Reviewerly/exHarmony huggingface dataset:
It's not what you say but how you say it: Examining the influence of perceived voice assistant gender and pitch on trust and reliance

Voice assistants (VA) are virtual agents used to aid information seeking. Cues contained in speech, such as perceived gender and vocal pitch, may influence attitudes towards, and interactions with these agents. Given expansion of telehealth efforts and the potential for VA to advise patients on health-related topics outside of formal healthcare settings, the influence of VA vocal characteristics is examined in the context of medication instructions. Participants rated trust and reliance on VA after viewing medication labels and hearing recommendations from male and female agents with varying pitch. Results suggest agents perceived as female were rated as more trustworthy, while vocal pitch did not significantly influence trust. However, a trend of lower reliance with increasing pitch was observed. Additionally, participants relied significantly more on VA advice than medication labels when making decisions. Post-hoc analyses revealed trust and reliance primarily varied between participants. Pitch and gender explained only a small portion of within-participant variance. We found suggestive evidence for social categorizations distilled from vocal cues influencing interactions with agents delivering health-critical information. Future work should explore additional samples, vocal cues, and participant-level sources of variation.
HML4Rec: Hierarchical meta-learning for cold-start recommendation in flash sale e-commerce

Recommender systems (RSs) have been extensively studied in academia and industry, while few works focus on flash sale recommendations. In flash sale scenarios, period-specific high discounts are applied on ordinal sales during each flash sale period to attract users. According to periodic sales strategies, available and discounted items change significantly across periods. Users are attracted by the high discounts and show a period-specific preference. Besides, the frequently changed available items provoke a cold-start problem, i.e., an impaired recommendation caused by lacking interactions. However, most existing RSs either cannot handle users’ period-specific preferences or suffer from the cold-start problem. Therefore, this work proposes a novel meta-learning-based RS to mitigate the cold-start problem and simultaneously handle users’ period-specific preferences. Moreover, we introduce a novel hierarchical meta-training algorithm to guide the learning of our recommendation model via period-and user-specific gradients. In this way, the learned model contains user- and period-shared knowledge and can fast adapt to the recommendations for new flash sale periods and users. To evaluate the effectiveness of our system in flash sale recommendations and non-flash sale recommendations, we conduct experiments on a real-world flash sale e-commerce dataset and a widely used recommendation dataset, considering both warm and cold scenarios. The experimental results show that our proposed model is remarkably improved over the current state-of-the-art methods in flash sale recommendations and most of the non-flash sale cold-start recommendations.
Recent advances of bat-inspired algorithm, its versions and applications

Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.
Simple and Efficient Architectures for Semantic Segmentation

Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware. This paper demonstrates that a simple encoder-decoder architecture with a ResNet-like backbone and a small multi-scale head, performs on-par or better than complex semantic segmentation architectures such as HRNet, FANet and DDRNets.Naïvely applying deep backbones designed for Image Classification to the task of Semantic Segmentation leads to subpar results, owing to a much smaller effective receptive field of these backbones. Implicit among the various design choices put forth in works like HRNet, DDRNet, and FANet are networks with a large effective receptive field. It is natural to ask if a simple encoder-decoder architecture would compare favorably if comprised of backbones that have a larger effective receptive field, though without the use of in-efficient operations like dilated convolutions.We show that with minor and inexpensive modifications to ResNets, enlarging the receptive field, very simple and competitive baselines can be created for Semantic Segmentation. We present a family of such simple architectures for desktop as well as mobile targets, which match or exceed the performance of complex models on the Cityscapes dataset. We hope that our work provides simple yet effective baselines for practitioners to develop efficient semantic segmentation models. The model definitions and pre-trained weights are available at https://github.com/Qualcomm-AI-research/FFNet.
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-intensive. Although some efforts have been made in synthetic data generation, the naturalistic aspect of data remains less explored. In our study, we propose two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces; and Random Occlusion Generation (RandOcc), a more general synthetic occluded data generation method (Figure 1). We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. To facilitate model evaluation, we present two high-resolution real-world occluded face datasets with finegrained annotations, RealOcc and RealOcc-Wild, featuring both careful alignment preprocessing and an in-the-wild setting for robustness test. We further conduct a comprehensive analysis on a newly introduced segmentation benchmark, offering insights for future exploration. Our code and dataset are available at https://github.com/kennyvoo/face-occlusion-generation.
Paired and Unpaired Deep Learning Methods for Physically Accurate Super-Resolution Carbonate Rock Images

Abstract X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise the pore-scale geometry of subsurface porous rocks. Recent developments in super-resolution (SR) methods using deep learning allow for the digital enhancement of low-resolution (LR) images over large spatial scales, creating SR images comparable to high-resolution (HR) ground truth images. This circumvents the common trade-off between resolution and field-of-view. An outstanding issue is the use of paired LR and HR data, which is often required in the training step of such methods but is difficult to obtain. In this work, we rigorously compare two state-of-the-art SR deep learning techniques, using both paired and unpaired data, with like-for-like ground truth data. The first approach requires paired images to train a convolutional neural network (CNN), while the second approach uses unpaired images to train a generative adversarial network (GAN). The two approaches are compared using a micro-CT carbonate rock sample with complicated micro-porous textures. We implemented various image-based and numerical verifications and experimental validation to quantitatively evaluate the physical accuracy and sensitivities of the two methods. Our quantitative results show that the unpaired GAN approach can reconstruct super-resolution images as precise as the paired CNN method, with comparable training times and dataset requirements. This unlocks new applications for micro-CT image enhancement using unpaired deep learning methods; image registration is no longer needed during the data processing stage. Decoupled images from data storage platforms can be exploited to train networks for SR digital rock applications. This opens up a new pathway for various applications related to multi-scale flow simulations in heterogeneous porous media.
Designing optimal convolutional neural network architecture using differential evolution algorithm

Convolutional neural networks (CNNs) are deep learning models used widely for solving various tasks like computer vision and speech recognition. CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time consuming, and challenging. To solve these, our study develops an improved differential evolution of convolutional neural network (IDECNN) algorithm to design CNN layer architectures for image classification. Variable-length encoding is utilized to represent the flexible layer architecture of a CNN model in IDECNN. An efficient heuristic mechanism is proposed in IDECNN to evolve CNN architecture through mutation and crossover to prevent premature convergence during the evolutionary process. Eight well-known imaging datasets were utilized. The results showed that IDECNN could design suitable architecture compared with 20 existing CNN models. Finally, CNN architectures are applied to pneumonia and coronavirus disease 2019 (COVID-19) X-ray biomedical image data. The results demonstrated the usefulness of the proposed approach to generate a suitable CNN model.
Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation Using Region and Category Adaptive Domain Discriminator

By reason of factors such as terrains, weather conditions, sensor imaging methods and cultural and economic development, there is a large shift between the remote sensing imagery collected from different geographic locations and different sensors, which makes the state-of-the-art semantic segmentation models trained on source domain (a image set gathered from specific geographic locations and sensors) difficult to generalize to target domain (another image set collected from other geographic locations and sensors). Currently, unsupervised domain adaptation using adversarial training whose purpose is to align the marginal distribution in the output space between source and target domain, is the most explored and practical approach to address this issue. However, this global alignment approach does not take into account diversities of different regions in a specific image nor the category-level distribution, which leads to the consequence that some regions and categories which are already well aligned between the source and target domain may be incorrectly remapped. Therefore, we propose a region and category adaptive domain discriminator, aiming to emphasize the differences in regions and categories during the process of alignment. Specifically, on the one hand, we propose an entropy-based regional attention module in domain discriminator to emphasize the importance of difficult-to-align regions. On the other hand, we propose a class-clear module to update only the distribution of existing categories in one iteration without affecting all categories. Finally, a lot of experiments are introduced to indicate that the proposed method can obtain better results when compared with other state-of-the-art unsupervised domain adaptation methods using adversarial training.
Investors' sentiments and the dynamic connectedness between cryptocurrency and precious metals markets

The connection between the cryptocurrency and precious metal markets has attracted a lot of interest among academics, investors, and policymakers. The focus of this paper is to examine the role of bullish, neutral, and bearish investors' sentiments on the connection between the five (5) most traded cryptocurrencies and the precious metals market. The following findings are perceptible from our analyses. First, in the spillover tests, there exists a significant high level of connection between the two markets. Second, the spillover results also reveal that tetherum and silver are net receivers of the shocks, while most of the cryptocurrencies are net transmitters of the volatility. Third, the BDS test shows strong evidence of non-linearity is a very crucial factor to be put into consideration when examining the role of investors' sentiments on the interactions between the cryptocurrency and precious metals markets. Fourth, the nonparametric causality-in-quantile test confirms that the connectedness between the cryptocurrency and precious metal markets is speculation driven. These results have important policy implications for policymakers and market participants.
Hybrid teaching–learning-based optimization for solving engineering and mathematical problems

In this work, a new and effective algorithm called hybrid teaching–learning-based optimization (TLBO) and charged system search (CSS) algorithms (HTC) are proposed to solve engineering and mathematical problems. The CSS is inspired by Coulomb and Gauss’s electrostatic laws of physics as well as the Newtonian mechanic laws of motion. The TLBO is inspired by the interaction between teacher and student in a classroom. Usually, the TLBO gets trapped in the local optimal due to the lack of a system for measuring the distance between the student and the optimal point. In order to solve this problem, the CSS algorithm, which is based on the electrical physics laws, is utilized. In the CSS algorithm, each factor is stored under the influence of the best local and global positions, and it is used in subsequent iterations as the possible optimal answers. In fact, this leads to a better balance between exploration and exploitation. In order to validate the proposed method, the CEC2021 and CEC2005 mathematical functions are optimized. Additionally, to show the applicability of the proposed algorithm and to evaluate its performance and convergence rate, several benchmark truss structures are optimized. The weight of the structural elements is taken into account as the objective function, which is optimized under displacement and stress constraints. The results of the proposed algorithm are compared with some other well-known meta-heuristic methods. The results show that the hybrid HTC algorithm improved the convergence rate and quickly obtained the optimal and desired design. The hybrid HTC algorithm can be adapted to solve other complex mathematical and optimization problems.
Applying machine learning methods to predict geology using soil sample geochemistry

In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and SMOTETomek having slightly lower effectiveness. Nine machine learning algorithms (naïve Bayes, logistic regression, quadratic discriminant analysis, nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifier, and gradient boosting classifier) were compared and AdaBoost classifiers and gradient boosting classifiers were found to be most effective. Finally, we experimented with multiple classifier systems (MCS) testing different combinations of algorithms and various combinatorial functions. It was found that MCS can outperform individual models, and the best MCS combined nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifiers, and gradient boosting classifier, then applied a logistic regression to the probabilities output by the models. Ultimately, we created a tool that is able to adequately predict underlying geology in the study area using soil geochemistry.
Examining the cultural dimension of contact-tracing app adoption during the COVID-19 pandemic: a cross-country study in Singapore and Switzerland

Contact-tracing applications (CTAs) have been introduced as part of the COVID-19 containment strategy worldwide. In most countries, however, their uptake has been too low to realize their full potential. This study contributes to the understanding of CTA adoption by investigating the influence of public perceptions on adoption and the role of media in forming these perceptions in Singapore and Switzerland. In a comparative approach, online surveys in both countries (Singapore: N = 998; Switzerland: N = 1,022) and multigroup structural equation modeling reveal national differences. First, attention to media was associated more strongly with app-related perceptions in Singapore than in Switzerland, with news media attention correlating positively with favorable perceptions in both countries (i.e., perceived usefulness of the CTA, perceived social norms of adoption) and social media attention correlating negatively with these perceptions in Singapore. Second, regarding the influence of these perceptions on CTA adoption, perceived usefulness was associated with CTA adoption in Switzerland but not in Singapore; conversely, perceived social norms were more important in Singapore than in Switzerland. These results suggest that the communicative formation of public perceptions and their behavioral relevance are contingent on media systems (authoritarian vs. democratic media system) and cultural values (collectivism vs. individualism), highlighting the theoretical value of a country-comparative approach and the practical need for a culturally sensitive implementation of health technologies.
Research on Piano Informatization Teaching Strategy Based on Deep Learning

Applying information technology to piano teaching can effectively avoid these problems. The application of information teaching means can not only enrich teaching methods but also enhance students’ interest in learning. Deep learning (DL) focuses on immersing students in knowledge and learning situations, emphasizing critical thinking, and realizing the intrinsic value of knowledge. Understanding DL theory is of great significance to deepening the teaching reform in China. BP algorithm is a very effective algorithm for prediction and evaluation. In this paper, the complex factors affecting the quality of piano information teaching are comprehensively considered, and the improved BP neural network (BPNN) algorithm is used to evaluate and predict the quality of piano information teaching. The model structure of BPNN for evaluating and predicting the quality of piano information teaching is given and simulated in MATLAB. The results show that the evaluation and prediction method overcomes the subjective factors of expert evaluation and obtains reasonable results. Compared with the traditional BPNN algorithm, it has good applicability.
Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (&lt;10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts.
High-performance hardware architecture of a robust block-cipher algorithm based on different chaotic maps and DNA sequence encoding

Nowadays, secure digital data in store and transmission is an important issue. Cryptographic methods are widely used to provide optimal security for multimedia data. In this paper, a good performance implementation of a strong block-cipher system is proposed. This article's contribution is to develop a new method to block-cipher hardware system based on DNA biological properties and various 3D chaotic maps. In this scheme, the high-level security is achieved by chaotic sequences generated by a robust chaos-based PRNG based on the Lorenz, Chua, Rossler, and Chen chaotic maps. The latter is used to generates high-quality keys that are applied for encryption. Thus, a high-security block cipher approach for encrypting and decrypting images has been developed. To increase the confusion process complexity, several biological operations, such as DNA-XOR, are added to the encryption process. Furthermore, a novel hardware architecture of the proposed block-cipher system is put forward. The latter achieves a low power consumption, good frequency of 192.813 MHz and high throughput of 24,576,153 Mbps built. The security analysis demonstrates that the cryptosystem provides effective security. The proposed PRNG validated successfully both the NIST SP 800–22 test suite and the U01-Test. Various tests are performed, such as statical tests, phase analysis and differential attacks applied to different images. A comparison of the proposed algorithm with several newly developed encryption algorithms demonstrates that our system generates good results.