Reviewerly/exHarmony huggingface dataset:
A Survey of Semantic Analysis Approaches

Semantics is a branch of linguistics, which aims to investigate the meaning of language. Semantics deals with the meaning of sentences and words as fundamentals in the world. Semantic analysis within the framework of natural language processing evaluates and represents human language and analyzes texts written in the English language and other natural languages with the interpretation similar to those of human beings. This study aimed to critically review semantic analysis and revealed that explicit semantic analysis, latent semantic analysis, and sentiment analysis contribute to the leaning of natural languages and texts, enable computers to process natural languages, and reveal opinion attitudes in texts. The future prospect is in the domain of sentiment lexes. The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. This study has covered various aspects including the Natural Language Processing (NLP), Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Sentiment Analysis (SA) in different sections of this study. However, LSA has been covered in detail with specific inputs from various sources. This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5).
Large-scale Ising emulation with four body interaction and all-to-all connections

Abstract Optical Ising machines with two-body interactions have shown potential in solving combinatorial optimization problems which are extremely hard to solve with digital computers. Yet, some physical systems cannot be properly described by only two-body interactions. Here, we propose and demonstrate a nonlinear optics approach to emulate Ising machines containing many spins (up to a million in the absence of optical imperfections) and with tailored all-to-all two and four-body interactions. Our approach employs a spatial light modulator to encode and control the spins in the form of the binary-phase values, and emulates the high-order interaction with frequency conversion in a nonlinear crystal. By implementing adaptive feedback, the system can be evolved into effective spin configurations that well-approximate the ground-states of Ising Hamiltonians with all-to-all connected many-body interactions. Our technique could serve as a tool to probe complex, many-body physics and give rise to exciting applications in big-data optimization, computing, and analytics.
The effect of workplace ostracism on knowledge sharing: the mediating role of organization-based self-esteem and organizational silence

Purpose This study aims to evaluate the effect of workplace ostracism on organization-based self-esteem (OBSE), organizational silence and knowledge sharing. Design/methodology/approach The statistical population of this research includes the employees of University of Isfahan in Iran. The structural equation modeling method is used to analyze the data and the conceptual model using 130 participants. Findings The empirical results suggest that ostracism and organizational silence influence knowledge sharing, negatively. Ostracism influences organizational silence, positively; and ostracism influences OBSE, negatively. Furthermore, organization-based self-esteem influences knowledge sharing, positively. In addition, the indirect relationship from ostracism to knowledge sharing through organizational silence was confirmed; however, the mediating role of OBSE in the relationship between workplace ostracism and knowledge sharing was not confirmed. Originality/value This research is among the first to evaluate the relationship between workplace ostracism, knowledge sharing, organizational silence and OBSE simultaneously.
Certificateless two-party authenticated key agreement scheme for smart grid

Smart grid is a fully automated power transmission network. It monitors and controls each user and grid node to ensure bidirectional flow of information and power between all nodes. It is an important issue that how to achieve secure sharing of information among numerous communicating agents in the smart grid environments. Authenticated key agreement (AKA) is a good option to enable secure communication between the smart meter and utility. In recent years, several AKA schemes have been put forward for smart grid environments. There are two shortcomings in these schemes: First, they are constructed from traditional public key infrastructure (PKI) or identity based cryptography (IBC), so they suffer from certificate management problem or key escrow problem. Second, the security proofs of these schemes were done in the random oracle model(ROM). It is well known, a cryptographic scheme proven to be secure in ROM is not necessarily safe in practical applications. In this paper, we present a certificateless two-party authenticated key agreement (CL2PAKA) scheme for smart grids, then provide the security proofs in the standard model. Our scheme does not require pairing operations and requires only four scale multiplication operations, so it is more efficient than previous ones.
Kernel and Rich Regimes in Overparametrized Models

A recent line of work studies overparametrized neural networks in the regime, i.e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution. This stands in contrast to other studies which demonstrate how gradient descent on overparametrized multilayer networks can induce implicit biases that are not RKHS norms. Building on an observation by Chizat and Bach, we show how the scale of the initialization controls the transition between the (aka lazy) and rich (aka active) regimes and affects generalization properties in multilayer homogeneous models. We also highlight an interesting role for the width of a model in the case that the predictor is not identically zero at initialization. We provide a complete and detailed analysis for a family of simple depth-$D$ models that already exhibit an interesting and meaningful transition between the kernel and regimes, and we also demonstrate this transition empirically for more complex matrix factorization models and multilayer non-linear networks.
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models

Advances in language modeling have led to the development of deep attention-based models that are performant across a wide variety of natural language processing (NLP) problems. These language models are typified by a pre-training process on large unlabeled text corpora and subsequently fine-tuned for specific tasks. Although considerable work has been devoted to understanding the attention mechanisms of pre-trained models, it is less understood how a model's attention mechanisms change when trained for a target NLP task. In this paper, we propose a visual analytics approach to understanding fine-tuning in attention-based language models. Our visualization, Attention Flows, is designed to support users in querying, tracing, and comparing attention within layers, across layers, and amongst attention heads in Transformer-based language models. To help users gain insight on how a classification decision is made, our design is centered on depicting classification-based attention at the deepest layer and how attention from prior layers flows throughout words in the input. Attention Flows supports the analysis of a single model, as well as the visual comparison between pre-trained and fine-tuned models via their similarities and differences. We use Attention Flows to study attention mechanisms in various sentence understanding tasks and highlight how attention evolves to address the nuances of solving these tasks.
Domain-Specific Face Synthesis for Video Face Recognition From a Single Sample Per Person

In video surveillance, face recognition (FR) systems are employed to detect individuals of interest appearing over a distributed network of cameras. The performance of still-to-video FR systems can decline significantly because faces captured in unconstrained operational domain (OD) over multiple video cameras have a different underlying data distribution compared to faces captured under controlled conditions in the enrollment domain with a still camera. This is particularly true when individuals are enrolled to the system using a single reference still. To improve the robustness of these systems, it is possible to augment the reference set by generating synthetic faces based on the original still. However, without the knowledge of the OD, many synthetic images must be generated to account for all possible capture conditions. FR systems may, therefore, require complex implementations and yield lower accuracy when training on many less relevant images. This paper introduces an algorithm for domain-specific face synthesis (DSFS) that exploits the representative intra-class variation information available from the OD. Prior to operation (during camera calibration), a compact set of faces from unknown persons appearing in the OD is selected through affinity propagation clustering in the captured condition space (defined by pose and illumination estimation). The domain-specific variations of these face images are then projected onto the reference still of each individual by integrating an image-based face relighting technique inside the 3-D reconstruction framework. A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD. In a particular implementation based on sparse representation classification, the synthetic faces generated with the DSFS are employed to form a cross-domain dictionary that accounts for structured sparsity, where the dictionary blocks combine the original and synthetic faces of each individual. Experimental results obtained with videos from the Chokepoint and COX-S2V data sets reveal that augmenting the reference gallery set of still-to-video FR systems using the proposed DSFS approach can provide a significantly higher level of accuracy compared with the state-of-the-art approaches, with only a moderate increase in its computational complexity.
A Framework for the Computational Linguistic Analysis of Dehumanization

Dehumanization is a pernicious psychological process that often leads to extreme intergroup bias, hate speech, and violence aimed at targeted social groups. Despite these serious consequences and the wealth of available data, dehumanization has not yet been computationally studied on a large scale. Drawing upon social psychology research, we create a computational linguistic framework for analyzing dehumanizing language by identifying linguistic correlates of salient components of dehumanization. We then apply this framework to analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. Overall, we find increasingly humanizing descriptions of LGBTQ people over time. However, we find that the label "homosexual" has emerged to be much more strongly associated with dehumanizing attitudes than other labels, such as "gay". Our proposed techniques highlight processes of linguistic variation and change in discourses surrounding marginalized groups. Furthermore, the ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
Show, Attend, and Translate: Unsupervised Image Translation With Self-Regularization and Attention

Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous domains, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
Coherence, Interference and Visibility

The interference observed for a quanton, traversing more than one path, is believed to characterize its wave nature. Conventionally, the sharpness of interference has been quantified by its visibility or contrast, as defined in optics. Based on this visibility, wave-particle duality relations have been formulated for two-path interference. However, as one generalizes the situation to multi-path interference, it is found that conventional interference visibility is not a good quantifier. A recently introduced measure of quantum coherence has been shown to be a good quantifier of the wave nature. The subject of quantum coherence, in relation to the wave nature of quantons and to interference visibility, is reviewed here. It is argued that coherence can be construed as a more general form of interference visibility, if the visibility is measured in a different manner, and not as contrast.
A binned likelihood for stochastic models

A bstract Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which is the key ingredient in order to assess the plausibility of model parameters given observed data. In some complex systems or experimental setups, predicting the outcome of a model cannot be done analytically, and Monte Carlo techniques are used. In this paper, we present a new analytic likelihood that takes into account Monte Carlo uncertainties, appropriate for use in the large and small sample size limits. Our formulation performs better than semi-analytic methods, prevents strong claims on biased statements, and provides improved coverage properties compared to available methods.
Multi-Angular Epipolar Geometry Based Light Field Angular Reconstruction Network

Densely-sampled light field (LF) image is drawing increased attention for its wide applications in 3D reconstruction, digital refocusing, depth estimation, and virtual/augmented reality, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> In order to reconstruct a densely-sampled LF with high angular resolution, many computational methods have been proposed. However, most existing methods consider LF angular reconstruction based on neighboring views, local epipolar plane images (EPIs) or EPI volume information, which overlook the rich LF angular information and fail to restore more texture details, especially for occlusion regions. In order to mitigate this problem, we introduce a multi-angular epipolar geometry (MA-EG) structure for LF angular reconstruction. The MA-EG structure contains multi-angular directional LF geometry information and can provide multi-angular geometry characteristics in LF reconstruction process, which benefits in recovering more texture details. Based on the MA-EG structure, we further put forward a multi-angular LF angular reconstruction network (MALFRNet) to learn a mapping from sparse LF to a densely-sampled LF. The proposed MALFRNet adopts a multi-stream framework, which can fully explore rich LF angular information and implicitly learn LF angular consistency and spatial geometry information from LF MA-EG. Comprehensive experiments on real-world and synthetic LF scenes demonstrate that the proposed MALFRNet can recover more texture details and achieve a better reconstruction quality. Moreover, ablation studies and LF depth estimation applications also illustrate the advantages of using more angular information in LF angular reconstruction through the proposed MALFRNet.
Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression

High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the hyperparameters.In addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, an improving grasshopper optimization algorithm (GOA) is proposed by adapting a new function of the main controlling parameter of GOA to enhance the exploration and exploitation capability of GOA. This improving is utilized to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on four datasets, show that our proposed algorithm performs better than cross-validation method, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the proposed improving confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared to other nature-inspired algorithms, which proves the ability of GOA in searching for the best hyperparameters values and selecting the most informative features for prediction tasks.
Measuring e-learning systems success: Data from students of higher education institutions in Morocco

The COVID-19 pandemic has forced Higher Education Institutions (HEI's) to rethink the teaching approach taken. In response to this emergency state, Moroccan universities switched to the e-learning approach as an alternative to face-to-face education. At this level the assessment of e-learning systems success becomes a necessity. This data article aims to identify e-learning systems success determinants during the COVID-19 pandemic. The data was collected from students of the Moroccan Higher Education Institutions. The research data are collected via an on a self-administered online questionnaire, from a sample of 264 university students. The responses are collected from students of 12 Moroccan universities and 31 Moroccan educational institutions. The data were analyzed using a structural equation modeling method under the Partial Least Squares approach (PLS-SEM). Data analysis was performed using SmartPLS 3 software. Universities managers can use the dataset to identify key factor to enhance e-learning system success.
Introduction to topical collection: Reproducibility challenges and solutions with a focus on guides to XPS analysis

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Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.
EnD: Entangling and Disentangling deep representations for bias correction

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an "information bottleneck" at a certain point of the deep neural network, where we dis-entangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that it does not require additional training complexity (like de-coders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on real-case scenarios, like removing hidden biases in the COVID-19 detection from radiographic images.
Aspects of visual avatar appearance: self-representation, display type, and uncanny valley

The visual representation of human-like entities in virtual worlds is becoming a very important aspect as virtual reality becomes more and more "social". The visual representation of a character's resemblance to a real person and the emotional response to it, as well as the expectations raised, have been a topic of discussion for several decades and have been debated by scientists from different disciplines. But as with any new technology, the findings may need to be reevaluated and adapted to new modalities. In this context, we make two contributions which may have implications for how avatars should be represented in social virtual reality applications. First, we determine how default and customized characters of current social virtual reality platforms appear in terms of human likeness, eeriness, and likability, and whether there is a clear resemblance to a given person. It can be concluded that the investigated platforms vary strongly in their representation of avatars. Common to all is that a clear resemblance does not exist. Second, we show that the uncanny valley effect is also present in head-mounted displays, but-compared to 2D monitors-even more pronounced.