles during close range operations.
Deep Learning for SAR-Optical Image Matching

The automatic matching of corresponding regions in remote sensing imagery acquired by synthetic aperture radar (SAR) and optical sensors is a crucial pre-requesite for many data fusion endeavours such as target recognition, image registration, or 3D-reconstruction by stereogrammetry. Driven by the success of deep learning in conventional optical image matching, we have carried out extensive research with regard to deep matching for SAR-optical multi-sensor image pairs in the recent past. In this paper, we summarize the achieved findings, including different concepts based on (pseudo-)siamese convolutional neural network architectures, hard negative mining, alternative formulations of the underlying loss function, and creation of artificial images by generative adversarial networks. Based on data from state-of-the-art remote sensing missions such as TerraSAR-X, Prism, Worldview-2, and Sentinel-1/2, we show what is already possible today, while highlighting challenges to be tackled by future research endeavors.