Reviewerly/exHarmony huggingface dataset:
Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone

Feedforward backpropagation artificial neural networks (ANNs) have been increasingly employed in many engineering practices concerning materials modeling. Despite their extensive applications, how to achieve successfully trained ANNs is not thoroughly explained in the literature, nor are there lucid discussions to delineate influential parameters obtained from analyses. Long bones are composite materials possessing nonhomogeneous and anisotropic properties, and their mechanical responses exhibit dependency on numerous variables. Material complexity hinders researchers from arriving at a consensus in implementing an optimal constitutive model or encourages them to adopt a simple constitutive model including many simplifying assumptions. However, such exceptional features and engineering challenges make long bones materials worth investigating, enriching our comprehension of complex engineering structures using novel techniques where traditional methods may present limitations. This paper reports on the prediction of loading, displacement, load and displacement simultaneously, and strains using feedforward backpropagation ANNs trained with experimental recordings. The technique was used to find optimum network structures (architectures) that encompass the best prediction ability. To enhance predictions, the influence of several elements such as a network training algorithm, injecting noise to datasets prior to training, the level of injected noise which directly affects model fitting and regularization, and data normalization prior to training were investigated and discussed. Essential parameters influencing decision making in identifying well-trained and well-generalized ANNs were elaborated. A considerable emphasis in this study was placed on examining the generalization ability of the already trained and tested ANNs, thus guaranteeing unbiased models that avoided overfitting. Gaining favorable outcomes in this study required three years of performing experiments and data collection before establishing the networks. The subsequent training, testing, and determination of the generalization of more than 60,000 ANNs are promising and will assist researchers in comprehending mechanical responses of complicated engineering structures that exhibit peculiar nonlinear properties.
Virtual Reality Exposure Therapy for Armed Forces Veterans with Post-Traumatic Stress Disorder: A Systematic Review and Focus Group

Virtual reality exposure therapy (VRET) is an emerging treatment for people diagnosed with Post-Traumatic Stress Disorder (PTSD) due to the limited accessibility of psychotherapies. This research aims to determine the guidelines for developing a Virtual Reality-War Scenario program for Armed Forces veterans with PTSD and encompasses two studies: Study 1, a systematic electronic database review; Study 2, a focus group of twenty-two Portuguese Armed Forces veterans. Results showed a positive impact of VRET on PTSD; however, there were no group differences in most of the studies. Further, according to veterans, new VRET programs should be combined with the traditional therapy and must consider as requirements the sense of presence, dynamic scenarios, realistic feeling, and multisensorial experience. Regardless, these findings suggest VRET as a co-creation process, which requires more controlled, personalized, and in-depth research on its clinical applicability.
Factors Associated with Online Hate Acceptance: A Cross-National Six-Country Study among Young Adults

The Internet, specifically social media, is among the most common settings where young people encounter hate speech. Understanding their attitudes toward the phenomenon is crucial for combatting it because acceptance of such content could contribute to furthering the spread of hate speech as well as ideology contamination. The present study, theoretically grounded in the General Aggression Model (GAM), investigates factors associated with online hate acceptance among young adults. We collected survey data from participants aged 18-26 from six countries: Finland (n = 483), France (n = 907), Poland (n = 738), Spain (n = 739), the United Kingdom (n = 959), and the United States (n = 1052). Results based on linear regression modeling showed that acceptance of online hate was strongly associated with acceptance of violence in all samples. In addition, participants who admitted to producing online hate reported higher levels of acceptance of it. Moreover, association with social dominance orientation was found in most of the samples. Other sample-specific significant factors included participants' experiences with the Internet and online hate, as well as empathy and institutional trust levels. Significant differences in online hate acceptance levels and the strength of its connections to individual factors were found between the countries. These results provide important insights into the phenomenon, demonstrating that online hate acceptance is part of a larger belief system and is influenced by cultural background, and, therefore, it cannot be analyzed or combatted in isolation from these factors.
Implications of blockchain‐powered marketplace of preowned virtual goods

Abstract The first‐sale doctrine, which protects consumers’ rights to resell purchased products, has been recognized by the US Supreme Court since 1908. In recent years, consumers have begun to purchase an increasing amount of virtual goods, which renders the first‐sale doctrine unclear. There are two main challenges leading to the uncertainty of the first‐sale doctrine in the digital age: lack of proper technology, and economic implications for developers and consumers. The advent of the blockchain solves the technology challenge, as it can track provenance and establish the chain of custody. In this study, we construct an analytical model to investigate the economic impact of trading preowned virtual items. Specifically, our model captures the decentralized nature of blockchain technology by allowing consumer‐to‐consumer trading, and considers the possibility that consumers prefer preowned virtual items over new ones because preowned items may be upgraded between purchase and resale. Lawmakers seek to strike a balance between the interests of virtual item developers and individual consumers. We show that, surprisingly, the introduction of a blockchain‐based preowned virtual item transaction can actually benefit both developer and consumer. The main intuition is that the developer can adjust the price when forward‐looking consumers incorporate the expected future transaction into their purchase decision. Our analysis also reveals that developers are more willing to embrace the secondary market when they can take a cut during the transaction. Our results provide important policy implications to the burgeoning debate of the first‐sale doctrine in the new digital world.
Stain transfer using Generative Adversarial Networks and disentangled features

With the digitization of histopathology, machine learning algorithms have been developed to help pathologists. Color variation in histopathology images degrades the performance of these algorithms. Many models have been proposed to resolve the impact of color variation and transfer histopathology images to a single stain style. Major shortcomings include manual feature extraction, bias on a reference image, being limited to one style to one style transfer, dependence on style labels for source and target domains, and information loss. We propose two models, considering these shortcomings. Our main novelty is using Generative Adversarial Networks (GANs) along with feature disentanglement. The models extract color-related and structural features with neural networks; thus, features are not hand-crafted. Extracting features helps our models do many-to-one stain transformations and require only target-style labels. Our models also do not require a reference image by exploiting GAN. Our first model has one network per stain style transformation, while the second model uses only one network for many-to-many stain style transformations. We compare our models with six state-of-the-art models on the Mitosis-Atypia Dataset. Both proposed models achieved good results, but our second model outperforms other models based on the Histogram Intersection Score (HIS). Our proposed models were applied to three datasets to test their performance. The efficacy of our models was also evaluated on a classification task. Our second model obtained the best results in all the experiments with HIS of 0.88, 0.85, 0.75 for L-channel, a-channel, and b-channel, using the Mitosis-Atypia Dataset and accuracy of 90.3% for classification.
Theory of Gating in Recurrent Neural Networks

Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive interactions. However gating i.e., multiplicative interactions are ubiquitous in real neurons and also the central feature of the best-performing RNNs in ML. Here, we show that gating offers flexible control of two salient features of the collective dynamics: (i) timescales and (ii) dimensionality. The gate controlling timescales leads to a novel marginally stable state, where the network functions as a flexible integrator. Unlike previous approaches, gating permits this important function without parameter fine-tuning or special symmetries. Gates also provide a flexible, context-dependent mechanism to reset the memory trace, thus complementing the memory function. The gate modulating the dimensionality can induce a novel, discontinuous chaotic transition, where inputs push a stable system to strong chaotic activity, in contrast to the typically stabilizing effect of inputs. At this transition, unlike additive RNNs, the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity). The rich dynamics are summarized in phase diagrams, thus providing a map for principled parameter initialization choices to ML practitioners.
Dual constraints and adversarial learning for fair recommenders

Recommender systems, which are consist of common artificial intelligence technology, have a profound impact on the lifestyles of people. However, recent studies have demonstrated that recommender systems have fairness problems which means that some people with certain attributes are treated unfairly. A fair recommender means that users with different attributes achieve the same recommender accuracy. In particular, the recommender systems completely rely on users’ behavior data for preferences learning, which leads to a high probability of unfair problems because that the behavior data usually contains sensitive information of users. Unfortunately, there are a few studies exploring unfair problem in recommender systems. To alleviate this problem, we present a novel fairness-aware recommender with dual fairness constraints (FRFC) to improve fairness in recommendations and protect the user’s sensitive information from being exposed. This model has several advantages: one advantage is that an adversarial-based graph neural network (GNN) is proposed to prevent the target user being infected by sensitive features of neighbor users; another advantage is that two fairness constraints are proposed to solve the problems of adversarial classifier failures in whole data and unfair ranking losses. With this design, the FRFC model can effectively filter out users’ sensitive information and give users of different attributes the same training opportunities, which is helpful for making a fair recommendation. Finally, extensive experiments demonstrate that the proposed model can significantly improve the fairness of recommendation results.
Automated Essay Scoring Using Transformer Models

Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While many machine-learning approaches for AES still rely on a bag-of-words (BOW) approach, we consider a transformer-based approach in this paper, compare its performance to a logistic regression model based on the BOW approach and discuss their differences. The analysis is based on 2,088 email responses to a problem-solving task, that were manually labeled in terms of politeness. Both transformer models considered in that analysis outperformed without any hyper-parameter tuning the regression-based model. We argue that for AES tasks such as politeness classification, the transformer-based approach has significant advantages, while a BOW approach suffers from not taking word order into account and reducing the words to their stem. Further, we show how such models can help increase the accuracy of human raters, and we provide a detailed instruction on how to implement transformer-based models for one's own purpose.
Use of hybrid MCDM methods for site location of solar-powered hydrogen production plants in Uzbekistan

Uzbekistan is a developing country where energy demand is rising, and it has many problems in the energy supply sector. This issue leads to a mismatch between energy consumption and supply in this country. Most of the country's energy supply comes from natural gas. A major part of Uzbekistan's energy demand can be provided by solar power. The purpose of this study is to determine the position of using solar energy to generate hydrogen in 13 provinces of Uzbekistan. This study is performed by using the Stepwise Weight Assessment Ratio Analysis (SWARA) for criteria weighting and using the Weighted Aggregated Sum Product Assessment (WASPAS), the COmplex PRoportional Assessment of alternatives (COPRAS), the Evaluation Based on Distance from Average Solution (EDAS), and the Weight Sum Model (WSM) for ranking locations. The results of criteria weighting with SWARA show that the top three most important criteria are solar radiation, sun hours, and average wind speed with weights of 0.248, 0.2, and 0.154, respectively. All ranking methods identify Bukhara province as the most suitable place in Uzbekistan for solar-powered hydrogen production. It is estimated that 1539.2 MW of solar power and 24.92 tons of hydrogen are produced annually in Bukhara.
An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.
Perceptual Image Hashing With Locality Preserving Projection for Copy Detection

Perceptual image hashing is an effective and efficient way to identify images in large-scale databases, where two major performances are robustness and discrimination. A better tradeoff between robustness and discrimination is still a severe challenge for the current hashing research. Aiming at this issue, we design a novel perceptual image Hashing with Locality Preserving Projection (LPP) (hereafter HLPP). Specifically, to improve the robustness against content-preserving operations, Gabor filtering is leveraged to adaptively extract the orientation and structure features, which are consistent with the response of human visual system. The LPP is adopted to learn intrinsic local structure from the maximum Gabor filtering response. The use of LPP can discover meaningful low-dimensional information hidden in the maximum Gabor filtering response and thus improves discrimination of HLPP. During hash similarity calculation, the Hamming distance is selected as the metric. The tradeoff performance between robustness and discrimination is validated on benchmark databases, and the results indicate that the proposed HLPP is superior to some state-of-the-art algorithms. In addition, extensive experiments of copy detection also demonstrate that the proposed HLPP can provide higher accuracy than the compared algorithms.
A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study

In manufacturing industries, it is of fundamental importance to detect anomalies in production in order to meet the required quality goals and to limit the number of defective products that are accidentally delivered to the customers. Nevertheless, monitoring systems currently employed in production are typically very simple and rely on a set of univariate control charts that fail to capture the multivariate and complex nature of real-world industrial systems. In such context, Machine Learning (ML)-based approaches for Anomaly Detection (AD) have proven to be extremely effective in increasing anomalies detectability and, in general, in enhancing monitoring procedures. However, industrial data are typically very complex and not suitable to be fed directly to classical ML-based AD tools making feature extraction procedures a necessary step that unfortunately may lead to information loss and low scalability. Deep Learning, has proven very effective at learning useful representations of complex data in an automatic way. In this paper, we propose an AD pipeline that makes use of convolutional autoencoders to extract useful features from two-dimensional, non-image, data. We test our approach on real world Optical Emission Spectroscopy data that are typical of semiconductor manufacturing and we achieve improved performance over classical monitoring methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Advanced monitoring is one of the most important task in the context of Industry 4.0. Some of the main issues in developing Machine Learning-based solutions in industrial environment are: (i) the lack of reliable tagged data; (ii) the complexity of data structures present in real-world scenarios. In this paper we investigate unsupervised anomaly detection for 2-dimensional data in manufacturing environment: we provide an approach that exploit Deep Learning-based architecture for handling the data at hand. We show the effectiveness of the proposed approach in a real world case study related to optical emission spectroscopy data in semiconductor manufacturing process providing satisfactory classification accuracy.
Late Fusion Multiple Kernel Clustering With Local Kernel Alignment Maximization

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data’s inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against noise.We input the local kernels of each view into the kernel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TiejianZhang/TMM21-LF-MKC-LKA</uri> .
Smartphones and Learning: An Extension of M-Learning or a Distinct Area of Inquiry

The smartphone has become an integral part of the education landscape. While there has been significant smartphone research in education under the guise of m-learning, the unique role of the device suggests that m-learning may not be an appropriate characterization. The purpose of this paper is to review the use of m-learning as a primary descriptor for smartphone- and learning-related research. In support of this goal, the paper reviews the definitions associated with m-learning, smartphones, and related technologies from the perspective of educational research. In addition, a review of author keywords of research on smartphones in education is used to provide context to the classification of the research. Finally, three theoretically guided smartphone programs are presented as evidence of the unique nature of smartphone and learning research. This review concludes with recommendations for the characterization of future research.